Reliability and Statistics in Transportation and Communication: Selected Papers from the 22nd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication: Artificial Intelligence in Transportation, RelStat-2022, October 20–21, 2022, Riga, Latvia 3031266544, 9783031266546

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Lecture Notes in Networks and Systems 640

Igor Kabashkin Irina Yatskiv Olegas Prentkovskis   Editors

Reliability and Statistics in Transportation and Communication Selected Papers from the 22nd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication: Artificial Intelligence in Transportation, RelStat-2022, October 20–21, 2022, Riga, Latvia

Lecture Notes in Networks and Systems

640

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

Igor Kabashkin · Irina Yatskiv · Olegas Prentkovskis Editors

Reliability and Statistics in Transportation and Communication Selected Papers from the 22nd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication: Artificial Intelligence in Transportation, RelStat-2022, October 20–21, 2022, Riga, Latvia

Editors Igor Kabashkin Transport and Telecommunication Institute Riga, Latvia

Irina Yatskiv Transport and Telecommunication Institute Riga, Latvia

Olegas Prentkovskis Vilnius Gediminas Technical University Vilnius, Lithuania

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-26654-6 ISBN 978-3-031-26655-3 (eBook) https://doi.org/10.1007/978-3-031-26655-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

In this volume of “Lecture Notes in Networks and Systems,” we are pleased to present the proceedings of the 22nd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication” (RelStat 2022), which took place in hybrid form in Riga, Latvia, on October 20–21, 2022. This event belongs to a conference series started in 2001 and organized annually by the Transport and Telecommunication Institute (TTI) in Riga, Latvia. The mission of RelStat is to promote a more comprehensive approach supporting new ideas, theories, technologies, systems, tools, applications, as well as work in progress and activities on all theoretical and practical issues arising in transport, information, and communication technologies. Results of previous editions RelStat were published by TTI Publishing House (RelStat 2001–2015) in the journal “Transport and Telecommunication” (ISSN 1407-6160), by Elsevier in the “Procedia Engineering” (RelStat 2016) and by Springer in “Lecture Notes in Networks and Systems” volume no.117 (RelStat 2019), volume no.195 (RelStat 2020), and volume no.410 (RelStat 2021). Design, implementation, operation, and maintenance of contemporary complex systems have brought many new challenges to “classic” reliability theory. We define complex systems as integrated unities of assets: technical, information, organization, economical, software, and human (users, administrators, and management) ones. Their complexity comes not only from their technical and organizational internal structure, which is built upon diverse hardware and software resources, but also from the complexity of information processes (data processing, monitoring, management, etc.) that must be executed in their specific environment. A system approach to the evaluation of the efficiency of complex systems at all phases of their life cycle is the contemporary answer to new challenges in the use of such systems. The dependability approach in theory and engineering of complex systems (not only computer systems and networks) is based on a multi-disciplinary approach to system theory, technology, and maintenance of the systems working in real, very often unfriendly, environment. Usability and dependability concentrate on efficient realization of tasks, services, and jobs by a system considered as a unity of all technical, information, and human assets, in contrast to “classical” reliability, which is more restrained to analysis of technical resources. This difference has caused a natural evolution in the topical range of subsequent RelStat conferences, with an increased focus on dependability approaches over the classical reliability approach. Efficiency of different modes of transport; transport for smart city; reliability, safety, and risk management for transport applications; statistics, modeling, and multi-criteria decision making in transportation and logistics; smart solutions, telematics, intelligent transport systems, innovative economics, and education and training in engineering are the main topics of RelStat. The conference had a special focus on the applications of artificial intelligence in various areas of transport.

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Preface

The program committee of the 21st International RelStat Conference, the organizers, and the editors of these proceedings would like to acknowledge participation of all reviewers who helped to refine contents of this volume and evaluated conference submissions. Our thanks goes to all members of the program committee: • Prof. Igor Kabashkin, Transport & Telecommunication Institute, Latvia – Chairman • Prof. Irina Yatskiv (Jackiva), Transport & Telecommunication Institute, Latvia – Co-Chairman • Prof. Irina Kuzmina-Merlino, Transport and Telecommunication Institute, Latvia – Co-Chairman • Assoc. prof. Giedr˙e Adomaviˇcien˙e, Kaunas University of Applied Engineering Sciences • Prof. Lutfihak Alpkan, Gebze Institute of Technology, Turkey • Univ.-Prof. Dr. Constantinos Antoniou, Technical University of Munich, Germany • Prof. Liudmyla Batenko, Kyiv National Economic University named after Vadym Hetman, Ukraine • Prof. Maurizio Bielli, Institute of System Analysis and Informatics, Italy • Dr. Brent D. Bowen, Purdue University, USA • Dr. Ilia B. Frenkel, Industrial Engineering and Management Department, Sami Shamoon College of Engineering, Israel • Assoc. prof. Lina Girdauskien˙e, Kaunas University of Applied Engineering Sciences • Prof. Alexander Grakovski, Transport and Telecommunication Institute, Latvia • Prof. Stefan Hittmar, University of Zilina, Slovakia • As. Prof. Ishgaly Ishmuhametov, Transport and Telecommunication Institute, Latvia • Prof. Dr. Nicos Komninos, Aristotle University of Thessaloniki, Greece • Dr. Gatis Krumins, Vidzemes Augstskola, University of Applied Sciences, Latvia • Prof. Natalja Lace, Riga Technical University, Latvia • As.Prof. Nikolova Christina Lazarova, University of National and World Economy, Bulgaria • As. Prof. Jacek Mazurkiewicz, Wroclaw University of Technology, Poland • Prof. Boriss Misnevs, Transport and Telecommunication Institute, Latvia • Prof. Dr. Andres Monzon de Caceres, Universidad Politécnica de Madrid, Spain • As. Prof. Eftihia Nathanail, University of Thessaly, Greece • Prof. Andrzej Niewczas, Lublin University of Technology, Poland • Prof. Lauri Ojala, Turku School of Economics, Finland • Prof. Ramunas Palšaitis, Vilnius Gediminas Technical University, Lithuania • Prof. Dmitry Pavlyuk, Transport and Telecommunication Institute, Latvia • Prof. Gunnar Prause, Tallinn Technical University, Estonia • Prof. Cristina Pronello, Polytechnic Torino, Italy • Prof. Olegas Prentkovskis, Vilnius Gediminas Technical University, Lithuania • Prof. Klaus Richter, Fraunhofer Institute for Factory Operation and Automation IFF Magdeburg, German • Prof. Svetlana Saksonova, University of Latvia, Latvia • Prof. Mihails Savrasovs, Transport and Telecommunication Institute, Latvia • Dr. Paulius Skaˇckauskas, Vilnius Gediminas Technical University, Lithuania • As. Prof. Ilze Sproge, Transport and Telecommunication Institute, Latvia

Preface

• • • • •

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Prof. Inna Stecenko, Transport and Telecommunication Institute, Latvia Prof. Julia Stukalina, Transport and Telecommunication Institute, Latvia Prof. Juri Toluyew, Transport and Telecommunication Institute, Latvia Prof. Tatjana Volkova, BA School of Business and Finance, Latvia Prof. Edmundas Zavadskas, Vilnius Gediminas Technical University, Lithuania

Thanking all the authors who have chosen RelStat as the publication platform for their research, we would like to express our hope that their papers will help in further developments in design and analysis of complex systems, offering a valuable and timely resource for scientists, researchers, practitioners, and students who work in these areas. Igor Kabashkin Irina Yatskiv (Jackiva) Olegas Prentkovskis

Contents

AI in Transportation Autonomous Mobile Robot Navigation Issues in Terms of Obstacles Detection in Disrupting Operating Conditions – Case Study . . . . . . . . . . . . . . . . . Robert Giel, Sylwia Werbi´nska-Wojciechowska, and Jakub Malewicz

3

Contribution Determination of the Statistical Loading of the Crossroads by Means of the YOLO5 Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrei Kazinski and Aliaksandr Puptsau

15

Attention-Based Spatio-Temporal Graph Convolutional Networks – A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jelena Perevozcikova and Dmitry Pavlyuk

26

Aviation Model of Decision Support System with Artificial Intelligence for Aircraft Fuselage Damage Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irina Bodrova

37

Factors Influencing Value Proposition in the Aviation Industry in the Context of Customer-Centric Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olga Zervina and Yulia Stukalina

50

Impact of Unpredictable Major Events on Aviation Industry: Challenges, Benefits and Prospects for Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Viktorija Gorodnicka and Iyad Alomar

61

Value Entity Recognition Task in the Air Transportation on the Base of E-Texts Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olga Zervina, Yulia Stukalina, and Dmitry Pavlyuk

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Aircraft Intellectual Records Management System . . . . . . . . . . . . . . . . . . . . . . . . . Vitalii Susanin and Leonid Shoshin

89

Jet Engine Health Assessment Using EGT Time Series . . . . . . . . . . . . . . . . . . . . . . 101 Vladislav Zhdanov and Alexander Grakovski

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Contents

Smart Logistics Impact of Stockout-Based Substitution on Optimal Inventory in Management Science and Microeconomic Implications . . . . . . . . . . . . . . . . . . . 113 Berdymyrat Ovezmyradov Classification of Inventory Management Methods Based on Demand Analysis in Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Valery Lukinskiy, Vladislav Lukinskiy, Darya Bazhina, Ekaterina Gazizova, and Igor Bernadskii Stakeholder-Oriented Investment Activities for Sustainable Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Harald Kitzmann and Gunnar Prause Use of Operational Research in Car Transport Logistics . . . . . . . . . . . . . . . . . . . . . 141 Martin Jurkovic, Tomas Kalina, Piotr Gorzelanczyk, and Maria Stopkova Reduction of Supply Chain Risks by Using Blockchain Technology . . . . . . . . . . 151 Meike Schroeder and Gunnar Prause Efficiency Assessment System Based on Analytical Approach for Sustainable Development of Transport Logistics . . . . . . . . . . . . . . . . . . . . . . . . 162 Anna Strimovskaya, Galina Sinko, and Elena Tsyplakova Smart Technology Smart Process Observer for Crane Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Daniel Sopauschke, Erik Trostmann, and Klaus Richter Analysis of Air Pollution Monitoring System in Lithuania . . . . . . . . . . . . . . . . . . . 191 Marius Mažeika and Darius Juodvalkis Piezoelectric Films Application for Vibration Diagnostics . . . . . . . . . . . . . . . . . . . 201 Aleksey Mironov, Pavel Doronkin, Aleksejs Safonovs, and Vitalijs Kuzmickis Autonomous Mobile Robot Study in the Context of Maintenance 4.0 . . . . . . . . . 213 Robert Giel and Alicja D˛abrowska Improvement of the Feature Selection Method for Network Attacks Classification Using Machine Learning in Digital Forensics . . . . . . . . . . . . . . . . . 223 Boriss Misnevs, Aleksandr Krivchenkov, and Alexander Grakovski

Contents

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Optimisation of Quay Crane Scheduling Problem at the Port of Algeria . . . . . . . 232 Hizia Amani, Linda Bouyaya, Rachid Chaib, Fatma Zohra Djekrif, and Mouna Aizi Scripting Complex Events and Behaviors in Computer Simulation of a Security Monitored Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 Jarosław Sugier Avatar: A Telepresence System for the Participation in Remote Events . . . . . . . . 252 Dietrich Trepnau and Klaus Richter Anomaly Detection for Predictive Maintenance on the Example of an Induced Draft of a Waste Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Nicolas Dolle, Christian Wilhelm, and Kirill Anikin Development of a Reliable and Offline Capable Hard- and Software Technology for Long-Term Machine Data Acquisition, Data Storing and Data Exportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Nicolas Dolle, Christian Wilhelm, and Kirill Anikin CargoTube: Next Generation Sustainable Transportation by Hyperloop Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Walter Neu, Heiko Duin, Lukas Eschment, James Napier, Thomas Nobel, Stephan Wurst, and Thomas Schüning Training Environment for Rare Events Learning a Feasibility Study . . . . . . . . . . . 285 ´ Przemysław Sliwi´ nski, Jarosław Sugier, Jacek Mazurkiewicz, and Tomasz Walkowiak Smart Transport and Mobility Comparison Analysis Between Pneumatic and Airless Tires by Computational Modelling for Avoiding Road Traffic Accidents . . . . . . . . . . . . 295 Mykola Karpenko, Olegas Prentkovskis, and Paulius Skaˇckauskas A Single-Level Joint Formulation for Travel Demand Estimation Under Stochastic User Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Mohamed Eldafrawi and Guido Gentile Assessing the Mobility Impact on the Corporate Social Responsibility . . . . . . . . 320 Mahnaz Babapourdijojin and Guido Gentile Bicycle Sharing Systems: A Comparative Analysis in Greece and Cyprus . . . . . . 336 Georgia Savva, Giannis Adamos, and Eftihia Nathanail

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Detection of Edges in Transport Networks Which are Critical for Public Service Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Peter Czimmermann Reliability Model of Autonomous Transport with Life Support Systems Based on Closed Biotechnological Complexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 Sergey Glukhikh Economics and Business Effective Change Management and Continues Improvements on Smart Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Ioseb Gabelaia and Abdelra Sherif Accounting Outsourcing: Increasing the Possibility of Its Use in Latvia . . . . . . . 384 Natalia Konovalova and Ludmila Rozgina Advances in the Research Domain of Crowdfunding: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 Oksana Adlere and Svetlana Saksonova Formation of the Insurance Market of the Region Taking into Account the Impact of Specific Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Evgenia Prokopjeva, Svetlana Saksonova, Tatyana Shibaeva, and Natalya Chezybaeva Possibilities and Barriers in Implementing of AI-Based Automation Techniques in Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 Nicolas Dolle and Irina Kuzmina-Merlino Assessment of the Dependence of Insurance Volumes on Various Socio-Economic Factors of Regional Development in Countries with a Transitive Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Svetlana Saksonova, Evgenia Prokopjeva, and Oksana Adlere The Impact of User-Generated Content on Customer Purchase Intentions of Online Shoppers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Ioseb Gabelaia Internet Retailers’ Valuation: Why Intangible Assets Matters and How to Assess Them . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450 Neli Abramishvili, Anthony Herman, Nadezhda Lvova, Nino Pailodze, and Ekaterina Yanshina

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Sustaining Well-Being of Teachers in Higher Education . . . . . . . . . . . . . . . . . . . . . 462 Ioseb Gabelaia and Ramune Bagociunaite Problems of Banking Stability and Efficiency: Comparative Analysis of Latvia and Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Natalia Konovalova, Mustafa Akan, and Luís Moreira Pinto Education and Training in Engineering Blended Learning as One of the Factors for Attractiveness of Studies: Case Study of KTK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Lina Girdauskien˙e, Giedr˙e Adomaviˇcien˙e, and Judita Štreimikien˙e Online Collaborative Learning: Use and Efficiency Evaluation . . . . . . . . . . . . . . . 498 Ryhor Miniankou and Aliaksandr Puptsau Human Resources Management and Training in Aviation . . . . . . . . . . . . . . . . . . . . 510 Federico de Andreis, Ubaldo Comite, Federico Maria Sottoriva, and Ilaria Cova Services Delivery Model for Education-as-a-Service Based Framework . . . . . . . 523 Boriss Misnevs and Igor Kabashkin Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539

AI in Transportation

Autonomous Mobile Robot Navigation Issues in Terms of Obstacles Detection in Disrupting Operating Conditions – Case Study Robert Giel1(B)

, Sylwia Werbi´nska-Wojciechowska1

, and Jakub Malewicz2

1 Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27,

50-370 Wroclaw, Poland {robert.giel,sylwia.werbinska}@pwr.edu.pl 2 General Tadeusz Kosciuszko Military University of Land Forces, Piotra Czajkowskiego 109, 51-147, Wroclaw, Poland [email protected]

Abstract. The chapter is focused on the issues of internal logistics process automation. Such operational processes’ performance needs appropriate systems to ensure safety and reduce the likelihood of eradicating undesirable situations. Therefore, the main problem addressed in the paper is investigating the possibility of obstacle detection to ensure the correctness and efficiency of the internal transport process performance. Indeed, the paper presents the results of conducted tests for obstacle detection by an autonomous mobile robot (AMR). Performance tests were carried out with an AMR placed on a hard surface. The tested navigation system consists of two laser scanners (SICK, HOKUYO), 3D cameras and an Inertial Measurement Unit. The obtained results allowed for the conclusion on the reliability of a material handling process in the context of AMR behaviour for static/dynamic obstacle detection. The work ends up with conclusions and directions for further research. Keywords: Autonomous mobile robot · Navigation process · Navigation accuracy · Obstacle detection · Disrupting conditions

1 Introduction The technologies of the Industry 4.0 concept have revolutionized industrial logistics today. As a result, significant development of storage systems can be noticed that partially or totally work in an autonomous way. One example of the objects mentioned above is huge distribution warehouses (e.g. Amazon Robotics) that use autonomous robots whose task is to locate, identify, take and transport loads between selected locations. Such operational processes’ performance needs appropriate systems to ensure safety and reduce the likelihood of eradicating undesirable situations [1]. In the context of automation of internal logistics processes, one of the interesting issues is the possibility of obstacle detection in the context of ensuring the correctness and efficiency of the internal transport process performance [2]. The problem under © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 3–14, 2023. https://doi.org/10.1007/978-3-031-26655-3_1

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consideration lies in ensuring the safety of autonomous transport systems, aimed at providing solutions that allow the system to respond correctly to the presence/occurrence of humans or other obstacles. In the known literature, different classifications of AMR safety issues can be found. The authors present the summary of conducted research in this area in work [1] and report [3]. Based on the conducted research, one can divide the known literature into three main categories: 1. a safe workplace for human safety, 2. development of collision avoidance systems, and 3. risk management. In this article, the authors focus on the second problem, the development of anticollision systems in the context of obstacle detection and the storage area problem. This research problem is widely investigated in the literature. The essential AMR safety solutions analyzed in the literature consider several performance parameters and safety indicators, the infrastructure required for their implementation, or the type of robot used (service/industrial) [4]. Additionally, one of the most crucial aspects is the type of obstacle detected (static or dynamic). The report [3] presents the classification of selected solutions to the robot obstacle detection problem. Among others, there were distinguished such aspects as the inaccuracy of the obstacle location error (e.g. [5]), deceleration distance (e.g. [6]), robot speed (e.g. [7]), light intensity (e.g. [8]), predicted collision location (e.g. [9]), or the distance between the robot and the obstacle (e.g. [5]). However, despite meeting safety standards, unforeseen problems may arise due to the peculiarities of the different navigation systems and technical solutions used in robots, like changing the range of scanning zones (e.g. [10]) or the influence of obstacle trajectory on detection speed (e.g. [11]). These problems may also occur in the implementation of automated processes in the warehouse area. Therefore, the paper presents the results of conducted tests for obstacle detection by an autonomous mobile robot (AMR). Performance tests were carried out with an AMR placed on a hard surface. The tested navigation system consists of two laser scanners (SICK, HOKUYO), 3D cameras and an Inertial Measurement Unit. The obtained results allowed for the conclusion on the reliability of a material handling process in the context of AMR planned route execution when a static or dynamic obstacle is detected.

2 Autonomous Mobile Robot – Main Assumptions for the Carried Out Research Research on obstacle detection was carried out using AMR (autonomous mobile robot) designed to transport a load from point A to point B. The innovation of the task was to develop a solution for stabilizing the platform’s path and detecting potential obstacles, which would analyze information from sensors in real-time and react to signals indicating loss of stability/potentially dangerous situations. Potential obstacles include objects such as stones, curbs, pallets and cardboard boxes (more than 5 cm high) or humans. This

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includes, for example, the intrusion of any object (including a person) into the robot’s path or the prevention of a possible collision and damage to the goods. In order to carry out the tests, the main assumptions regarding, e.g., sensors layout, navigation system or route planning have to be developed. A short introduction is provided in the next sub-sections. More information can be found in [3]. 2.1 Sensor Layout and Navigation System In order to carry out the tests, the sensors’ layout on the platform was planned in the first step. For this purpose, a simulation model of the Gazebo environment was developed with layouts defined accordingly. The robot’s base_link layout, which determines its configuration vector in space, was defined in the platform’s non-torsional axis, symmetrically between the wheels. The IMU inertial sensor was placed directly above this arrangement, at the height of 0.25 m. This avoided close contact with interference factors and ensured sufficiently accurate measurements of changes in the platform’s orientation and acceleration (Fig. 1). The 3D cameras and laser scanners were arranged in such a way as to allow 360degree coverage of the area around the truck, along with zones partially covered by two scanners simultaneously. The 3D cameras are arranged in such a way as to allow observation of the environment in front of the pallet and directly behind the forklift during reversing.

Fig. 1. Layout of sensors and cameras (visualization in Gazebo Sim software).

The analyzed AMR was equipped with an appropriate navigation system that distinguishes between two location systems: global and local. The global layout is defined in space by a pattern in the form of a static map (map layout). Its definition in relation to static obstacles in space is constant in time. On the other hand, the local layout is defined during system start-up (Odom layout). Because the platform can be launched in any place or space, defining the current position of the platform requires knowledge of the transformation between the local layout and the map layout - global localization is

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responsible for this. Dividing the localization problem into two system transformations allowed to make corrections based on global references to the real world and local displacement measurements. Figure 2 shows the systems used to determine the platform’s position in space. Both the implementation of global and local planning requires knowledge of the environment, the platform and its movement capabilities - kinematics. Cost maps are responsible for representing the current state of space. These are occupancy grids, each cell of which contains the cost of realizing a route through that cell. Cost maps can use static maps and available sensors to represent information about environmental obstacles. The developed system uses separate grids to implement global and local planning.

Fig. 2. Scheme of systems used to determine the position of the platform in space.

In the developed system, the costmap_2d module is responsible for representing the cost map. Representations of the state of the environment, constructed with its use, consist of layers: • • • •

Static – built based on a static map, 2D obstacles – built based on 2D sources (laser scanners), 3D obstacles – built based on 3D sources (3D cameras), inflation – built based on obstacles present in the other layers.

In order to analyze the current state of the environment, the definition of the robot’s structure was adopted in the form of a geometric figure - a cuboid. Bringing the problem to a two-dimensional space, which does not consider the robot’s height, the analyzed representation is called the platform footprint. 2.2 Route Planning and Execution In order to plan the route of the robot’s movement, it is necessary to implement an algorithm to determine the trajectory of its movement from point A to B. In the process

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of planning the route to the destination point, a global planner is used. Before starting the task, it uses a static map, determining a path that maps the route segment in terms of points (x, y) defined in space. To implement the global planner process, it was decided to use an implementation of the A* algorithm, along with the use of interpolation of the determined points to smooth the delivered path. The A* algorithm finds a path in the graph using heuristics by minimizing the objective function, resulting in an optimal solution - the least expensive path. The use of A* ensures the determination of the path in sufficient time, and the smoothing carried out allows its fluent execution in the context of traffic dynamics. During the execution of the task, local planning is also carried out, which consists of continuous optimization of the realized trajectory in terms of the adopted criterion. The determined trajectory is a path in the discrete-time domain, defining the successive speeds the platform must have to realize a given trajectory. The condition for the execution of the displacement task is the feasibility of the given trajectory. If each of the provided points lies in an achievable cell (from the point of view of the global cost map and the platform’s footprint), and it is possible to plan a path running from the starting position through successively defined points, then the path is considered feasible.

3 Obstacle Detection Tests Testing of the navigation system was carried out in the developed virtual world in the Gazebo tool in the form of an outdoor environment of about 1,500 m2 . It contains an open space with characteristic elements and a pavement simulating the ground in the form of cobblestones. The environment is shown in Fig. 4. A map of the environment was built during the tests, shown in Fig. 3. The map obtained is free of distortions and unexpectedly thickened walls, which confirms the correct operation of modules combining data from laser sensors and those responsible for local localization - based on data from encoders and IMU.

Fig. 3. Environmental map.

Based on the space obtained, the cost maps’ parameters were adjusted. The result is shown in Figs. 5 and 6; it provided sufficiently safe and smooth implementation of paths. As a result, tests have been carried out in the direction of evaluating the safety of platform movement, human identification and track change, as well as detecting an obstacle that cannot be avoided. Selected results are presented in the next subsection.

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Fig. 4. Map of the external environment.

Fig. 5. Local cost map.

Fig. 6. Global cost map.

3.1 Obtained Results Within the research framework, the team has focused on several basic problems. First, the issue of safety in platform movement has been investigated. Ensuring safety in the movement of the platform is implemented based on sensory data and developed zones. The tests were conducted to verify the correctness of obstacle detection and the speed of the system’s response. Straight line movement was planned, obstacle avoidance functionality was disabled, and the system’s behaviour was checked to see whether it was correct. 100 trials were conducted, 50 with a 0.5 m × 0.5 m × 0.05 mm obstacle and 50 with a human obstacle, and in both tested situations, the

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obstacles remained stationary. As designed, when an obstacle appeared in the yellow zone (warning_zone – Fig. 7) of safety, the platform halved its speed, while immediate braking occurred when an obstacle was detected in the red zone (emergency_zone – Fig. 8). The covered braking distance of the platform, both with and without a 500 kg load, was within the range derived from the estimated characteristics. For maximum speed, the braking distance in the worst case was 3.1 m (impossible case - the object was immediately in the emergency_zone). On the other hand, for regular operation, an object appearing in the warning_zone caused a speed reduction and a stopping distance of 0.95 m.

Fig. 7. The appearance of an obstacle in the warning_zone.

Fig. 8. The appearance of an obstacle in the emergency_zone.

In addition to verifying the safety system’s responsiveness, the correctness of building a local cost map based on obstacles with heights in the range of 0.05–2.50 m was also examined. As the platform moved through the environment, the building of the VoxelGrid layer was observed based on the objects visible by the cameras, which, together with the laser scanner data, influenced the map construction. The resulting representation of local obstacles met the set assumptions, with both a 0.05-m high obstacle and a non-standard shape (in this case, a table) adequately reflected as inaccessible space. It is worth noting that an obstacle 0.05 m high is typically a dynamic obstacle - it was not reflected in any way on the static map. Another aspect related to the safety study is the performance of the platform’s driving stability analysis system. It was checked whether the platform would correctly respond to situations: • excessive slope (slope greater than 5°), • excessive slip (causing a sizeable local localization error), • collision with an obstacle (detecting high acceleration in the plane parallel to the ground).

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The primary source of information in each situation mentioned is the IMU, based on which the event is identified, and when it occurs, the platform should immediately stop and report the corresponding error. Tests of slope detection yielded positive results and were carried out by raising each wheel in turn to the appropriate height, which was at least 16.5 cm. On the other hand, the situation of excessive slip was investigated by blocking the movement of the platform so that it did not execute the displacement, but the odometry data changed. The Kalman filtering initially corrected the results, but the platform responded accordingly when the error increased significantly. In the case of a collision, the impact of an obstacle from 4 sides was simulated while the platform moved in different directions; no significant problems occurred here either, and the approach of observing the IMU signal proved sufficient and effective. The correct operation of the system analyzing the weight distribution of the load on the forks of the platform was also investigated. The platform stopped reporting an appropriate error message if there was a significant shift in the center of gravity of the load being transported. Another interesting issue is the human identification and path modification problem. The study of the navigation system for replanning the local path based on the current state of the environment was carried out using a dynamic obstacle such as a human. For this purpose, an actor was added to the simulated environment, which moved along the path realized by the platform. 100 trials were carried out, varying in terms of where the event occurred along the path. In each case, the platform, via laser scanners and 3D cameras, correctly identified the obstacle and included it in the local cost map. Based on the map, the local planner correctly replanned the local path and generated appropriate trajectories to ensure collision-free avoidance of the actor. Figures 9, 10, 11, 12, 13 and 14 present the stages of deviating from the global path and correctly and smoothly bypassing the dynamic obstacle. The platform then returned to the previously realized path in the same efficient manner. Due to the positive effects of repetitive obstacle avoidance from the scenario mentioned above, it was decided to conduct another series of 100 trials. During each of them, various dynamic obstacles with heights of 0.05–1.0 m and actors appeared at random locations along the platform’s path. In most trials, the platform adjusted the local path and generated trajectories to ensure collision avoidance. There were situations in which a moving object did not collide with the executed route - then the platform did not implement unnecessary actions. The next step was to check the behaviour of the mobile platform when it encountered an obstacle that was impracticable to pass around. For this purpose, several blocks were placed along the route from point A to point B to prevent passage (Fig. 15). In 100/100 trials, the robot reacted correctly to the situation, in which the platform stopped no closer than 0.5 m from the obstacle. The behaviour of the robot in most cases is as follows:

Autonomous Mobile Robot Navigation Issues in Terms of Obstacles Detection

Fig. 9. Path execution.

Fig. 11. Avoidance of a dynamic obstacle.

Fig. 10. Human detection and trajectory change.

Fig. 12. Return to the previously implemented path - time 1.

1. the robot travels straight to the designated target (Fig. 15).

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Fig. 13. Return to the previously implemented path - time 2.

Fig. 14. Return to the previously implemented path - time 3.

2. the robot detects an obstacle more than 4 m away (Fig. 16). 3. the robot tries to avoid the obstacle, but finding such a path is impossible (Fig. 17). 4. the robot stops and reports an error (Fig. 18). There were cases in which the mobile platform initially tried to avoid obstacles from one side and then turned around and attempted from the other side. The important thing is that at no time was the robot closer than 0.5 m from the obstacle, and each time it eventually stopped, after which it reported an error.

Fig. 15. Driving the robot along the route

Fig. 16. Detection of an obstacle in the yellow zone

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Fig. 17. Attempting to avoid the obstacle.

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Fig. 18. Stopping the robot due to the inability to avoid the obstacle.

4 Conclusions The paper focuses on the problem of obstacle detection by AMR operating in material handling systems. The necessary sensors were used to perform the planned tests correctly. Their application enabled the full scope of manoeuvres in choosing the best solution for detecting the different types of objects. The results obtained allowed, on the one hand, to identify fundamental problems in static and dynamic obstacle detection. On the other hand, they can be used in the design process of autonomous storage systems. The conducted studies have been supplemented with verification of the selection of the scanner to perform tasks in outdoor (open) conditions. This aspect is especially important for AMR operating in changing conditions. In this area, the effect of temperature and humidity changes on the operation of scanners and cameras has been identified as part of a study simulating temperature changes. The AMR is to perform tasks partly in a closed environment (e.g., warehouse hall) and an open environment (e.g., manoeuvring yard). Therefore, in addition to operating in given “fixed” conditions, the influence of variable conditions that may arise, among other things, when moving from a closed environment to an open one and vice versa, was examined with tests in a climatic chamber. The most important study from the point of view of sensor selection was to check to what extent the phenomenon of “frosting”, i.e., condensation of water vapour on measuring devices, occurs and to what extent this affects the performance of these scanners. In addition, the effects of dust and haze on sensor performance were also analyzed to verify the effect of temperature factors on sensor performance. Clear conclusions were obtained in both studies (both the effect of fog and dust). In both cases, particle size was not significant from the point of view of the scanners. On the other hand, the number of particles suspended in the air per volume of air is essential. This means that an increase in dustiness/haze limits the sensors’ field of view. The scanner measurements were narrowed to about 60 cm.

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Acknowledgements. This research has received funding from POIR.01.01.01-00-0691/19 Development and validation in real conditions of an autonomous forklift truck dedicated to operations in an open external environment.

References 1. D˛abrowska, A., Giel, R., Werbi´nska-Wojciechowska, S.: Human safety in autonomous transport systems - review and case study. J. KONBiN 51(1), 57–71 (2021) 2. Zhou, Y., Hu, H., Liu, Y., Lin, S.-W., Ding, Z.: A distributed approach to robust control of multi-robot systems. Automatica 98, 1–13 (2018) 3. D˛abrowska, A., Giel, R.: Development and validation in real conditions of an autonomous forklift truck dedicated to operations in an open outdoor environment. Report for project no. POIR.01.01.01-00-0691/19 (2022) 4. Halme, R.J., Lanz, M., Kämäräinen, J., Pieters, R., Latokartano, J., Hietanen, A.: Review of vision-based safety systems for human-robot collaboration. Procedia CIRP 72, 111–116 (2018) 5. Bostelman, R.B., Hong, T.H., Messina, E.: Intelligence level performance standards research for autonomous vehicles. In: CEUR Workshop Proceedings, pp. 48–54 (2015) 6. Bertozzi, M., Bombini, L., Broggi, A., Coati, A.: A Smart vision system for advanced LGV navigation and obstacle detection. In: Proceedings of the IEEE Conference on Intelligent Transportation Systems, ITSC, pp. 508–513 (2012) 7. Tanha, S.D.N., Dehkordi, S.F., Korayem, A.H.: Control a mobile robot in Social environments by considering human as a moving obstacle. In: Proceedings of the 6th RSI International Conference on Robotics and Mechatronics, IcRoM 2018, (IcRoM), pp. 256–260 (2019) 8. Hedenberg, K., Åstrand, B.: 3D sensors on driverless trucks for detection of overhanging objects in the pathway. In: Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor, pp. 41–56 (2016) 9. Pratama, P.S., Kwun Jeong, S., Sil Park, S., Bong Kim, S.: Moving object tracking and avoidance algorithm for differential driving AGV based on laser measurement technology. Int. J. Sci. Eng. 4(1), 11–15 (2012) 10. Yoon, S., Bostelman, R.: Analysis of automatic through autonomous - unmanned ground vehicles (A-UGVs) towards performance standards. In: Proceedings of the IEEE International Symposium on Robotic and Sensors Environments, ROSE 2019, pp. 1–7 (2019) 11. Bostelman, R., Shackleford, W., Cheok, G.: Safe control of manufacturing vehicles research towards standard test methods. In: International Material Handling Research Colloquium 2012, Gardanne, pp. 1–25 (2012)

Contribution Determination of the Statistical Loading of the Crossroads by Means of the YOLO5 Neural Network Andrei Kazinski1(B) and Aliaksandr Puptsau2 1 Brest State Technical University, Masherova, 26, Brest, Belarus

[email protected] 2 European Humanities University, Saviˇciaus 17, Vilnius, Lithuania

[email protected]

Abstract. The effectiveness of publicly available CCTV cameras should be significantly improved. Currently, webcams only allow users to observe the current situation in the online mode. Users do not have the opportunity to receive additional information for decision-making. This is due to the fact that there are not enough services on the network for automatic collection and processing of video information. Such services are not available to a wide range of users. The data obtained is used by a small number of companies and organizations. In this article, we describe the study of a neural network of the YOLO family for detecting and tracking road users. First, we offer a variant of training the YOLOv5 neural network on the assembled dataset. We have analyzed the qualitative indicators of training. Secondly, the method of collecting statistical loading of the intersection by traffic participants is described. The obtained results were used to create a web application for an operational assessment of situations that deserve attention. Situations that require attention are based on the time parameters of detected objects. The results of the study can be used in the development of the “Smart City” concept. Such a concept should be developed on the basis of publicly available open source access services. Keywords: Urban transport · Traffic intensity · YOLOv5 · Emergency situations

1 Problem Statement 1.1 Introduction Information about the congestion of urban highways and intersections has a significant impact on traffic, traffic safety and is closely related to the efficiency of urban infrastructure. When determining the statistical loading of an intersection using a neural network, we relied on some research in this area [1, 2].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 15–25, 2023. https://doi.org/10.1007/978-3-031-26655-3_2

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Statistical data on the flow of passengers and the movement of passenger transport were used to build a model of the transportation process [3]. The paper proposes an algorithm for estimating the waiting time of a vehicle. The algorithm is based on the use of information about the flow of passengers. Such information is represented by discrete random variables. The use of the algorithm in the conditions of an increase in the number of personal transport in megacities helps to predict traffic congestion, the occurrence of problems of air pollution by exhaust gases, etc. Xiaoyu Cai et al. [4] proposed a «System Of Road Safety Risk Assessment Indices Based On Deviant Driving Behavior». The study was carried out in a big city for a long time. Numerous resources are involved in data collection. These are on-board diagnostic data on driver behavior and a lot of additional data received from municipal traffic management authorities. Authors proposed a road traffic safety risk evaluation index system is established with the road traffic safety entropy (RATE) as the primary index. This system is based on non-trivial calculations and requires numerous resources to collect data. Peši´c D. et al. [5] proposed a new method for benchmarking traffic safety level (BTL). When calculating BTL, the following data were used: annual number of traffic accident fatalities per 100,000 inhabitants (PR), annual number of traffic accident fatalities per 10,000 registered vehicles (TR), percentage of drivers that do not drive under the influence of alcohol (IA%), percentage of drivers that non speeding (NS%), etc. The BTL, RATE methods are quite fundamental and solve the tasks well. However, they take a long time to collect information, based on data from many external sources and databases. Numerous resources have been used to collect data. The calculation results are static and do not reflect the operational situation on the roads. Our proposed method is based on automatic data acquisition from publicly available video surveillance webcams, simple calculations, and easily reproducible components. The use of neural networks minimizes the resources used, allows you to get operational information. After adapting and testing the method, the developed web applications can work independently. 1.2 A Brief Introduction to the Data for the Intersection Statistical Loading Method In the city of Brest there are several dozen webcams that broadcast in real time. Some of them are aimed at sections of roads with heavy traffic. However, most of the webcams are not accessible to the average user. Such cameras are rented by private companies. For example, only two of Wikilink’s 32 cameras were available at the selected time. Cameras aimed at areas with heavy traffic and provided for general access were selected for further use. Webcams allow you to get photos of a road section in real time. Photographs taken at regular time intervals allow you to get statistical information about traffic intensity. Statistics include the time the vehicle was at the intersection. A significant deviation of the time spent by the vehicle on the road section from the average value indicates a non-standard situation. Accidents are one of the causes of non–standard situations. Fixing a non-standard situation using a webcam and providing information to the user can be performed automatically.

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As part of the study we have determined the goal: to perform a statistical assessment of the traffic intensity in V. Kolesnik Street in the city of Brest (Belarus). The reasons for choosing this observation point are as follows: the researchers’ access to the webcam in an acceptable mode, high traffic intensity, proximity to the urban infrastructure (children’s institutions, public transport stops, etc.). The intersection near the Holy Resurrection Cathedral was chosen as an additional observation point. This intersection is included in the list of sections with the most intense traffic in the city of Brest. The study is represented by the following stages: problem analysis, choice of means and methods of implementation, collection and labeling of images, design and training of a neural network for the classification of motion objects, evaluation of the results and adjustment of the study stages, design of an application for a statistical assessment of the intersection load and assessment of situations worthy of attention.

2 Designing a System for Determining the Contribution of the Statistical Loading of the Intersection 2.1 Collection and Labeling of Images At this stage, out of a large amount of video a video recording with a total volume of more than 15 h was chosen. Image frames with a resolution of 1920 × 1080 pixels in the jpg format were extracted from the video. The recording rate is 30 frames per minute. For further use as the input data of the neural network, a dataset of 12,000 images was formed and labeled. Images for the dataset were selected taking into account different day light and significant fluctuations in traffic intensity. These parameters depend on the observation time. The primary analysis of the data made it possible to establish that the dataset is not balanced according to the selected parameters. For example, cars and pedestrians are the most common in the frames. Minibuses, trucks and buses are less common in the frames. There are practically no cyclists and motorcyclists at the intersection. The primary analysis allowed us to formulate the ultimate goal of the study. The purpose of the study is to build a technique based on statistical analysis to determine traffic safety parameters at the crossroads. Thus, the determination of the statistical load of the intersection required the solution of the following important tasks: the formation of a dataset, the modernization and retraining of neural network, taking into account the selected classes for objects’ classifying and tracking, and the implementation of an application for real-time data visualization. 2.2 System Implementation Tools As a neural network for detecting and tracking vehicles, we selected the YOLOv5 neural network [6]. The reasons for this choice were the results of the analysis of the properties of the YOLO family networks [7]. Let’s list the most important of them: • it is possible to achieve a processing speed from 30 to 140 frames per second, which is enough to process frames received from a webcam;

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• the average accuracy of object detection (mAP) up to 0.895 is achieved on some datasets in 100 epochs; • a small scale file (about 27 MB); • the availability of publicly available documentation [8] and open source code [9] compensates for complex configuration and lack of purpose for the production environment; • support for running YOLOv5 on a Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM). The authors’ motives in choosing the YOLOv5 neural network for detecting objects at intersections are the following: • YOLOv5 is built into Python 3 as a module, which allows you to use the Colaboratory environment for programming; • the YOLOv5 model has an open source journal to follow the experiment; • YOLOv5 includes the necessary dependencies, for example, initial pre-trained weights, which allows you to significantly reduce the time of training and the amount of computing resources. The authors have prepared a program code in Python in the Colaboratory environment for accessing the webcam and recording the received frames. The result of recording a frame in the Colaboratory environment is shown in Fig. 1.

Fig. 1. An example of a frame recorded from a webcam at an intersection near the Holy Resurrection Cathedral in Brest.

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The LabelImg application was used to mark up the dataset [10]. As a result of the labelling, the corresponding set of files in the xml format was obtained. The files contain coordinates of rectangular labels for classes: car, truck, bus, minibus, biker, cyclist, walker. Google services are used to host the dataset. An example of frame markup in labeling is shown in Fig. 2.

Fig. 2. Frame markup in LabelImg application.

The total number of frames exceeds 12,000. The number of labeled objects on frames often exceeds several dozens. For this reason, more than twenty students were involved in labelling the dataset. Despite a large amount of group work, a local utility was used in collaboration. The lack of networking capabilities of the labelling utility makes collaboration difficult. The choice of the LabelImg utility is explained by the need for quick staff training and short deadlines for work completing. Thus, the implementation tools are Google Colaboratory, Google Drive services, YOLOv5 neural network, LabelImg application. 2.3 Obtaining Weighting Coefficients for a Neural Network After filtering the dataset, 9,610 frames with photos of intersections were used to train the neural network. The main selection parameter was the quality of the markup. The training results for 100 epochs on the assembled and marked-up dataset are shown in graphs. Figure 3 shows the Mean Average Precision (mAP) dependence for detecting seven classes of objects with confidence thresholds of 0.5 and 0.5–0.95.

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0.8 0.6 0.4

mAP = 0,5 mAP = 0,5:0,95

0.2 0.0 0

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Fig. 3. The average accuracy of detecting mAP objects for training on 100 epochs with confidence thresholds of 0.5 and 0.5:0.95.

When recognizing traffic participants, the average detection accuracy of mAP = 0.66 with a confidence threshold of 0.5 was achieved. This accuracy is sufficient for detecting objects at the intersection. The detection result is shown in Fig. 4.

Fig. 4. Detecting objects at the intersection of Kolesnik Street.

Object tracking was performed using Dep Sort technology [11]. We used the repository code (Kinzo, Wojke 2019) for our data.

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2.4 Analysis of Statistical Information About the Loading of the Intersection The implementation of object detection using YOLOv5 and object tracking allowed us to collect and analyze data. Data on the travel time of transport at the intersection near the Holy Resurrection Cathedral in the city of Brest were collected in a test mode. The collection was held on weekends and working days. The following is an analysis for data collected during 4 h on one of the weekends. Data on the time of passage of the intersection was collected automatically. An online camera with shared access was used for the collection. The collected data is filtered out. When filtering, erroneous data obtained as a result of neural network errors are removed. Figure 5 shows a graph showing the time it takes for passenger cars to cross the intersection. The average time for a passenger car to cross an intersection is 93 s. The overwhelming number of light vehicles cross the intersection in a time from 10 to 60 s. 250

Time to travel to the intersecon, sec

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Number of passenger cars Fig. 5. The time of crossing the intersection by passenger cars.

The time required for crossing the intersection by other vehicles is shown in Fig. 6. The average time for crossing the intersection by these vehicles is 40 s. Most of these vehicles cross the intersection in a time from 10 to 50 s. The difference in indicators can be explained by the fact that drivers of minibuses, buses and trucks have more driving experience than drivers of light cars. Figure 7 shows a summary graph of the dependence of the intersection travel time for all vehicles.

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Time to travel to the intersecon, sec

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Number of vehicles (without passenger cars) Fig. 6. The time of crossing the intersection by vehicles (without cars).

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Number of vehicles Fig. 7. The time of crossing the intersection by vehicles.

We also calculated the intersection crossing time taking into account the PCU value (Passenger Car Unit). This parameter allows you to take into account the size of the vehicle. The PCU values we have accepted for different vehicles are as follows: • For a car (passenger car) the PCU value is assumed to be 1. • For a minibuses the PCU value is assumed to be 1.

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• For a trucks (Heavy Goods Vehicle) the PCU value is assumed to be 2. • For a bus (Articulated Buses) the PCU value is assumed to be 3. Figure 8 shows a summary graph of the dependence of the intersection travel time for all vehicles, taking into account the PCU indicator.

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Number of vehicles, taking into account the PCU indicator Fig. 8. The time of crossing the intersection by vehicles, taking into account the PCU indicator.

It can be seen from the graphs in Figs. 6, 7 and 8 that the main number of vehicles crosses the intersection in a time from 10 to 60 s. This conclusion was obtained using various evaluation methods. The analysis of general data allows us to consider: • to analyze the nature of traffic on the selected site, it is enough to track the movement of passenger cars; • it takes from 10 to 60 s for a vehicle to pass through the intersection near the Holy Resurrection Cathedral in the city of Brest. Exceeding this time by some vehicles, for example, three times may be caused by a situation that deserves attention. The increase in the relative number of vehicles that were delayed at the intersection also indicates the emergence of a situation that deserves attention. An important fact obtained during the study is the possibility of obtaining information about situations that deserve attention in automatic mode. To increase the accuracy of object detection and tracking, additional dataset collection and neural network training are needed.

3 Conclusion In conclusion, let’s turn to the indicators that determine the global Smart Cities Index (Quantum). Energy audit, ecology, and control of CO2 emissions are certainly important.

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However, the safety of moving around the city should also be monitored in real time. For this reason, it is important to create security assessment models using artificial intelligence. The model for assessing the safety of urban highways that we propose is based on the following requirements: • online availability, data collection is carried out using publicly available monitoring and surveillance tools and is offered to participants of the movement for decisionmaking; • automation involves the use of ready–made solutions based on artificial intelligence, free software, human participation at all stages of the system should be minimal; • the use of cloud services, data collection and analysis performed using cloud resources and services; • modularity and extensibility should allow to include various observation points in the system and integrate the results of the analysis. The authors are aware that we have an initial state of research. The research can be continued in the direction of the development of a platform for assessing situations that deserve attention. The platform can develop under the condition of increasing computing power and storage volumes. Meeting this requirement will allow you to collect sufficient data to calculate the capacity of intersections. The accumulation of data will allow comparing the results obtained by such methods as ICU and HCM [15]. Note that ICU and HCM methods require special software and take into account many parameters. But we got the results by attracting small computing resources that are used to automate calculations.

References 1. Czerepicki, A., Choroma´nski, W., Kozłowski, M., Kazinski, A.: Analysis of the problem of electric buses charging in urban transport. Sci. Tech. 19(4), 349–355 (2020) 2. John, L.C., et al.: Human Factors Guidelines, 318 p. National Academy of Sciences, Washington (2012) 3. Prolisko, E.E., Shuts, V.N., Kozinsky, A.A.: Managing the transportation process in the city passenger transport system. Appl. Quest. Math. Model. 3(2.1), 216–223 (2020) 4. Cai, X., Lei, C., Peng, B., Tang, X., Gao, Z.: Road traffic safety risk estimation method based on vehicle onboard diagnostic data. J. Adv. Transp. 2020, Article ID 3024101 (2020). 13 p. https://www.hindawi.com/journals/jat/2020/3024101/. Accessed 30 July 2022 5. Peši´c, D., Vujani´c, M., Lipovac, K., Anti´c, B.: New method for benchmarking traffic safety level for the territory. Transport 28(1), 69–80 (2013). https://doi.org/10.3846/16484142.2013. 781539. Accessed 02 July 2022 6. Nelson, J., Solawetz, J.: YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS (2020). https://blog.roboflow.com/yolov5-is-here. Accessed 02 July 2022 7. Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement (2018). https://pjreddie. com/media/files/papers/YOLOv3.pdf. Accessed 03 July 2022 8. YOLOv5 Documentation. https://docs.ultralytics.com. Accessed 01 July 2022 9. Jocher, G.: ultralytics/yolov5. https://github.com/ultralytics/yolov5. Accessed 01 July 2022 10. Tzu Ta. tzutalin/labelImg. https://github.com/tzutalin/labelImg. Accessed 01 July 2022

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11. Wojke, N., Bewley, A., Paulus, D.: Simple Online and Realtime Tracking With a Deep Association Metric. arXiv:1703.07402v1 [cs.CV], 21 Mar (2017) 12. Simple Online Realtime Tracking with a Deep Association Metric. https://github.com/nwo jke/deep_sort. Accessed 01 July 2022 13. Quatum. Energy Monitoring & Reporting. https://www.quantumesco.it/en/energy-monito ring-reporting. Accessed 01 July 2022 14. Passenger Car Unit. https://www.cycling-embassy.org.uk/dictionary/passenger-car-unit. Accessed 05 Aug 2022 15. Calculation of MOEs by Traffic Analytical Tools. https://ops.fhwa.dot.gov/publications/fhw ahop08054/sect4.htm. Accessed 05 Aug 2022

Attention-Based Spatio-Temporal Graph Convolutional Networks – A Systematic Review Jelena Perevozcikova(B) and Dmitry Pavlyuk Transport and Telecommunication Institute, Lomonosova 1, Riga 1019, Latvia [email protected]

Abstract. Spatio-temporal attention-based Graph Convolutional Networks will be the subject of the current review. The aim is to identify the status quo of the research done so far in this area and identify knowledge gaps still to be covered. Graph Convolutional Networks (GCN), which is one of the most powerful deep learning algorithms, is still a rather emerging field with applications in traffic modelling. GCNs are an important aspect of Intelligent Transportation System. Since traffic flows are connected with spatial and temporal correlations of multiple factors, Spatio-temporal GCN were introduced. They were meant to address the fact that traffic flow is highly non-linear and not time- and spacially independent. The idea of the attention mechanism is that more attention and thus more weight is given to the information bits that contribute the most to the prediction precision. Later attention-based method was applied to solve temporal problems. To identify relevant studies, we utilised the following academic search engines: TRID, Scopus, IEEE Xplore, IET Digital Library (search by titles and abstracts), Google Scholar and Science Direct (full-text search). Since the model under interest is a rather new method, the reviewed bibliographic database covers the period from 2017 to 2022. The focus was made on empirical studies with real or simulated data. Gaps were identified, research directions were classified and described. Keywords: Spatio-temporal · Attention-based · Graph convolutional networks · Traffic modelling

1 Introduction An increased interest in Graph Convolutional Networks (GCN) is observed in the traffic modelling field. One of the explanations of the popularity of this method is the fact that, e.g., road and public transport networks may be represented as graphs. At the same time many researchers admit that GCN do not demonstrate a desired precision, so original GCN get different add-ons in the hope of improvement. The aim of this review is to investigate how GCN have evolved since they were first introduced by Kipf et al. [5]. We will first study the added spatio-temporal aspect to GCN and later consider the additional attention mechanism. Attention-based spatio-temporal GCN will be the highlight of the current review. Attention mechanisms have been formulated outside of the GCN context but they present a high potential in improving the modelling precision of GCN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 26–33, 2023. https://doi.org/10.1007/978-3-031-26655-3_3

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1.1 Graph Convolutional Networks When data are visualized in graphs, the nodes represent various states of the system and connections among them signify what states are interrelated. Such a graphical structure appears in many examples in real life, including traffic prediction [1] or computer vision [2]. In traffic prediction one can imagine nodes as origin/destination points and connections as roads between the points. A graph can transmit spatial dependencies with longer connections indicating longer distances. However, working directly with graphs often does not yield satisfactory results since real world problems lead to extremely complicated graph structures. Despite applying various embedding methods, no universal method that would be good for every situation has been found so far. At the same time deep learning algorithms have been successful in numerous applications [3]. One of the algorithms is Convolutional Neural Networks (CNN) that have proven to be successful in several applications, e.g., in images [4]. One of the reasons of the high performance is the ability of CNNs to make use of the stationarity and compositionality of some data types especially dealing with images. The basic idea of CNNs is to apply convolutional layers to hierarchical patterns and extract high-level features. As a result, a set of filters should be learnt that are able to scan every pixel. We will refer to Kipf et al. [5] to give a formula of a convolution rule:   1   −2 − 21 (l) (l) (l) ˆ ˆ ˆ , (1) f H , A = σ E AE H W ˆ = A + I , where I is the identity matrix, A is graph structure in matrix form and with A ˆ W (l) is a weight matrix for the l-th neural Eˆ is the diagonal node degree matrix of A. network layer, σ(·) is a non-linear activation function, H (l) is the l-th neural network layer. Having said this, graphs do not represent such a regular structure as images do, as a result CNNs are not as successful there. It has been a work of many years to try to adapt CNNs to graphs [3]. One of the research directions was to define graph convolutions. Kipf et al. [5] provided a thorough review of operations and analyses commonly applied to graphs. As Fig. 1 shows, GCN consists of an input layer, several hidden layers and an output layer. GCN may be split into two groups - spectral graph convolutions and spatial graph convolutions [3]. Moreover, GCN can be regarded as a powerful variation of CNN. Often it naturally happens such that original methods are rather simple and then they are made more complicated to meet real-world needs. However, GCN, being an extension of neural networks, have demonstrated a relative complexity from the outset [1]. The nonsimplicity arrives from the fact that GCN layers are non-linear. Wu et al. [6] proposed a simplified version of GCN by removing nonlinearities and collapsing weight matrices between consecutive layers. GCN, which is one of the most powerful deep learning algorithms, is still a rather emerging field [7] with applications in traffic modelling. GCNs are an important aspect of Intelligent Transportation System (ITS). With the introduction of deep-learning technologies in the field of traffic forecasting, it broadened the variety of spatio-temporal dependencies that could be now investigated

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[20]. Since traffic flows are connected with spatial and temporal correlations of multiple factors, Spatio-Temporal GCN (STGCN) were introduced. They were meant to address the fact that traffic flow is highly non-linear and not time- and spatially independent [8].

Fig. 1. Schematic depiction of Graph Convolutional Network (GCN) with A input channels and B feature maps in the output layer. The graph structure (edges shown as black lines) is shared over layers, labels are denoted by Yi . Source: based on [5].

Kong et al. [12] propose a model which is supposed to reflect spatio-temporal dependencies, however, the authors call their model temporal graph convolutional network (T-GCN). The model is a combination of the GCN and the gated recurrent unit (GRU). The authors explain that GCN is responsible for the spatial dependencies and GRU for the temporal dependencies. It is claimed that T-GCN is more successful in obtaining spatio-temporal correlation than some state-of-art baselines. STGCN have been applied in train time delay [9], passenger flow [10, 14], origindestination ride-sourcing demand [11], diagnosis and treatment response prediction [13] and many more. In Jinjun et al. [9] it is claimed that train delay prediction is a common spatio-temporal problem. The data of neighbouring stations and time points are dynamically related to each other. In Jintao et al. [10] a combination of the GCN and the gated recurrent unit (GRU) is used similar to Kong et al. [12]. In Zhang et al. [13] STGCN framework was obtained by putting forward the modules of spatial graph attention convolution (SGAC) and temporal fusion at each stage which was obtained from using a sliding temporal window method on functional magnetic resonance imaging (fMRI).

2 Methodology of the Review 2.1 Search Strategy Since the area of our interest was very narrow, we looked for all the articles containing the words: spa∗, tempor∗, attention∗, graph convolutional networks, where * is a wildcard. These terms cover numerous references to the spatial dimension (“spatial”, “spatiotemporal”, “space”). All of these terms should have been present

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simultaneously, otherwise the articles showed big deviations from our topic. The reasons why all of the terms had to be in the name of an article together were: • Many articles deal with only one aspect, either spatial or temporal. As Pavlyuk in [22] claimed, many authors consider the temporal dimension but not the spatial dimension of the traffic forecasting. Furthermore, others admitted the potential use of considering the spatial aspect in the modelling but did not actually apply it. That is why we concentrated on those articles that explicitly used the spatial dimension in their empirical research. • We were interested in exactly GCN rather than in any other broader architecture of networks, e.g., Graph Neural Networks. The scope of the current review was based on the following aspect: • We focused on the empirical research but we did not exclude theoretical articles either. • We did not restrict ourselves to any particular areas of application of ASTGCN. • We also considered the various attention mechanism definitions brought forward by multiple authors. To identify relevant studies, we utilised the following academic search engines: TRID, Scopus, IEEE Xplore, IET Digital Library (search by titles and abstracts), Google Scholar and Science Direct (full-text search). The search brought forward 808 articles, which were further filtered on the basis of the three criteria specified above. Such a big number of articles can be explained with the fact that the search did not perfectly convey only those articles that contained all the keywords simultaneously. In reality, it turned out that only on ten occasions did all the keywords come all at once. Thus, the authors had to filter the suitable articles manually.

3 Attention Mechanisms As Zhaoyang et al. in [23] claimed, attention had become one of the vital ideas in the deep learning domain because it indeed could be applied in most models in various fields of the deep learning. We will further refer to a thorough review of attention mechanisms by Zhaoyang et al. in [23] to categorize attention mechanisms into four criteria: (a) the softness of criteria; (b) forms of input feature; (c) input representations; (d) output representations. The reader should note that these criteria are not necessarily mutually exclusive. Bahdanau et al. in [24] introduced the softness criteria where the authors explained why soft attention was more preferable than hard attention. Furthermore, depending on the input feature one can divide attention mechanisms into location-wise and item-wise. In the item-wise mechanism the input must be explicit items. The item-wise soft attention computes a weight for each item, and then makes a linear combination of them. The location-wise soft attention takes an entire feature map as input and creates a transformed version through the attention module [23]. Chaudhari et al. in [25] talked about the nature of the input feature – it can be a single or a multiple

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input. Zhaoyang et al. in [23] continued in a similar way discussing the output features that can also be single or multiple. Attention mechanisms have been used in areas like document classification [26], computer vision [27], machine translation [24], image-based analysis [28] and many more [28].

4 Attention-Based Spatio-Temporal Graph Convolutional Networks (ASTGCN) It is a very logical step forward to argue that closely lying to each other locations are more correlated with each other than some distant ones. That is why it is reasonable to add an attention-based aspect to the existing STGCN. For an example of an attention mechanism we will refer to Huang et al. in [21]. The proposed attention mechanism choses the top K-percent nodes from the current graph structure in accordance with the calculated attention values, and then accumulates the information from close to the selected nodes. Huang et al. [21] define an  neighbours  attention function fatt H l to generate a positive weight Atti for each node υi in the graph, which can be interpreted as the relative importance given to υi in the current graph. Then, the indexes of k-largest nodes are selected, where k is a dynamic scalar determined by down-sampling rate α. The process can be formulated as follows: Idx = fTopK (Att, k),

(2)

k = max(1, αNl ),

(3)

  Att = fatt H l , Att ∈ RNl ×1 ,

(4)

where fTopK(·) is a sorting function and produces indexes of the largest k nodes, and · is the operation of rounding down. Wang et al. [15] introduced Attention-based Spatio-Temporal GCN (ASTGCN) to address traffic flow prediction problem which is meant for collective traffic flow prediction at each location of the network. Various attention mechanisms had been applied before in different areas, such as, image caption and speech recognition. Wang et al. [15] applied the model on two real-world datasets and claimed that their model outperformed the existing baseline models. Wang et al. [15] used two kinds of attentions that addressed two types of correlation – spatial and temporal. Bai et al. [16] also claimed that the original Graph Convolutional Network cannot fully grasp dynamic traffic flows, that is why an additional attention mechanism was required. The authors proposed their version of such a mechanism which was incorporated into GCN. To do this, they formulated a so-called attention matrix. They also compared their model with Wang et al. [15]. Chen et al. [17] used the model to predict passenger flows. Attention-based approach was applied to depict the most influential time points. The idea behind was rather simple:

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what happened an hour ago is not so much influencing what is happening now as opposed to what was 5 min ago. Or, e.g., if a morning rush hour was less busy than usual, potentially, the evening rush hour will also be less busy. Fang et al. [18] use the attentionbased model from another angle. They claim that nearer nodes in a graph influence each other more than distant ones, thus, more attention should be paid to closer nodes. Byeonghyeop et al. [19] conclude that the attention mechanism makes additional use of the joint effects of spatial and temporal parameters. The authors claim that the model was applied on many large-scale real data bases and that it proved its efficiency. Jihua et al. [20] make a step further and proposed ASTGCN-EF indicating that their model considered some external factors. To conclude, ASTGCN is a new and still emerging model with researchers proposing their own versions of attention mechanisms. It seems that there is evidence that attention mechanism improves the performance when compared to ordinary STGCN. However, although some good performance has already been demonstrated by ASTGCN there is still space for further research. To the best of the authors’ understanding, the majority of the articles that considered ASTGCN dealt with item-wise soft attention. Thus, item-wise global attention was not covered by the articles observed. Potentially, this is the area for further research in order to understand if global attention also offers effective models.

5 Conclusions In this review we have observed one aspect of the development from transport modelling with GCN to ASTGCN. We have seen that the last add-on to the model – the attention mechanism in itself is not new but its use in the context of transport modelling seems to be at its dawn. It seems that there is evidence that attention mechanism improves the performance when compared to ordinary STGCN. However, although some good performance has already been demonstrated by ASTGCN there is still space for further research.

References 1. Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Datadriven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017) 2. Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: CVPR, vol. 1, p. 3 (2017) 3. Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Comput. Soc. Netw. 6(1), 1–23 (2019). https://doi.org/10.1186/s40649-019-0069-y 4. Guo, T., Dong, J., Li, H., Gao, Y.: Simple convolutional neural network on image classification. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 721–724 (2017) 5. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR 2017) (2017) 6. Wu, F., Zhang, T., Souza, A.H., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying Graph Convolutional Networks. ArXiv, abs/1902.07153 (2019)

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7. Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011) 8. Zhang, D., Peng, Y., Zhang, Y., Wu, D., Wang, H., Zhang, H.: Train time delay prediction for high-speed train dispatching based on spatio-temporal graph convolutional network. IEEE Trans. Intell. Transp. Syst. 23(3), 2434–2444 (2022) 9. Tang, J., Liang, J., Liu, F., Hao, J., Wang, Y.: Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Transp. Res. Part C: Emerg. Technol. 124, 102951 (2021) 10. Ke, J., Qin, X., Yang, H., Zheng, Z., Zhu, Z., Ye, J.: Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Transp. Res. Part C: Emerg. Technol. 122, 102858 (2021) 11. Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2020) 12. Kong, Y., et al.: Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity. Hum. Brain Mapp. 42(12), 3922–3933 (2021) 13. Junbo, Z., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-First AAAI Conference on Artificial Intelligence (2017) 14. Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc. AAAI Conf. Artif. Intell. 33(01), 922–929 (2019) 15. Wang, F., Xu, J., Liu, C., Zhou, R., Zhao, P.: MTGCN: a multitask deep learning model for traffic flow prediction. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds.) Database Systems for Advanced Applications. DASFAA 2020. LNCS, vol. 12112, pp. 435–451. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59410-7_30 16. Bai, L., Yao, L., Kanhere, S.S., Wang, X., Sheng, Q.Z.: STG2Seq: spatial-temporal graph to sequence model for multi-step passenger demand 55 forecasting. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 1981–1987. AAAI Press (2019) 17. Chen, W., Chen, L., Xie, Y., Cao, W., Gao, Y., Feng, X.: Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, (2020) 18. Fang, X., Huang, J., Wang, F., Zeng, L., Liang, H., Wang, H.: ConSTGAT: contextual spatialtemporal graph attention network for travel time estimation at Baidu maps. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining KDD 2020, pp. 2697–2705. Association for Computing Machinery, New York (2020) 19. Yu, B., Lee, Y., Sohn, K.: Forecasting road traffic speeds by considering area-wide spatiotemporal dependencies based on a graph convolutional neural network (GCN). Transp. Res. Part C: Emerg. Technol. 114, 189–204 (2020) 20. Ye, J., Xue, S., Jiang, A.: Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction. Digit. Commun. Netw. 8, 343–350 (2022) 21. Jingjia, H., et al.: AttPool: towards hierarchical feature representation in graph convolutional networks via attention mechanism. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019) 22. Pavlyuk, D.: Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review. Eur. Transp. Res. Rev. 11(1), 1–19 (2019). https://doi.org/10.1186/s12544019-0345-9 23. Niu, Z., Zhong, G., Hui, Y.: A review on the attention mechanism of deep learning. Neurocomputing 452, 48–62 (2021) 24. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

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Aviation

Model of Decision Support System with Artificial Intelligence for Aircraft Fuselage Damage Assessment Irina Bodrova(B) Transport and Telecommunication Institute, 1 Lomonosova Street, Riga, Latvia [email protected]

Abstract. Airline companies together with aircraft maintenance, repair and overhaul (MRO) organizations are entirely responsible for the safe flight. In these circumstances, to become the industry leaders, they also need to keep in mind that time is critical as well. Airlines and MRO organizations are permanently searching the adequate balance between the flight safety and the cost of aircraft maintenance. This makes the repair and overhaul the significant element of the industry. To speed up the process of aircraft maintenance MRO organizations need to accurately measure all its possible surface impact damages. Developing digital tooling can significantly improve the efficiency of the external surface damage definition. Additionally, the data collection, storage and analysis can prove advanced aircraft reliability monitoring. 3D scanner with artificial intelligence integration can be an optimal solution for the aircraft fuselage damage assessment. This paper starts with providing a brief observation and investigation of the scanning technology. It introduces the existing methods for the aircraft surface inspection in MRO organizations. Additionally, the article describes the models of decision support systems for the fuselage damage identification. The key area of the study is to formulate basic principles of modeling the advanced aircraft damage assessment. The conclusion formulates recommendations of the improvement of the described process. Keywords: Aircraft inspection · 3D scanner · Progressive tooling · Airline · Reliability

1 Introduction Due to commercial airplanes features, their long lifetime, the maintenance and repair tasks have a huge impact on the airlines business. The industry pays much attention to MRO processes development and standardization. Improving innovative techniques permits to eliminate unnecessary costs and boost the productivity of aircraft maintenance and repair. Civil aircraft repair process is a complex set of sequential operations. It includes coordinated work of engineers, logistics and maintenance personnel. The repair starts with © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 37–49, 2023. https://doi.org/10.1007/978-3-031-26655-3_4

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the defect detection and inspection. According to [1], these processes are still characterized by a high manual effort, because adequate digital product models for automation purposes are often not available. The aircraft damage assessment is considered to be the most time-consuming process for the MRO personnel. Under these circumstances developing advanced digital equipment becomes an actual task. Among the issues of diagnostics, a special place belongs to monitoring the state of fuselage. The ability to capture the required data quickly and precisely makes 3D scanning technique widely used in all industries. The scanners become more popular than ever in aircraft manufacturing, maintenance and repair. The new technic allows to make the surface inspection process well standardized. This means that using digital device excludes the influence of human factor. Consequently, it improves the accuracy of surface defect measurement. No doubt optical metrology will soon be used to perform continuous, high-speed inspections of items of all shapes, sizes, and materials [2]. Nevertheless, 3D scanners have several disadvantages. These can directly influence the aircraft repair quality. Some systems do not work in rain. It makes application of 3D scanners in the airport field impossible. Only MRO organizations with hangar facilities can use them. Additionally, lighting is critical for result output in the scanning process. Aircraft external surface is painted with very glossy paint. It causes light reflection and makes optical 3D scanning process inconclusive. Finally, the output data is provided in a picture format. It is difficult for analysis and not suitable for comparison and sorting. This article provides a research analysis of data accuracy received during 3D scanning of aircraft fuselage external surface damages. It proposes algorithms for the system of decision-making construction. The aim of the paper is to formulate recommendations for modeling the aircraft fuselage defect detection and inspection process within the frame of new digital maintenance ecosystem of MRO organizations.

2 3D Scanning Related Works A 3D scanner is a device that allows to digitize an object from the real world and get its three-dimensional model. The first prototype appeared more than 160 years ago. Nowadays, there are a huge number of scanner manufactures, and there is more than a dozen of surface digitization technologies. The paper [3] provides information concerning 3D scanners history and usage. According to [4], there are varieties of technologies for digitally acquiring the shape of a 3D object. Many of them are based on the human experience of information perception about the object volume. Standard classification divides them into two types: contact and non-contact 3D scanners (see Fig. 1). The implementation of 3D technology is changing industries [5]. In many cases using only one 3D scanning technique is impossible. Combination of several different methods gives a good result. Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use 3D scanning with sensors to perform automated and semi-automated inspections.

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Fig. 1. Classification of 3D scanners.

Today there are a lot of professional 3D scanners and 3D scanning technologies. [6, 7]. The scanner allows to obtain high density nodal points so the measurable object can be accurately imitated as a 3D model and its accuracy of generation can be analyzed [8]. Paper [9] presents a technique of 3D scanning applied to the training aircraft blade so as to copy its precise geometry after a specified period of operation. Scanning enables the use of a created geometry for numerical analyses, in particular of icing during aircraft operation. Using the special software for 3D scanning of damage assessment on airplanes such as hail damage, impacts, corrosion, seat rails, and more is studied in [10]. The current study provides comparison of accuracy and duration of manual damage definition technics and 3D scanning technology in real conditions of the aircraft surface diagnostics and discussion of approach for use of artificial intelligence model in the maintenance ecosystem of MRO organizations.

3 The Models of Decision-Making Systems Based on the Results of Aircraft Fuselage Damage Diagnostics In the general case, the decision-making process based on the results of fuselage damage diagnostics includes several stages (see Fig. 2).

Fig. 2. The main stages of aircraft fuselage damage diagnostics.

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The paper considers three models of decision support systems: • Classic visual inspection and decision-making by service personnel. • Damage identification using a 3D scanner and decision-making by maintenance personnel. • Damage identification using a 3D scanner and artificial intelligence application as a decision support system. 3.1 Model 1. Aircraft Fuselage Damage Assessment by Service Personnel Typical model of aircraft fuselage damage manual assessment is demonstrated below (see Fig. 3). Each step ends with paperwork completion, data sorting and saving. The process is considered to be very time-consuming.

Fig. 3. Model of aircraft fuselage manual damage assessment.

A dent on the aircraft metal fuselage is chosen for the case study. The damage is the external surface deformation without removal of material or net area change. The dent has smooth bottom where the metal is not creased or cracked. The aircraft damage assessment always starts with the surface visual inspection. It is the most basic and common method. Clean surface and good lightning are required for successful results at this step. Once the damage found it is measured using certified manual devices: ruler and depth gauge-meter (see Fig. 4). Additional instruments can be used, if required. The distance from the damage to the significant aircraft elements (frame stations, stringers, fasteners, cut-outs) is measured with ruler (see Fig. 5). Since all the required information is defined, it is analyzed in comparison with damage size allowed for current aircraft and fuselage zone location. If the dent length

Model of Decision Support System with Artificial Intelligence for Aircraft

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Fig. 4. Dent depth measurement.

or depth is greater than those described in technical documentation, maintenance staff prepare damage inspection sketch to be send to engineering team. After that it is analyzed for the aircraft airworthiness requirements and a repair scheme is provided.

Fig. 5. Dent locating.

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The current case shows the following dent dimensions: – dent depth is 0.21 mm; – dent length is 38 mm; – distance from aircraft significant item (fastener row) is 185 mm. The damage analysis shows that the dent is acceptable for the aircraft fuselage external surface. Further actions or repair scheme are not required. 3.2 Model 2. Aircraft Fuselage Damage Assessment Using 3D Scanner and Decision-Making by Maintenance Personnel An important aspect of aircraft engineering and maintenance involves the detection and measurement of damage on aircraft surfaces [11]. MRO processes and techniques are standardized by Original Equipment Manufacturers (OEMs). This is the reason why not all of the described 3D scanners are allowed to be used during the aircraft maintenance and repair. Optical technology is considered to be the preferred model. It has greater range and can capture huge data amounts very quickly. These features are very much desirable for such a big object as an airplane. Additionally, it enables the capture to adding graphic elements. It allows the damage sketch preparation and sending it to engineering staff with the purpose of its further assessment. To perform the scanning technology with manual aircraft external surface assessment comparison Damage Reporting Tool (DRT) manufactured by 8-tree company was used. The device was built specially for airlines and MRO organizations. It measures and analysis such surface defects as dents, bumps, lightning-strike damages and blend-outs on metallic, composite, curved and flat surfaces. The results are instantly projected on the aircraft surface. Table 1 provides major characteristics of DRT. Table 1. DRT characteristics. 18 × 32 cm

Field of view Resolution

Diameter

250 µm

Depth

25 µm

Measurement speed

as fast as 2 s

Battery life

up to 4.5 h (still usable while charging)

Nominal working distance

50 ± 5 cm

Weight

4 kg

Dimensions

35.4 × 30.0 × 9.0 cm

There are a lot of technology instructions for 3D scanning development [12]. The model of aircraft damage assessment with the digital tool is presented below (see Fig. 6).

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3D scanner locates and measures the damage just in seconds. However, the tool output data is still analyzed by engineers. The specialists sort, store the images and make the decision based on Structure Repair Manual (SRM) manually. The process of aircraft external surface damage assessment depends on MRO personnel knowledge and qualification. In case of big volume of the data it becomes a very complex task. Additionally, there is a risk of human factor presence. Digital product is still a major challenge especially for complex products such as aircraft [13].

Fig. 6. Model of aircraft fuselage damage assessment using 3D scanner and decision-making by maintenance personnel.

The same aircraft external surface dent is inspected using DRT. The first step is its assembly, configuration and calibration. Approximately it takes 10 min. The second step is to scan the damage. The result appears in 2 s (see Fig. 7). The case shows the following dent dimensions: – dent depth is 0.26 mm; – dent length is 35 mm; – dent width is 29.5 mm. Additionally, DRT defines the width to depth ratio. It is a very important parameter required for accurate damage assessment. DRT does not measure the distance from aircraft significant item to the dent. However, it can be defined during the damage sketch preparation with the help of special computer program. DRT shows that the dent can be considered as aircraft surface allowable damage. Manual damage assessment and 3D scanner technique show the same final result. The subject dent does not require its repair scheme proposal. However, the damage

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Fig. 7. Dent 3D scanning result.

dimensions differ. The dent depth measurement deviation is approximately 19%. It proves that human factor can significantly affect the aircraft maintenance and repair process, and finally, its airworthiness. The aircraft technical documentation defines the time required for the fuselage dent inspection. The practice shows that it takes 10 min for getting access to the damaged surface and approximately 10 min to measure all the required dimensions. The time includes the surface cleaning from oil and dirt, drawing additional lines with marker on it and finally deleting these drawings. After these operations the maintenance personnel register the damage and attach a full package of documents to it. It requires up to 60 min. Consequently, it takes 80 min for manual aircraft metal surface damage assessment. DRT usage requires 10 min for getting access to the damaged area, too. The device preparation takes additional 10 min. The final result preparation requires only 2 s for scanning and 5 min for the report printing out. The time safe is approximately 31% for the single dent assessment. Additionally, DRT can identify and assess multiple surface damages (see Fig. 8). It takes the same time as for one defect inspection. In this case the time safe can be up to 90%, as it is proposed by the device manufacturer. According to [14], using 3D scanning techniques, it is easy to identify surface deviations of less than 0.5 mm, and this makes the identification of damage features very easy where humans are so clearly limited in this respect. However, optical 3D scanner has a huge disadvantage. The object cannot be properly inspected in bright light (see Fig. 9). It is related to the aircraft external metal surface painted with very gloss paint. To remove the light gloss Ardrox 9D1B spray was tested. It is approved for aircraft external surface usage for non-destructive testing. Its main components are propanol and talc. The spay can be easily removed from surface with lint free cloth. The result of testing shows effectiveness of the proposal (see Fig. 10).

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Fig. 8. Multiple dents assessment.

Fig. 9. Light reflection during 3D scanning.

3.3 Model 3. Aircraft Fuselage Damage Assessment Using 3D Scanner and Artificial Intelligence as a Decision Support System At present, all prerequisites have been created for the transition to full automation of the processes of aircraft fuselage damage identification and assessment. At the same time, the model for solving this problem is reduced to automating two groups of operations (see Fig. 11): • robotic scanning of the fuselage surface using 3D scanner; • the use of artificial intelligence for the analysis and classification of images obtained as a result of scanning.

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Fig. 10. Usage of additional surface coating.

Fig. 11. Architecture of the system for automated identification of aircraft surface damage.

To solve the first task a digital twin of the aircraft fuselage surface is created with the help of mobile robots equipped with 3D visual sensors to perform automated inspections. It can be carried out, for example, by unmanned aerial vehicle or autonomous drone. The result of this scanning is a three-dimensional model of the aircraft surface, which is its digital twin from the point of view of damage diagnostics (see Fig. 12). Artificial intelligence is a branch of computer science that comprises machine learning and deep learning. These methods are successfully used as tools to solve the problem of detection and diagnostic. With science and technology development, data interpretation manually in the conventional computer-aided systems has gradually become a challenging task. However, convolutional neural networks (CNNs) are effectively used to enable machines to visualize things. It helps to perform analysis on images and visuals. These classes of neural networks can input a multi-channel image and work on it easily with minimal preprocessing required.

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Fig. 12. The digital twin 3D model for aircraft surface damage identification.

To solve the problem of the aircraft damage assessment CNN takes the result of 3D scanning in image format as an input, processes it and classifies it under certain categories. Convolution process is operated by a kernel or a filter over the whole image. It is the first part of the layer in CNN. The objective of the operation is to extract the highlevel features such as edges from the input picture. However, the convolution nets need not be limited to only one layer. Conventionally, the first convolution layer is responsible for capturing the low-level features such as edges, color, gradient orientation, etc. With addition of the layers, the architecture adapts to the high-level features and provides a network, which has the good understanding of images in the dataset. Similar to the convolutional layer, CNN pooling layer is responsible for decreasing the spatial size of the convolved feature of the image. This is to reduce the computational power required to process the data through dimensionality reduction. Moreover, it is useful for extracting dominant features which are rotational and positional invariant which maintains the process of effectively training of the model. There are two types of pooling: max pooling and average pooling. The first one yields the maximum value from the portion of the image enclosed by the kernel. On the other hand, the second gives the average of all the values from the portion of the image covered by the filter. CNN fully-connected layer is a cheap way of learning non-linear combinations of the high-level features as represented by the output of the convolutional layer. The fully-connected layer is learning a non-linear function in that space. Each layer to produces the convolved image as the input into the next layers. The activation function transforms the summed weighted input from the node into the activation of the node or output for that input. Afterwards, the rectified linear activation function (ReLU) outputs the input directly if it is positive, otherwise, it outputs zero. It has become the default activation function for many types of neural networks because a model that uses it is easier to train and often achieves better performance. The raw outputs of the neural network are often very difficult to interpret. The softmax activation function simplifies this for decision makers. It transforms the raw outputs of the neural network into a vector of probabilities, essentially a probability distribution over the input classes.

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In case of the aircraft surface damage assessment, at the output of the system, MRO personnel can get the probabilities of three main decisions that can be made as a result of diagnostics: • no damage detected; • no critical damage detected; no additional maintenance operations required; • critical damage detected; it is necessary to stop the aircraft operation and perform maintenance or repair tasks. The described model can be modified to solve the main problems of the aircraft fuselage damage assessment. To optimize the digital process of 3D scanning it is required to change the format of output data from images to text or tables. Artificial intelligence is considered to be a good solution of the described task. According to [15], it is basically a prediction technology. It has an ability to learn from the changing environment. This requires discovery of algorithms, and the ability to remember facts, skills and even learning strategies [16]. Consequently, machine intelligence can learn from the data provided by 3D scanner, predict the aircraft fuselage repair scheme and its total maintainability. MRO personnel do not need to analyze the damage with SRM in the proposed digital model. It is done with the help of artificial intelligence. All this gives an opportunity to exclude the most time-consuming steps of the process. It makes the artificial intelligence able to become the main driver of the development of digital process for the aircraft reliability monitoring.

4 Conclusion Aircraft surface gets different types of damages during its operation. Each of them is required to be properly inspected and investigated. This directly affects the flight safety. Additionally, it is important to be able to plan the aircraft repair, track data and run reports. The records utilizing ensures the efficiency of the resources allocating. All this can be implemented in digital ecosystem of the aircraft maintenance on the base of MRO organization. Provided machine intelligence model for aircraft fuselage damage assessment is going to be used for further development of digital process for monitoring the advanced aircraft reliability.

References 1. Grosser, H., Muller, P., Stark, P.: Integrated solutions of 3D scanning and generation of digital product models in MRO processes. In: Proceedings of the 1st International Conference on Through-life Engineering Services (TES-Conf)-Enduring and Cost-Effective Engineering Support Solutions. Cranfield University, UK (2012) 2. Larue, J.F., Brown, D., Viala, M.: How optical cmms and 3d scanning will revolutionize the 3D metrology world. In: Liu, Z., Ukida, H., Ramuhalli, P., Niel, K. (eds.) Integrated Imaging and Vision Techniques for Industrial Inspection. Advances in Computer Vision and Pattern Recognition, pp. 141–176. Springer, London (2015). https://doi.org/10.1007/978-1-4471-674 1-9_5

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3. Dyuzhev, V.: 3D Scanners. History and usage. Measurements world, Issue 4 (2021) 4. Ebrahim, M.A.-B.: 3D laser scanners’ techniques overview. Int. J. Sci. Res. 4(10), 323–331 (2015) 5. Javaid, M., Haleem, A., Pratap Singh, R., Suman, R.: Industrial perspectives of 3D scanning: features, roles and it’s analytical applications. Sens. Int. 2 (2021) 6. Artec3D Homepage. https://www.artec3d.cn/files/pdf/Artec3D-Scanners-Brochure.pdf 7. EMS Homepage. https://www.ems-usa.com/tech-papers/3D%20Scanning%20Technol ogies%20.pdf 8. Montusiewicz, J., Czyz, Z., Kesik, J.: Using 3D replication technology in preparing didactic aid sets in the area of cultural heritage on Proceedings, Barcelona, Spain (2015) 9. Babel, R., Sawicki, K., Gasiorowski, M.: Obtaining the 3D geometry of the blade in a training aircraft. J. Phys.: Conf. Ser. CMES 2020 (2021) 10. Allard, P.-H., Lavoie, J.-A., Fraser, J.-S.: Improvement of aircraft mechanical damage inspection with advanced 3D imaging technologies. https://www.creaform3d.com/sites/ default/files/assets/technological-fundamentals/wp__improvement_of_hail_damage_inspec tion_with_advanced_3d_imaging_technologies_19112013.pdf 11. Miranda, L., Paez, D.: Three-Dimensional Laser Scanning Test in Aircraft Surfaces. FIG Working Week 2015, Sofia, Bulgaria (2015) 12. Chen, T., Du, X., Jia, M., Song, G.: Application of Optical Inspection and Metrology in Quality Control for Aircraft Components. Institute of Electrical and Electronics Engineers, V5-294–V5-298 (2010) 13. Klunover, A.: Challenges in implementing digital aircraft technologies. In: ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis on Proceedings. Haifa, Israel (2009) 14. Mumford, P.: Aircraft Damage Assessment Limitations & Application of 3D Scanning Techniques. Airworthiness matters. The International Federation of Airworthiness, West Sussex, United Kingdom (2018) 15. Sadiku, M.N.O., Musa, S.M.: Machine intelligence. In: Sadiku, M.N.O., Musa, S.M. (eds.) A Primer on Multiple Intelligences, pp 125–132. Springer, Cham (2021). https://doi.org/10. 1007/978-3-030-77584-1_10 16. Mikolov, T., Joulin, A., Baroni, M.: A roadmap towards machine intelligence. https://arxiv. org/pdf/1511.08130.pdf

Factors Influencing Value Proposition in the Aviation Industry in the Context of Customer-Centric Digital Economy Olga Zervina(B) and Yulia Stukalina Transport and Telecommunication Institute, 1 Lomonosova Street, Riga 1019, Latvia {Zervina.O,Stukalina.J}@tsi.lv

Abstract. In the face of intensified competition and accompanying challenges associated with business digital transformation, value proposition in the Aviation Industry is becoming a strategic priority. Creating value is regarded as a core thing among the potential components of a successful business strategy. Value proposition is derived from the firm’s strategy to allocate resources in the most efficient way, which is crucial for capturing opportunities and avoiding threats in the extremely competitive global environment. Recent changes in value shift from entertainment and comfort to social responsibility, and from consumerism to eco-friendliness and sustainable growth have significantly affected the aviation domain. Air transportation businesses are now adopting new values in order to boost their profitability and support sustainability of their operations in the agenda of digital transformation. The use of novel customer-centric business models, which are based on new values and are aimed at formation of new relationships, enables the sustainability advantage of an aviation enterprise. The aim of the paper is to identify and discuss the main factors that may influence value proposition in the Aviation Industry in the context of customer-centric digital economy. Research methods involve the review of secondary sources, ICAO, and IATA documents, as well as in-depth expert interviews and the online expert panel discussion on the main challenges faced by the given sector. The obtained results would be used by senior managers of an air transportation enterprise for effective decision-making in the agenda of creating a unique value proposition to their customers in uncertain times. Keywords: Value shift · Air transportation enterprise · Sustainability

1 Introduction Current changes in value shift from entertainment to social responsibility, and from consumerism to environment-friendliness have considerably influenced the air transportation domain. Aviation is a highly technological industry, so a recent value shift from the exploitation of the environment to its protection has affected aviation as well. Developing the company’s strategy in this industry, managers take into account changes in consumer perception of values. New-fangled values are now accepted by modern © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 50–60, 2023. https://doi.org/10.1007/978-3-031-26655-3_5

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companies to increase profitability and support sustainability of business operations. For aviation enterprises it is closely associated with maintaining their fundamental commitments to safety and sustainability in the face of Covid-19 pandemic [1, 2], which had a significant impact on the air transportation sector in terms of decreased passenger traffic, reduced revenues and increased financial losses [3]. Introducing new values is one of the main tasks for today’s successful company. Competitive advantage delivers new challenges; a requirement to identify and provide values for the passengers, as well as delivering the internal value, should be taken into consideration by the Aviation Industry. Value proposition results from the company’s strategy to distribute resources in the most efficient way, which is vital for seizing opportunities and eluding threats in the extremely competitive global environment. The aim of the paper is to identify and discuss the main factors that may influence value proposition in the Aviation Industry. The research methodology includes the review of secondary sources, ICAO and IATA documents, as well as in-depth expert interviews and the online expert panel discussion on the main challenges faced by the Industry. The results of the study are supposed to be applied by senior managers of an air transportation enterprise to facilitate decision-making in the context of creating a unique value proposition to their customers in the uncertain business environment.

2 Literature Review 2.1 Searching for Innovative Approaches to Achieving Values Creation in the Aviation Industry According to the value study conducted by [4], success of companies is driven by ability to deliver value to customers. Though [5] utilized a term “value proposition” (VP) in the article prepared for the consulting firm “McKinsey and Co” in a 1988, [4] suggested an original strategy on the basis of a value proposition differentiated for customers. The proposed value collection is termed “Elements of Value”. Figure 1 presents the Elements of Value; the categories are associated with the Maslow’s Hierarchy of Needs. [6] claims that an appropriately formulated value proposition is critical for creating values in commercial settings; developing of value also provides “building blocks” in terms of value protection mechanism in the contemporary economics. [7] conducted a research grounded on the survey data of 285 digital start-ups, analysing the relationship between technological innovation and value proposition (“exploitative” vs “explorative”) regarding start-ups. Their study revealed that exploratory innovation reinforces constructive outcome of innovative value proposition on the business results of start-ups. The numerous problems which aviation businesses confront necessitate the search for innovative approaches to achieving values creation, market share and profitability. In this respect, innovations in business models can be connected with a new perspective for the enterprise [8]. Introducing new value proposition demonstrates its relevance and to prove it, [9] claim that companies are competing differently when they innovate in their business model. [10] also state that modifications in the business model are critical for business success, since they generate value and business opportunities; they also decrease the “risk of inaction” when a firm still uses similar model that has been effective.

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Fig. 1. Heuristic model demonstrating Elements of Value (with examples). Source: based on [4].

With airline companies, the innovative application of new business models based on new values empowers the “sustainability advantage of anticipation”, named “First-Mover Advance”/FMA, which was suggested by [11]. Putting a successful business model into practice can place an airline ahead of the competition with a well-thought model that enables to add value from innovations implemented [11, 12]. In aviation, traditional dominant values are price, quality, and speed, and it has been dominating for decades [13, 14]. Since business models customarily adopted by airline companies and based on the strategies of low-cost (full-service) may be inadequate to fit to the new market reality [15], “hunting for” new ways to operate an airline in the market for delivering greater value to customers is vital [16]. This trend is also supported by [17, 18]. 2.2 Air Transportation Value Shift Through History Throughout the history of air transportation, the focus placed on consumer value has shifted. In the early days of air transportation, cost was not a significant consideration; the emphasis was on fundamental aspects such as air connections or routes, as well as the mode of transportation itself [19]. In an environment of more regulated airfares, labours to enhance customer value focused on on-board service: flights started their differentiation by introducing classes for passengers, and added value was demonstrated in the difference of seats and cooks [19]. Later, new features for passenger comfort, including

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television and audio systems, have been introduced recently. The emphasis, on the other hand, was on pricing and expenses [23]. Reduced expenses of mass transportation, along with an increase in the number of passengers per plane, enabled more price difference. In addition to the official Foreign Air Transport Association rates for international services, lower-cost, grey-market pricing for international services has arisen. Customer value is no longer just dependent on transportation quality but is increasingly dependent on cheaper fares as well. In the recent decades commercialization of air transportation initiates competition and, subsequently, new values search. An example of new values successfully introduced in aviation: business low cost [26], Finnair flights between two start-up capitals networking on board [27], KLM launched Meet & Seat program [28]. The bilateral systems of air services agreements (ASAs) between countries were formed as a result of the 1944 Chicago Convention on International Civil Aviation [29] that has taken control over the global air transportation since that time. In the developing worldwide air transportation industry, every country’s national airline served routes, air companies charged essentially equal rates, and airlines shared markets as well as earnings on a frequently basis. Some bilateral aviation services agreements (ASAs) also included rules controlling accountability for things such as passenger and aircraft ground management, which were not always followed. When it came to the conditions of bilateral agreements, it was a reflection of the bargaining and present air transportation legislation of the nations, and the resultant efficiency was sometimes relatively poor, while the expenses were frequently extremely high [30]. Because of the ongoing deregulation of air transportation markets, the international air transport sector regulatory system has been characterized by a steady decline since the end of the 1970s. These trends began from the liberalization of the United States domestic market in 1978 [31], was followed by the deregulation of the domestic markets in the 1980s in Australia, the United Kingdom, New Zealand, and Canada, and culminated with the full deregulation completion of the European Union in 1997. As a result, airlines are more able to deploy their resources according to their needs in terms of space and time. As a result of deregulation, the world’s air transportation sector has grown significantly more competitive [32]. Airports of the past were competing mostly in terms of finances: the price was the key element of competition. Nonetheless, today’s reality dictates the idea of insufficiency for airports to compete on prices only. Airlines have now more sophisticated needs than ever before; they are utilizing financial incentives no longer has the impact it once did, as John Jarrell, Head of Airport IT Amadeus, stated [20]. Competing from other airports and newer transportation hubs is a significant motivator toward modification, and airports realize it not to be feasible any more incentivizing only on the basis of cost savings. Instead, they should concentrate on providing clients with more complete advantages (ibid.). The changes dictated by the air transport liberalization process led to an increase in competitiveness [21]. [22] emphasizes the differences in the approach to maintaining value and the ability to generate value. [24] states that today, cost competition is not the only driver of modern business. Instead, for example, airports should concentrate on developing a more complete value offer that takes into account the economic requirements of the surrounding communities as well as their own. It expresses the value it

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creates as a liable business which takes a serious responsibility for the area around, as evidenced by its unwavering commitment to development and sustainability. Each airport establishes value proposition aimed at every client goal which is supported by the data collected throughout the research. Communicating that value proposition effectively is now an essential feature of any modern airport [24]. Table 1 provides an insight into value shift derived from the ratio graphs in aviation transport through history, from speed and safety in the early 20th century to ecology and sustainability in the early 2000s. It is worth noting that values were added instead of changing one to others. Table 1. Air transportation value shift through history. Source: authors’ construction based on the literature review. No

Value

Period

Example

1

Speed, safety

1910–1920

No posters with price, speed only

2

Speed, safety, comfort

1930–1960

On-board service introduced

3

Speed, safety, comfort, price

1970–2000

Deregulation of air transport markets leads to price competition

4

Speed, safety, comfort, price, ecology, sustainability

2000–present

Airport as a driver for growth for the surrounding region

As seen from Table 1, the evolution of values is associated with value shift from entertainment and comfort to social responsibility, from consumerism to eco-friendliness and sustainable growth. The following question arises in the context of value shift in the field: What factors have the biggest impact on the managerial decisions regarding value proposition in the Aviation Industry? Below, the main factors influencing value proposition in air transportation are discussed in more detail.

3 Research Results and Discussion 3.1 Economic Characteristics that Drive Value Proposition Within the Air Transportation Industry The Aviation Industry is characterized by a number of features – unstable fuel prices, incompetent management, confrontational trade unions, overcapacities, economic decline and recession, insufficient cost-effectiveness, such occasions as terrorist attacks, outbreaks of disease and natural disasters, which intensify its turbulent and highly volatile nature [25, 33–35]. Due to the relevantly recent deregulation of air transportation and high dependence on the security policies, common specific features define value proposition in the Industry. [36] developed a range of influential factors and associated economic characteristics that influence and drive value proposition within the Industry, which are presented below.

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• Perishable product. In fact, the airline product is intangible; the transportation capacity is available for a certain period and “vanishes” after the aircraft has departed. • Similar products. The airline product is also homogenous; consequently, is very similar across various airline companies, both low-cost carriers and full service commercial airlines. • Low margin costs structures. High fixed costs in aviation could be tracked back to individual airlines. Additionally, the marginal expenses of additional passengers are rather low; this bolsters the carrier’s price discounting. • Substantial withdrawal barrier. Aircraft capacity normally stays in the market; it vanishes in the extended long-term, also despite the fact that concrete airline company may be out of business. Moreover, the plane could be moved into a different business location fairly easily. Airports’ infrastructure never entirely disappears; it is capable of being reintroduced to service at a low marginal cost; for instance, Berlin Brandenburg Airport was constructed in 2011 and licensed in 2020. • Capacity. The so-called “incremental” capacity can be required for different purposes: a) growth within the existing route network (purchase of new aircraft is necessary to provide traffic growth resulted from an expanding market or increased market share); b) for satisfying new missions. Besides, airport infrastructure remains available for a long period of time since investment decisions are made. • Industry growth. The growth of the Industry was fast, but unpredictable and very heterogeneous across geographic locations, which resulted in recurrent short periods of profitability, yet for the periods when the average revenues were low. These unstable periods also cause a conservative approach to value proposition. Three top start-up airlines serve as a good example of market volatility: “Eos”, “MaxJet” and “Silverjet” (providing business-class-only flights “London-New York”). They offered some appealing extra features at reasonable prices. This was highly appreciated by some passengers; however, they were incapable of replicating some other appealing features in the value propositions of their competitors. At that time, finance for start-ups was effortlessly accessible, and the demand for business-class travel between London and New York was growing. Nevertheless, the competitors obviously were focused on defending their shares of this money-making premium market. Naturally, it was vital for “Eos”, “MaxJet” and “Silverjet” to prove that their individual value propositions would provide them with a sustainable competitive advantage that fullservice carriers could not copy. Unfortunately, they were not able to sustain competition against better-resourced airlines, and subsequently they all failed. • Heterogeneity of conditions. In the individual air travel segment, airlines have a tendency to stay in heterogeneous situations. Whereas the market for an air transportation company between a couple “origin-and destinations” or “city pairs” is its main market, providing direct services can be a marginal market for another airline company that offers services through transfer connections. These companies then are regarded as heterogeneous competitors – relatively close rivals. It should be noted that between such competitors, the ability to evade “deep price” competition is less possible. • Recent liberalization/monopolization. Recent liberalization brings about new challenges. Obviously, consumers are expected to pay lower prices in the segments, which

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are more exposed to competition. In these circumstances, a requirement to identify and provide values for the passengers, as well as delivering the internal value is also taken into consideration by the Aviation Industry. On the other hand, natural monopolizing position of airports and national airlines in certain parts of the world dictates their own rules for competitiveness, for instance, competition with railroad transportation. 3.2 Main Factors Influencing Value Proposition in Aviation For assessing the factors that may influence value proposition in aviation, a series of expert interviews were conducted with representatives of the Industry. The purpose of standardized expert interviews was to get in-depth expert opinions on the impact the factors derived from Baxter’s research on value proposition. Qualitative methods were chosen for this stage of the study, as they enable the exploration of complex questions about human experiences [37]. Semi-structured interviews can be particularly informative as they enable the respondent to discuss ideas in their own words [38], and ensure that the interview is focused while allowing for rich data collection, including additional data from areas outside the topic [39]. For the in-depth expert interviews, subject matter professionals with at least five years of experience in the field of Aviation Management were involved; they are working in Riga International Airport, Odessa International Airport, and Swiss business jet airline. The interview questions were written to align with good practice as recommended by [40], and [38]. This included essential questions covering the topic of interest; similar but differently worded questions to check reliability; ‘throw-away’ questions to build rapport and pace the interview, and probes to encourage elaboration and draw out details. The interview questions were informed by both the theoretical literature and preliminary expert discussion results; the authors also used [36] classification of dominant factors and associated economic characteristics that may drive value proposition within the Industry. The questions were developed based on formed to cover four areas: • • • •

Introduction and background information. Nature and importance of value proposition in aviation. Factors that influence values in air transportation. Benefits of automation of value identification and expansion process – as part of a bigger research dedicated to value automation process [41].

A total of five interviews were conducted with Principal Investigator/PI (one of the authors), between May and December 2020. An additional session of questions was conducted with PI during the online expert panel discussion “Aviation: A New Reality” held in Transport and Telecommunication Institute (Riga, Latvia) in December 2020 [42]; the obtained data were used in this analysis as well. The interviews were conducted face-to-face in the academic’s office or aviation enterprise meeting room, and all video-recorded with permission. A topic guide of 30–45 min was used, but every interview’s length varied from 20–90 min dependent on participant’s availability and the level of detail provided in

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responses. The interviews were all undertaken by the same researcher (one of the authors) to increase consistency. Table 2 summarizes the main factors influencing value proposition in the Aviation Industry based on [36] categorization, the results of the in-depth expert interviews and the online expert panel discussion. Table 2. Key factors influencing value proposition in the Aviation Industry. Source: authors’ construction based on [36] categorization, the results of the in-depth expert interviews and the online expert panel discussion. Factor

Economic characteristic

Impact on value proposition

1. Short-term product

Aviation product is immaterial in In the short-term, expenses to nature, rapidly perishable, could not provide capacity are unmet; be stored pressing price discounting

2. Homogeneous products

The airline product is homogenous in nature, similar across different airlines

3. Low marginal cost structure

Low marginal costs, high fixed costs Dependence on high fixed costs for additional passengers restricts value proposition range

4. Substantial barriers to exit

Long-term aircraft and airport capacity; rigid full-service network carrier business model

Fixed unalterable value proposition; any extra ways of using existing resources are beneficial (e.g. by pursuing new values)

5. Fixed capacity

New aircraft and airport capacity: long-term return on investment

Capacity is fixed/inflexible problem: underutilization (if insufficient demand) inability to increase capacity easily

6. Growth of business in the sustainable context

Volatile and heterogeneous business Unstable periods may cause a conditions conservative approach to value proposition

7. Divertive legal terms as a barrier for sustainability

Airports and air carriers are subject to the specific policies at their domestic market

It impacts the competitive collaboration between the companies in a way not related to value proposition or efficacy

8. Market liberalization/monopolization

Recent liberalization (intensified competition), at the same time total monopoly in some areas (less competition)

New values as an afterthought, making a recent or current monopoly to be a reason for traditional values only

9. Environmental effects of aviation/sustainability of operations

Increasing concern for environmental issues forces airports and air carriers to develop innovative solutions and implement sustainability-related initiatives, require heavy investments

Increased focus on consumer-conscious environmental value propositions

Novel products initiative is quickly imitative by competitors; no differentiation value

(continued)

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O. Zervina and Y. Stukalina Table 2. (continued)

Factor

Economic characteristic

Impact on value proposition

10. Anticipated social impact

Growing concern for diversity and social equity and inclusion makes airlines spend more money on their contribution to communities

A zero-tolerance policy for all types of discrimination as a value

As seen from Table 2, the factors having impact on value proposition in the Aviation Industry can be grouped into: 1) “product factors” directly related to the product offered by an aviation enterprise; 2) “non-product factors” indirectly related to the product. Both groups of factors may influence consumer’s decisions in the aviation domain.

4 Conclusions, Proposals, Recommendations The following conclusions, proposals and recommendations have been drawn by the authors based on the conducted research. • There is the need for increased attention to value proposition as an important element of the company’s strategy in the Aviation Industry, as the competition in the domain intensifies. • Value proposition in the Aviation Industry has undergone significant changes over time: more values have been added instead of changing one to others. • Recently, there has been a value shift from the values associated with individual consumption towards permanent values such as ecology (environmental concerns), social impact and sustainable development. • The use of novel business models based on new values enables the sustainability advantage of an aviation enterprise. • The findings of the study demonstrate that main factors that influence value proposition in the domain are determined by the economic characteristics inherent to the aviation business. They are also closely linked to environmental and social issues in the context of sustainable aviation development. • The findings of the research also show that both product and non-product factors may impact consumer’s choices in the aviation domain. • So, multiple perspectives should be considered in the process of decision-making aimed at creating a unique value proposition in the Aviation Industry, which would facilitate the process in the modern uncertain environment. • The main limitation of the study is the perspective of analysis selected by the authors. Managers may exhibit bias in their perceptions, which in turn, can affect their judgements. Thus, it would be highly recommended to increase the number of respondents in further analysis.

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References 1. IATA: Annual Review 2020 (2020). https://www.iata.org/contentassets/c81222d96c9a4e0 bb4ff6ced0126f0bb/iata-annual-review-2020.pdf. Accessed 05 Oct 2021 2. IATA: Annual Review 2021(2021a). https://www.iata.org/contentassets/c81222d96c9a4e0 bb4ff6ced0126f0bb/iata-annual-review-2021.pdf. Accessed 21 Oct 2021 3. IATA: World Air Transport Statistics. Plus Edition 2021 (2021b). https://www.iata.org/conten tassets/a686ff624550453e8bf0c9b3f7f0ab26/wats-2021-mediakit.pdf. Accessed 20 Oct 2021 4. Almquist, E., Senior, J., Bloch, N.: The elements of value. Harv. Bus. Rev. 46–53 (2016) 5. Lanning and Michaels: Delivering value to customers. Harvard Business Review, President and Fellows of Harvard College (1988) 6. Li, Su-C.: The role of value proposition and value co-production in new internet startups: how new venture e-businesses achieve competitive advantage. In proceedings PICMET 2007 - 2007 Portland International Conference on Management of Engineering & Technology, Portland, OR, 2007, pp. 1126–1132 (2007). https://doi.org/10.1109/PICMET.2007.4349435 7. Guo, H., Yang, J., Han, J.: The fit between value proposition innovation and technological innovation in the digital environment. IEEE Trans. Eng. Manag. 68(3), 797–809 (2019) 8. Pereira, B.A., Lohmann, G., Houghton, L.: Innovation and value creation in the context of aviation: a systematic literature review. J. Air Transp. Manag. 94, article 102076 (2021) 9. Casadesus-Masanell, R., Ricart, J.E.: From strategy to business models and onto tactics. Long Range Plan. 43, 195–215 (2010) 10. Achtenhagen, L., Melin, L., Naldi, L.: Dynamics of business models: strategizing, critical capabilities and activities for sustained value creation. Long Range Plan. 46(6), 427–442 (2013). https://doi.org/10.1016/j.lrp.2013.04.002 11. Markides, C., Sosa, L.: Pioneering and first mover advantages: the importance of business models. Long Range Plan. 46(4–5), 325–334 (2013) 12. Teece, D.: Business models, business strategy and innovation. Long Range Plan. 43, 172–194 (2010) 13. IATA Passenger Surveys and Reviews: Database (1991–2019). www.iata.org/contentassets/. Accessed 22 Sept 2021 14. Aviation Benefits Report(2019). https://www.icao.int/sustainability/Documents/AVIATIONBENEFITS-2019-web.pdf. Accessed 15 Sept 2021 15. Gassmann, O.G., Frankenberger, K., Csik, M.: The St. Gallen Business Model Navigator. St. Gallen: University of St. Gallen (2014) 16. O’Connell, J., Williams, G.: Passengers’ perceptions of low-cost airlines and full service carriers: a case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines. J. Air Transp. Manag. 11, 59–272 (2005) 17. Franke, M.: Innovation: the winning formula to regain profitability in aviation? J. Air Transp. Manag. 13(1), 23–30 (2007) 18. Daft, J., Albers, J.: A conceptual framework for measuring airline business model convergence. J. Air Transp. Manag. 28, 47–54 (2013) 19. Bieger, T., Wittmer, A., Laesser, C.: What is driving the continued growth in demand for air travel? customer value of air transport. J. Air Transp. Manag. 13(1), 31–36 (2007) 20. Jarrell, J.: The Future of Digital Technology in the Aviation Industry. International Airport Review (2018). https://www.internationalairportreview.com/article/76057/future-digital-tec hnology/. Accessed 25 Oct 2021 21. Lima, L., Okana, M.: The air sector as generator of resources and development in Brazil. In: Proceedings of the 3rd International Conference Contemporary Issues in Theory and Practice of Management, 2020, Czestochowa, 2020, 1st edn., pp. 182–189 (2020), ISBN 978-83-7193-732-3

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22. Rudny, W.: The Business Model and Value Creation. In: Samborski A. (ed.), Governance – Corporations, Public Institutions, Networks. Economic Studies Faculty Scientific Journals. University of Economics in Katowice. (2013) 23. Towfiqi, D.A.A.: A Model for Airport Strategic Planning and Master Planning in the Arabian Gulf. Doctoral Dissertation, Loughborough University (2018) 24. Michael, A.: Strengthening the Airport Value Proposition. A Frost & Sullivan White Paper (2017). https://www.frost.com/wp-content/uploads/2017/12/FS_WP_Amadeus-Airport-A4_ 11Dec17_FINAL.pdf. Accessed 25 Aug 2021 25. Brunger, W.G.: The impact of the internet on airline fares: the “internet price effect.” J. Revenue Pricing Manag. 9(1/2), 66–93 (2010) 26. JetBlue: JetBlue Ready to Launch Low-cost New York to London. The Guardian (2021). https://www.theguardian.com/business/2021/aug/01/jetblue-ready-to-launch-low-cost-newyork-to-london-flights. Accessed 10 July 2021 27. Finnair: Bring together the Brightest Minds (2016). https://www.finnair.com/int/gb/slushone-way-2016. Accessed 02 Sept 2021 28. KLM: With Meet & Seat, KLM Integrates Social Media with Air Travel (2012). https://news. klm.com/klm-lanceert-applicatie-meet-e-seat-en/. Accessed 25 July 2021 29. ICAO: Appendix 5 ICAO Template Air Services Agreements (2021). https://www.icao.int/ Meetings/AMC/MA/ICAN2009/templateairservicesagreements.pdf. Accessed 11 July 2021 30. OECD: The Future of International Air Transport Policy: Responding to Global Change (2021). https://www.oecd.org/sti/futures/38303767.pdf, Accessed 27 Oct 2021 31. Burghouwt, G.: Airline Network Development in Europe and its Implications for Airport Planning. Routledge, Abingdon (2016) 32. Bieger, T., Wittmer, A.: Air transport and tourism – perspectives and challenges for destinations, airlines and governments. J. Air Transp. Manag. 12(1), 40–46 (2016) 33. Franke, M., John, F.: What comes next after recession? Airline industry scenarios and potential end games. J. Air Transp. Manag. 17(1), 19–26 (2011) 34. Malighetti, P., Meoli, M., Paleari, S., Redondi, R.: Value determinants in the aviation industry. Transp. Res. Part E 47(3), 359–370 (2011) 35. Morrell, P.: Current challenges in a distressed industry. J. Transp. Manag. 17(1), 14–18 (2011) 36. Baxter, G.: Capturing and delivering value in the trans-Atlantic air travel market: the case of the air France-KLM, delta air lines, and virgin Atlantic airways strategic joint venture. MAD – Mag. Aviat. Dev. 7(1), 17–37 (2019). https://doi.org/10.14311/mad.2019.01.03 37. Creswell, J.W.: Qualitative Inquiry and Research Design: Choosing Among Five Approaches. SAGE Publications, Thousand Oaks (2007) 38. Rubin, H. J., Rubin, I. S.: Qualitative Interviewing: The Art of Hearing Data. SAGE Publications, Thousand Oaks (2012) 39. Patton, M.Q.: Two decades of developments in qualitative inquiry: a personal experiential perspective. Qual. Soc. Work 1(3), 261–283 (2002) 40. Berg, B.L.: Qualitative Research Methods for the Social Sciences. Pearson, Boston (2014) 41. Zervina, O., Stukalina, Y., Pavlyuk, D., Rubens, N.: Value creation in air transportation: beyond price, quality, and speed. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2020. LNNS, vol. 195, pp. 119–129. Springer, Cham (2021). https://doi.org/10.1007/978-3030-68476-1_11 42. TSI: Aviation: A New Reality. Expert Panel Discussion. Transport and Telecommunication Institute (2020). https://www.youtube.com/watch?v=qmrmebj4tAU&ab_channel=TSIRiga. Accessed 15 Oct 2021

Impact of Unpredictable Major Events on Aviation Industry: Challenges, Benefits and Prospects for Recovery Viktorija Gorodnicka(B)

and Iyad Alomar

Transport and Telecommunication Institute, Lomonosova 1, Riga 1019, Latvia [email protected], [email protected]

Abstract. The aviation sector has experienced both the positive and negative impacts of such events on its operations. The adverse effects include a standstill on the ride due to the significant reduction of passengers and the strict guidelines imposed by the governments and WHO. The main aim of this research is to review the effects and impacts of unpredictable event on Aviation Industry with the development of recommendations based on suitable strategies. In order to achieve this aim, review of the most affected components was done, along with identification the individual causes of affected areas within Latvian based aviation company – Flight Consulting Group Ltd and development and evaluation of recovery strategies for the company management. The result of the paperwork shows importance of implementing strategical planning and business optimisation for the recovery process, as well as advises the strategies for risk mitigation and response plan. Hence, the proposed recommendations were generated and suggested to company management in order to be better prepared for future possible events. Keywords: Aviation industry · Crisis · Pandemic · Recovery · Risks

1 Sars-Cov-19 and the Aviation Industry Any business activity in the transport-related industry is associated with risk, which increases the probability of an occurrence of danger, an adverse event with specific consequences, and an uncertain amount of damage. At the beginning of 2020, the entire world became introduced to the risk of unpredictable epidemiological events such as SARS-CoV-2 (COVID-19). Unfortunately, this particular circumstance affected the aviation industry, as evident in its’ production, commerce, innovation, and other activities [1]. Governments around the world have enforced full or partial isolation regimes, closed borders, imposed tight travel restrictions, and issued guidelines warning against traveling unless absolutely required as part of the worldwide effort to contain the pandemic and preserve human health [2]. As these measures have led to an unprecedented drop in demand for air transportation, therefore aviation has become one of the most affected sectors by the pandemic [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 61–74, 2023. https://doi.org/10.1007/978-3-031-26655-3_6

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Research states that it is more correct to compare 2020 with the situation in the economies of Europe, the USA, and Japan at the end of the Second World War and immediately after its end (1944–1946), when economic dynamics were also determined by non-economic factors: military actions on the territory of European countries and the end of the period of military mobilization of the economies. International organizations estimate a 4.2–4.3% drop in the global GDP last year, which is significantly better than earlier forecasts at the peak of the pandemic, when a drop of 6–7% was expected. The positive revision is primarily due to the rapid recovery of business activity in developed countries in the third quarter, following a record recession in the second quarter of 2020 as well as the steady recovery of the Chinese economy [4]. According to the OECD, the lifting of restrictive measures in the third quarter contributed to a significant slowdown in the rate of decline in US GDP relative to the corresponding quarter of the previous year, when they slowed from −9.0% in the second quarter to −2.8% in the third quarter of 2020. Among the EU countries, the most rapid recovery was registered in France; the decline in the country’s corresponding period of the previous year slowed from −18.9% in the second quarter to −3.9% in the third quarter (Table 1) [4]. Table 1. GDP growth rates in 2020, % compared to the corresponding quarter of 2019 OECD, 2020. I

II

III

I

II

III

USA

0.3

−9

−2.8

EU

−2.6

−13.9

−4.2

Canada

−0.3

−12.5

−5.2

China

−6.8

3.2

4.9

Australia

1.4

Japan

−2.1

−6.4

−3.8

Brazil

−1.3

−10.9

−3.9

−10.3

−5.7

Mexico

−2.2

−18.7

−8.6

Germany

−2.1

−11.2

−4

India

3.3

−23.5

−7.5

France

−5.7

−18.9

−3.9

Indonesia

3

−5.4

−3.6

Italy

−5.6

−18

−5

Russia

1.6

−8

−3.8

Spain

−4.2

−21.6

−9

Turkey

4.7

−8.5

UK

−2.4

−20.8

−8.6

Saudi Arabia

−1.1

−6.4

−4.5

Eurozone

−3.2

−14.7

−4.3

RSA

−0.2

−17.5

−6.1

According to the Minister of Foreign Affairs and Vice-Chancellor of Germany from 1998 to 2005, Joschka Fischer, “the COVID-19 pandemic brought the most technologically capable generation in the history of civilization to its knees in just a few weeks” [5]. The coronavirus has cruelly exposed the inadequacies in institutions that the overwhelming majority of people rely on. These institutions include both national governments and international structures. Most likely, in their current form, they will not – and should not – survive. The crisis has shown that the existing political institutions no longer meet their goals and must be created anew [5]. Under the 2030 Agenda framework, ICAO was designated as the custodian agency for the global indicator for Passenger and Freight Volumes by Mode of Transport. The

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63

International Civil Aviation Organization (ICAO) maintains a record of and reports on countries’ progress in building resilient infrastructure, promoting equitable and sustainable business, and encouraging innovation. The air transportation industry is expanding, and aviation has a bright future ahead of it. In 2017, aircraft transported around 4.1 billion passengers around the world. 56 million tons of freight were transported on 37 million commercial flights. Airlines move over 10 million people and about USD 18 billion in freight every day [6]. These figures show how important aviation is to the global economy, as proven by the fact that it accounts for 3.5% of global GDP (2.7 trillion dollars) and has created 65 million employments globally [6]. 1.1 Global Pandemic and Its Impact on Aviation Sector Several economic behaviors were implemented during the COVID-19 pandemic that affected the aviation sector. For example, the travel restrictions in the aviation industry resulted in a reduction in demand for airline services. IATA established that, by April 2020, passenger air transport dropped by about 90%, and by August the same year, air travel was still down by about 75%. Trade and other economic activities were affected as a result. In 2021, it became clear that, once COVID restrictions were lifted, people became eager to return to the skies. However, surges of coronavirus variants such as Delta and Omicron hampered recovery efforts. The apparent amounts of pent-up demand as well as the global economy’s general rebound, encouraged optimism that recovery would continue apace. To achieve the fastest possible recovery moving forward, a more internationally coordinated approach to vaccination roll-out and health travel passports is required, and leaders must take a more nuanced and thoughtful approach to new variants [7]. The shock from the pandemic put liquidity buffers of airline companies under pressure. As a result of safety and health requirements, operation costs in the aviation industry increased. For example, the airline sectors have implemented measures to disinfect planes and airports to reduce infection rates [8]. These actions have increased the operating costs in the aviation industry, reducing the total amount of economic profits in the industry. Additionally, the enforcement of social distancing rules in the sector means that the planes can carry a small number of passengers per trip compared to those they carried before. The COVID-19 pandemic also threatened the loss of some skilled workers in the aviation sector. Due to the reduction in the revenue generated by different aviation companies across the world, some organizations needed to cut operational costs, which meant that some workers got let go. IATA estimates that approximately 65.5 million jobs globally are either dependent on or directly involved in aviation. Out of these, about 2.7 million jobs are at the airlines, while the rest, adding up to almost 63 million jobs, are in other companies in the aviation industry [9].

2 Russian Airspace Closure, 2022 In the face of its conflict with Ukraine, Russia banned 36 countries from accessing its airspace. This ban on the thirty-six nations, which include all 27 members of the European Union, was a way of retaliating for the ban by the European countries on civil

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aviation registered or operated by Russian-related parties [10]. As a result of the ban, the aviation sectors of the banned countries have been hit hard. Air transportation is critical for international trade in manufactured goods, notably in the components that now account for a significant portion of cross-border trade. Due to the EU regulus and sanctions raised for anybody involved with the Russian Federation, the world’s international trade by air as well as passenger traffic will face a stark decline, since Russia played an important role in this sector. In 2021, passenger and freight traffic connecting Russia and the rest of the globe made up 5.2% of all international traffic, although it accounts for just 1.3% of total traffic, as per IATA (2022) report. International aviation travel to and from Russia accounted for 5.7% of total European traffic in 2021 (Table 2) [11]. Table 2. Traffic shares for selected markets impacted by the conflict, IATA. Share of passenger, %

Total European traffic Excl. Russia domestic, %

Global traffic, %

Ukraine

3.3

0.8

Belarus

0.3

0.1

Moldova

0.4

0.1

Russia International

5.7

1.3

A global recession is improbable at this moment, given that the IMF anticipated global GDP growth of 4.4% prior to the war. It’s also worth noting that, although global GDP growth averaging approximately 3%, the world’s Brent oil price stuck around USD 100 per barrel from 2011 to 2014 [11]. Russian Federation and Ukraine together account for nearly 2% of world GDP, notwithstanding their importance to the global economy as significant players in terms of exportation of several commodities such as oil, precious metals, wheat, and many others. Most major economies consider Russia to be a modest trading partner. Russia represents alone for 0.5% of US trade, but 2.4% of Chinese trade.

3 Case Study As per practical part, Flight Consulting Group LTD has been selected. Company has been established in 2000 [12]. Holding itself is focusing on non-scheduled and business aviation flight support. FCG consists of six subsidiary companies in different aviationrelated fields. FCG has over 20 years of involvement with the business flying region and offers complete answers regarding flight support, ground handling, composure of private charters and trading airplanes, and of course – provides professional aviation consulting. The work and progressing activities of FCG Holding and its organizations are exceptionally valued among experts in the business aviation field worldwide [12]. Surprisingly, 2021 has been a very difficult year, with an extreme increase in business aviation field generally. According to the company’s’ data, it experienced an increase of 50% in flights as well as 25% increase in professional staff.

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In early March, the European aviation sector became one of the global pandemic epicenters. The aviation industry has been functioning at fewer than 20% of 2019 levels. For internal flights, the situation began to somehow improve in June 2020, and a regional opening of air traffic worldwide for passenger flights occurred on July 1st (Table 3). Table 3. Flights supported between 2019–2021.

According to the table above, it is stated, that the pandemic restrictions worldwide has affected the flight operations significantly. Flights support count has decreased on 115 flights (from 211 per month to 96 per month) in comparison between March 2020 and April 2020, which represents an unbelievable impact on revenues within such a company (Table 4). Table 4. Flights leg supported between 2019–2021. Legs number 2019

2020

2021

2019

2020

2021

January

561

885

768

July

1,114

1,064

1,693

February

533

860

921

August

1,200

1,311

1,668

March

678

April

660

902

1,053

September

1,021

1,001

1,490

324

1,010

October

1,037

862

1,320

May

762

408

1,230

November

864

782

978

June

826

724

1,346

December

953

848

1,052

Industry professionals mention a high chance avoidance of such a sizeable impact if the organisation had responded to the news of the pandemic in China in a timely manner when the initial news was shared.

4 Impact Minimisation and Recovery 4.1 Primary Research Primary research is an important tool for any type of study since it involves the researcher directly participating in the data collection process and gathering appropriate data samples on their own rather than depending entirely on already acquired data in the research context. In a study situation, this strategy allows the researcher to acquire first-hand data, which can be considered more genuine and authentic. For the current study, the certain

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form of survey was distributed to the target audience. Survey is aimed to the employees of different aviation industry companies worldwide according to their managerial level. All the participants were given the link for the survey (personally by the researcher during EBACE (The European Business Aviation Convention & Exhibition) in Geneva (May 2022) and by emails). To strengthen the trustworthiness of the data acquired, the study adopted a semi-structured approach. 4.2 Secondary Research Risk Management and Mitigation. COVID-19 pandemic yielded other subsequent risks despite the pandemic itself being a risk on its own. Predominantly, these risks lay in the supply chain management (SCM) and health concerns of employees and clients. The pandemic caused a severe decline in demand for passenger aviation services. Governments worldwide imposed travel bans to prevent the disease from spreading. This decline had deleterious effects on the industry, such as declining revenue, leading to supply issues such as employee layoffs, and more than one million airline personnel lost their livelihoods during the pandemic period [13]. This rise in demand has led to supply risks, despite this being the opportune moment for industry players to get back to their winning ways. The labor shortage is a risk that follows up by losing customers and revenue for inadequate staff members. Consequently, the other supply chain-related risk companies face is gaining access to material that will help them enhance their daily operations. Particularly, aviation service provider companies grapple with fuel shortage issues and the inordinate rise in global fuel prices [14]. Gordon recommends various risk management strategies, including automated recommendations and risk matrixes. Automated recommendations include implementing artificial intelligence-based mechanisms that analyze big data quickly and inform airline companies of impending fuel supply shortages following an unstainable increase in demand for airline transportation services [14]. Using automated recommendations in the AI realm is an adept risk management strategy since companies will make adequate plans on inventory management to avoid the notorious last-minute rush or clamp in operations due to fuel and other operational shortages. When a hazard cannot be completely avoided, risk mitigation is used, and it focuses on the unavoidability of certain events. Mitigation is concerned with the aftermath and consequences of different disasters and the efforts that can be taken before the event to mitigate negative and potentially long-term outcomes, rather than preparing to avoid a risk (Fig. 1). a) A risk mitigation strategy considers not just the organization’s priorities and missioncritical data security, but also any hazards that may arise due to the nature of the field or its geographic location. Employees and their demands must be considered while developing a risk reduction strategy. b) Carry out a risk assessment, which entails determining the level of risk associated with the occurrences FCG identified. Measures, methods, and controls are used in risk assessments to mitigate the impact of risk. The author proposes that the company

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Fig. 1. Five steps to creating a risk mitigation plan.

use Risk Assessment Matrix (Fig. 2), along with the possibility to introduce to the company a new platform “nTask” (Fig. 3).

Fig. 2. Risk Assessment Matrix.

Fig. 3. Risk analysis, nTask platform.

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The next step is to assess all potential hazards after the project team has described them. The built-in Risk Assessment Matrix in nTask generates the fields required to construct a matrix. The risk levels can then be determined using this matrix. All stakeholders should be involved, and the matrix should be supported by genuine data. During this session, all stakeholders must recognize and agree on the risks in order to implement a contingency plan. Prioritize risks by assigning a severity level to the hazards that have been measured. One aspect of risk mitigation is prioritization, which requires accepting a certain level of risk in one area of the business in order to better protect another. This could be done by using functions described above. 4.3 Opportunities and Recommendations Mass Reassurance. The first strategy this industry has to accomplish, in general, is reassuring the masses. COVID-19 scared many people, making them skeptical of international travel. Thus, returning the faith should be the first step. Eventually, it is these customers who will bring in the income. Essentially, this first step involves reestablishing the relationship caused by the uncertainty the pandemic brought. Strong advocacy for health regulations should accompany this move. COVID-19 showed that aviation must play a critical role in maintaining health standards worldwide. Thus, in the wake of the fear surrounding the pandemic, it seems pragmatic for the industry to continue with the measures implemented during the event. This move reassures the masses that the sector is always considering their well-being. Besides, since COVID has not been completely eradicated, reassuring the travelers that these firms have not forgotten their commitment to health and safety will be suitable for encouraging travelers with clients and customers face-to face, to already factor these expenses into operations [15]. Cost-Reduction Strategy. Fabiani et al. (2015) found that corporations chose to use cost-cutting methods in their investigation of the effects of the 2008–2009 economic crisis and the actions taken by businesses. Despite the fact that cutting non-labor costs was the most popular technique, enterprises that cut labor costs primarily did so by laying off temporary workers and reducing hours worked. Findings also validated the firms’ adamant opposition to base wage cuts: according to it – firms rarely executed such reductions, with the exception of Estonia, where over 45% of firms slashed nominal wages in reaction to the crisis [16]. Company has ability to encourage remote work, hence this process can help to save revenue on new office equipment, utilities, and more office space. Company might consider recruiting people to work from distant stations to expand team, if needed, at a lower cost. Employee engagement, discipline, and communication will all be hampered by remote working. They are, however, only momentary setbacks because, once the remote employee system is in place, you may assist lower expenses significantly in the long run. According to the law of demand, people are more likely to purchase a product or a service if its price is affordable. Thus, setting lower amounts is bound to attract more users and be more competitive on the market during these times. However, with strict profit margins to maintain, it does not simply help set low prices. Company should consider attractive packages that allow them to generate revenue while giving a reasonable cost [16].

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Import Substitution. Import substitution (IS) is a policy that prevents a commodity from being imported and instead permits it to be produced on the domestic market. The goal of this policy is to transform the economy’s structural makeup. The structural shift is brought about by establishing holes in the process of decreasing imports, allowing non-traditional industries to invest [17]. Backbone concepts in the establishment of a balanced developing meso economy oriented on modernization of growth are used to form non-primary development strategies in a mixed economy [18]. New import substitution plan guidelines call for maximal involvement and exploitation of endogenous regional resource cluster interactions. Business Optimization. The quality of aviation industry offerings is assessed based on timeliness, functional efficiency, accuracy, and price. Air transportation customers implement this criterion by mainly focusing on the flexibility of schedules, safety, ontime flights, in-flight service satisfaction rating, reasonable pricing, appropriate handling of cargo, and the convenience experienced during ticket purchases [19]. Aviation companies therefore implement optimization decision support systems critical to fostering the attainment of the customers’ concerns. From the organization’s point of view, business optimization offers the opportunity to widen operations into such areas as cargo flights and military flights operations; realize higher profits; invent cost-effective fare classes; effectively plan flight schedules, aircraft routes, maintenance, and training schedules; and achieve effective cargo handling mechanisms. Efficiency in Operations. Optimization in the aviation industry has achieved much in terms of promoting efficiency. Optimization of flight schedules addresses the problems associated with high demand [19]. Through business optimization, the process of issuing tickets has become seamless, promoting customer satisfaction. Again, business optimization translates into adopting technologies that have contributed to advancing the quality of schedules, thus attaining timeliness. Advanced technologies have solved high-security concerns across airlines through efficient cargo management and other operations. This efficiency has helped airlines achieve accurate plan flight schedules and manage their aircraft routes effectively [19]. Furthermore, business optimization comes with cost-effectiveness. Significant cost savings are realized that could be invested in further optimizing the efficiency of operations. Therefore, optimization presents excellent benefits to the aviation industry. Through business optimization, airlines can venture into the highly promising military and cargo markets, ensuring high revenue and profit margins. New Markets. It is essential for any aviation industry players to seek for new market opportunities, as it sets a goal to improve the aviation industry’s international competitiveness while still ensuring high-quality services for passengers (MEMO/15/6145). International air transport has traditionally been managed by bilateral air services agreements, facilitating in a patchwork of market access and rules for companies. As a result, the EU has developed an external aviation policy since 2003 with the goal of finalizing comprehensive aviation accords and aviation safety agreements with important aviation partners across the world for the benefit of consumers and business. New market agreements provide new economic prospects by ensuring market access, encouraging investment, facilitating air travel, and giving customers more options.

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Applying for Government Grants. The adverse effects associated with COVID-19 forced governments worldwide to set aside funds to cushion organizations and citizens from an economic meltdown. A projection by OECD (2020) indicated that the global domestic product would decline by 4.9% in 2020, a significant drop capable of destabilizing the aviation industry [4]. However, this industry could recover if it applies for government funding available in loans, aids, or stimulus packages. According to Abate et al. (2020), aviation industry players could apply for government aid in recapitalization through state equity, government-backed commercial loans, flight subsidies, tax waivers, or nationalization [20]. The form of funding that companies select for depends on liquidity, amount of debt, and short and long-term objective. Adrienne et al. (2020) confirmed that by the end of March 2020, aviation organizations had requested up to $300 billion in financial support from their respective governments to activate airport operations [21]. If granted, this support could go a long way in helping resume airport operations. It is essential for companies to collaborate with governments and IATA, their global representative, to secure funding. It is important to note that this funding may be impractical without additional safeguards to secure financial future. Xuan et al. (2021) advocated for regulatory relief to reduce the taxation threshold, which threatens to cripple the industry [22]. Operations Resources Allocation. Most aviation service providers could support flights at any time that best meets their objectives. However, due to weather, airspace restrictions, and other conditions, these freely established timetables can become infeasible at certain times within the company, airport operations, and in the airspace. After that, a plan must be put in place to alleviate congestion in a safe, efficient, and equitable manner. Current planning techniques tackle each congested resource separately, employing a variety of principles to reduce interoperation delays to match the capacity reduction. However, many resources may be congested at the same time, and neglecting such dependencies could result in infeasible allocations for flights using multiple resources [23]. Therefore, one strategy could be to focus on the Air Traffic Flow Management (ATFM) system, whose principal function is to distribute capacity at overloaded resources. These techniques work based on the assumption that congested resources have been identified as well as the predicted length of the demand-capacity imbalance.

4.4 Individual Strategy – Issue Response Plan As an aviation industry worker, the author brainstormed the following protocol for creating issue response plan. The following protocol has been proposed to the management of company and has been partially implemented to understand priorities and steps toward a solution during an unpredictable time, which will guarantee the time management efficiency and resource allocation. Knowing what steps to take and how to allocate resources during a hard situation saves time, prevents confusion, and gives employees a clear idea of what they need to accomplish. Additionally, within FCG, some employees may fill several roles, and this could also decrease the possibility of firing workers when business faces crisis; hence, this step can be counted as an optimization of the workflow process within the company. Before a crisis arises, management should assign personnel to the

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roles of team leader and issue leader. The role allocation will help to prioritize problems and the way they can be solved. The current plan will be beneficial for time management, ensuring that some issues can be solved without bringing them up to the FCG board, which will not only save time for shareholders and board of directors, but also develop independent decision-making within departments, which will lead to increase efficiency. The author believes that the proposed plan can be a helpful tool for structuring the decision process in a stressful and unpredictable situation and would work as a smooth tool for issue solving process (Fig. 4).

Fig. 4. Issue response plan.

5 Conclusions The COVID-19 epidemic has wreaked havoc on the global aviation and tourism industries, prompting governments throughout the world to impose a slew of new restrictions, including travel bans, lockdowns, stay-at-home orders, and quarantine measures, in an effort to stem the disease’s rapid spread. Primary research in a form of survey was conducted within the managers of different aviation organizations worldwide. As a result of this study with intercorrelation of secondary research, it is possible to pinpoint the implementations, obstacles, and advantages of various strategies for Latvian-based company Flight Consulting Group LTD. Based on the experience of how company response to unpredictable events, it is essential for the company to incorporate different kind of strategies, to minimize impact of events, as mentioned in sections above, and especially focus on strategic planning and business optimisation. Optimisation will be useful tool

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to develop and apply new ideas for more efficient and cost-effective business process, would additionally shorten turnaround time and reduce expense while increasing level of performance. In-house risk management strategy would be highly beneficial in recovery period, as well as could be the most efficient tool in case of the future crises. Management should consider digitizing and automating companies’ services and operations, join new markets and seek new opportunities, optimize business structure, efficiently use help from the governments. The majority of countries view government assistance for the air transportation sector to be in their strategic interests due to its favorable effects on GDP, employment, and connectivity. Additionally, the issue response plan will be highly beneficial for the company management to undertake due to its strategical advantage in terms of decision-making process. Overall, the changes needed in the aviation industry cannot be one-dimensional. The goal is to make a system-wide change that will allow firms to overcome the existing challenges and plan for the future. Thus, reassuring the masses, including company’s’ employees and working on marketing strategies are also an important move since it ensures that people know that aviation environment is in a safe state. However, it is also essential that all needed measures are followed by the establishment to ensure that this industry can sustain a positive reputation in an era where individuals have become more health and regulation cautious. To conclude, this paper will be continued to work on with the main goal of developing authors anti-crisis management set, as per figure below (Fig. 5).

Fig. 5. Anti-crisis management set.

References 1. Suau-Sanchez, P.: An early assessment of the impact of COVID-19 on air transport: just another crisis or the end of aviation as we know it? (2020). https://doi.org/10.1016/j.jtrangeo. 2020.102749

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2. WHO – World Health Organisation COVID-19 Public Health Emergency of International Concern (PHEIC) Global Research and Innovation Forum (2021). https://www.who.int/ publications/m/item/covid-19-public-health-emergency-of-international-concern-(pheic)global-research-and-innovation-forum 3. ICAO. Effects of Novel Coronavirus (COVID-19) on Civil Aviation: Economic. Impact Analysis (2022). https://www.icao.int/sustainability/Documents/Covid19/ICAO_coronavirus_E con_Impact.pdf 4. OECD. The territorial impact of COVID-19: managing the crisis across levels of government (2020). https://www.oecd.org/coronavirus/policy-responses/the-territorial-impactof-covid-19-managing-the-crisis-across-levels-of-government-d3e314e1/ 5. Fisher, J.: The virus that changed the world (2020). https://www.project-syndicate.org/com mentary/the-virus-that-changed-the-world-by-joschka-fischer-2020-04 6. ICAO. Future of Aviation (2017). https://www.icao.int/Meetings/FutureOfAviation/Pages/ default.aspx 7. KMPG. Covid-10 and the Airport Industry (2021). https://home.kpmg/ru/en/home/insights/ 2020/04/covid-19-and-the-airport-industry.html 8. Fu, S.: Quantifying the impacts of COVID-19 on US aviation economy. Acad. J. Bus. Manage. 4(2) (2022) 9. Alkan, A.D.: The effects of COVID-19 on human resource management in aviation companies: the case of Europe. In: Digitalization and the Impacts of COVID-19 on the Aviation Industry, pp. 225–242 (2022) 10. Majka, A., Ostr˛ega, P., Pasich, A.: Influence of airspace avoidance due to political and safety issues on flight efficiency and environment. In: IOP Conference Series: Materials Science and Engineering, vol. 1226, No. 1, p. 012023. IOP Publishing (2022) 11. IATA. The Impact of the War in Ukraine on the Aviation Industry (2022). https://www.iata. org/en/iata-repository/publications/economic-reports/the-impact-of-the-conflict-betweenrussia-and-ukraine-on-aviation/ 12. Flight Consulting Group (2021). https://www.fcg.aero 13. Singh, P., et al.: Alternative strategies to avoid layoff in airlines industry during the Covid-19 pandemic. Int. J. Tour. Hospital. Asia Pasific 4(2), 69–83 (2021) 14. Gordon, A.: Four Strategies to Help Airlines Manage and Mitigate Supply Risks (2022). https://interactive.aviationtoday.com/avionicsmagazine/march-april-2022/opinion-four-str ategies-to-help-airlines-manage-and-mitigate-supply-risks/ 15. Kurnaz, S., Žilinskien˙e, D.: The effects of the covid-19 pandemic and the future of aviation. In: Kurnaz, S., Argın, E. (eds.) Digitalization and the Impacts of COVID-19 on the Aviation Industry, pp. 53–72. IGI Global (2022). https://doi.org/10.4018/978-1-6684-2319-6.ch004 16. Fabiani, S., Lamo, A., Messina, J., R T.: European firm adjustment during times of economic crisis. IZA J. Labor Policy 4(1), 1–28 (2015). https://doi.org/10.1186/s40173-015-0048-3 17. Bruton, H.: The import substitution strategy of development: a survey. Pak. Dev. Rev. 10(2), 123–146 (1970) 18. Tyaglov, S.G., Kushnarenko, T.V., Khokhlov, A.A., Qeropyan, M.A.: The development of cluster relations within the state and business structures in terms of strategy of non-primary sector import-substitution. Eur. Res. Stud. J. XX(1), 198–207 (2017). https://doi.org/10. 35808/ersj/609 19. Tian, Y., He, X., Xu, Y., Wan, L., Ye, B.: 4D trajectory optimization of commercial flight for green civil aviation. IEEE Access 8, 62815–62829 (2020). https://doi.org/10.1109/ACCESS. 2020.2984488 20. Abate, M., Christidis, P., Purwanto, A.J.: Government support to airlines in the aftermath of the COVID-19 pandemic. J. Air Transp. Manage. 89(23), 19–31 (2020). https://doi.org/10. 1016/j.jairtraman.2020.101931

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21. Xuan, X., Khan, K., Su, C.W., Khurshid, A.: Will COVID-19 threaten the survival of the airline industry? Sustainability 13(21), 11–32 (2021). https://doi.org/10.3390/su132111666 22. Adrienne, N., Budd, L., Ison, S.: Grounded aircraft: an airfield operations perspective of the challenges of resuming flights post COVID. J. Air Transp. Manage. 89(4), 10–29 (2020). https://doi.org/10.1016/j.jairtraman.2020.101921 23. Churchill, M.: Coordinated and robust aviation network resource allocation (2010). https:// drum.lib.umd.edu/bitstream/handle/1903/11212/Churchill_umd_0117E_11776.pdf?seq uence=1&isAllowed=y

Value Entity Recognition Task in the Air Transportation on the Base of E-Texts Analysis Olga Zervina(B) , Yulia Stukalina, and Dmitry Pavlyuk Transport and Telecommunication Institute, 1 Lomonosova Street, Riga 1019, Latvia {Zervina.O,Stukalina.J,Pavljuks.D}@tsi.lv

Abstract. One of the main goals of companies is to create value. In defining specific values as part of their unique value proposition, companies use comparison analysis and market research. These value identification methods are very resource-intensive and time-consuming. Automation of this process would substantially reduce costs, as well as significantly expand the number of identifiable values. The aim of this research is to introduce a novel approach to Information Extraction in the form of a new Entity Recognition task: Value Entity Recognition (VER) and to develop a methodology for the automated value identification from e-texts in air transportation domain finalizing a series of studies on automation of value identification. The result of automation of value identification is presented as a neural network model trained on a unique database of values derived from start-ups’ online promotional texts of air transportation domain. To train a model, a dataset from annotation of Twitter profiles of start-ups was utilized. The air transportation industry was taken to narrow down the domain and to facilitate an experiment. The experiment is conducted in English language as English is the international language of civil aviation and English unites air transportation industries. A trained neural network can be useful to aviation managers in their decision-making process. The methodology offered is a baseline for value identification automation and is applicable to different industries, elaborated subject to the tasks set. Keywords: Digitalization · Automation · Aviation · Value entity recognition

1 Introduction One of the main goals of companies is to create value [1]. The company offers values as part of its value proposition to the market. To identify their values, according to [2] companies study the market: conduct marketing research, compare the value propositions of competitors. The research problem is that the manual processes of value identification are resource-intensive and time-consuming. In addition, companies can manually identify a limited number of values, while automation and deep learning capabilities expand the number of values that can be identified. The following question arises: can values as a part of value proposition be identified and extended automatically? © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 75–88, 2023. https://doi.org/10.1007/978-3-031-26655-3_7

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To narrow down the domain and to address real-world challenges, the authors applied their novel research to the air transportation industry. Recent liberalization and course on sustainable development makes air transportation sector to utilize new values. Traditional number values that can be found manually using industry landing pages analysis is 3– 7 (speed, safety, comfort). A database of extended number of values in aviation can facilitate categorization and conceptualization of the value proposition. This paper aims to introduce a novel approach to Information Extraction in the form of a new Entity Recognition task: Value Entity Recognition (VER)and to offer a methodology that results in creating a neural network model for automated identification of values from texts as a part of value proposition. The methodology uses texts of 796 Twitter profiles of start-ups as a data source. The following methods were used: data was obtained via the annotation of values as a Value Entity Recognition (VER) task and resulted in a dataset of values creation. The methodology was based on the following approach: one word – one value. A model – a deep learning neural network based on Stacy library – was trained on using this dataset. Authors use start-ups’ profiles as the source of data due to the following reasons: the nature of a start-up is the openness of information to attract investors and secure a constant growth. Another reason to use start-ups as a source of value is the principle of innovation approach which among others differentiates start-ups from small businesses or well-established companies. Capital requirements for steady and constant technological development are very high; and start-ups as new companies don’t have access to it; with less funds they typically experiment with new technologies and novel business models, developing new values. To summarize, as start-up’s goal is to grow and scale rapidly [3] with “high reliance on innovation of product, processes” [4], information on their landing pages is open and actual; it provides a good data source for analysing existing and prospective value proposition. Figure 1 presents a conceptual model reflecting start-ups as a source for identifying domain values. The research base is the Crunchbase [5] selection of air transportation start-ups in the form of Twitter profiles: a Twitter profile of a start-up company is an e-text expressing a concentrated value proposition.

Fig. 1. Startups as a source for values (constructed by authors).

This research attempts to establish a baseline and to demonstrate feasibility of the automated approach to value identification and values’ number increasing. Annotation

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process of multiple start-ups’ profiles by experienced annotators secure the impartiality of value selection; a trained neural network has proved the accuracy of methodology. Decision-makers in the field of air transportation can utilize this neural network model in the process of developing novel values for their companies. Limitations of the research: number of annotators and their level of expertise, ambiguity of natural language, objectivity of semantic perception, cross-domain applicability, cross-language applicability, accuracy of identification of new values by the model.

2 Identification of Values, Data Driven Decision-Making, and NLP This research is devoted to the value identification as a part of value proposition. The notion of value, its development into the strategical management and marketing tool are key things for better comprehension of the possible mechanisms for automotive value identification. 2.1 Notion of Value Values have been inherent in human civilization from the very moment of its inception. Realizing the need for things, processes, emotions, humans endowed them with value. They learnt to identify the worth of values and sell them when economic ties developed. Explanatory dictionaries give a large number of uses for the concept of value. From one of them, at the end of the 20th century, the understanding of value as part of a value proposition emerged. In the version of the explanatory dictionary [6], the entry value has 8 meanings. From the meaning of “something intrinsically desirable” as it is shown on Fig. 2, the concept of a value proposition emerged as it was first described in a report by [7] as “a clear, simple statement of the benefits, both tangible and intangible, that the company will provide, along with the approximate price it will charge each customer segment for those benefits”.

Fig. 2. Definitions of value and the separation of the concept of value proposition (authors).

This paper utilizes a notion of value as a part of value proposition concept defined by Lannings and Michaels in1988.

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2.2 Development of Value Proposition Concepts The concept of value proposition has been gaining popularity which is reflected by Fig. 3: how this term occurred in the corpus of Google Books using Google Ngram Viewer starting from 1800 till 2019.

Fig. 3. Value proposition term usage, 1800–2019. Source: books.google.com/ngrams

Almquist et al. [18] made a significant contribution to value proposition theory by defining value proposition as a strategic business objective based on differentiated values for consumers. Elements of Value was proposed as a collection of values, including categories based on Maslow’s famous Hierarchy of Needs (Fig. 4).

Fig. 4. The elements of value [18].

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Authors found 30 “value elements” – fundamental characteristics in their simplest and most distinct forms. These factors are classified as functional, emotional, lifechanging, and societal effect. Certain components are more inward-looking, focusing on the consumers’ own wants. For instance, Fitbit’s exercise-tracking gadgets are built on the life-changing ingredient motivation. Others are more outward-looking, assisting clients in interacting with or navigating the external environment. One of the possible implications of a dataset of values obtained using automation is to systematise values according different categorization systems. 2.3 Data-Driven Decision Making and Natural Language Processing as a Tool [8] state that data-driven decision making (DDDM) depends on factors which could be contained in and retrieved by data-driven approaches such as process execution, inputs and outputs of data, while other contingency aspects refer are intangible such as governance, culture, or law. In the age of big data, DDDM remains important and have been enhanced with data-driven components such as business intelligence (BI) or analytics. One of the key data sources for DDDM is texts and the analysis of natural language texts is known as Natural Language Processing (NLP). With the explosive growth of the World Wide Web, there is a vast wealth of information in semi-structured and unstructured documents (e.g., web pages and full-text documents) available on the Web as well as a flow of companies’ inner reports and papers. Efforts to automatically access and distil the information from these sources have thus been ongoing for the past decades. This area is known as Information Extraction (IE). Information extraction on text extracts factual metadata that conveys the information of the original text. The extracted metadata is assigned to the text, presenting part of the text information content for further processing. This task was initially explored by the Natural Language Processing (NLP) community, and now has been extensively studied in research communities including information retrieval and text mining. IE has a wide range of applications in domains such as biomedical informatics [9], business intelligence in finance and E-commerce [10], serving as a bridge between raw data and knowledge. Metadata extracted by IE tasks for understanding the raw text is treated as the semantic annotation, and the text analysis programs or human experts tackling IE tasks as semantic annotators. To the best of our knowledge, there is no well-established definition to determine whether an annotation is “semantic” or not. In this study, the term “semantic” covers a broader scope than what it usually does in the context of NLP, which is not only the meaning within sentences but also the meaning of the text beyond the sentence level, such as the context. For example, the output from the text analysis tasks with different purposes are all considered as semantic annotations – named entity extraction (NER). In some cases, it also involves a subtask named entity disambiguation resolving entity identifiers of the entity instance detected [11].

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Machine learning techniques is a straightforward way of combining annotators, especially to determine the optimal combination of annotators with respect to their accuracy. Among others, the most popular techniques are neural networks [12], support vector machines, classifier stacking and conditional random fields (CRFs) [13]. Using the techniques of NER, the current research is based on extracting values as a part of value proposition from texts of start-up companies which is a novel task for NLP. To address this task, a new approach is introduced in the next chapter – Value Entity Recognition (VER).

3 Research Design The experiment for this research was conducted based on annotations of 796 Twitter profiles of the start-ups of air transportation domain from Crunchbase [5], a popular resource for start-ups seeking funding. A novel task of Information Extraction – Value Entity Recognition – was performed to identify values from startups’ Twitter profiles. 3.1 Value Entity Recognition Usually, named entities are a kind of proper names while others are general entities within the domain. Therefore, NER can be carried out based on orthographic features, wordlevel features, and gazetteers. Data analytic techniques are used to classify named entity tags and the disambiguation among named entities based on their features. However, these features have very little contribution to the Value Entity Recognition (VER) task because, as [14] showed based on the Agricultural Entity Recognition (AGER), general entities are not proper names, and they have no orthographic features like named entities in NER task. This is a challenge for any entity recognition system. This research proposes an entity recognition process in a specific domain which is air transport, and the entity is a value as a part of value proposition. The key highlights of the VER are: (1) design rule-based annotation experiment for value entities; (2) develop a semantic-based Value Entity Recognition (VER) tasks and a new efficient approach for VER in air transport domain; (3) use deep learning (DNN) to train and identify value entities from texts of air transportation Twitter profiles of start-up companies. The semantic-based VER approach combines a NER-based approach and binary classification approach (if an entity is a value or is not a value). Figure 5 shows the place of Value Entity Recognition (VER) as an NER approach to Information Extraction Process (adopted from [15]).

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Fig. 5. Value Entity Recognition (VER) as an NER approach to Information Extraction Process (based on [15]).

Determining the methods of identifying values from texts, several alternatives were evaluated as reflected in Table 1. Table 1. Alternative methods for value identification from texts. Method of Information Extraction

Reason for accepting/rejecting

1. Multiclass (speed, cost, comfort), predetermined classes

1. Numerous classes and no opportunity to find new classes/values

2. Sentiment analysis

2. Whole text/sentence is evaluated; multiclass (values can belong to a mixture of sentiment classes)

3. Binary term classification

3. Allows to identify new values

4. Named Entity Recognition (NER)

4. Allows to create a new entity for recognition

NER approach allows to create a new entity type – value with predefined semantic rules for annotators: they learn a basic theory of a value proposition and they are trained to spend the same time as an average online consumer spends on a webpage – 62 s [16].

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3.2 Experiment Procedure The main technique used in the experiment was text annotation. Data annotation is the text-mining technique through which data is labelled so as to make objects (in this research – values expressed by words) recognizable by machines. An online interface reflected on Fig. 6 was created and a database of 796 start-ups Twitter profiles from air transportation domain was downloaded.

Fig. 6. Annotation process web interface (authors developed).

Two annotators were tasked to label texts for values as a part of value proposition. Both annotators were experts in value proposition concept, one was an expert in natural language processing, and one was an expert in aviation management. As a result, a corpus of values from air transportation domain was created. The design of value identification and systematization faced the following problem: the number of words that constitute a value in the majority of cases is unknown. It was hypothesized that the majority of values could be captured by one word. The validity of the hypothesis was investigated via numerical experiments in the domain of aviation, e.g. it was evaluated on the Twitter profiles of 796 start-ups in the domain of aviation and a survey where 96 respondents were given a task to formulate values in air transportation industry. While annotating for the Twitter profiles and during the value formulation experiment, respondents were given the freedom to perceive textual forms of values. Respondents could identify as value any part of speech, any part of the sentence, and any number of words on the Twitter profile of the start-up. Furthermore, it was at the discretion of respondents to choose either a phrase reflecting the value or one word. The analysis of the Tweets annotation shows that 78% of values were annotated as one-word value. Value formulation survey reflects 71% of all values being captured

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by one-word value. The one-word approach to value identification is beneficial as it provides simplification and efficiency, and it allows for a more fine-grained analysis. 3.3 Training and Evaluation To finalize the process of automation, a neural network was trained to identify values from texts of air transportation domain. One of the top performing libraries for NLP tasks is widely considered to be spaCy, so it was chosen for the task of value identification from the texts. The scores then are used to predict the label for the word. Metrics are as follows: precision 0.85, recall 0.89 and F1-score of 0.87. Supervised method of value extraction was applied with the following steps: 1. Set of documents (564 Twitter profiles of aviation start-ups out of 796 in the initial dataset; 232 were non-informative for different reasons like no value proposition or non-English content) was used as a source base for value recognition. 2. Labelling (annotation of values) by experts assumed words containing values as part of value proposition to be tagged as value entities. Combined method of semantic and binary annotation was utilized. 3. Dataset of values (763 words) was finally obtained as the result of annotation. 4. Pre-processing (filtering, normalization: Python) included One-word principle: one value is expressed by one word. 5. Simplifying task as binary classification: binary classification (value / not value) instead each value being a class allows to pick up new values. 6. A neural network model from spaCy library is used as a binary classifier. 7. Training and evaluation: Fig. 7 presents the process of training spaCy model, where Gradient is the direction and rate of change for a numeric value. Minimising the gradient of the weights should result in predictions that are closer to the reference labels on the training data [17].

Fig. 7. Process of training the model [17].

The word corpus used is “spacy.Corpus.v1” for English language. Model architecture is Spacy.TransitionBasedParser.v2. Transition-based parsing is an approach to structured prediction where the task of predicting the structure is mapped to a series of state transitions.

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Architecture. Hidden layer size = 64 neurons, a parser consists of 3 subnetworks: 1. tok2vec layer (transforms words (tokens) into vectors), 2. “lower” layer (the “meat” of neural network, converts vectors to internal representation), 3. “upper” layer (feed-forward layer that calculates scores from the previous layers’ vectors). The scores then are used to predict the label for the word. Figure 8 shows the training progress.

Fig. 8. Pipeline.

Start from Score 0.56, after 10 training iterations the score raised up to 0.72, after 60 training iterations the score raised up to 0.83. The experiment was stopped at 100 iterations with the score at 0.84 because of the following reason: – improvement flattens out (becomes incremental) (see Fig. 9), – to avoid overfitting.

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Fig. 9. Graph of iterations/score correlation.

4 Results This research has resulted in the developing of Methodology for Automatic Value Identification from Air Transportation Texts, presented on Fig. 10:

Fig. 10. Methodology for automatic value identification from air transportation texts.

The Methodology consists of 5 blocks: Exploratory Data Analysis, Data Source Identification, Text Annotation, Data Systematization and Modelling. Each block proposes the sequence of steps. As an example, Data Source Identification supposes the stages that lead to the choice of Data Source: first, the decision was made to analyse texts of webpages, all the webpages contain too many texts, so the choice was made for start-ups landing pages and twitter profiles as they bear concentrated value proposition. According to the set-up goals and tasks, the domain was narrowed down to the Air Transportation.

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5 Practical Importance and Industry Reviews Practical importance of this research resulted in developing a Methodology For Automatic Identification Of Values in the domain of air transportation can be reflected in the following: 1. Strategic decision-making scope is widened by providing extended choice of values based on innovations and novel business models. 2. Automation provides opportunities for identification industry value shift through time and new values can be documented using time- and resource-saving technique. 3. Novel methodology has a potential applied to different domains enabling identification and extending values beyond air transportation. Air Transportation industry experts’ reviews were positive. Among others: – International Hub Airport Commercial Director, Nordic-Baltic region: “The unexpected finds and formulations of values that were not previously in sight are of great interest for marketers”. – Commercial Director of Reginal International Airport, Eastern Europe: “Automation and systematization of values in air transportation allows for faster decision making on strategic questions as it shows new values and value chain transformation”.

6 Conclusion The research aim has been achieved: Value Entity Recognition task was introduced and methodology for automatic identification of values in the domain of air transportation has been provided. The question can values as a part of value proposition be identified and expanded automatically? is answered affirmatively. To achieve the research aim, the following steps were performed: 1. The philosophical ground and factual implication of value concept was investigated and traced the path of becoming a component of the value proposition concept. 2. A novel approach to Information Extraction in the form of a new Entity Recognition task Value Entity Recognition (VER) was introduced. 3. An experiment annotating texts of the start-ups of air transportation domain was conducted, values were successfully identified, and a corpus created 4. The quantity of the obtained values were calculated. The quantity (763) substantially outnumbered the traditional number of values in aviation organization (3–7) that can be obtained manually. 5. One-word approach was established as the basis for a binary classification. 6. A neural network model was trained on the corpus of Twitter profiles of air transportation start-ups with the precision of 0.85.

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Limitations of the research: number of annotators and their level of expertise, ambiguity of natural language, objectivity of semantic perception, cross-domain applicability, cross-language applicability, accuracy of identification of new values by the model. Implications for the further research could be aimed at developing possibilities for air transportation values comparison in the specific time periods and elaboration the methodology to suit different domains.

References 1. Hillstrom, L.: Value Creation – strategy, organization, definition, school, company, business, competitiveness. https://www.referenceforbusiness.com/management/Tr-Z/Value-Cre ation.html (2018) 2. Stephens, E., Martin, B.: Business policy and strategic management. Scientific e-Resour. (2019) 3. Paternoster, N., Giardino, C., Unterkalmsteiner, M., Gorschek, T., Abrahamsson, P.: Software development in startup companies: a systematic mapping study. Inf. Softw. Technol. 56(10), 1200–1218 (2014) 4. European Commission: Europe’s next leaders: the start-up and scale-up initiative. https://ec. europa.eu/growth/content/europes-next-leaders-start-and-scale-initiative_en 5. Crunchbase: Crunchbase profile. https://www.crunchbase.com/organization/crunchbase. Last accessed 3 Dec 2021 (2021) 6. Merriam-Webster: “value” entry. In: Merriam-Webster.com. Retrieved 8 May 2021, from https://www.merriam-webster.com/dictionary/value (2021) 7. Lanning & Michaels: Delivering value to customers. Harvard Business Review, President and Fellows of Harvard College (1988) 8. Fleig, C., Augenstein, D., Maedche, A.: A process mining-enabled decision support system for data-driven business process standardization in ERP implementation projects: KIT scientific working paper. In: Proceedings of the KSS Research Workshop 2017, pp. 48–52 (2019). https://doi.org/10.5445/IR/1000104369 9. Gaizauskas, R., Demetriou, G., Artymiuk, G., Willett, P.: Protein structures and information extraction from biological texts: the PASTA system. J. Bioinform. 19(1), 135–143 (2003) 10. Malik, H., Vikas, S. B., Huascar, F.: Accurate information extraction for quantitative financial events. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, pp. 2497–2500, New York, NY, USA (2011) 11. Ortona, S., Chen, L., Orsi, G.: Roseann: taming online semantic annotators. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, WWW Companion ’14, pp. 591–594 (2014) 12. Speck, R., Ngonga Ngomo, A.-C.: Ensemble learning for named entity recognition. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 519–534. Springer, Cham (2014). https:// doi.org/10.1007/978-3-319-11964-9_33 13. Wang, H., Zhao, T., Liu, J.: Multi-agent classifiers fusion strategy for biomedical named entity recognition. In: 2008 International Conference on BioMedical Engineering and Informatics, vol. 1, pp. 311–315. IEEE (2008) 14. Ngo, Q.H., Kechadi, T., Le-Khac, N.A.: Domain specific entity recognition with semanticbased deep learning approach. IEEE Access 9, 152892–152902 (2021) 15. Alexander, F.: Information extraction – lecture 3: rule-based named entity recognition. In: Lecture slides, pp. 512–517, published at https://www.cis.unimuenchen.de/fraser/information_ extraction_2015_lecture/. Last accessed 10 Nov 2020 (2013)

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16. Contentsquare Homepage: Digital Experience Benchmark, https://go.contentsquare.com/en/ digital-experience-benchmark. Last accessed 7 Jul 2021 (2020) 17. Spacy.io: spaCy Training Pipeline. https://spacy.io/usage/training. Last accessed 24 Jul 2021 (2022) 18. Almquist, E., Senior, J., Bloch, N.: The elements of value. Harvard Bus. Rev. 46–53 (2016)

Aircraft Intellectual Records Management System Vitalii Susanin1,2(B)

and Leonid Shoshin1,3

1 Transport and Telecommunication Institute, Riga, Latvia 2 “S7 ENGINEERING” LLC, Mozzherina Prospect, 12, Novosibirsk, Russia

[email protected] 3 “S7 ENGINEERING” LLC, Building 6/1, Airport Domodedovo, Moscow, Russia

[email protected]

Abstract. The paper reviews the current process of working with aircraft technical records and proposes a new methodology for extracting and processing data and metadata. The proposed methodology includes a continuous process of working with documents, which includes the corresponding developed algorithms for recognition, semantic analysis and subsequent processing of documents. As part of the approbation, the methodology showed a positive result, which allows solving the research problem and the corresponding tasks. Keywords: Technical records · Extracting data · Recognition · Records management system

1 Introduction In order to ensure flight safety and harmonize standards, the Chicago Convention was signed, on the basis of which the relevant laws on initial and continued airworthiness were developed and implemented. Taking into account the high cost of aircraft, spare parts, serious overregulation of processes, various approaches to the organization of aircraft maintenance have been developed and implemented, as well as many schemes for interaction between airlines and the aircraft owner (lessor). In order to standardize and optimize these processes, International Air Transport Association (IATA) has developed guidelines for the return of aircraft to the lessor, as well as for the paperless maintenance process. The subject of attention of all these guides is the technical record drawn up as part of the maintenance of the aircraft. Without a fully completed technical records, the aircraft is considered unairworthy and has no right to fly. The signed technical records are proof that all scheduled maintenance, airworthiness directives, service bulletins, defects have been eliminated on the aircraft, etc. In fact, the completed technical record is a large number of archive boxes and carries the cost of the aircraft. In case of loss of technical record, the value of the aircraft is practically depreciated. To minimize this risk, many airlines prefer to additionally store copies of all completed technical record in an electronic, secure form. Due to the fact that tens of thousands of pages of technical record are issued annually for a civil aircraft, as well as taking into account © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 89–100, 2023. https://doi.org/10.1007/978-3-031-26655-3_8

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the requirements of aviation authorities and lessors, airlines are forced to employ a large number of specialists who filter, check and systematize all technical record depending on the types of records. In order to minimize these labor costs, airlines are implementing various software products and approaches that automatically or semi-automatically perform the tasks of scanning, storing, filtering, systematizing, checking and completing technical record. Such software products are called Records Management system (RMS). Accordingly, the research problem is formulated as current methodologies and algorithms for working with records do not provide the required level of process automation, the cost of working with record is quite expensive, the risks of errors and loss of records are quite high, current systems do not allow ensuring the required level of records readiness for the delivery of aircraft to lessors, and also for audits by aviation authorities. The purpose of the study is defined as the development of a new process and methodology for working with record using the principles of machine learning as part of the development and implementation of digital ecosystems. The following tasks investigated are indicated: – Development of an effective algorithm for working with record to ensure the processes of maintaining airworthiness and handing over the aircraft to the lessor. – Development of methods for extracting and working with data and metadata of scanned technical record. – Formulation and development of requirements for Records management system. In the following paragraphs, in the beginning related research works are reviewed for subject actuality confirmation. The next appropriate aircraft records are classified by several criteria. Then general information for records management process for paperless, optimization, reliability and quality of aviation maintenance processes is described. After that methodology for exporting and processing data from records is proposed. Then requirements for records management system are formulated and taxonomy of main components of the digital ecosystem is presented. Finally in the conclusion actuality of the subject is also confirmed and appropriate inferences are defined.

2 Related Works In the paper [1] authors developed a secure blockchain that can store aircraft service records and information in a digital distributed ledger. Also the paper defined the need to enhance the integrity and transparency of aircraft maintenance records in the aviation industry by using blockchain technologies. Ali Furkan Biten et al. [2] published the OCR annotations for IDL documents using commercial OCR engine given their performance over open source OCR models. In paper [3] the status of structural maintenance record management was analyzed from four aspects of view. This paper analyzed the existing problems of structural maintenance reliability of airline operators in China. It showed some shortcomings including the scattered data sources, lacking of effective data collecting method, no analysis

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model, hard to quantify the inspection effect, and no structural reliability analysis standard. Finally, this paper made some suggestions on standardizing structural maintenance records from both the authorities and the airline operators’ points of the view. Authors of paper [4] developed a syntactic and semantic rules-based approach and an approach leveraging a pre-trained language model, fine-tuned for a question-answering task on top of our base dictionary lookup to extract entities of interest from maintenance records. Authors also developed a preliminary ontology to represent and capture the semantics of maintenance records. Their evaluations on a real-world aviation maintenance records dataset show promising results and help identify challenges specific to named entity recognition in the context of noisy industrial data. Jiuxiang Gu et al. [5] presented UDoc, a new unified pretraining framework for document understanding. UDoc is designed to support most document understand- ing tasks, extending the Transformer to take multimodal embeddings as input. Each input element is composed of words and visual features from a semantic region of the input document image. An important feature of UDoc is that it learns a generic representation by making use of three self-supervised losses, encouraging the representation to model sentences, learn similarities, and align modalities. Extensive empirical analysis demonstrates that the pretraining procedure learns better joint representations and leads to improvements in downstream tasks. In the paper [6] Authors proposed LayoutLMv3 to pre-train multimodal Transformers for Document AI with unified text and image masking. Additionally, LayoutLMv3 is pre-trained with a word-patch alignment objective to learn cross-modal alignment by predicting whether the corresponding image patch of a text word is masked. Experimental results show that LayoutLMv3 achieves state-of-the-art performance not only in text-centric tasks, including form understanding, receipt understanding, and document visual question answering, but also in image- centric tasks such as document image classification and document layout analysis. Authors of paper [7] investigated the task of extracting parts and associated conditions from maintenance records in the aircraft domain. Authors experimented with a bidirectional LSTM-CRF network to extract parts and conditions simultaneously. The model achieved a reasonable accuracy for the task across diverse data sets. All the above-mentioned articles confirm the relevance of the topic and research problem, however, all authors solve these problems within narrow areas and do not consider research on the creation of methodologies for the operation of the aircraft records management system. Also, the authors have developed appropriate products and algorithms that allow solving a specific problem, however, these developments do not allow solving universal problems within industry 4.0 and the concept of a digital ecosystem, which also confirms the relevance of this work.

3 Aircraft Records Classification A classification of types of records used in the framework of the aviation industry has been developed by authors. As a rule, airlines manage all types of records and maintain appropriate records in various information systems. The following requirements are general and are followed by all information systems:

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The data must be formatted in accordance with a single standard; All records should be in compliance with standard SPEC2500 [8]; Records should be accurate enough, fully readable and written in English; Data formats are to be compatible with corresponding information systems.

All documents can be divided into 3 different categories, which are a static set of records, a dynamic set of records updated on a periodic basis, and a set of records that must be kept up to date in online format. The static set of documents includes: – – – – –

airworthiness certificate; noise and radio certificates; passenger compartment / cargo compartment configuration; layout of emergency equipment; list of used fuels and lubricants, etc. The dynamic documents set includes:

– maintenance program; – list of minimum equipment; – aircraft weighing report; The set of statuses that are maintained online include: – – – – – –

last done and next due status; airworthiness directive status; service bulletin and modification status; on condition and condition monitoring status; hard time component and life limit parts status; structure repair status.

The data for the above statuses is collected as a rule from the execution of the relevant tasks of the aircraft maintenance. The execution of maintenance forms must be carried out by the relevant certified organizations, after each maintenance of the aircraft, the technicians sign off a document allowing the aircraft to fly - a certificate of release to service. For the technician to put the appropriate signature, it is necessary to sign off and prepare a set of evidentiary documentation, which constitutes a working package of technical records. For several of maintenance events, the package may exceed 15,000 pages. At the same time, the package of technical records itself consists of the relevant work cards for performing routine work and performing non-routine work, as well as relevant documents for the implementation of airworthiness directives, service bulletins. The package includes the relevant certificates for the replaced components and materials, as well as the relevant evidence documents confirming the replacement of the component. The package also contains documents on the elimination of defects and the implementation of structural repairs. Also, the package may contain any other documents that confirm the fact that a particular task has been completed as part of

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maintenance. For the formation and indication of the basic work package composition the block diagram is created which is shown on Fig. 1.

Fig. 1. Work package composition.

4 Records Management Process for Paperless, Optimization, Reliability and Quality of Aviation Maintenance Processes The process of aircraft maintenance begins with the organization of the airworthiness maintenance system, which includes a number of mandatory tasks, such as: • Development and support of maintenance programs and reliability programs for various types of aircraft. • Development and maintenance of the minimum list of equipment. • Development and implementation of special procedures: flight, weighing and others. • Organization and implementation of the aircraft storage program. • Development of work cards and other technical records. • Aircraft maintenance planning.

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• Maintenance, accounting and storage of aircraft technical records. • Monitoring of aircraft engine fleet operation parameters. • Airworthiness Statuses management. Airworthiness statuses are defined as statuses of Components with assigned and overhaul resources, Airworthiness Directives, Service Bulletins and Modifications, Structural Aircraft Structure Repairs, Maintenance Program Implementation, etc. The purpose of performing these tasks is to perform activities and maintenance that ensure the required level of flight safety. After the preparatory work, a list of tasks to be performed on this form of maintenance is formed. When performing each of the tasks, the engineering and technical staff draws up records in a classic format to prove the performance of the relevant work on the aircraft. This record is further completed and scanned for further indefinite storage. At the same time, in order to comply with the requirements for maintaining airworthiness, the aviation authorities and the clauses of the leasing agreement, the airlines additionally form additional groups of records from the received package of technical record to confirm the performance of certain mandatory works, replacement of components, etc. Given the high labor intensity of these processes, many aviation companies implement the philosophy of paperless maintenance. This philosophy is part of Industry 4.0 innovation and requires compliance with all requirements. Paperless aircraft maintenance involves performing similar tasks on an aircraft, with the exception of preparing technical record in paper form. To implement this approach, airlines or maintenance organizations should write these processes into their respective procedures and operations manual. It is also necessary to organize a system for registration of technical record in electronic form. This is possible when using a qualified electronic signature, which requires a serious approach and additional conditions in the operation of the system information. As a rule, these conditions are automatically met in the presence of a digital ecosystem. With the introduction of paperless maintenance technology, labor costs for the preparation of technical record are reduced, it is not required to draw up each item with a corresponding signature, a few clicks are enough to close the corresponding work in the system. The quality of technical record is significantly improved due to the abolition of the human factor. However, this technology does not allow to significantly reduce the labor costs for splitting the completed packages of technical record (i.e., the entire archive), completing and grouping technical record by types and records.

5 Methodology for Data and Metadata Extracting from Aircraft Records At the moment, the approach for working with technical records is manual without extracting any data and metadata from documents. Data includes all information from page including barcodes, tables, etc. and metadata includes cross-references to information system data, position of document through work package, etc. Records are divided into types according to the results of manual maintenance by a large number of employees. After that, the records are scanned one by one for the

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purpose of further uploading to the server for storage and backup. Further work with records is also much more complicated. In fact, the engineer works with tens of thousands of images, searching for and compiling the required number of documents. As part of the study of the possibilities of working with documents and extracting relevant data from documents, the authors of the article proposed an appropriate methodology for extraction data from records, working with such data, organizing appropriate records and using artificial intelligence to train the system to solve relevant research problems. The current and proposed methodologies are shown in Figs. 2 and 3. The methodology is a closed cycle of the algorithm for working with documents. This cycle includes streaming scanning of documents, without the need to divide the work package into the corresponding documents, and documents can also be loaded into the system in other ways, such as photos, mail messages, etc. Usually process of recognition consists of: 1. 2. 3. 4. 5. 6. 7.

Convert pdf to image stack (1 sheet pdf - 1 image). Image preprocessing (image alignment, noise removal, etc.). Finding the area or form field that needs to be recognized. Character detection and bounding box definition. Recognition of each character using a computer vision model (mainly OCR). Validation of the acquired knowledge for correctness. Adding the received data to the database.

To detect objects in a strictly defined zone, preprocessing and the findContours function of the OpenCV library were used. Simplified code example:

import cv2 as cv src = cv.imread('area.jpg') gr = cv.cvtColor(src, cv.COLOR_BGR2GRAY) canny = cv.Canny(bl, 10, 250) kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5)) closed = cv.morphologyEx(canny, cv.MORPH_CLOSE, kernel) contours = cv.findContours(closed.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)[0] for cont in contours: #smoothing and quantifying corners sm = cv.arcLength(cont, True) apd = cv.approxPolyDP(cont, 0.02*sm, True) if len(apd) == 4: cv.drawContours(src, [apd], -1, (0,255,0), 4) cv.imwrite('result.jpg', src) To assess the accuracy of recognition of individual elements of the extracted data, we enter the corresponding parameters Xi and Yi for data. Wherein, X1 = Number of recognized machine written words out of the total number Y1 ; X2 = Number of recognized handwritten words out of the total number Y2 ; X3 = Number of recognized tables out of the total number Y3 ;

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Xi = number of recognized signs out of total numbers Yi . The recognized item of data can be understood as a random variable that takes the values X1 , …, Xn , with the corresponding probabilities p1 = XN1 , . . . , pn = XNn , where N = X1 + X2 + . . . + Xn , since the recognition probability pi item of Xi is equal to XNi . Thus, arithmetic mean value of recognition quality can be determined using the relation Qav = x1 p1 + . . . + xi pi =

n 

xi pi ,

(1)

i=1

Further the system according to the algorithms embedded in it, sorts the corresponding documents according to the basic features, recognition of all data in the document is performed, each of the pages is classified according to the corresponding type of document. Relevant data and metadata are retrieved and stored in the respective tables. Then the next stage begins, which includes semantic analysis of data for relation to a particular type of documentation. One part of semantic analysis code is shown below:

our_table = [] for cell in cells: cell_key = "" cell_value = "" for block in blocks: if block.text in ["MJC №", "Job title", "A/C Type", "A/C REG", ...]: cell_key = block.text else: cell_value = block.text if cell_value in ["WORD ORDER", "MJO", ...]: # type of document cell_key = "TITLE DOCUMENT" cell_value = block.text our_table.append({"cell_key":cell_key, "cell_value":cell_value}) After determining the type of each document, the package is divided into the corresponding documents with separation by type. After separating and extracting the relevant information the system according to this methodology, creates appropriate links between current documents and related documents for example confirming certificates for the materials used are attached to the report on the modification performed. If the system detects inconsistencies in the completeness or position of the pages, the system at the initial stage proposes and agrees with the specialist the appropriate changes, after receiving the necessary training, the system performs corrections on its own. Documents also undergo additional quality control, fields without appropriate signatures are analyzed, as well as the correctness of entering the relevant information into work cards, as well as the availability of all necessary certificates for the materials used. After completion of this stage, the system prepares a processed package of documents for subsequent work with documents for the relevant specialists in a module with a user interface. If a specialist identifies certain shortcomings in the work of the system or incorrect responses of the methodology and algorithms, the specialist performs the

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necessary changes in the package of documents prepared by the system. The system tracks all corrections, determines the algorithm why the corrections were made and makes appropriate changes and improvements to the basic algorithms for documentation recognition. Also, within the framework of this work, the authors developed and proposed a user interface for the corresponding work by specialists - shown in Fig. 4.

Fig. 2. Current methodology for records processing.

Fig. 3. Proposed methodology for records processing.

Fig. 4. Proposed user interface for records management system.

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6 Requirements for RMS For the functioning of the developed and proposed algorithm, the authors have compiled basic requirements that should be used in the development of the necessary software products. These basic requirements are divided into 4 categories - Recognition, Processing, System settings. Transition and are presented in Table 1. Table 1. Requirements for records management system Recognition

Typescript, table, handwriting recognition Recognition of quick response code and barcodes, empty fields Distribution and grouping of records, neural network Record Type Definition Extract and group records according to a predefined algorithm Ability of the system to offer new algorithms for grouping records Extracting the necessary data inside the record (part number, serial number, airworthiness directive number, etc.) Creating links between records (for example airworthiness directive - > service bulletin - > engineering order - > material certificate) Completing records by package, by type, attaching certificates, etc Record editing, manual page replacement, page reordering

Processing

Record audit by maintenance organization, lessor or operator Ability to create groups of records, such as Redelivery Binders Uploading the necessary records in the required format of a set of records or sending by mail, saving on a computer Checking the quality of records by the internal quality control Advanced search through records data and metadata Preparation and submission of records to the operator or lessor Fixing comments and eliminating them by explaining inside the communication system or uploading new or revised records Preparation of airworthiness statuses from the system

System settings

Main viewer module: filters, folder tree, record viewer, module for editing records and recognized data Module for scanning and creating custom statuses and reports Comments module and their corrections Module for receiving records by e-mail, from remote servers Integration with maintenance and operation system Setting access rights (reading, changing records of the 1st level, changing data in records, etc.)

Transition

Reservation and storage of records Scanning and printer connection Streaming scan, rescanning discrete pages Alternative methods of obtaining records (e.g. by E-mail or photo) Downloading on an internal server in a dedicated folder Cloud storage integration

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7 RMS Integration into Digital Ecosystem As described in the article [9] the digital ecosystem consists of relevant blocks integrated with each other. To function, the Records Management System is a separate unit that exchanges data with other sub-systems. The integration and data format are designed in such a way that the system can both receive data from related subsystems of the digital ecosystem and receive data and metadata directly from paper. Once the data and metadata are extracted from paper, the data is shared within the digital ecosystem for further validation and, if necessary, adjustments. The taxonomy of the main components of the digital ecosystem is presented in Fig. 5.

Fig. 5. Digital ecosystem of aircraft operation.

8 Conclusion Proposed methodology provide possibility to control data and metadata of scanned copies of records for correct execution, to analyze, systemize as per lessors and aviation authority’s requirements. 1. All archives of technical records undergo a process of syntactic and lexical content analysis. 2. By means of zonal text recognition, the facts of the absence of signatures for the work performed are revealed. 3. Records are collected and systemized in accordance with set classification by automatic process.

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References 1. Aleshi, A., Seker, R., Babiceanu, R.F.: Blockchain model for enhancing aircraft maintenance records security, In: 2019 IEEE International Symposium on Technologies for Homeland Security (HST). Presented at the 2019 IEEE International Symposium on Technologies for Homeland Security (HST), IEEE, Woburn, MA, USA, pp. 1–7 (2019). https://doi.org/10.1109/HST 47167.2019.9032943 2. Biten, A.F., Tito, R., Gomez, L., Valveny, E., Karatzas, D.: OCR-IDL: OCR annotations for industry document library dataset (2022) 3. Cheng, H.: Analysis and improvement of aircraft structural maintenance records. In: Proceedings of the 2020 International Conference on Aviation Safety and Information Technology. Presented at the ICASIT 2020: 2020 International Conference on Aviation Safety and Information Technology, pp. 42–48. ACM, Weihai City China (2020). https://doi.org/10.1145/343 4581.3434588 4. Dixit, S., Mulwad, V., Saxena, A.: Extracting Semantics from Maintenance Records 7 5. Gu, J., et al.: Unified Pretraining Framework for Document Understanding (2022) 6. Huang, Y., Lv, T., Cui, L., Lu, Y., Wei, F.: LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking (2022) 7. Niraula, N.B., Kao, A., Whyatt, D.: Part and condition extraction from aircraft maintenance records. In: 2020 IEEE International Conference on Prognostics and Health Management (ICPHM). Presented at the 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1–7. IEEE, Detroit, MI, USA (2020). https://doi.org/10.1109/ICP HM49022.2020.9187064 8. Airlines for America (A4A). 2017 Aircraft Transfer Records, Spec2500. https://global.ihs. com/ 9. Shoshin, L., Susanin, V.: Digital Transformation of an Aircraft Operation Ecosystem. Relstat2021 (2021)

Jet Engine Health Assessment Using EGT Time Series Vladislav Zhdanov(B) and Alexander Grakovski Transport and Telecommunication Institute, Lomonosova 1, Riga 1019, Latvia [email protected], [email protected]

Abstract. In the presented work, the engine and its sets of parameters in two conditions were observed: before and after repair. All parameters show, that engine was deteriorated and engine performance was restored during repair, however, all parameters were within limits. Exhaust gas temperature (EGT) was selected as the main parameter for analysis due to its high impact on engine health. EGT time series for the engine before and after repair were compared and it was shown, that the fractal dimension is higher for the engine after repair. That means that deteriorated engine processes are less complex compared to processes in the repaired engine. Based on this information, two nonlinear models were developed for EGT prediction. Keywords: Jet engine · Technical state model · Decision-making · Predictive maintenance · Time series · Fractal dimension · EGT

1 Introduction A modern jet engine has many sensors, which are capable to record a different kind of information about engine performance, such as temperature, vibration, fuel flow, etc., among all phases of flight. This information is widely used for engine health monitoring and analysis [1]. Interest in engine health monitoring and deep analysis of parameters is mainly called for two reasons: improving the safety of flights and reducing costs. On the one hand, earlier detection of problems and preventive maintenance helps to avoid serious incidents [2]. On the other hand, good knowledge of engine technical state provides operators flexibility in flight schedules and repair planning. For example, focusing on exhaust gas temperature (EGT) prediction during the cruise phase allows ensuring the optimal functioning of the engine [3]. The main goal of this paper was to determine critical changes in parameters, which led to engine repair. To achieve this goal engine data was investigated to find differences between the engine’s technical state before and after repair, and a nonlinear model for parameter prediction was developed. After this introduction, the remaining parts of the paper are divided into four sections. Section 2 presents a description of the research object, while EGT time series analysis is considered in Sect. 3. Section 4 presents the development of the nonlinear models and EGT predictions. Conclusions are discussed in the last section. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 101–109, 2023. https://doi.org/10.1007/978-3-031-26655-3_9

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2 Research Object Description One of the most popular and mass-produced engines in civil aviation was chosen as an object of current research – CFM56-7B27, which is installed on Boeing 737. 2.1 Engine Under Study The particular engine which was studied has the highest thrust available for this model: 27 lbs. Engines of the same model working with different thrust levels have no technical distinctions. Engine software allows burning more fuel to provide higher thrust. This leads to increasing of the temperature inside the engine combustor and turbine. For monitoring of engine temperature, the EGT parameter is usually used.

Fig. 1. HPT Nozzles missing material.

During the exploitation of the engine, total efficiency is going low due to increasing clearances, contamination on the airfoils, etc. To keep the level of thrust, the engine has to burn more and more fuel, which leads to increasing of the EGT and faster deterioration of the engine gas path. When the EGT is high and the EGT margin (EGTM) is close to zero, the engine should be routed to the repair (shop visit, SV), which is usually called “Performance restoration”. The engine under study was operated for about 24 thousand hours and 8 thousand cycles since the last repair when it was routed to another repair. The main reason for this SV is performance restoration. In Fig. 1 the engine condition is presented. Usually, the engines with the same thrust level and same operating time have missing material and thermal barrier coating, cracks, and even missed blades in hot section parts such as the combustion chamber and high-pressure turbine.

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2.2 Available Engine Data For the engine, about 300 points are available before and after SV for 15 parameters. A summary of the data is recorded in Table 1. There is a notable difference between values before SV and after SV, vibration and exhaust gas temperature are higher before SV, EGT margin is lower, which confirms that engine condition and performance are not the same, however, all values are within limits for both cases. The EGT margin parameter is shown in Fig. 2. Pale lines correspond to raw data and bright correspond to show 10 points averaged data. There is a gap between the last point before SV and the first point after SV because EGT margin recovery is the main purpose of the shop visit. Table 1. Summary of available data. Parameter

Before SV

After SV

Limit

MIN

AVG

MAX

MIN

AVG

MAX

EGT to, °C

697

764

836

670

749

838

950

EGT cr, °C

606

652

705

555

612

680

N/A

EGT Margin, °C

6

18

33

18

44

70

0

Fan vib fwd to

0.4

0.65

1

0

0.18

0.6

4

Fan vib fwd cr

0.3

0.6

0.8

0

0.22

0.7

4

Fan vib rear to

0.6

0.82

1.2

0

0.16

0.6

4

Fan vib rear cr

0.2

0.57

1

0

0.16

0.7

4

Core vib fwd to

0

0.13

0.2

0

0.06

0.1

3

Core vib fwd cr

0

0.08

0.4

0

0.03

0.1

3

Core vib rear to

0

0.2

0.4

0

0.1

0.2

3

Core vib rear cr

0

0.28

0.6

0

0.02

0.1

3

Oil pressure to, psid

58

62.3

67

53

57.7

64

N/A

Oil pressure cr, psid

49

53.9

56

45

48.9

53

60

Oil temp to, °C

42

61.9

79

43

62.4

80

140

Oil temp cr, °C

70

84.8

98

66

80.6

96

140

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Fig. 2. EGT margin time series visualization.

2.3 EGT Selection as the Main Parameter for Analysis For the current research, EGT was taken as the main parameter to focus on. Three time series were selected for deep analysis: takeoff EGT, cruise EGT, and EGT margin. There are several reasons for such a selection. First, EGT is a well-known engine health indicator, and literature analysis shows that other researchers actively used EGT for engine health assessment [4]. Then, EGT increasing is a real problem especially for high thrust engines, as for the engine under study. Also, the consequences of high EGT are visible during parts inspections. This allows you to compare the data from sensors and the real condition of parts. After the engine is started EGT is going up and reaches its peak during the takeoff phase when the thrust is maximum, and after that EGT slowly goes down. For the deteriorated engine, EGT is higher during all phases of flight. If the engine is in bad condition, EGT could achieve the limit or even be higher than it. If this happens, the engine may be seriously damaged because engine parts are not designed to work at such temperatures. Therefore, EGT monitoring and analysis is key important for engine health assessment.

3 Time Series Analysis Using Fractal Dimension and Hurst Exponent Approaches Time series analysis aims to confirm, that there is a difference between the parameters of the deteriorated engine and the repaired engine, using well-known mathematical approaches.

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3.1 Fractal Dimension and Hurst Exponent Fractal dimension is a coefficient which is describing sets based on their complexity. For the calculation of fractal dimension, the following formula was used: D = lim

a→0

log N (a) , log(a)

(1)

where N (a) is the number of samples included in the hypersphere of radius a in ndimensional pseudo-phase space [5]. Hurst exponent (index) is another measure for time series analysis. It decreases as the delay between two identical pairs of values in the time series increases. For the calculation of the Hurst exponent, the following formula was used: log(R/S) , r→0 log r

H = lim

(2)

where R(r) is the difference between the maximum value of the cumulative deviation and the minimum value of the cumulative deviation, S is the standard deviation in data range r [6]. 3.2 Self-similarity Analysis Results Fractal dimension and Hurst index were calculated for EGT takeoff, EGT cruise, and EGT margin parameters taken before and after SV. Calculation results are shown in Table 2. Obtained results confirm that characteristics of the engine time series are differing: for all three time series, fractal dimensions are lower for the engine before SV. This could mean that the processes in the deteriorated engine are less complex compared to the repaired engine.

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V. Zhdanov and A. Grakovski Table 2. Hurst index and fractal dimension calculation results. Before SV Data

Hurst index

Fractal Dimension

EGT-TAKEOFF

0.8331

2.680

EGT-CRUISE

0.6930

2.477

EGT Hot Day Margin

0.9156

1.8824

Data

Hurst index

Fractal Dimension

EGT-TAKEOFF

0.6256

4.491

EGT-CRUISE

0.7855

4.248

EGT Hot Day Margin

0.8638

3.115

After SV

4 Developing the Nonlinear Model for EGT Prediction Short-term EGT forecasting might be helpful for engine performance assessment and SV planning reasons. The main idea of parameter prediction is that if there is any pattern in the time series, then it could be possible to develop a nonlinear model [7], which would be able to predict future values using a combination of historical values. Fractal analysis showed that the possible pattern in the data is nonlinear. Therefore, nonlinear models were used for parameter prediction, and the orders were selected based on Takens’ theorem [8], which is defining the relationship between fractal metrics and the nonlinear model order. To predict the next EGT value of deteriorated engine, three previous points should be used in the nonlinear model, while for repaired engine five previous points. For the prediction of the EGT for the engine before SV, the following nonlinear model was used: xk = c1 xk−1 + c2 xk−2 √ √ √ + c3 xk−3 + c4 xk−1 · xk−2 + c5 xk−1 · xk−3 + c6 xk−2 · xk−3 .

(3)

Coefficients were calculated using the Ridge model from Python 3 library Scikitlearn. 70% of raw data was used to find the optimal coefficients of the model. Then using these obtained coefficients, predicted EGT was calculated and compared with the remaining 30% of real data. The model didn’t show good data fit for EGT take off and EGT margin, but the EGT cruise predicted line shows better agreement with real data. For the prediction of the EGT for the engine after SV, the following nonlinear model was used: √ √ xk = c1 xk−1 + . . . + c5 xk−5 + c6 xk−1 · xk−2 + . . . + c15 xk−4 · xk−5 . (4) For coefficient calculation, the same approach was used. Again, the model predictions don’t fit well with the real data.

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Fig. 3. Results of EGT prediction.

The results are shown in Fig. 3. Pale red and blue lines correspond to real data used for coefficient calculation (red for the engine before SV and blue for the engine after SV). Bright lines correspond to real data used for model verification.

Fig. 4. Models’ coefficients ci before (3) and after (4) SV for EGT M prediction.

Gaps between pale lines and bright lines are caused by the display future: the last point of the data used for calculation is not connected visually with the first point of the data used for verification. There is no interruption in raw data. Green lines are models’

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predictions. To get the first prediction point 3 or 5 previous real EGT values were used together with calculated coefficients using expressions (3) and (4) accordingly. For the next prediction point, one real value was replaced by the predicted value. Finally, starting from the fourth and sixth points only, predicted values were used to get the new point. Figure 4 shows the distribution of the models’ coefficients. These coefficients may also be used for health assessment. Their evaluation during engine life could be another health indicator and such comparison will be the next step of current research.

5 Conclusion Modern engines provide a great opportunity for researchers to develop and implement different data-driven approaches for engine health assessment, and huge demand for such solutions is observed. In the presented work, the full cycle of developing such a solution is presented: starting from the engine description, data collection and analysis, and finishing with the models, which are trying to predict EGT values based on historical data. It was shown that engine time series have notable differences for the engine before and after SV. Then, fractal analysis confirmed that engine time series before and after SV have different dimensions: 3 and 5. Finally, the selected nonlinear model was not able to predict EGT correctly, but its coefficients may be applied to the analysis of the engine health on the base of the evolution of the prediction model order. The idea is to calculate coefficients for different parts of raw data and then investigate their progress. In addition, it might not be enough to use only the EGT parameter for engine health assessment, for example, if the EGT margin is not a problem for a specific engine. In this case, more complex models should be used, and EGT should be combined with other parameters.

References 1. Powrie, H.E.G., Fisher, C.E.: Engine health monitoring: Towards total prognostics. In: 1999 IEEE Aerospace Conference. Proceedings (Cat. No.99TH8403), pp. 11–20, vol. 3. IEEE, Snowmass at Aspen, CO, USA (1999). https://doi.org/10.1109/AERO.1999.789759 2. Jaw, L.C., Lee, Y.-J.: Engine diagnostics in the eyes of machine learning. In: Volume Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy, p. V006T06A029. American Society of Mechanical Engineers, Düsseldorf, Germany (2014). https://doi.org/10.1115/GT2014-27088 3. Raphael, L., Jerome, L.: Turbofan exhaust gas temperature forecasting and performance monitoring with a neural network model (2022). https://www.researchgate.net/publication/363350 797_Turbofan_exhaust_gas_temperature_forecasting_and_performance_monitoring_with_ a_neural_network_model. Last accessed 28 November 2022 4. Fentaye, B.: Gilani, kyprianidis: a review on gas turbine gas-path diagnostics: state-of-the-art methods. Challenges and Opportunities. Aerospace. 6, 83 (2019). https://doi.org/10.3390/aer ospace6070083 5. Falconer, K.J.: Fractal Geometry - Mathematical Foundations and Applications. John Wiley and Sons, United States (2003)

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6. Hurst, H.E.: Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 116, 770–799 (1951) 7. Meade, N., Islam, T.: Prediction intervals for growth curve forecasts. J. Forecast. 14, 413–430 (1995). https://doi.org/10.1002/for.3980140502 8. Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980, pp. 366–381. Springer Berlin Heidelberg, Berlin, Heidelberg (1981). https://doi.org/10.1007/BFb0091924

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Impact of Stockout-Based Substitution on Optimal Inventory in Management Science and Microeconomic Implications Berdymyrat Ovezmyradov(B) Transport and Telecommunications Institute, Lomonosov Str. 1, Riga 1019, Latvia [email protected]

Abstract. This study aims at identifying different outcomes of stockout-based substitution for optimal inventory depending on modeling settings. The optimal order size in mathematical modeling of the stockout-based substitution depends on the underlying assumptions in the reviewed studies. The difference between related models in microeconomic theories with several streams of research in management science, as this review demonstrates, is that the former focus on competing firms, whereas the latter mostly describes the same independent firm that owns an assortment of substitutable products. The substitution can increase the expected profits of firms and change their inventory levels if managers adapt business strategies to prevailing market conditions. Another implication of the substitution is important for sustainable supply chain management in that policymakers and retailers might reduce inventory waste by encouraging consumers’ certain responses to stockout. Keywords: Management science · Microeconomics · Inventory management · Stockout-based substitution · Sustainability

1 Introduction Existing literature on stockout-based substitution demonstrates how it can improve profitability and change optimal inventory. There exist reviews of research on substitutable products (for instance, Pentico 2008 and literature review sections of the papers discussed in this study) [1]. However, they do not provide an overview of how stockout-based substitution affects optimal inventory depending on modeling settings. How do studies in management science employing mathematical models of stockout-based substitution differ in terms of the change in optimal inventory? This is the research question that the study attempts to answer. The topic is important now that sustainable development concerns increasingly pay attention to the consumption of resources, and this study discusses the corresponding implications of managing inventory. The next section explains the used method; reviews of empirical and theoretical studies with accompanying analysis are presented in the subsequent two sections; then a section discusses how models in management science can be different from those in microeconomic theory with implications for sustainability; and the study concludes with the conclusion section summarizes its main findings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 113–122, 2023. https://doi.org/10.1007/978-3-031-26655-3_10

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2 Method The method employed by this study is a limited review of studies in the field of management science and operations research. The method aims at filling the corresponding gap in the literature and is based on a review of selected influential or novel publications on consumer responses to stockout in terms of their research impact (citations) in operations research and management science. The objective here is not to cover as many sources as possible but rather to provide a representative set of papers. The main limitation of the review is thus the limited number of selected papers. The focus of the review is on the effect on inventory since reducing the consumption of materials has become one of the urgent priorities in sustainable supply chain management.

3 Empirical Evidence and Significance of Substitutability Before the following review of theoretical studies in the next section, it is reasonable to show the practical importance of substitution for real business. Corsten and Gruen (2005) found that 9% of stockout situations resulted in consumers not buying anything in response; 31% switching to another store to buy the same product; 19% substituting for another product of the same brand; 26% choosing a different substitutable brand; and 15% delaying the purchase [2]. Some empirical evidence suggests up to 40% of customers do not ask the store staff for help when a searched item is out of stock; furthermore, even when they do, managers are not able to assist due to being busy or unaware of misplaced items locations [3]. In addition, there are other less common responses to stockout not reviewed in this study, such as apology, raincheck, home delivery, trade-up and discounts as being insignificant, which could become effective remedies for retailers managing stockout situations at stores in certain situations [4]. The incorporation of these alternative aspects of substitution into modeling research is not common in the literature on stockout-based substitution, and they are not included in the following review.

4 Review and Analysis of Stockout-Based Substitution Models The main feature of numerous models in operations research and management science distinguishing them from microeconomic models is the focus on responses to stockout occurring in a newsvendor model setting (balancing stockout and overstock). Admittedly, there exist price-based substitution models in both fields, but stockout-based substitution appears to attract more attention in management science relative to microeconomics literature. Research featuring games between competing or otherwise strategically interacting retailers or supply chain coordination found the likelihood of an increase in an optimal inventory with stockout-based substitution and supply chain contracts. Conversely, research modeling independent symmetric retailers, games between strategic consumers and retailers, and product assortment often indicates quite the opposite outcome is possible: retailers’ optimal inventory might be lower with consumers’ stockout-based substitution. This difference, as Table 1 demonstrates, might be explained by the model

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settings: in the first group of the above-mentioned research, the papers describe a model of competing firms, whereas the second group of papers describes the same retailer that owns an assortment of substitutable products or faces strategic consumers. It should be noted that Table 1 only outlines the prevailing effects of each research stream on inventory, which does not exclude a possibility the opposite outcome. Under certain conditions, optimal order size might decrease in the models of product assortment and substitutable brands. Inventory increase conditions with exponential demand distribution can be considered an anomaly in risk pooling [5]. Table 1. Streams of mathematical modeling research on stockout-based substitution and its prevailing effect on optimal inventory in management science. Stream No

Representative paper

Inventory change

Model type

1

Anupindi and Bassok (1999) [6]

Mostly non-decreasing

Games between competing or otherwise strategically interacting retailers

Mishra and Raghunathan (2004) [7] Netessine and Zhang (2005) [8] Hopp and Xu (2008) [9] Wan et al. (2017) [10] Huang et al. (2011) [11] Liu et al. (2013) [12] 2

Rajaram and Tang (2001) [13] Khouja et al. (1996) [14]

Independent symmetric retailers Mostly non-increasing

Kurata and Ovezmyradov (2018) [15] Ovezmyradov and Kurata (2022) [16] 3

Su and Zhang (2008) [17] Cachon and Swinney (2009) [18]

Games between strategic consumers and firms

Cachon and Swinney (2011) [19] 4

Smith and Agrawal (2000) [20]

Product assortment

Kök and Fisher (2007) [21] Fadılo˜glu et al. (2010) [22] Transchel (2017) [23] Kurata et al. (2017) [24]

(continued)

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Stream No

Representative paper

5

Yang and Schrage (2009) [25]

6

Inventory change

Model type Risk pooling

Dong and Rudi (2004) [5]

Ambiguous, both increase and decrease are possible

Cachon (2003) [26]

Non-decreasing for most contract types

Supply chain coordination with contracts

The remaining part of this section presents mathematical models believed to be fairly representative of each stream. The first stream covers game-theoretical models with Nash equilibrium and an increase in the inventory. They describe various supply chain structures with uncertain demand: one manufacturer and two dealers [6]; vendor-managed inventory with two manufacturers and one retailer [7]; competing retailers likely to overstock substitutable products [8]; and other models of response to stockout increasing the optimal orders [9, 10]. Despite certain differences, these models demonstrate the benefit of product substitution due to decreased lost sales and increased profits, which can be achieved by ordering larger optimal sizes in competitive settings. It is well known from microeconomic theory that competition affects order quantities, as the models with substitution based on capacity or price (Cournot or Bertrand, correspondingly) demonstrate: strategic interactions between competing companies leads to suboptimal decisions. In those models, rival firms’ market output is greater if they choose output simultaneously, but the output generally lies between the competitive and monopoly levels [27, 28]. Unlike models of businesses facing stochastic demand in management science, microeconomics mostly considers deterministic demands with equilibrium prices and output in a market, mostly neglecting stockouts at individual firms. Notably, the first stream (Table 1) and the microeconomic models appear different in the outcome of the competition in the equilibrium output (inventory). The analysis continues with retailers not being direct competitors in a market with uncertain demand. A key assumption here is that they are symmetric (the same price and other parameters). Assume a supply chain comprises two independent retailers, 1 and 2, each procuring two substitutable brands a and b at wholesale price, w, from a supplier (representative paper is Ovezmyradov and Kurata) [15]. Retailers have to place an order, q, before the regular sales season at p price ending in a clearance sales at discounted v price. When the desired brand is out of stock, a portion α of consumers who experience stockout might delay purchase if backlogging is allowed. With the brand-switching, the overstock of one product brand can be used to substitute a β portion of the spillover demand from another stockout brand at the same retailer. With store switching, γ portion of the spillover demand is derived from the second retailer that experiences stockout. The expected profit from a product a at retailer 1 selling substitutable brands that consumers

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can substitute or delay buying can be formulated in general form as follows:   π1,a = p1,a S1,a − w1,a q1,a + p1,a − w1,a α1,a L1,a    +p1,a min β1,b L1,b + γ2,a L2,a , I1,a    +v1,a max I1,a − β1,b L1,b − γ2,a L2,a , 0

(1)

where the expected sales of brand a at retailer 1 is q1,a ∞     S1,a = ∫ x1,a f x1,a dx1,a + q1,a ∫ f x1,a dx1,a , 0

1,a

the expected overstock of the brand a at retailer 1 is q1,a     I1,a = ∫ q1,a − x1,a f x1,a dx1,a 0

the expected stockout of the brand a at retailer 1 is ∞    L1,a = ∫ x1,a − q1,a f x1,a dx1,a , q1,a

∞    the expected stockout of brand b at retailer 1 is L1,b = ∫ x1,b − q1,b f x1,b dx1,b ; the q1,b ∞

   expected stockout of the brand a at retailer 2 is L2,a = ∫ x2,a − q2,a f x2,a dx2,a . q2,a

The complexity of the responses to stockout explains the challenges of finding an analytical solution. The outcome depends on restrictive assumptions, and effects on inventory can be ambiguous. Notably, simulations suggest the prevailing effect of decreasing inventory [16]. This outcome is particularly likely with a delay of purchase (backlogging), as the analysis of Eq. (1) reveals. A lower optimal inventory is also a common result of the stream of research considering an assortment of substitutable products in a store. A different type of model considers a game between retailers and strategic consumers, assuming the rationality of consumer beliefs with the Nash equilibrium result [17–19]. The corresponding stream of research is different from all others in the review because strategic consumers consider their responses to the possibility of future stockout, not the current one. Each consumer has a utility of consumption or reservation price, u, and a discount of future consumption, δ. For consumers with rational expectations, the chances of getting a discount after waiting for clearance sales, r, is equal to the probability of buying the overstock unit at the lower price, v. This is possible only if the retailer has sufficient inventory, q, to satisfy the demand, so that r = F(q∗ ). The retailer maximizes the profit with the prices satisfying u − p ≥ δr(u − v)

(2)

So that all consumers purchase the product at the regular price instead of waiting for a discount (representative paper is Cachon and Swinney) [19]. It is then straightforward

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to find from analyzing Eq. (2) that the presence of strategic consumers leads retailers to holding less inventory in an equilibrium with rational expectations. In addition to the same or horizontally differentiated products considered so far, several studies have shown the important role of customers’ switching between vertically differentiated products in case of unavailability for ordering policies of manufacturers [29]. The benefits of the multiproduct downward substitution are obvious when demand variability is high, substitution cost is low, the profit margin is low, salvage values is high, and similarity of products (in terms of prices and costs) is increased [30]. Tsay and Agrawal studied the competition between firms for substitutable products based on price and product attributes [31]. The outcome for inventory in this stream is predominantly uncertain and their details are consequently excluded from this review. There is a small possibility of higher inventory with partial risk pooling for certain distributions such as the exponential one. In a more likely scenario, three critical conditions under risk pooling when a decrease in stock occurs are: the overstock cost per unit is higher relative to the cost of shortage per unit; the probability distribution of demand is not too right-skewed; while the pooling effect is not too high [25, 32]. Overall, the outcome of risk pooling on inventory can be ambiguous in specific settings such as transshipment [5]. A vast literature on supply chain contracts shows they often encourage higher inventory levels to avoid lost revenues due to stockouts [26]. In this stream context, a decrease in order size is often a sign of double marginalization, a negative outcome for supply chain partners that decreases the total supply chain profit and should be avoided by designing various contract types to encourage higher inventory and share the ensuing monetary gain between the partners. To summarize, streams 1 and 6 in this review (Table 1) show a more likely outcome of an increase in inventory, while the other streams allow either decreasing or ambiguous changes in inventory. The outcomes have implications for society as the next section discusses.

5 Implications for Microeconomics and Sustainability Optimal order size, as the previous section finds, can be defined by the consumers’ response to stockout and whether the research stream setting is competitive or not. A quick search of scientific and popular literature on sustainability reveals a paramount priority of reducing excessive consumption of raw materials for society. This is particularly the case in sectors with considerable stockouts and heavy environmental pollution, such as fashion supply chains. It is perhaps not surprising that a reduction of waste in the form of overstock and excessive consumption has implications not only for lean management but also for sustainability. Microeconomic models traditionally emphasized the societal benefits of competitive business environments in terms of higher output and lower equilibrium prices for consumers. This study argues for a more balanced approach considering the harm of excessive inventory as a result of the higher competition. Perhaps, lack of competition is not a concern for certain industries such as fast fashion, where saturated markets with plenty of rival firms produce a massive amount of waste in the form of overstock and returns with unacceptable shares of rejected products ending up in landfills or

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microplastic (especially in oceans). The cut-throat competition also frequently results in a race-to-the-bottom in the low pricing policies that negatively impacts the environmental (encourages higher consumption of raw materials), financial (squeezes profit margins), and social (lowers wages in the developing countries where most textiles are sourced) dimensions of sustainability. Regulatory bodies and international treaties in such cases could shift attention from an anti-monopolistic focus to policies reducing firms’ wasteful output even if it would mean higher costs for consumers. Managers, on the other hand, could pay more attention to certain microeconomic effects of their inventory policies on the environment. In pursuit of profit and revenue maximization, they should not neglect the damage from the overstock to social welfare and other dimensions of sustainability. Another interesting managerial implication from the review is the counterintuitive result of the benefit of widespread behavior of strategic consumers who were traditionally considered harmful to businesses. Since such consumers force the retailers to hold less stock, society benefits in terms of the aforementioned effects of reduced inventory on sustainability. Policymakers at higher levels of national governments and international institutions are more likely to consider the opinions of economists than management scientists. In fact, the field of economics has a higher research impact than management science [33]. Students in many majors initially learn the concept of substitution from the discipline of microeconomics. An overview of recent textbooks in microeconomics reveals they often do not explicitly discuss stockout-based substitution even though it can be as important in real business as other kinds of substitution that receive attention in this discipline, such as those based on utility, output, and price [27, 28]. Perhaps, textbooks in both disciplines of microeconomics and management science could briefly mention the main findings on substitution outside their main subject that are relevant in terms of inventory management for current and future practitioners. This need for the cross-fertilization of management science and economics is an important implication of the findings in this review. Finally, the complex relationships between substitutability and inventory redundancies in the international logistics have to be mentioned in light of the fundamental transformations taking place in the global supply chains since the start of the COVID-19 pandemic in 2020. Empirical and modeling evidence suggests the policies of holding minimum or zero inventory according to the just-in-time approach that were popular before the coronavirus pandemic proved to be costly after the supply chain disruptions between 2020 and 2021 (Sanci, 2021). Supply chain disruption studies before 2020 primarily focused on the consequences of natural and humanitarian disasters on businesses’ supplies. Lean manufacturing can help save inventory costs but becomes risky during natural and other disasters. Proactive firms, such as Toyota, shifted away from a just-in-time approach for critical supplies despite previously being leaders in such an approach [34]. Resilience in supply chains can be crucial during pandemics [35]. Decentralized systems holding more facilities with inventory are desirable when disruption risks are present [36]. The inventory diversity can be redundant, but it also helps cope with shocks in supply [37]. Substitutability can speed up the validation of emergency orders for international humanitarian organizations [38]. High product variety

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indeed contributed to the operational resilience during the recent pandemic [39]. Substitutability does not necessarily lead to greater product variety and capacity during the pandemic if a low SKU number reduces changeover times as a part of a manufacturer’s reconfiguration [40]. However, extra inventory and capacity during an emergency similar to COVID-19 in certain product categories such as groceries could benefit the adequate cooperation between retailers and food banks [41]. To summarize, the sustainability considerations might counterbalance a tendency for adding higher capacity and inventory observed in the global supply chain disruptions following the pandemic outbreak in 2020. Substitutability-driven reduction in inventories with the relative ease of stockoutbased substitution due to omnichannel fulfillment might mitigate the negative effect of excessive inventory [16]. If everyone can win from the stockout-based substitution, how can regulators and businesses encourage it? The retail sector and omnichannel fulfillment can encourage suppliers to support substitutability even if it means lower order sizes. Independent retailers and upstream supply chain members could mutually cooperate to promote specific responses to stockout with specially designed contracts, somehow changing strategic interactions between market players towards favorable characteristics of monopolistic competition for higher stockout-based substitution. Even competing retailers could share data in a manner that promotes the substitution, as its effects could benefit all firms in a monopolistic competition setting.

6 Conclusions The review of the extant literature on stockout-based substitution in operations research and management science confirms that corresponding implications for decision-makers can depend on several common modeling settings. First, it is a game-theoretic model where two or more companies compete. The related streams of research describe a likelihood of larger optimal inventory when stockout-based substitution is high, or supply chain agreements prevent double marginalization. Second, it is streams of noncompetitive models where a single retail chain or several non-competing stores select optimal order sizes for their stock or interaction occurs among retailers and strategic consumers. Third, the results of the remaining research streams do not clearly indicate the direction of change in inventory. Inventory tends to increase in the streams of models of competing companies in the research as mentioned above, while the opposite is true for the other less-competitive interaction streams. Differences in how the substitution is approached in microeconomics and management science suggest the need for those two study areas to become more interdisciplinary, complementing each other. A shift in focus when stockout-based substitution becomes of higher importance should support policymaking geared towards a decrease in optimal inventory for all dimensions of sustainability to counter the tendencies towards redundancies with higher output after the global supply chain disruptions happening since 2020. The impact of stockout-based substitution on policymaking in the textiles market with vertical product differentiation is currently the next direction of the ongoing work based on this study. While the presented review did not cover the increasing number of studies on multichannel retail from a microeconomic perspective of competition, there is

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empirical evidence of an essential role of stockout-based substitution in online shopping. It suggests another desirable direction for future research. Acknowledgements. This study received funding from the MSCA RISE Program under grant agreement No. 870647.

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19. Cachon, G.P., Swinney, R.: The value of fast fashion: quick response, enhanced design, and strategic consumer behavior. Manag Sci. 57(4), 778–795 (2011) 20. Smith, S.A., Agrawal, N.: Management of multi-item retail inventory systems with demand substitution. Oper. Res. 48(1), 50–64 (2000) 21. Kök, A.G., Fisher, M.L.: Demand estimation and assortment optimization under substitution: Methodology and application. Oper. Res. 55(6), 1001–1021 (2007) 22. Fadılo˜glu, M.M., Kara¸san, O.E., Pınar, M.Ç.: A model and case study for efficient shelf usage and assortment analysis. Annals of Operations Research 180(1), 105–124 (2010) 23. Transchel, S.: Inventory management under price-based and stockout-based substitution. Eur. J. Oper. Res. 262(3), 996–1008 (2017) 24. Kurata, H., Ovezmyradov, B., Meuthia, Y.: Stocking decision and supply chain coordination under the occurrence of backlogging, brand switching, and store switching. The J. Japan Indu. Manage. Asso. 68(2E), 33–63 (2017) 25. Yang, H., Schrage, L.: Conditions that cause risk pooling to increase inventory. Eur. J. Oper. Res. 192(3), 837–851 (2009) 26. Cachon, G.P.: Supply chain coordination with contracts. Handbooks Oper. Res. Manage. Sci. 11, 227–339 (2003) 27. Pindyck, R.S., Rubinfeld, D.L.: Microeconomics. Pearson Education (2018) 28. Perloff, M.J.: Microeconomics theory and applications with calculus (2018) 29. Bitran, G.R., Dasu, S.: Ordering policies in an environment of stochastic yields and substitutable demands. Oper. Res. 40(5), 999–1017 (1992) 30. Bassok, Y., Anupindi, R., Akella, R.: Single-period multiproduct inventory models with substitution. Oper. Res. 47(4), 632–642 (1999) 31. Tsay, A.A., Agrawal, N.: Channel dynamics under price and service competition. Manuf. Serv. Oper. Manag. 2(4), 372–391 (2000) 32. Gerchak, Y., Mossman, D.: On the effect of demand randomness on inventories and costs. Oper Res. 40(4), 804–807 (1992) 33. Frey, B.S., Eichenberger, R.: The ranking of economists and management scientists in europe a quantitative analysis. Journal des économistes et des études humaines 10(4), 575–582 (2000) 34. Shih, W.: From Just-In-Time to Just-In-Case: Is Excess and Obsolete Next? Forbes (2022) 35. Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58(10), 2904–2915 (2020) 36. Schmitt, A.J., Sun, S.A., Snyder, L.V., Shen, Z.J.M.: Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions. Omega 52, 201–212 (2015) 37. Hecht, A.A., Biehl, E., Barnett, D.J., Neff, R.A.: Urban food supply chain resilience for crises threatening food security: a qualitative study. J. Acad. Nutr. Diet. 119(2), 211–224 (2019) 38. Saïah, F., Vega, D., de Vries, H., Kembro, J.: Process modularity, supply chain responsiveness, and moderators: The Médecins Sans Frontières response to the Covid-19 pandemic. Production and Operations Management (2022) 39. Li, Y., Wang, X., Gong, T., Wang, H.: Breaking out of the pandemic: How can firms match internal competence with external resources to shape operational resilience? Journal of Operations Management (2022) 40. Dohmen, A. E., Merrick, J. R., Saunders, L.W., Stank, T.T.P., Goldsby, T.J.: When preemptive risk mitigation is insufficient: the effectiveness of continuity and resilience techniques during COVID-19. Production and Operations Management (2022) 41. Penco, L., Ciacci, A., Benevolo, C., Torre, T.: Open social innovation for surplus food recovery and aid during COVID-19 crisis: the case of Fondazione Banco Alimentare Onlus. British Food Journal (2021)

Classification of Inventory Management Methods Based on Demand Analysis in Supply Chains Valery Lukinskiy(B) , Vladislav Lukinskiy, Darya Bazhina, Ekaterina Gazizova, and Igor Bernadskii National Research University Higher School of Economics (HSE University), St. Petersburg, Russia [email protected], [email protected]

Abstract. Improving the efficiency of inventory management in supply chains remains relevant for industrial, commercial, service and other enterprises. The results of a supply chains survey (distribution center - stores) with weekly deliveries throughout the year demonstrated that inventory management strategies may be classified into three groups: smooth (more than 47 weeks per year deliveries are recorded), low (from 20 to 46 weeks per year), and sporadic and lumpy (less than 20 weeks per year). The conducted studies have shown that for three groups of inventory management strategies in supply logistics, it is advisable to form three groups of calculation models that allow obtaining quantitative estimates of supply amount and making decisions under conditions of certainty, risk and uncertainty. The first group of calculation models includes Last period or Naive, as well as Exponential smoothing with trend, Multiple regression, etc. The second group consists of combined models, including estimates of the first group and expert estimates under risk conditions. The third group involves models based on decisionmaking under conditions of uncertainty. The proposed approach allows making reasonable management decisions based on the identification of the demand type to improve the efficiency and reliability of the supply chain. Keywords: Supply chain · Inventory management · Demand · Reliability

1 Introduction The effective management of logistics operations in supply chains requires a continuous focus on updating theoretical knowledge and solutions to practical problems in such areas as transportation, inventory management, warehousing, assessment of reliability and efficiency of customer satisfaction, design of logistics systems, etc. (among many others, [1]). A fast and reliable supply chain turns out to be particularly important for quality assurance [2]. A number of papers have pointed out that systematization of the various approaches to supply chain inventory management theory is a complicated task (among many others, [3]). A mass retailer offering general merchandise and food often exceeds 50,000 stock keeping units. Faced with this width of inventory, retailers attempt © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 123–130, 2023. https://doi.org/10.1007/978-3-031-26655-3_11

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to reduce risk by pressing manufacturers and wholesalers to assume greater and greater inventory responsibility [4]. The dilemma for every manager, whether they are a provider, producer or vendor, is not only to obtain a service rate of 100% to satisfy all their clients, but also to have the lowest possible storage cost [5]. Supply chains need to have a very assertive demand forecasting method, otherwise, the inventory and transportation costs will significantly increase [6]. Despite significant advances in recent decades, it is still too early to speak of a solution to this problem. This paper considers an approach to the classification of inventory management methods based on an analysis of the different types of demand in supply chains. The research has shown that demand can be classified into at least three groups: regular (smooth); low, intermittent, and irregular; lumpy, sporadic, and erratic demand. The proposed approach makes it possible to make justified managerial decisions based on the identification of the type of demand in order to improve the efficiency and reliability of supply chains.

2 Literature Review Over the past ten years, the International Conference on Reliability and Statistics in Transportation and Communication (RelStat) has presented the results of our research in three main areas: analysis of methods for choosing logistics intermediaries, inventory management and transportation, assessment of the reliability and efficiency of supply chains in conditions of uncertainty and risk. Table 1 includes a summary of the main scientific questions we have been studying since 2013. Table 1. Analysis of the research results presented at RelStat conferences 2013–2021. Research course

Source Paper abstract

Assessing the reliability and efficiency of [7] supply chains in conditions of uncertainty and risk (RelStat 2013–2016)

The article presents developed and improved failure models for a number of logistics operations

Analysis of methods for choosing logistics [8] intermediaries (RelStat 2015, 2017–2018)

Some of the indicators may be considered as sets of random variables representing an intellectual product and reflecting the characteristics of human thinking

Inventory management and transportation [9] (RelStat 2016–2021)

The proposed approach makes it possible to achieve such results as, on the one hand, avoiding a shortage in the distribution network and, on the other hand, avoiding unnecessary safety stocks in warehouses

An analysis of various sources [1–13] showed that logistics operations related to supply, production and distribution are usually performed under conditions of uncertainty. The random nature of demand causes various supply chain failures, and an inaccurate

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forecast may cause inventory shortage or excess. Therefore, a key factor in improving the effectiveness of supply chain management is the ability to minimize the possible consequences of such negative conditions.

3 Formation of a Classification of Inventory Management Methods Based on the Demand Analysis in the Supply Chain At present, a significant number of forecasting methods have been developed. The most commonly used are extrapolation methods such as exponential smoothing. At the same time, there is no universal model in the available sources that allows taking into account various options for the time series of actual sales for the past periods. Moreover, the limitations of existing models include the need to have information about demand for a long period to identify factors such as seasonality, while in a number of cases it is not possible to obtain objective information about the uneven demand during the day or week (working days and weekends). It is worth mention that the formation of demand is influenced by various external factors, for example, the nature of the competitive environment. Short-term predictable and unpredictable extreme increases, decreases or lack of demand, etc., should also be taken into account. Supply is one of the functional logistics areas aimed at obtaining the goods and services necessary for the company. There is a fundamental difference in the processes of planning the need for purchased products in industrial and trading companies. For trading companies, the basis of planning is sales plans with taking into account forecast estimates of demand, for industrial companies, the main guideline is to increase the reliability of timely delivery to ensure the smooth operation of production lines. To improve the supply reliability and meet demand, it is necessary to optimize the inventory of trading companies, in particular, to answer two questions: how much to buy and when to deliver. An enlarged algorithm for optimizing decision-making on inventory management in supply chains based on demand analysis for trading companies includes the following main blocks: – – – –

demand forecasting for 12 months based on time series for past periods; calculation of turnover and inventory levels; delivery tracking and control; procurement planning that takes into account the demand forecast, turnover and inventory availability, etc.

The system for planning purchases and optimizing stocks of trading companies is formed in accordance with one of the typical structures, for example, Hub&Spoke. At the center of such a structure is a distribution center (Hub), which receives goods from suppliers (for example, once a week), and completes orders for a chain of stores. To determine the number of units of a specific product item supplied to the distribution center, may be used an iterative procedure and performed calculations based on the dependency: Qt = (Qt−1 + St−1 + Qss ) − Lt−1 ,

(1)

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where Qt – number of production units at the beginning of the week (forecast); Qt−1 – number of production units at the beginning of the previous week; S t−1 – planned receipt in the previous week, production units; Qss – safety stock, production units; L t−1 – planned sales (demand) in the previous week, production units. For instance, if Qt−1 = 5 units, S t−1 = 20 units, Qss = 3 units, L t−1 = 18 units, then the forecast value of the units number of production at the beginning of the next week Qt = 10 units. The presented iteration model (Eq. 1) includes different variants reflecting the diversity of manufacturing and trade supply systems. This diversity is defined by three main features. First, the number of parameters considered (Eq. 1) may range from two (S t−1 , L t−1 ) to four (Qt−1 , S t−1 , Qss , L t−1 ) parameters. Second, the iterative model may include deterministic (planned) and random (predicted) quantities. For instance, Table 2 presents deterministic data on production and sales of furniture products by the Pennington company [13]. Table 2. Production and sales of cabinet sets [13, p.327]. Month

Qt−1

S t−1

Qss

L t−1

Qt

December

-

-

-

-

100

January

100

840

0

750

190

February

190

840

0

760

270

Third, according to the classification of decision-making methods for good, weak, and unstructured problems, it is possible to form various analytical models of unit quantity estimation Qt . If the information about the supply and consumption of products is shown in the form of deterministic values, the calculation is performed as a simple (arithmetic) dependence (Table 2 presents the results of calculations based on deterministic values). In case the data on supply S t , consumption L t and safety stock values Qss are in the form of dynamic (time) series, the calculation of Qt is based on forecasting methods (Last period, Exponential smoothing, Multiple regression, etc.). In addition to the above methods, probabilistic models based on the composition of distribution laws of random variables may be used to estimate the number of units Qt . Thus, with normal distribution laws for the random variables included in Eq. (1), the probability of no shortage is defined as   M1 − M2 , (2) PQ = 1 − F(x) = 1 − F − √ D12 where F(x) is a tabulated normal law distribution function [12]; M 1 = M(Qt−1 ) + M(S t−1 ) + M(Qss ), where M(Qt−1 ), M(S t−1 ), M(Qss ) are the mean values of Qt−1 , S t−1 , Qss ; M 2 = M(L t−1 ), where M(L t−1 ) is mean value of L t−1 ; D12 = D(Qt−1 ) + D(S t−1 ) + D(Qss ) + D(L t−1 ), where D(Qt−1 ), D(S t−1 ), D(Qss ), D(L t−1 ) are variances of random variables Qt−1 , S t−1 , Qss , L t−1 .

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Consider calculating the probability of no shortage based on the mean values and variances presented in Table 3. Table 3. Mean values and variances of the variables included in Eq. (2). Random variable

Mean value, M

Variance, D

S t−1

800

2,500

Qss

50

225

Qt−1

150

784

L t−1

850

3,025

Then, when substituted into Eq. (2), we obtain the value of the probability of no shortage   150 PQ = 1 − F − √ = 1 − F(−1.85) = 0.968. () 6534 Let’s try to specify the relationship of supply processes between stores and distribution centers. The analysis showed that Eq. (1) needs to be clarified and adjusted depending on the dynamics of deliveries to distribution centers and consumption in stores. The conducted studies of a number of supply chains (distribution center - store) showed that the values Q, S, L reflect a three-dimensional random variable x i,j,k , where i – the number of stores (i = 1, …, N); j – the number of items supplied to stores (j = 0, …, M); l – the number of the week during the year in which the delivery was made (l = 1, …, 52). An analysis of various sources showed that the inventory management strategies in each of the stores (Spoke) that receive products from the distribution center may be divided into the following groups: periodic (constant period between orders, t = const); reorder point (ROP) (the order is made when the stock balance reaches a certain level); combined (the order is placed when the time point t and/or ROP is reached); low-volume (insignificant volume of products consumption, the moments of deliveries are separated by long periods); – impulse (demand is discrete and reaches large values, the moments of supply are separated by long periods).

– – – –

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Thus, the considered options for inventory management strategies may be classified into three groups that characterize the demand for products: smooth, low, sporadic and lumpy demand (demand). For each of the specified groups, it is proposed to enter the corresponding boundary values, determined by the number of weeks in which deliveries were recorded. So, the first group (smooth) includes items with the number of deliveries from 47 to 52 per year; in the second group (low) from 20 to 46 supplies, respectively; the third group involves items with less than 20 deliveries per year. As for the numerous variants differentiation of forecasting models, they may also be divided into three groups. The first group may include the following models and methods: Last period (LP) or Naive, Moving average (MA), Weighted moving average (WMA), etc.; Exponential smoothing (ES), Exponential smoothing with trend (TES); Linear regression (LR), Multiple regression (MR), ARMA, ARIMA, SARIMA, etc. The second group includes synthesized models, which are a combination of predictive estimates of the first group and estimates based on decision-making methods under risk conditions. It should be taken into account that the reliability of the obtained estimates is lower than for the methods of the first group. The third group includes models that cover time series with a predominant number of zero values, for instance, Croston’s method or models based on expert estimates under uncertainty (for example, AHP proposed by T.Saaty). Table 4 shows the data obtained from the supply chain survey (distribution center stores). The company name is not disclosed for confidentiality reasons. Table 4. Results of the supply chain survey (distribution center - stores). Parameter

Quantity (range)

Comments

Number of stores, N

29 (35)

Of the total (35 stores), demand was in 29

The total number of products types supplied during the year, M

32 (34)

Two products were discontinued within a year

Number of products supplied to one store, K

1–18

Number of deliveries per store per year, L

1–52

Weekly deliveries

Table 5 shows the values for one of the items supplied to six stores, a group of inventory management strategies and predictive models (for quantitative assessment and the possibility of planning for subsequent periods), as well as possible management decisions (1 - purchases synchronous with the consumption process; 2 - creation of stocks; 3 - emergency / unscheduled deliveries).

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Table 5. Fragments of time series of deliveries of one of 32 nomenclatures, in units of production. Store Week number 1 2

Delivery number 3

4

5

6

7

8

1

53

100 68 103 73 115 230 15 8

52

2

5

1

3 4

0

3

8

51

7

0

6

47

185 0

29 1

0

0

11

9

5

24

0

0

1

0

24 0

96

0

3

21

5

0

495 0

0

0

0

0

0

1

8

6

0

0

0

0

0

0

0

0

2

0

* ** ***

Per 8 weeks Per year, L 1 1

1

2 2

2

3 3

3

* stock management strategy group, ** forecast model group, *** management decisions

4 Conclusion Increasing the inventory management efficiency in supply chains despite the obvious progress and results achieved remains relevant for industrial, commercial, service and other enterprises. Various analytical models may be generated to determine the number of units of a particular product item supplied to the distribution centre. In case the supply and consumption information is presented as deterministic values, the calculation is provided as a simple (arithmetic) dependence. In case the supply, consumption and safety stock data are in the form of dynamic (time) series, the quantity calculation is based on forecasting methods (Last period, Exponential smoothing, Multiple regression, etc.). In addition to these methods, probabilistic models based on the composition of the distribution laws of random variables may be used for estimating unit quantities. An analysis of a significant number of sources devoted to the study of inventory management models and methods revealed that various inventory management strategies are currently being used (periodic, ROP, combined, low-volume, impulse, etc.). The survey results of supply chains (distribution center - stores) with weekly deliveries during the year showed that these strategies may be classified into three groups: smooth (deliveries are recorded in more than 47 weeks per year), low (from 20 to 46 weeks per year), and sporadic and lumpy (less than 20 weeks per year). The iterative formula for the weekly forecast of the values of the production units number should be refined as a three-dimensional random variable that reflects the number of stores in the chain, the number of delivered items, and the number of weeks per year in which deliveries were carried out. The conducted studies have shown that for three groups of inventory management strategies in supply logistics it is advisable to form three groups of calculation models that allow obtaining quantitative estimates of supply amounts and making decisions under conditions of certainty, risk and uncertainty. The first group of calculation models included Last period or Naive, as well as Exponential smoothing with trend (TES), Multiple regression (MR), SARIMA, etc. game theory). The third group involves models

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based on decision making under uncertainty (for example, AHP proposed by T. Saaty, as well as Croston’s method). As directions for further research on improving the efficiency and reliability of supply chains based on demand analysis it should be noted the use of methods and models of machine learning, for example, neural networks, random forest, and others.

References 1. Prause, F., Prause, G.: Inventory routing analysis for maritime LNG supply of German ports. Transp. Telecommun. 22(1), 67–86 (2021) 2. Treiblmaier, H., Garaus, M.: Using blockchain to signal quality in the food supply chain: the impact on consumer purchase intentions and the moderating effect of brand familiarity. Int. J. Inf. Manage. (2022). https://doi.org/10.1016/j.ijinfomgt.2022.102514 3. Jackson, I., Tolujevs, J., Kegenbekov, Z.: Review of inventory control models: a classification based on methods of obtaining optimal control parameters. Transp. Telecommun. 21(3), 191–202 (2020) 4. Bowersox, D., Closs, D., Cooper, M.: Supply Chain Logistics Management, 2nd edn. McGraw-Hill, New York (2007) 5. Yalaoui, A., Chehade, H., Yalaoui, F., Amodeo, L.: Optimization of Logistics. John Wiley & Sons, Inc., USA (2012) 6. Pereira, M., Frazzon, E.: A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains. Int. J. Inf. Manage. 57, 102165 (2021) 7. Lukinskiy, V., Lukinskiy, V.: Formation of failure models for the evaluation of the reliability of supply chains. Transp. Telecommun. 16(1), 40–47 (2015) 8. Lukinskiy, V., Lukinskiy, V., Bazhina, D.: Increasing the adequacy of management decision making for choosing intermediaries in supply chains. Lect. Notes Netw. Syst. 68, 374–388 (2019) 9. Lukinskiy, V., Lukinskiy, V., Bazhina, D., Nikolaevskiy, N., Averina, E.: A combined strategy of centralized and decentralized inventory allocation. Lect. Notes Netw. Syst. 410, 270–278 (2022) 10. Ballou, R.: Business Logistics Management. Prentice-Hall International Inc, New York (1999) 11. Taha, H.: Operations Research: An Introduction, 9th edn. Prentice Hall, Upper Saddle River, New Jersey (2011) 12. Axsäter, S.: Inventory Control. International Series in Operations Research Management Science, vol. 225. Springer International Publishing (2015) 13. Bozarth, C., Handfield, R.: Introduction to Operations and Supply Chain Management, 5th ed. Pearson Education (2019)

Stakeholder-Oriented Investment Activities for Sustainable Supply Chain Management Harald Kitzmann1(B)

and Gunnar Prause2

1 University of Tartu, Narva College, Raekoja plats 2, 20307 Narva, Estonia

[email protected] 2 Wismar, University of Applied Science, Philipp-Müller-Str. 14, 23966 Wismar, Germany

[email protected]

Abstract. While in the traditional understanding managers are single individuals that takes decisions as fractal parts of macro economy, the situation within supply chains is more complex. With the rise of sustainability considerations as well as with smart concepts, classical approaches of supply chain management were transformed from the “dilemma of operational planning”, described as the “magic square” into “magic cubes” integrating sustainability dimensions as well as flexibility. Beside these aspects, future supply chains have to consider that not only human stakeholder are involved in the interactions, but also artificial participants comprising weak Artificial Intelligence and Machine Learning based solutions. These artificial approaches force the reshaping of traditional supply chain structures together with their underlying supporting and management processes. The aim of the research is to outline a framework of design parameters for relevant decision situations in supply chain management, so that they can be designed and processed digitally and implemented into human-task-technology systems, with outline the way how managerial decision-making will be changed. First, the study creates a model of strategic and operative management decisions within supply chains by using approaches that are suitable for realizing as Artificial Intelligence (AI) solutions together with methods from conjoint analysis, logistics curve theory and distributed investment appraisal to safeguard stakeholder-orientation. Since the authors were involved in several green transportation projects with a special focus on autonomous vehicles and related management solutions for the lastmile transportation, the developed model is empirically validated in the context of smart supply chains. Keywords: AI · Stakeholder-oriented decision theory · Sustainable supply chains management · Investment appraisal

1 Introduction The main daily tasks of the managers in organisations are measuring, analysing, and communicating the degree of stakeholder satisfaction as well as planning activities to achieve the satisfaction. Additionally, to this diagnostic task, the managers should develop improvement activities to reach a higher level of stakeholder satisfaction [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 131–140, 2023. https://doi.org/10.1007/978-3-031-26655-3_12

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As the environment is constantly changing and the information is not always complete and available, management decisions are made in certain, uncertain, and risky conditions. To achieve the company’s goals in a volatile, uncertain, complex, ambiguity and dynamic (VUCAD) environment, managers are forced to make faster decisions but also to revise them as quickly as possible. With the rise of sustainability considerations as well as with smart concepts, classical approaches of supply chain management were transformed from the “dilemma of operational planning”, described as the “magic square” into “magic cubes” integrating sustainability dimensions as well as flexibility. These approaches force the reshaping of traditional supply chain structures together with their underlying supporting, the behaviour of decision makers and management processes. Since the restructuring of supply chains must cope with the objectives and intensions of all types of stakeholders of the supply chain comprising the allocation of resources and investments within the supply chain as well as the assessing KPI systems that must obey the stakeholder interests. To achieve these objectives powerful approaches were developed in solving tasks of the operative management but shows its limitations in activities of organisational development and strategic management, which is reasoned in the different mind-set in solving these decision situations. In consequences, present behaviour in preparing decision making is focusing on the task in finding an effective combination of failure prevention and safety philosophy as well as uncertain, chaotic trial and error culture [2]. Reshaping of this behaviour and processes together with effective and efficient usage of resources are in the focus of organisational development which is currently discussed also in the context of AI [3]. Prerequisite for digitalization of management activity is the creation of theoretical concepts in supporting describing, explaining, prediction, simulation, decision, optimizing and gives orientation and reflection of the activities [4]. Hereby it should be considered modelling case, organization and situation-based individual, dynamical and process depending elements, the modelling of the decision making-process on different management levels, the modelling of the cognitive process of intuition and deliberation in decision-making, as well as the tight integration of human decision activities and technology support [5]. Partially, the tasks and activities of preparing management decisions like sorting, classification and monitoring decision variables and business processes, their optimizing and forecasting of their outputs, are implemented in industrial and commercial applications as machine learning (ML) algorithms and solutions like process mining or digital decisioning [6] and represent only the realization of manual and cognitive routine activities in the decision preparation. However, solving complex decision situations and combining complex applications into one overall solution are dominated by the significant contributions from human beings [4]. Models like the Viable System Model (VSM), systemic economy, theory of living systems and the sensitivity model by Vester explaining the processes in the systems, but these reasoning is based on causal-deterministic processes without considering dualistic, complementary effects in decision making and processes in organisations [5]. Holistic modelling approach by Johannes Rüegg-Stürm, and Simon Grand [4], as well as the approach of the model-based management of Schwaninger and Gösser [7] overcovering partly this gap, but these approaches transfer the task of reflection to the management [4], or transfer the solving into the empirical practise where to develop and use of models and methods [7]. Therefore, it was developed a framework for explaining

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how complex and complementary decision situations are structured so that they can be designed and processed digitally as well as implemented into human-task-technology systems.

2 Framework of Managing Stakeholder-Orientated Investment Activities Investment activities are classified according to the three attractors of development: optimizing equilibrium (stable equilibrium and steady state), instability and bounded instability. Both, the stable equilibrium, and the steady state characterised the optimising development direction and shows the suitableness as value-driver based planning in traditional budget planning approaches. Focusing only on these directions prevents dealing with fast changing and innovation, which are seen in the limitation on Lean Management approaches [8]. In investment activities the relevance is in the third state, the bounded instability which is the mixture between stability and imbalance. In this type of status, the system real innovation has the best possibility to initiate, because the interconnections between positive and negative feedback loops forming irregular structures, which is the best basic for spontaneous self-organisation and flexibility in the system and therefore supporting sustainable development of the organisation. Task for management is therefore to handle in a proper way the tightrope walks between instability and stability and therefor the monitoring of stabilising and destabilising activities. The duality between these two forces is the real challenging question to get solved in renewal developments and shows the complementary character of designing new potentials and designing the usage of resources in strategic thinking. Handling this duality require to be aware of the management variables and parameter describing the investment situation. The variables needed to consider and provided for decision-making involve therefore not only the traditional information relevant for the accounting and investment decisions, but cover additional aspects like management task, management level, sensitivity of change, lever on content, dependencies between variables, the purpose, methods of identification, analysis and evaluation, significance [5]. The results in the management processes have impact on the management activities themselves, as well as on the interconnection and behaviour with the environment and the design of the organisation; these interconnections consist of the development modes, structuring forces, management practice, orientation level and value creation (see Fig. 1). The interconnection levers based on the modelling approach of the actual St. Gallen management model [4] and is described with the targets to be achieved, the processes of designing potentials, processes of designing usage of potentials and the design of implementation processes as well as the control of these attributes and activities, which follow the main understanding of management activities. Following the main idea of the receptor model in organisational and manufacturing design the task of the interconnection is to channel their impacts on the basic organisational characteristics [9].

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Fig. 1. Interaction between management and environment.

Considering the whole supply chain, the focus in this research is covering the activities between client and product provider, the adjustment between financial and operational planning at the product provider and set-up of investment planning of the supply chain provider which are mainly activities in the interconnection part, but also in the management part. 2.1 Integrated Budget and Operational Planning Due to growing globalization, fast development of information and communication technologies, shortened product life-cycles, increasing the speed of changing client demands and market process, the task of planning is particularly challenging and important. Advanced budgeting approaches focusing on the rolling forecast idea supporting these challenges and increase the importance of operative planning in organisations. Methods and models for operative planning have shown their success especially for production planning tasks, but the connection with budget planning still indicates improvement areas. Budget planning is therefore a core task in connecting the planning of the normative, strategic, and operational management. Beside the in management inside tasks, budget planning has their interconnections (input and impact) to the outside environment (partners and issues of interactions) as well to the interconnection to the organisational framework. Although the budget planning is an integral part and monetary result of the operative planning in organisation, the connection of operative planning and their monetary results are mainly not sufficient elaborated and solving budget questions are mainly in the obligation of the financial departments dealing with Accounting or Controlling. Different from the financial department, in the company’s departments responsible for internal

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supply chain questions, the subject is the planning, performing and control activities of the value stream in the organisations and these internal supply chains are targeting on the logistic, contradicting objectives (“dilemma of operational planning”) resource utilization, order throughput time, order delivery reliability and Work-in-process [10]. Operational planning in the internal supply chain is mainly done based on experiential solutions or with using of modelling approaches like simulations, queuing theory models or logistic-operating-curves (LOC). Input parameter regarding LOC is therefore the planned output in each period as given by the sales plan and the planned order fulfilment time of production orders, which are given by the engineers [11]. With the modelling approach of LOC, the impact of sales plans and company targets on the situation in the production could be demonstrated as well as the needed improvements in investments, organisational development, and adjustments in sales planning to achieve the stated company targets. These solutions continue the causal-chain and correlation understanding in the activities, and allow split complex tasks into smaller steps to transfer them into ML solutions [12]. Although traditionally these parameters are deterministic and mainly operationalized with traditional mathematic expression, but considering flexibility give the parameters additional complemented character of a “magic cube” [13] enabling operative control without replanning targets and activities. Therefore, the analytical measuring and optimizing task turns into a management assignement, which shows the limitations and challenges of present AI and ML solutions [14, 15]. 2.2 Conjoint Analysis Understanding organisations as systems means that successful organisations can describe and use the interactions between all partner of interactions for satisfying the wants and needs, which are the trigger for management activities. Depending on the approaches and limitation of the partners of interactions this applies to solutions like shareholder values, sustainable management, circular economy, and balance of nature, but also to solutions known as customer focused activities. Sure, as part of the control activities the feedback loop to the stakeholders is a significant part of the organisational and product design which are fulfilled with methods like conjoint analysis as a well-known tool in target costing. Stakeholder-oriented corporate management requires information about the assessment of supply chain performance and related services from the stakeholder perspective in order to successfully align activities of supply chain management. Business processes can only be planned and controlled effectively once these variables are known. A frequently used procedure for this is the conjoint analysis, which uses a linear, statistical model to determine the utility values of interviewed subjects. The utility concept follows the basic idea of ranking various objects, whether they are decisions, products or other economic factors, by preferences of test persons through an individual utility function. The shape of the individual utility function is then obtained from the sequence formation of the beneficial properties. The starting point for the conjoint analysis is the definition of common, relevant properties of the different objects, through which they can be characterized and differentiated. The objects are then replaced by the characteristics that specify them, i.e. by

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so-called stimuli, so that two objects can only be distinguished if there is at least one characteristic in which the two objects differ. Within the framework of a sample of test persons, combinations of different stimuli are presented in the form of surveys, for which the test persons have to form a ranking or preference. From these surveys, the individual part utility values are then determined using the statistical tools like rang regression. The so gained individual part utility values are then aggregated into standardized importance of the different properties. The aggregated values of the standardized importance of the different properties serve as target values corresponding to user-oriented importance and weights that are attributed to the different properties. These target values are then compared with the management-internal weights, which correspond to the actual values, leading to a target/actual comparison. The differences that arise are analysed and then, if deemed necessary, new actions and measures to eliminate the differences are developed. The aim is to align the managements internal use of resources with user expectations in order to ensure a stakeholder-oriented supply chain policy. Ideally, the target values for the stakeholder-oriented use of resources in relation to the realised management activities, interpreted as budget shares for the realisation of the stakeholder-oriented properties, correspond to the actual values. In the area of target costing, the ratio of target values and the budget shares for each property and give orientation for future management decisions. If the benefit-cost index for an activity is close to one, the stakeholder benefit determined with the conjoint analysis corresponds to the use of resources. If the index is higher than one, there is a risk that the current use of resources does not take sufficient account of stakeholder benefits. If it is below one, this could be an indication that an excessively large use of resources flows into the activity under consideration, which is not appreciated due to the smaller relative benefit share. In any case, however, it must also be pointed out that the benefit-cost index can only be viewed as a starting point for a more detailed analysis, since the index compares benefit and cost variables, which always requires further economic interpretation [16]. Although the conjoint analysis is developed to solve decision and optimize the cost and benefits of various objects, there are advantages in using it in balancing questions between different point of views together with the main ideas of LOC. Hereby the combination of the two deterministic, analytical approaches allow them to transfer the analytical tool into tool solving management assignments with focus on the optimizing the benefits of all stakeholders. 2.3 Set-Up of Investment Planning Understandings in strategic management consist of a time-dependent view with orientation to the future situation and a time-independent view of the design of potential and complementary design of the ways how to use the potential. This differentiation exists in existing literature, but clearly not always expressed [4, 17]. Whereas the time-dependent view defines the development direction of the activities and determines whether the investment is without or with changing of the organisation’s potential. In case of not changing the potential, the investments are possible to analyse and calculate with traditional operational investment planning approaches. In case of investments with changing of the potential like first investment, enlargement of potentials

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strategic planning approaches following the idea of bounded instability, which is the mixture between stability and imbalance. Schwaninger and Gälweiler developed a time-independent view in strategic management as a framework of control variables and identified differences in the purpose of these variables, their types and goals representing [17]. This approach allows strategic thinking and considering additional to the usage of the potential the design of the potential. Although this complementary thinking is rarely considered in management, in investment planning this understanding is the main subject of discussions, whereas the main focus is on the usage of potentials, even when considering transfer nonmonetary goals like transformability and agility into monetary variables [18]. With considering flexibility the measures on the operative management level get an extended, complex, and complemented character, and allow therefore the complementary analysis of design and usage of potential without replanning targets and activities [19]. However, these models show its limitation in its impractical usage for evaluating investments objects and request to develop additional approaches for considering the appropriate expenses.

3 Sustainable Supply Chain Management Activities Green and sustainable supply chain management is based on the principle of supply chain management with an extra add-on on green impacts, meaning environmentally friendly and efficient aspects. Supply chain management aims at providing not only the logistic aspects of the production process in the company in the most efficient way, but means also involving suppliers, manufactures, customers and disposal companies. In the context Seuring & Müller [20] define sustainable supply chain management as “the management of material, information and capital flows as well as cooperation among companies along the supply chain while taking goals from all three dimensions of sustainable development, i.e., economic, environmental and social, into account which are derived from customer and stakeholder requirements. In sustainable supply chains, environmental and social criteria need to be fulfilled by the members to remain within the supply chain, while it is expected that competitiveness would be maintained through meeting customer needs and related economic criteria”. This definition includes the approach on how ecological aspects can be considered in the whole business processes in the most effectively way. It can be assumed that the involvement of green aspects in the supply chain of a company also initiates changes in the supply chain itself [21]. With still growing e-commerce market volumes the structure of the supply chain is enlarging and involves the delivery of goods and services directly to the client (last-mile delivery), which was done in traditional approaches by the clients themselves [22]. Investment activities in the supply chain are part of the entrepreneurial collaboration and so are always reaching beyond the classical individualistic view of singular company orientation. In this sense, supply chain management realises to different extent the coordination and allocation of common pool resources that have to safeguard and optimize smooth downstream flows of materials and upstream flows of information and finance. Such an understanding of supply chain management represents a challenging task due complex and distributed structures of stakeholder and inter-company business processes. Prause & Hoffmann [23] highlighted the crucial role of common-pool resource management allowing the use of a limited quantity of units for exploitation keeping in mind that

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these core resource has to be protected and fostered so that a continuous exploitation is safeguarded. The frame conditions of smart and sustainable logistics concepts like the concept of a Green Transport Corridor (GTC), proposed by European Union for sustainable long and medium range cargo transport, or in the context of maritime supply chains, new technologies can be used to improve efficiency of supply chain flows. Philipp et al. [24] investigated the impact of blockchain technology and smart contracts to maritime supply chains and detected significant cost reductions as well as improvement potential in entrepreneurial collaboration. The key approach is based on the use of innovative blockchain-based technology – especially in form of Smart Contracts – that can furthermore facilitate the implementation of collaborative governance structures of supply chains and of common-pool resources in general. Supply chain management in all these cases takes place in multi-stakeholder environments very often combined with publicprivate partnerships, which significantly increases the complexity of management and governance structures. In case of GTC for example, the participation of public and even political sector for infrastructural decisions requires multi-level policy approaches that are well-known from EU institutions. Here, the research of stakeholder interests has to be reflected and a successful supply chain management has to orientate their activities on the requirements of the stakeholders and should be considered and analysed according to their political, economic, social, technological, legal, and ecological impact [25]. With Smart Contracts based on the blockchain technology one decision situation is solved with focus on the agreed condition of cooperation between two parties in the supply chain. This decision situation was additional analysed with using other methods described to optimize the benefits of the agreement. Table 1 gives an overview and focus of these methods. Table 1. Decision situations and corresponding methods for analysing. Focus

Method

Alignment of contract conditions with market behaviour

Blockchain – smart contract

Optimizing the benefits of all stakeholders

Conjoint analysis

Alignment of contract benefits with applying efforts

Budgeting with LOC

Cost and efforts of capacity building

Investment planning

4 Conclusions The high complexity of management activities in VUCAD environments requires a holistic approach to consider, analyse, and evaluate the interests of all stakeholders in development activities of sustainable supply chain management assignments. Fragmenting of the whole and complex tasks into different, smaller steps enable to analyse the assignment from different point of views, but also enable to consider and use advanced approaches of weak AI and ML for solving these fragmented assignments.

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The study shows that the complexity of decision situations allows using different proper solving methodologies with different reliable and suitable results, as well as enables and recommends continuous reviewing the design of decision situations. Furthermore, it underlines on one hand the organisational and time specific individualised character of decision situations especially in development activities. On the other hand, it revealed reciprocal dependency between semantics, procedural model, and the user of the decision environment. The research result closes the gap in finding implementation approaches for supporting the management in solving complex decision situations considering holistic system understandings and highlights the development areas for novel ideas. In the context the presented approach of this paper can be used as a blueprint for a good governance approach of solving decision situations in supply chains management assignments.

References 1. Rüegg-Stürm, J., Grand, S.: Das St. Galler Management-Modell 4. Generation – Einführung, 3rd ed. Haupt, Bern (2017) 2. Schäffer, U., Weber, J.: Digitalization will radically change controlling as we know it. WHU Control. Manag. Rev. 60(6), 34–40 (2016) 3. Haefner, H., Wincent, J., Parida, V., Gassmann, O.: Artificial intelligence and innovation management: a review, framework, and research agenda. Technol. Forecast. Soc. Chang. 162, 120392 (2021) 4. Rüegg-Stürm, J., Grand, S.: Das St. Galler Management-Modell - Management in einer komplexen Welt, 2nd ed. Haupt, Bern (2020) 5. Kitzmann, H.: Holistic Modelling Approach for the Management of Organisations. In: International Scientific and Practical Conference Sustainable Development in the Post-Pandemic Period (SDPPP-2021). SHS Web of Conferences 126, 06003 (2021) 6. Maschler, B., White, D., Weyrich, M.: Anwendungsfälle und Methoden der künstlichen Intelligenz in der anwendungsorientierten Forschung im Kontext von Industrie 4.0. In: ten Hompel, M., Vogel-Heuser, B., Bauernhansl, T. (eds.) Handbuch Industrie 4.0, pp. 1–15. Springer Vieweg, Berlin, Heidelberg (2020) 7. Schwaninger, M., Grösser, S.: Kybernetische grundlagen eines modelbasierenden management. In: Grösser, S., Schwaninger, M. (eds.) Modellbasierendes Management, Konferenz für Wirtschafts- und Sozialkybernetik KyWi 2013, pp. 15–34. Duncker and Humblot, Berlin (2014) 8. Klemke, T., Nyhuis, P.: Lean changeability – evaluation and design of lean and transformable factories. Int. J. Econ. Manag. Eng. 3(5), 454–461 (2009) 9. Cisek, R., Habicht, C., Neise, P.: Gestaltung wandlungsfähiger produktionssysteme. ZWF Zeitschrift für Wirtschaftlichen Fabrikbetrieb 97(9), 441–445 (2002) 10. Schmidt, M.: Beeinflussung Logistischer Zielgrößen in der Unternehmensinternen Lieferkette Durch die Produktionsplanung und -Steuerung und das Produktionscontrolling. PZH, Hannover (2018) 11. Nyhuis, P., Wiendahl, H.-P.: Fundamentals of Production Logistics. Springer, Berlin (2009) 12. Kitzmann, H.: Production budget planning with logistic operating curves. Controlling 78(4), 2–7 (2020) 13. Kitzmann, H., Falko, S.: Uppavlenie gibkoct ppedppiti na opepativnom ypovne. Innovacii v menedmente 11(1), 26–31 (2017)

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14. Khrennikov, A.: Quantum-like modeling: cognition, decision making, and rationality. Mind Soc. 19(2), 307–310 (2020). https://doi.org/10.1007/s11299-020-00240-6 15. Yukalov, V.I.: Evolutionary processes in quantum decision theory. Entropy 6(22), 681 (2020) 16. Prause, G.: Marktorientiertes Controlling mit der Conjoint-Analyse. Controlling-Berater, pp. 57–88. Haufe, Freiburg (2001) 17. Schwaninger, M.: Intelligent organizations - an integrative framework. Syst. Res. Behav. Sci. 18(2), 137–158 (2001) 18. Schulze, P., Brieke, M., Seidel, H., Sallaba, G.: Erweiterter wirtschaftlichkeitsrechnung in der fabrikplanung. In: VDI (eds.) Strategien und nachhaltige Wirtschaftlichkeit in der Fabrikplanung, pp. 75–157. Beuth, Berlin, Wien, Zürich (2012) 19. Pachow-Frauenhofer, J.: Planung Veränderungsfähiger Montagesysteme. PZH, Hannover (2012) 20. Seuring, S., Müller, M.: From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16, 1699–1710 (2008) 21. Hunke, K., Prause, G.: Sustainable supply chain management in German automotive industry: experiences and success factors. J. Secur. Sustain. Issues 3(3), 15–22 (2014) 22. Hoffmann, T., Prause, G.: On the regulatory framework for last-mile delivery robots. Machines 6(3), 33 (2018) 23. Prause, G., Hoffmann, T.: Innovative management of common-pool resources by smart contracts. Marketing and Management of Innovations 1, 265–275 (2020) 24. Philipp, R., Prause, G., Gerlitz, L.: Blockchain and smart contracts for entrepreneurial collaboration in maritime supply chains. Transp. Telecommun. J. 20(4), 365–378 (2019) 25. Kitzmann, H., Falko, S., Prause, G.K.: Risk Assessment of logistics hub development along green transport corridors: the case of paldiski port. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2019. LNNS, vol. 117, pp. 341–350. Springer, Cham (2020). https://doi.org/ 10.1007/978-3-030-44610-9_34

Use of Operational Research in Car Transport Logistics Martin Jurkovic1(B)

, Tomas Kalina1 , Piotr Gorzelanczyk2 and Maria Stopkova3

,

1 University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia

{martin.jurkovic,tomas.kalina}@fpedas.uniza.sk 2 Stanislaw Staszic University of Applied Sciences in Pila, ul. Podchorazych 10, Pila, Poland

[email protected] 3 Department of Transport and Logistics, Faculty of Technology, Institute of Technology and

Business in Ceske Budejovice, Okruzni 10, 370 01 Ceske Budejovice, Czech Republic [email protected]

Abstract. This study is focused on a model design of electric vehicle transport to Western Europe from Slovakia using operational research. In addition to the still key aspect in the choice of transport of goods, which is the price of transport, other aspects that were not so important for carriers in the past are becoming more and more important. In the study, we designed the transport of cars in all available transport modes on the Bratislava - Passau model route. When designing them, we based on the process of optimizing the transport route in order to efficiently, economically, ecologically and as quickly as possible transport the selected commodity. We used combined transport for transport modes that do not have a direct connection to the starting or destination point of transport. As part of the multicriteria assessment, we used various techniques of operational research to achieve transparent results. In the first phase, we defined a panel of experts from various fields, who proposed evaluation criteria, which they then compared with each other and assigned a degree of importance. In the next phase, three methods of operational research were applied, the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, the AHP (Analytical Hierarchical Process) method and the WSA (Weighted Sum Approach). The results of the study demonstrate and present the advantages and disadvantages of individual modes of transport in the transport of cars on a selected route. At the same time, they provide a pattern for use in any destination and point out aspects of transport that influence those interested in transport in choosing a suitable mode of transport. Keywords: Transportation · Logistics · Operational research · Multi-criteria assessment

1 Introduction The expansion of the automotive industry has been gaining momentum in recent years [1]. A slight slowdown can be felt due to the impact of the COVID 19 pandemic, but the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 141–150, 2023. https://doi.org/10.1007/978-3-031-26655-3_13

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long-term trend is still progressive. The export of the final products of automobile plants is primarily oriented towards road and rail transport [2]. However, there the transport capacity reaches its limits, therefore it is necessary to look for an alternative to these dominantly used modes of transport [3]. A real alternative is the use of water transport, where there is always sufficient free transport capacity [4]. Of course, water transportation is limited by the geographical availability of waterways as well as other factors that are crucial for the transportation of cars [5]. It is necessary to start implementing water transport in the logistics of forwarding and transport companies [6]. The current trend of increasing transport on roads and railways is unsustainable in the long term [7]. The aim of this article is to propose an optimal way of transporting the selected product - a car on the selected route. Operational research methods are used in the design, which reflect the criteria and their importance in transportation. The increasing pressure on rail and road transport forces carriers to look for alternative solutions that will be sustainable and competitive. Such a solution can be inland water transport, which has still available infrastructure. Designing the logistics of transporting the final products of the automotive – automobile on the selected route Bratislava – Passau means, first of all, selecting the route and options for transporting the cargo, accurately determining times, speeds of means of transport, locations and number of transshipments. For our purposes, a fully electric Volkswagen e-UP has been chosen. It is a small car intended primarily for the city with a range of up to 260 km. The Volkswagen e-UP is manufactured at the Volkswagen plant in Bratislava. Bratislava was chosen as the starting point of the transport process. The plant already has an existing connection to the road and railway infrastructure. When involving water transport in the transport process, it is necessary to use the port of Bratislava, where it is necessary to transport cars by road or rail transport. Passau in the south of Germany was chosen as the destination. Emission limits for internal combustion cars already apply in Germany. An increase in electrified traffic is expected there, and that is one of the reasons why we chose the city of Passau. Another reason is that the city has a port, thanks to which we will ensure the competitiveness of water transport compared to other modes of transport.

2 Methods When choosing the most suitable solution for the transportation of cars on the selected route, we chose three methods of operational research, the results of which we confronted each other. 2.1 Application of the WSA Method In WSA method, the optimal and basal variants are defined. The ideal variant H represents the maximum value of a specific indicator, while the basal variant D represents the lowest rate for the given criterion [8]. The next step is the creation of a normalized matrix of criteria according to Eq. 1: rij =

gij − dj , hj − dj

(1)

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where rij – normalized values for alternative i and criterion j; gij – elements of the matrix located in the i-th row in the j-th column; dj – the lowest value in the jth column of the matrix (values of the basal alternative); hj – the highest value in the jth column of the matrix (values of the ideal alternative). The third step includes the calculation of the utility function for individual variants. Benefit means the overall evaluation of the given alternative according to all criteria. The calculation of the utility function for individual alternatives uses the following Eq. 2: n vj × rij , (2) u= j=1

where vj – level of importance; rij – normalized elements of the matrix located in the i-th row and j-th column. The last step is to arrange the variants in descending order according to the calculated values. 2.2 Application of the TOPSIS Method One of the methods used to assess the problem of choosing the most suitable mode of transport is the TOPSIS method. The task of this method is to find such a solution to the decision problem (in our case, the choice of variant) which is closest to the ideal variant (the ideal solution, after taking into account all aspects, actually rarely exists) [9]. The first step is to create a normalized criterion matrix (see Eq. (3)) [10]: yij gij =  m

; i = 1, 2, . . . m; j = 1, 2 . . . n,

(3)

i=1 y2ij

where yij – elements in ith line in jth column; y2 ij – all values of the respective column. The second step is to calculate the normalized weighted criteria matrix – Eq. (4): zij = wj gij ,

(4)

where wj – normalized matrix elements. The subsequent step is to create an ideal variant (h1 , h2 ,…) and basal variant (d1 , d2 ,…) (see Eq. (5)): hj = max zij ; dj = min zij .

(5)

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The calculation of the distance from the ideal variant (IH) is as follows (see Eq. (6)):  2 n  IH = zij − hj ; i = 1, 2, . . . m. (6) j=1

The quantification of the distance from the basal variant (BH) is as follows (see Eq. (7)):  2 n  zij − dj ; i = 1, 2, . . . m. (7) BH = j=1

In the next step, the criteria ideal and basal values are searched for (these are the maximum and minimum criteria in each row). gij -level of importance. The final step of this procedure is to quantify the relative indicator of the distance from the basal variant (UV) – Eq. (8) [11]: UV =

BH . (IH + BH)

(8)

The variant with the highest UV score is considered to be the most suitable variant. 2.3 Application of the AHP Method The Analytical Hierarchy Process is based on a pairwise comparison of the degree of significance of individual criteria and the degree to which the evaluated solution variants meet these criteria. The rating scale can be more complex. The value is based on expert estimation, in which experts in their fields compare and evaluate the mutual effects of individual criteria [11]. The first step is to determine the values of the vector of the matrix in the ith row of the decision matrix based on Eq. 9:  n wi = n Sij = n Si1 ∗ Si2 ∗ . . . ∗ Sim , (9) j=1

where wi – values of the vector; Sij – elements of the matrix located in the i-th row in the j-th column. Determination of the normalized vector of the matrix: wi vi = , (10) w1 + W2 + . . . + wn where vi – normalized vector; wi – value of the vector. The largest weighted sum represents the most suitable decision analysis solution.

3 Results and Discussion When applying the decision-making process, it was necessary to identify a panel of experts. They can choose appropriate criteria and identify their importance (Table 1).

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Table 1. Panel of experts. Expert area

Country

Logistics and warehousing

Slovakia

Car transport and logistics

Slovakia

Car transport and logistics

Czech Republic

International carrier

Germany

International carrier

Austria

The following criteria were chosen for assessment: C1 – Delivery time plays an important role in car shipping in today’s world. C2 – The cost of transportation is also one of the most important factors. C3 – The criterion includes the risk of cargo damage, its subsequent repair and insurance. C4 – The environmental aspects is a criterion that is currently being looked at more and more and is becoming a priority for manufacturers and forwarders. C5 – Number of transshipments made during transport influence the transport safety. C6 – Difficulty of transshipment processes. C7 – Technical equipment of the transshipment operation. The importance ratings refer to the preference values of the criteria, while these values were assigned to each factor using a modified pairwise comparison method – Saaty’s method (Table 2). Table 2. Pairwise comparison of the criteria. Mode of transport

C1

C2

C3

C4

C5

C6

C7

Road

4

4

2

1

2

5

4

Rail

3

3

4

4

4

4

3

Water

1

5

1

3

5

3

2

Air

5

1

3

2

1

2

1

Importance

0.17

0.28

0.28

0.03

0.10

0.10

0.06

Based on the information provided by the questionnaire, we determined the importance of the criteria. In most cases, the individual criteria have different weights and there is no dominant criterion among them. 3.1 Evaluation of the Results of the WSA Method The first step of selecting the ideal variant by the WSA method is to determine the ideal H and basal D variant. H = {5; 5; 4; 4; 5; 5; 4}

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D = {1; 1; 1; 1; 1; 2; 1} The calculations in Table 3 are made by normalized matrix and Eq. 1. The table lists the results of the normalized criteria. Table 3. Matrix of normalized criteria by the WSA method. Mode of transport

C1

C2

C3

C4

C5

C6

C7

Road

0.75

0.75

0.33

0

0.25

1

0.66

Rail

0.50

0.50

1

1

0.75

0.66

0.33

Water

0

1

0

0.66

1

0.33

0

Air

1

0

0.66

0.33

0

0

1

Importance

0.17

0.28

0.28

0.03

0.10

0.10

0.06

The next step is to calculate the utility function for individual variants according to Eq. 2: u1 = 0.75 ∗ 0.17 + 0.75 ∗ 0.28 + 0.33 ∗ 0.28 + 0 ∗ 0.03 + 0.25 ∗ 0.10 + 1 ∗ 0.10 + 0.66 ∗ 0.06 = 0.59 u2 = 0.50 ∗ 0.17 + 0.5 ∗ 0.28 + 1 ∗ 0.28 + 1 ∗ 0.03 + 0.75 ∗ 0.10 + 0.66 ∗ 0.10 + 0.33 ∗ 0.06 = 0.69 u3 = 0 ∗ 0.17 + 1 ∗ 0.28 + 0 ∗ 0.28 + 0.66 ∗ 0.03 + 1 ∗ 0.10 + 0.33 ∗ 0.10 + 0 ∗ 0.06 = 0.43 u4 = 1 ∗ 0.17 + 0 ∗ 0.28 + 0.66 ∗ 0.28 + 0.33 ∗ 0.03 + 0 ∗ 0.10 + 0 ∗ 0.10 + 1 ∗ 0.06 = 0.42

The last step includes the classification of the resulting variants in descending order according to the quantification itself (Table 4). Table 4. Results of the WSA method. Rail transport

0.69

1

Road transport

0.59

2

Water transport

0.43

3

Air transport

0.42

4

The results of the WSA method show that rail transport is the most suitable mode of transport. 3.2 Evaluation of the Results of the TOPSIS Method The TOPSIS method minimizes the distance from the ideal variant and at the same time maximizes the margins from the basal variant. The analysis makes it possible to choose the optimal variant from individual transport modes.

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Table 5. Matrix of weighted normalized criteria. Mode of transport

C1

C2

C3

C4

C5

C6

C7

Road

0.093

0.156

0.101

0.005

0.028

0.065

0.043

Rail

0.070

0.117

0.203

0.021

0.056

0.052

0.032

Water

0.023

0.194

0.051

0.016

0.070

0.039

0.021

Air

0.117

0.039

0.152

0.011

0.014

0.026

0.011

Importance

0.17

0.28

0.28

0.03

0.10

0.10

0.06

The first point of the analysis is the compilation of a matrix of weighted normalized criteria (Table 5). Next step determines the ideal H (variant highest values according to specific criteria) and basal D alternatives (variant lowest values according to specific criteria): H = {0.117; 0.194; 0.203; 0.021; 0.070; 0.065; 0.043} D = {0.023; 0.039; 0.051; 0.005; 0.014; 0.026; 0.011} The next step is to determine the distance from the ideal variant. √ d1+ = A = 0.1202 d2+ =

√

B = 0.093

d3+ =

√ C = 0.1808

d4+ =

√ D = 0.1800

d1− = d2− = d3+ = d4− =

√ √ √

A = 0.1543

B = 0.1859

C = 0.1660

√

D = 0.1381

In the last step the indicators of the relative distance from the basal variant according to Eq. 7 is solved. c1 =

0.1543 = 0.562 0.1202 + 0.1543

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c2 =

0.1471 = 0.667 0.1059 + 0.1471

c3 =

0.1660 = 0.518 0.1543 + 0.1660

c4 =

0.1788 = 0.434 0.1726 + 0.1788

The following Table 6 summarizes the findings related to the use of the TOPSIS method. Rail transport is the most advantageous, followed by road, water and air transport. Table 6. The results of the TOPSIS method. Rail transport

0.667

1

Road transport

0.562

2

Water transport

0.518

3

Air transport

0.434

4

3.3 Evaluation of the Results of the Analytical Hierarchical Process The starting point for solving the AHP is a pairwise comparison of the criteria and then a comparison of how individual variants meet the weights of the selected criteria. The solution is based on the normalized vector matrix, which compares individual criteria with each other (Table 7). Table 7. Normalized vector matrix. Criterion

C1

C2

C3

C4

C5

C6

C7

Sij

wi

vi

C1

1

1/3

1/3

5

3

3

3

15.0

1.968

0.119

C2

3

1

1

5

5

5

5

1875.0

6.580

0.398

C3

3

1

1

5

5

5

5

1875.0

6.580

0.398

C4

1/5

1/5

1/5

1

1/3

1/3

1/3

0.0003

0.132

0.008

C5

1/5

1/5

1/5

3

1

1

3

0.0720

0.518

0.031

C6

1/5

1/5

1/5

3

1

1

3

0.0720

0.518

0.031

C7

1/5

1/5

1/5

3

1/3

1/3

1

0.0027

0.228

0.014

Sum

-

-

-

-

-

-

-

12.87

16.52

1.000

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The results of the AHP in the Table 8 consists of the results of the mutual comparison of the criteria with each other and the respective weight assigned to each criterion. Table 8. Comparison of the variant by AHP. Criterion

Weight

Mode of transport Road

Rail

Water

Air

C1

0.119

0.270

0.126

0.039

0.565

C2

0.398

0.270

0.126

0.565

0.039

C3

0.398

0.120

0.574

0.038

0.268

C4

0.008

0.056

0.563

0.263

0.118

C5

0.031

0.085

0.291

0.582

0.042

C6

0.031

0.563

0.263

0.118

0.056

C7

0.014

0.563

0.263

0.118

0.056

Weighted sum

0.215

0.319

0.270

0.194

Order

3

1

2

4

The highest weighted sum in the decision table was achieved by rail transport, which represents an ideal variant for model transport. Table 9 combines the results of all analyses and compares their final ranking. Table 9. Comparison of the results of different methods. TOPSIS

WSA

AHP

1

1

1

Road transport

2

2

3

Water transport

3

3

2

Air transport

4

4

4

Rail transport

4 Conclusion In this study, we realized the research on the use of different modes of transport in the transport of cars on the selected route. In the analysis, transport options using road, rail, inland water, and air transport were compared. The individual routes for each mode of transport were chosen in an optimized way. This reflects the real possibilities and at the same time represent the most effective usable option. When using inland water and air transport, a combination of the initial and final section of transport with road transport is considered. This is due to the lack of the infrastructure of inland waterway and air

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transport in the selected destination. The research was carried out using operational research techniques - AHP, TOPSIS and WSA methods. In the conclusion, the results of all analyses are proven and confronted, from which it follows that the most suitable mode of transport for transporting cars on the selected route is rail transport. Acknowledgements. This work is the result of the Project VEGA No. 1/0128/20: Research on the Economic Efficiency of Variant Transport Modes in the Car Transport in the Slovak Republic with Emphasis on Sustainability and Environmental Impact, Faculty of Operation and Economics of Transport and Communications: University of Zilina, 2020–2022.

References 1. Kalina, T., Jurkovic, M., Binova, H., Gardlo, B.: Water transport - The challenge for the automotive industry in Slovakia. Commun. – Sci. Lett. Univ. Zilina 18(2), 26–29 (2016). https://doi.org/10.26552/com.C.2016.2.26-29 2. Nadanyiova, M.: Implementation of the green marketing principles in the Slovak automotive industry. In: Transport means 2016: proceedings of the 20th international scientific conference: October 5–7, 2016, Juodkrante, Lithuania. Kaunas University of Technology, Kaunas (2016) 3. Galierikova, A., Sosedova, J.: Environmental aspects of transport in the context of development of inland navigation. Ekol. Bratisl. 35(3), 279–288 (2016) 4. Minken, H., Johansen, B.G.: A logistics cost function with explicit transport costs. Econ. Transp. 19, 100116 (2019). https://doi.org/10.1016/j.ecotra.2019.04.001 5. Garceau, T., Atkinson-Palombo, C., Garrick, N., Outlaw, J., McCahill, C., Ahangari, H.: Evaluating selected costs of automobile-oriented transportation systems from a sustainability perspective. Res. Transp. Bus. Manag. 7, 43–53 (2013). https://doi.org/10.1016/j.rtbm.2013. 02.002 6. Stoilova, S., Munier, N., Kendra, M., Skrúcaný, T.: Multi-criteria evaluation of railway network performance in countries of the TEN-T orient-east med corridor. Sustainability 12, 1482 (2020). https://doi.org/10.3390/su12041482 7. Madudova, E., David, A.: Identifying the derived utility function of transport services: Case study of rail and sea container transport. In: 13th International Scientific Conference on Sustainable, Modern and Safe Transport, TRANSCOM 2019, vol. 40, pp. 1096–1102 (2019) 8. Ozcan, E., Ahiskali, M.: 3PL service provider selection with a goal programming model supported with multi-criteria decision making approaches. GAZI Univ. J. Sci. 33, 413–427 (2020). https://doi.org/10.35378/gujs.552070 9. Ge, X., Yang, J., Wang, H., Shao, W.: A fuzzy-TOPSIS approach to enhance emergency logistics supply chain resilience. J. Intell. Fuzzy Syst. 38, 6991–6999 (2020). https://doi.org/ 10.3233/JIFS-179777 10. Zhu, C., Gu, P., Xu, Z.: Domestic airport competitiveness evaluation based on entropy weight TOPSIS method. J. Beijing Jiaotong Univ. 43, 124–130 (2019). https://doi.org/10.11860/j. issn.1673-0291.2019.20180112 11. Li, X., Zhao, X., Bai, D.: Marine transport efficiency evaluation of cross-border logistics based on AHP-TOPSIS method. J. Coast. Res. 110, 95–99 (2020). https://doi.org/10.2112/ JCR-SI110-023.1

Reduction of Supply Chain Risks by Using Blockchain Technology Meike Schroeder1(B) and Gunnar Prause2 1 Hamburg University of Technology, Am Schwarzenberg-Campus 4, Hamburg, Germany

[email protected] 2 Wismar University of Applied Sciences, Philipp-Müller-Str. 14, 23966 Wismar, Germany

[email protected]

Abstract. Transparency in the supply chain is essential to maintain efficient supply chain risk management (SCRM). Blockchain technology can be applied to improve visibility in supply chains. But with increased transparency, SCRM is faced with new challenges. In this article, the application of blockchain technologies’ effects on supply chain risks are analyzed. For this purpose, a fictitious case is created in which blockchain technology is applied to a manufacturer of medical technology products’ supply chain. The findings of the paper include the effects on supply chain risks. Keywords: Case study · Literature review · Transparency

1 Introduction The push for ever more efficient supply chains has a decisive influence on the individual supply chains’ competitiveness [1]. Blockchain has, therefore, attracted increasing attention both in research and in practice in recent years. The use of blockchain technology can assure ownership tracking and traceability across the supply chain. It would also improve the availability and dissemination of information, thus, increasing the transparency for all supply chain partners involved [2]. The use of blockchain technology, therefore, shows considerable potential for the future improvement of cooperation within supply chains. Since the blockchain research field is still quite young, there are only a limited number of scientific publications. However, none of the identified scientific publications has yet provided a holistic view of Supply Chain Risk Management (SCRM) in regard to a given technology. Holistic means that – in addition to internal risks – supply, demand, and environmental risks are also considered in the analysis. Furthermore, the consideration includes the individual process steps of SCRM, such as risk identification, analysis, assessment, handling, and control [3]. Thus, a first attempt is made with this paper, addressing the following research question: How might the application of blockchain technology affect supply chain risks? © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 151–161, 2023. https://doi.org/10.1007/978-3-031-26655-3_14

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The paper is organized as follows: first we give a brief introduction to the topics of SCRM, supply chain management and blockchain. Second, a description of the methodological approach follows; relating the literature review, which was applied, using a snowballing method. Blockchain use cases were analyzed to answer the given research question. A fictitious use case is described in this paper, applying blockchain technology to a manufacturer’s supply chain of medical technology products. Based on this case, blockchain technologies’ effects on supply chain risks can be analyzed, using the categorization of the risks developed by Christopher and Peck [4]. The article closes with a summary and limitations.

2 Theoretical Background 2.1 Supply Chain Risk Management Supply Chain Risk Management (SCRM) can be understood as “a part of Supply Chain Management which contains all strategies and measures, all knowledge, all institutions, all processes, and all technologies, which can be used on the technical, personal, and organizational level to reduce supply chain risks.” [5, p. 157]. The main objective of SCRM is to increase the transparency and robustness of processes to withstand any kind of supply chain disruptions [6, 7]. The typical SCRM process encompasses four steps: risk identification, analysis and assessment, handling (also called mitigation), and control. This paper focuses on the risk identification step. As part of risk identification, supply chain risks can be grouped according to different classifications [8]. This article is based on the approach of Christopher and Peck [4]. They classify supply chain risks into three groups: company internal risks, supply chain internal risks, and environmental risks [4]. Internal risks emphasize process and control risks within the company. While process risks relate to internal disruptions and to the value-adding and managerial activities of the company. Control risks arise from the misapplication of rules and procedures about how supply chain processes, like order quantities or batch sizes, are controlled. Supply chain risks consider both supply and demand risks generated by supply chain partners. While supply chain risks relate to the flow of goods and information within the network, up the supply stream of the focal company, demand risks relate to the flow of goods and information between said focal company and the market. Environmental risks cover all risks caused by socio-political, macroeconomic, or natural disasters [4]. 2.2 Supply Chain Management and Blockchain Technology Supply chain management (SCM) coordinates and optimizes cross-company business processes based on downstream flows of goods and services as well as upstream flows of information and finance [8]. The coordination of supply chain flows represents a challenging task and recent research results advocate a big potential in blockchain technologies for facilitating supply chain management [9]. The reason for this enthusiasm toward blockchains stems from the underlying technological concept that uses timestamped ledger of transactions without a central authority. In other words, transactions

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are not recorded centrally, and each party maintains a local copy of the ledger consisting of a linked list of encrypted blocks comprising a set of transactions that are hashed and grouped in blocks and thus broadcasted and recorded by each participant in the blockchain network [10, 11]. A closer look to the term blockchain indicates that often two meanings are mixed, namely a distributed database and a data structure consisting of a linked list of blocks of transactions, where each block is cryptographically chained to the previous one by including their hash value and a cryptographic signature, in such a way that it is impossible to alter an earlier block without re-creating the entire chain since that block). Additionally, blockchain platforms usually offer additionally the possibility of executing scripts on top of a blockchain, which is called smart contracts allowing parties to encode business rules like negotiated legal agreements. Thus, a smart contract can be considered as an electronic transaction protocol to enforce digitally the negotiation and execution of the terms of an underlying legal contract designed to fulfil conditions like payments, legal obligations, and enforcement without third parties. Such a smart contract realizes the digital execution of legal agreements and linked transactions between distributed units within a network or supply chain with reduced transaction costs by being trackable and irreversible [12]. There are two types of (mainly) used blockchain configurations: (1) Public blockchains: In this type, every transaction is public and permissionless accessible. (2) Private blockchains: Within this type, the companies which like to access the network, need permission to access the network. Besides the access, also read and validation rights can be controlled by the host of the network. 2.3 Methodology We conducted a literature review, using a snowballing approach (Wohlin, 2014) to answer our research question, listed in the introduction. A set of internationally renowned research papers about blockchain was assembled and then evaluated using keywords ((“Blockchain”) AND (“supply chain” AND “risk”)). For context, Scopus was the primary source. The paper collection and analysis took place between June and July 2022. In total, 170 relevant papers, with a focus on blockchain and risks (limited to journal papers and written in the English language), were identified. Blockchain publications with no clear connection to risk management in the logistics and supply chain management field were excluded. Additionally, the literature research was extended by searching for well-described blockchain use cases to analyze them in order to answer the research question. Several use cases in the literature were identified: blockchain implementation at British Airways, IBM and Maersk, Wal-Mart, UPS, Provenance, and Carrefour [1, 13]. However, none of these were suitable for this research project. In most of the cases, the handling was described only very superficially, hence, relevant information, which is needed for an analysis of a holistic SCRM approach, is missing. Therefore, a fictitious use case, applying blockchain technology to a manufacturer’s supply chain of medical technology products is described.

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3 The Use of Blockchain Technology from an SCRM Perspective 3.1 Results of the Literature Analysis Current literature reviews on blockchain in supply chain and logistics concentrate on specific industries or general discussions on where to use it. Table 1 contains some examples of relevant work in the area of risk management in supply chain and logistics. Only articles that deal with the topic of risk management are listed here. Taken together only a few articles focus on the general approach to supply chain risk management. The majority of the articles focus on specific areas of supply chain management, such as supply chain finance or logistical functions, like the tracking of goods. Even in the overview shown below, the articles are very focused on specific use cases like supply chain finance. The generalist approach to this new technology in SCRM is usually left out. Therefore, the focus of this article is the influence of the new technology on SCRM and how risks need to be reassessed if this technology is to be standardized. Table 1. Examples of the literature review results. Article

Key findings

[14]

The authors “analyze the strength of correlation between digital technology [among them blockchain] and different risk types [macro environment, operational, functional and microenvironment risks].”

[15]

The authors “make big production enterprise as the research object to analyze by constructing a model its endogenous risk management mechanism, and analyze the management mechanism produced economic value.”

[16]

The authors “explore Supply Chain Financing pledge risk controlling taking advantage of Blockchain technology, which is known for information transparency and tamper-proof, and proposes a strategy of Real-Time Stare in Market to mitigate risk pressure brought by a pledge of movables.”

[17]

The authors conduct an integrative literature review method to select relevant literature related to blockchain technology implementation in the supply chain. “The integrative literature review method is suitable for this study since the risk of implementing blockchain technology is often shadowed by the benefits of implementing blockchain technology in SCM”

[18]

The authors study “the risk decision-making problem faced by participants in a spacecraft supply chain, considering the adoption of the Blockchain technology to facilitate information sharing.”

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3.2 The Use Case: Blockchain Technology for a Medical and Safety Products Manufacturer The fictitious use case describes the supply chain of a leading international company in the fields of medical and safety technology (Fig. 1). With more than 10,000 employees worldwide and distribution and services companies in over 40 countries, the OEM manufactures a large variety of medical and safety products in India, the UK, the Czech Republic, South Africa, China, and the USA. The manufacturer is known for products, like ventilators, for intensive and emergency care items (gas measuring technology), and for personal protection equipment that is produced for use in clinical, industrial, firefighting, and rescue services. Suppliers (finished and semifinished goods)

tier -n

tier -2

OEM (Production facilities, distribution and services)

tier -1

End customers (B2B, B2G, B2C)

tier +1

Fig. 1. Supply chain of the manufacturer of medical and safety products.

The industry of medical technology is subject to high design and production requirements, which results from numerous legal standards for manufacturers and importers regarding the quality and traceability of medical devices (see, e.g., Medical Production law, EU Guideline 67/548/EWG, FDA’s Regulation of Medical Devices). The legal requirements require each part used for the manufacturing of the medical and safety products to be documented. To make this possible, the OEM needs access to information about serial and batch numbers, as well as about certifications from suppliers, production facilities, and distribution partners of the entire supply chain. Here it is often difficult to gain visibility within the supply chain as the OEM usually has limited direct access to information on their direct suppliers (tier −1 respectively) or on their direct customers (tier +1) [8]. Changes in documents and products must be traceable and tamper-proof, if product charges have to be recalled. In the use case, the OEM decided to implement a private-access blockchain. The company and transport symbols presented in Fig. 1 illustrate the nodes of the blockchain. This includes both the OEM and supplier subgroups. The end customers within the supply chain are not included, as they bring too much uncertainty into supply chain planning. The blockchain presented here is a private blockchain in which the tracking data of the individual products are stored. With the help of the blockchain, all data on the sub-products are tracked, thereby preventing information asymmetries and gaps.

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3.3 Effects of Blockchain Technology on Different Risks Categories In the following section we describe how blockchain technology can improve SCRM by affecting different kinds of risks. To structure our procedure, we rely on the risk categorization system developed by Christopher and Peck [4] (as described before), which divides risks according to their origin, into internal – containing process and control risks – supply chain, demand, and environmental risks. Process and Control Risks As described before, the asymmetrical distribution of information between the primary company, the suppliers, and the customers can be a major problem in the process flow. The OEM is often not able to track the activities of the other suppliers. But within blockchain technology, the participants apply smart contracts, as explained in the blockchain section. Any supply chain partner using such a contract posts all applicable transactions to the blockchain – for example, when the products are transferred to a logistics service provider and loaded onto a truck. By using smart contracts, the blockchain can operate in a fully autonomous and decentralized manner and replace the need for human intervention [19]. As defined by Christopher and Peck [4], control risks might arise from the application or misapplication of pre-defined rules. By using blockchain technology, the handling process ensures that all supply chain partners meet the defined standards and that problems of confidence or concerns of manipulation are reduced and limited to the data entry phase [1]. Therefore, using blockchain technology decreases control risks. Furthermore, applying blockchain technology reduces the risks associated with information transfer and those associated with accrued during the overall handling process [2]. Multiple parties, like shippers and carriers, logistics services providers, distribution and service companies, insurance providers, and customs agents, are involved in the transport process of medical and safety products, and they all need specific information at particular times. By using blockchain technology, each supply chain and service partner can view the progress of goods through the entire supply chain and track where products are in transit. Furthermore, these stakeholders can view bills of lading and see the status of customs documents, so that the transparency from end-to-end can be enhanced. This level of transparency helps to reduce errors in the process execution and increases the speed of transit as well as the shipping process [1]. From an SCRM perspective, the composition of the blockchain network, in connection with the distribution of power and defined standards, should also be considered to reduce process risks. As of the publication of this document, uniform blockchain standards that govern the processing of transactions are still missing. In consequence, use of case-specific standards must be set [20]. In the use case, the manufacturer of medical and safety products is a powerful actor. They handle the private network by defining the standards and administrating smart contracts to reduce process and control risks. The OEM determines who gets access rights to the blockchain technology. For that, they must convince all supply chain partners, including all distribution and services companies from India, UK, Czech Republic, South Africa, China, and the USA, to take part in the blockchain. Implementing blockchain technology requires financial, personnel, and technical resources, as well as

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the ubiquitous use of smart contracts by all partners [21]. Convincing partners is particularly challenging when they are involved in multiple supply chains which do not use blockchain technology. On the one hand, the OEM can motivate the supply chain partners to participate – e.g., by offering web interfaces or maintenance services. On the other hand, they could exploit their market power by terminating the collaboration if the supplier does not agree to accept these defined standards and use the blockchain [22]. Here, the typical discussion around bargaining powers between buyers and sellers is explored. Supply and Demand Risks Unforeseeable disruptions, which cannot be identified in time and consequently lead to an interruption of the supply chain, are the biggest challenges in supply chain management [23]. By using blockchain technology, transparency can be increased [2]. Supply chain partners can reliably track goods along the entire supply chain. Critical data points along the supply chain can be accurately collected so that information related to the medical and safety products can be continuously updated. Blockchain technology allows data to be linked directly to all the OEM logistics services providers and customers. Based on realtime data, it is possible to map the supply chain, which protects it more robustly against disruptions and reduces the incidence of errors. Supply chain partners can proactively take measures to counteract the risks and promptly react when the events occur [2]. Blockchain technology makes it possible to analyze the transaction time-stamps. In this way, a disturbance at the beginning of the supply chain – for example, the spread of the coronavirus (COVID-19) in Asia – can be detected in real-time. Thereby, each supply chain partner could foreseeably estimate the consequences of the interruption and adapt their respective forecasting and projections (Schlegel and Trent, 2014). The party responsible for the disruption as well as the immediately subsequent supply partner could thereafter be identified and traced [24]. Due to the spread of the coronavirus, there is a significant worldwide increase in demand for ventilators and personal protective equipment – in particular, FFP masks and half masks. In the use case, the OEM produces the masks in Sweden and SouthAfrica. When the OEM recorded the increasing demand, all production capacities were ramped up and safety stocks were increased. By using blockchain technology, the OEM can track all materials and resources. Additionally, bottlenecks, due to the global supply chain, and reduced transport capacities can be identified quickly. By merely comparing incoming and outgoing goods, as well as serial numbers and descriptions, every supply chain partner can compare the information and thus check whether the product comes from the real manufacturer or is a counterfeit. The use of blockchain technology prevents the circulation of falsifications and recalls can be executed faster [2]. The value this provides can be exemplified in the FFP masks. In the corona crisis, masks have become a sought-after and valuable product, leading to questionable providers entering the world market: packaging can be misleading, advertised products may not really exist, and new masks are replaced with expired ones on the way to the

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customer. Blockchain technology helps the OEM to protect themself against buying counterfeit goods, and to avoid legal difficulties that may result from mistakenly selling counterfeits. In doing such, the OEM reduces risks to their reputation and avoids demand-side risks, by creating trust among direct customers, who can individually check where the goods come from [21]. These advantages can lead to better customer services, combined with reduced costs, and faster delivery. A lack of transparency and the withholding of important information and (realtime) data between partners in the supply chain not only poses risks on the flow of goods, but can also be crucial for supply chain finance, as missing information can result in payment delays [3]. By using blockchain technology, paper-based transactions can be automated. This automation can approve and verify invoices more quickly. With blockchain technology, everyone involved in the supply chain has easy access to realtime, finance-related data – such as the current invoice status – so that those payment transactions can be processed and monitored transparently. This extended period for financing improves the liquidity of the supply chain partners. The quick payment of invoices reduces the risk of liquidity bottlenecks and, thereby, the risk of supplier failures. Environmental Risks As described above, the occurrence of environmental risks, like natural disasters or worker strikes, cannot be influenced by individuals. However, the use of blockchain technology can help to identify these earlier so that measures can be taken before supply chain interruptions occur. Conversely, the transparency of the supply chain can help to accelerate the recovery process. Besides environmental risks, political risks also remain. In the use case, the supply chain partners are in different countries. Data must be stored in the blockchain to document the transactions and to achieve transparency. Therefore, the OEM must consider which data is subject to confidentiality by law [25]. If political requirements prohibit the use or removal of personalized data, the transactions will be de-restricted and all the advantages of transparency, resulting from the use of blockchain, will be negated. But due to the increased transparency in the supply chain, the OEM might react better to regulations, as with the obligation to report identified risks in products that can directly or indirectly lead to death or severe health impacts (Medical Devices Act; Medical device safety plan regulation). Should this reporting obligation be expanded in the future with further information, it could be saved or retrieved from the blockchain. Figure 2 summarizes the results of how blockchain might influence the different risk classifications, based on Christopher and Peck [4]. Worth noting is that a non-overlapping allocation of the characteristics of different risk categories is not possible. An increase in transparency not only has a positive effect on internal processes and controls, but also on the entire supply chain (end-to-end-view) [2].

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Environmental risks • • •

High dependence on political decisions Better reaction to regulations Faster reaction to environmental disruptions

Supply risks • • • • • •

Validly collected critical data points along the supply chain Linked data to all participants Better protection towards disruptions Allocation of disruption causes and their consequences Reduction of incidence of faults Reduced risk of liquidity bottlenecks

Process & Control risks • • • • • • • • • •

Reduced risk of information Harmonized processes Reduced human intervention Reduced confidence problems Impossible secret deletion or modification of transactions Limitation of risks of manipulation to data entry Increased quality of data Reduced errors in process execution Reduced forecasts errors Reduced product quality risks

Demand risks •

•

•

Better informed customer Increased customer confidence in the company's performance Improved protection against buying counterfeit goods and avoiding legal consequences

Fig. 2. Effects of blockchain technology on different risk categories.

4 Discussion Applying the risk categorization developed by Christopher & Peck [4], we analyzed how various risks might be influenced by the use of blockchain technology. While internal (process and control), demand, and supply chain risks might be reduced, due to defined blockchain standards and the transparency of the flow of goods and information, environmental risks remain volatile and cannot be influenced. On the one hand, the management of these risks will decrease in scope, but, on the other hand, risks related to the distribution of power must be given more importance. Those risks, which could previously be classified as low in probability, should now be given greater weight. The changing power position of a given company in a private blockchain, such as the OEM in the fictitious use case, might have a significant influence on the bargaining power of buyers and sellers, and thus on their overall competitiveness. If the OEM is not the main customer for certain components, they have less bargaining power and thus, are dependent on the material or products of their suppliers. Here, the OEM could use the transactions entered in a private blockchain to deduce the interdependencies between the various suppliers and main customers. As a result, the OEM has information about their suppliers and main customers and can change the priorities of their supplier network. We also detected a shift of the SCRM focus: the OEM should apply additional effort towards the strategic alignment of SCRM – such as the election of blockchain users or the development of blockchain standards. Consequently, this means that thorough planning is required. By contrast, the OEM can reduce the management of the operating supplier actions, because supply chain operations will fully conform to defined blockchain standards. From the analyzes of the use case, it was discovered that the processes can be automated, which means that the individual phases of the risk management process

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(risk identification, analysis and assessment, handling and control) could also be further automated. The availability of uniform data records facilitates the use of risk analysis methods and enables the easy integration of risk control variables (e.g., delayed provision of goods or delivery reliability at various times).

5 Conclusion and Limitations An efficient SCRM relies on transparency about the flow of goods and information within the network. The fictitious use case, describing the supply chain of a leading international, medical and safety technology manufacturing company, explores the changes in supply chain risks’ scope and weight, due to increased visibility and supplier monitoring with blockchain technologies. Human risks might decrease, while risks associated with the division of power might become more critical. A company’s traditional SCRM must be adapted to the new conditions in the supply chain when using blockchain technology. Current risk measures should take an overall view of the supply chain, and not only refer to individual links or supply chain partners. A cooperative risk management approach is needed to mitigate supply chain interruptions through measures taken in parallel by multiple partners. The results of the research are limited to the aforementioned fictitious use case, and a more detailed examination of the use case is required. The results should also be compared with other blockchain configurations, such as public blockchain, and additional use cases should be examined in terms of SCRM. The empirical review of the findings is still pending. This paper provides a useful starting point to deepen research and scientific discussions on this subject.

References 1. Chang, Y., Iakovou, E., Shi, W.: Blockchain in global supply chains and cross border trade: a critical synthesis of the state-of-the-art, challenges and opportunities. Int. J. Prod. Res. 58, 2082–2099 (2020). https://doi.org/10.1080/00207543.2019.1651946 2. Azzi, R., Chamoun, R.K., Sokhn, M.: The power of a blockchain-based supply chain. Comput. Ind. Eng. 135, 582–592 (2019) 3. Jüttner, U., Peck, H., Christopher, M.: Supply chain risk management: outlining an agenda for future research. Int. J. Log. Res. Appl. 6, 197–210 (2003) 4. Christopher, M., Peck, H.: Building the resilient supply chain. The Int. J. Logistics Manag. 15, 1–13 (2004) 5. Kersten, W., Hohrath, P., Boeger, M., Singer, C.: A supply chain risk management process. Int. J. Logistics Syst. Manag. 8, 152–166 (2011) 6. Tang, C.S.: Perspectives in supply chain risk management. Int. J. Prod. Econ. 103, 451–488 (2006) 7. Schroeder, M., Prause, G.: Transportation of dangerous goods in green transport corridorsconclusions from Baltic Sea region. Transp. Telecommun. J. 17, 179–191 (2016) 8. Jacobs, F.R., Chase, R.B.: Operations and supply chain management: The core. The McGrawHill/Irwin series in operations and decision sciences, 5th edn. McGraw-Hill Education, New York, NY (2020)

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9. Gligor, D.M., et al.: Utilizing blockchain technology for supply chain transparency: a resource orchestration perspective. J. Bus. Logistics 43, 140–159 (2022) 10. Sternberg, H.S., Hofmann, E., Roeck, D.: The struggle is real: insights from a supply chain blockchain case. J. Bus. Logist. 42, 71–87 (2021) 11. Philipp, R., Prause, G., Gerlitz, L.: Blockchain and smart contracts for entrepreneurial collaboration in maritime supply chains. Transp. Telecommun. J. 20, 365–378 (2019) 12. Prause, G.: Smart contracts for smart supply chains. IFAC-PapersOnLine 52, 2501–2506 (2019) 13. Jabbar, K., Bjørn, P.: Infrastructural grind: introducing blockchain technology in the shipping domain. In: Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work [Online], pp. 297–308 (2018) 14. Barczak, A., Dembi´nska, I., Marzantowicz, Ł: Analysis of the risk impact of implementing digital innovations for logistics management. Processes 7(11), 815 (2019) 15. Fu, Y., Zhu, J.: Big production enterprise supply chain endogenous risk management based on blockchain. IEEE Access 7, 15310–15319 (2019) 16. Gao, B., Zhou, Q., Li, S., Liu, X.: A real time stare in market strategy for supply chain financing pledge risk management. In: 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1116-1119. Bangkok, Thailand, 16–19 Dec 2018 17. Rasi, R.Z., Rakiman, U., Radzi, R., Masrom, N.R., Sundram, V.: A literature review on blockchain technology: risk in supply chain management. IEEE Eng. Manage. Rev. 50, 186– 200 (2022) 18. Zheng, K., Zhang, Z., Chen, Y., Wu, J.: Blockchain adoption for information sharing: risk decision-making in spacecraft supply chain. Enterp. Inform. Syst. 15, 1070–1091 (2021) 19. Zhao, N.: Managing interactive collaborative mega project supply chains under infectious risks. Int. J. Prod. Econ. 218, 275–286 (2019) 20. Lacity, M.: Addressing key challenges to making enterprise blockchain applications a reality. MIS Quart. Executive 17(3), 201–222 (2018) 21. Montecchi, M., Plangger, K., Etter, M.: It’s real, trust me! Establishing supply chain provenance using blockchain. Bus. Horiz. 62, 283–293 (2019) 22. Nikhilesh, D.: Walmart Tells Produce Suppliers to Use Blockchain by Next September, Homepage (2018). https://www.coindesk.com/markets/2018/09/24/walmart-tells-produce-suppli ers-to-use-blockchain-by-next-september, last accessed 15 Dec 2022 23. Manuj, I., Mentzer, J.T.: Global supply chain risk management. J. Bus. Logist. 29, 133–155 (2008) 24. Svensson, G.: A conceptual framework for the analysis of vulnerability in supply chains. Int. J. Phys. Distrib. Logist. Manag. 30, 731–750 (2000) 25. Tönnissen, S., Teuteberg, F.: Analysing the impact of blockchain-technology for operations and supply chain management: an explanatory model drawn from multiple case studies. Int. J. Inform. Manage. 52, 101953 (2020)

Efficiency Assessment System Based on Analytical Approach for Sustainable Development of Transport Logistics Anna Strimovskaya1(B)

, Galina Sinko2

, and Elena Tsyplakova3

1 National Research University Higher School of Economics, Mishkinskaya Street, 78,

Saint-Petersburg, Russian Federation [email protected] 2 Pushkin Leningrad State University, Krasnogo Kursanta Street, 30, 13, Saint-Petersburg, Russian Federation 3 Pushkin Leningrad State University, 15th Line of Vasilievskiy Island, 12-13, Saint-Petersburg, Russian Federation

Abstract. Sustainable development of transport and related logistics activities, as being a part of international business, is becoming one of the major tendencies worldwide. It increases social responsibility of companies involved through reasonable resource consumption and lean approach to exceeding logistics costs. Logistics includes many activities with indicators comparing planned values with actual results. There are several methods of indicators assessment: based on mathematical models ones, conceptual frameworks and others. Nevertheless there is still a perspective for further investigation in terms of systematic approach to efficiency assessment of sustainable development. The paper presents a modified analytical approach to efficiency assessment of performance indicators with combination of integral method of analysis and linear programming; proposes to use a set of parameters reflecting sustainable development of transport and related logistics operations. For relative indicators it is proposed to use binomial distribution in order to approximate final values. The results obtained help to improve logistics management solutions in terms of a wide range of tasks: choosing an intermediary, shifting to alternative mode of transport, route planning and others. The proposed approach includes several steps, such as a conceptual framework for sustainable development, an algorithm for designing efficiency assessment system, set of key indicators, analytical models. Keywords: Sustainable development · Efficiency · Assessment tools · Analytical methods · Transport logistics

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 162–173, 2023. https://doi.org/10.1007/978-3-031-26655-3_15

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1 Sustainable Development of Transport Logistics: Trends and Challenges 1.1 Sustainability and Opportunities for Development Logistics as a science and a business practice covers a wide range of issues devoted to technical, economic and social activities [1–5]. It plays a significant role in modern transport systems because cargo flow is constantly increasing, and the major part of it refers to motor road transportation penetrating to cities as well [6]. Freight road transportation is actively used due to its flexibility for transporting small cargo volumes, parcels and providing door-to-door services. However, it is generally considered to be less environmentally friendly and have higher CO2 emissions per ton-mile compared with, for example, rail, pipeline or maritime transportation. Demand-driven nature of modern supply chains [2, 7] requires taking into consideration environmental issues as well. Understanding the importance of sustainable development will make the decision making process in logistics more efficient and able to improve corporate responsibility, increase customer loyalty and make environmentfriendly decisions. According to Oxford Dictionary sustainability can be defined as the ability to maintain a desired level of function without depleting required natural resources in the process1 . Specialists claim that sustainability means taking into consideration social and economic factors while targeting to more effective resource consumption with minimum wastes and emissions [8–10]. What steps should be taken toward sustainability is still under discussion. In the article [11] it is highlighted that there are two main groups of measures for sustainable development of transport logistics. They are soft ones, aimed on giving incentives toward using alternative transport modes: promotion of walking and cycling delivery; improved access to public transport terminals, etc. Hard measures are based on restriction policy and include: reduction of motorized vehicle usage; infrastructure changes; increase of motorized vehicle operational costs. Both soft measures and hard ones are aimed on reducing CO2 emission and increasing logistics efficiency as well. Another approach is based on designing a framework called Sustainable Urban Logistics Plan (SULP). According to [12] SULP considers a wide range of smart techniques, such as pick-ups and deliveries by electric vehicles, drone delivery, information and telecommunication systems for planning, routing and controlling vehicles, crowdsourcing and others. Obviously both SULPs and the decision-making process for designing sustainable supply chains should be based on many aspects. In the article [13] emphasis is made on regional one. According to [14] the process of choosing certain technology should be done from technical, operational, logistical and economical dimensions. Despite the fact that many active steps are taking towards sustainable transport development, some researches are concerned about efficiency of the measures taken. For example, there are still some discussions about internationally recognized actions towards sulphur emission control area (SECA) in Mediterranean Sea [15, 16]. The literature review has showed that there are many research papers devoted to sustainable logistics. Nevertheless proposed approaches seem to be too general, do not 1 Oxford English Dictionary Online, https://www.oed.com, last accessed 2022/02/08.

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consider logistics functions separately (transportation, inventory management, service control) and rarely include comprehensive analytical tools. 1.2 Conceptual Attitude to Sustainable Development of Transport Logistics In order to solve complicated management tasks it is important to attract specialists from different fields. That is why plan for sustainable transport development and further evaluation of it should be based on multidisciplinary approach, based on synergy of several sciences and social areas (Fig. 1).

Technical opportunities

Social responsibility

Society

Public administration

Ecology

Logistics

Balance of costs and profit

Sustainability

Business intelligence

Economics

Fig. 1. Conceptual framework for sustainable development of transport logistics.

Talking about importance of sustainable development, it is important to highlight possible outcomes and benefits not only for society, but for business as well. Feasible financial goals may stimulate entrepreneurs and companies to take more actions towards sustainable logistics. Taking into consideration environmental issues should be a ‘must have’ for almost all logistics companies operating worldwide. For example, using sustainable transportation increases not only costs in short-term, but it increases customer loyalty in long-term perspective. Despite the importance of integral approach to sustainability, it is necessary to take into account specific factors and restrictions that are typical for certain region or city. For example there are districts with low self-restoration potential (such as northern cities). This specificity makes it impossible to apply advanced experience mechanically. The solutions vary according to the region, because there are different economic situations, infrastructure, distance, collaboration level, access to green techniques, etc.

2 Performance Indicators 2.1 Basic-Level Indicators and Their Role in the Efficiency Assessment Analysis of logistics market has showed that companies tend to increase their performance in all possible ways. For reaching a complicated goal of performance excellence it is important to define a set of key indicators for comparing target and actual values

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even at the stage of planning. At the same time many companies prefer to use averaged indicators: average time, average delivery distance, etc. Obviously it leads to large data scatter and significantly restricts opportunities for precise assessment. To overcome the obstacles towards comprehensive assessment approach we suggest designing a set of basic indicators to reflect sustainability. Thus it will be possible to assess them first, and then analyze whether contribution to sustainable development was sufficient. Number of basic indicators is varied in a wide range and depended on many external aspects. We also assume that a conveyance is finished at urban zone, so it is needed to consider terms for sustainable development of city logistics. Moreover for different modes of transportation there are special indicators (speed of oil track in a pipeline, for example). At the same time there are universal ones typical for all transport system configuration. These indicators form a basic set: • time (cargo loading, delivery itself, cargo unloading, invoice statement), • vehicle’s performance (defined as multiplication of cargo weight and km), • net costs per voyage, demand satisfaction rate (delivered orders divided on total number of orders received), • response speed (time between order receiving and goods delivered), • agility (time needed for changing requested delivery window), • flexibility (time needed for changing one product by a new one according to customer’s request), • number of returns, • frequency of defectives occurring (number of defective goods divided on total goods number), etc. Let us consider the equation for transportation costs (C T ) in multimodal conveyance. It is important to introduce to the equation indicators related to sustainable issues. For example, decreasing coefficient according to the type of vehicles’ engine. Less CO2 emission reduces coefficient and makes overall transportation costs smaller. Including the coefficient of agility reflects customers’ interests in demand-driven market. From the other side, this coefficient has much potential in terms of value added services provided by speed of reaction on changing requirements. (C T ) are defined as: CT = gybk,

(1)

where g – transportation tariff per a voyage with cargo, $; y – number of voyages with cargo; b – decreasing coefficient according to the type of engine of a vehicle; k – agility coefficient (percent of agile orders on total order quantity). Note, that Eq. (1) is focused on sustainable issues more rather than on productivity (transportation workout or averaged delivery speed). By adding more components to the Eq. (1) we may shift from productivity to sustainability. In order to introduce efficiency assessment system based on analytical approach, we suggest using the following sequence of actions: 1. Designing company’s profile with its performance indicators. 2. Efficiency assessment process: for basic indicators, for complicated indicators, for relative indicators.

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3. Gap-analysis. 4. Decision-making process in accordance with management level correlated. At the first stage (1) it would be beneficial to make a set of indicators in order to make the assessment of current state and perspectives for sustainable development in particular area. At the second stage (2) performance indicators should be analyzed with analytical tools depending on their type separately (certain models for particular indicators are presented below in the article). At the third step (3) it is proposed to use Gap-analysis in order to check the assessment process. 2.2 Correlation Between Management Level and Logistics Performance Indicators After having proposed an analytical correlation between indicators that are most reliable to sustainable development of transport, we should define the assessment method. Assessment process might be divided to three groups according to the basic methodological approach: empirical, analytical and combined. It is complicated to claim that one approach has benefits for the other, because there are different management levels, different tasks that should be solved at each level [17, 18]. Table 1. Classification of methodology approach according to management level in logistics. Management level

Decisions

Planning horizon

Objective

Methodological approach

Strategic planning

Number of facilities, locations, capacity, demand allocation

Long-term

Network definition, cost minimization, profit maximization

Empirical (often)/Combined (rarely)

Tactical planning

Service level, Medium-term lead time, safety stock

Order fulfillment Combined processes, (often)/Analytical material flow (rarely) management

Operational planning

Allocation of Short-term demand from customers to retailers

Logistics requirement planning

Analytical (often)/combined (rarely)

As we can see from the Table 1, at the higher management level with greater responsibility, decision-making process is tending to rely more on expert assessment. Obviously, it is important to consider experts’ opinions, their experience and expertise. Nevertheless, analytics in logistics is becoming a strong tendency occupying higher management levels [19, 20 and others]. There are several reasons for it: opportunity of process modelling, possibility to get explicit results of calculations, ability to strengthen expert view

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by figures. Thus, design and application of analytical models seems to be an efficient tool toward decision-making support systems for top management. A paper [21] presents comparative analysis for several economics and mathematical methods: index method, substitution method, differential calculus method and others. Integral method of analysis (IMA) is recommended to use when there is a need to track influence caused by changing of certain factor on the objective function [21, 22]. For example, by using this method we can see how changing in cargo loading improves transportation costs reduction and at what rate. That type of correlation between operations and economic targets brings explicit answers for management tasks.

3 Analytical Approach to Efficiency Assessment 3.1 Integral Method of Analysis IMA helps to get accurate results and see the influence of each factor on the target function (CT ). The Eq. (1) might be presented as a sum of functions of changing the variables considered: fCT = fg + fy + fb + fk .

(2)

Let us introduce additional variable: α = gyb.

(3)

So equation for transportation costs may be represented as follows: CT = αk.

(4)

And equation for the objective function will take the following form: fCT = fα + fk .

(5)

By applying standard forms of IMA basic equations [21], we can define correlations for all the factors:   f0 g1 + f1 g0 (6) fg = g 2g0 g1   f0 y1 + f1 y0 fy = y (7) 2y0 y1   f0 g1 + f1 g0 fg = g (8) 2g0 g1   f0 k1 + f1 k0 fk = k (9) 2k0 k1 As was mentioned earlier, costs devoted to sustainable logistics should be included to completion of certain logistics function. Consider transportation, we take Eqs. (1) – (9)

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Table 2. Influence factors on the objective function for multimodal international conveyance. Factor

Planned value, 0

Actual value, 1

Difference (1–0)

IMA

g

2000

1800

– 200

– 328

y

4

4

0

0

b

0.5

0.4

– 0.1

– 688

k

1

0.8

– 0.2

– 688

CT

4000

2304

– 1696

– 1704

and make the analysis how each of the parameter of multimodal international conveyance influence the target function (Table 2). As we can see from the Table 2, the objective function has declined. The critical influence on (C T ) is caused by the efficiency brought by agility (k) and decreasing coefficient according to the type of engine of a vehicle (b). At the same time more evident reduction of transportation tariff doesn’t have the same impact on the decrease of (C T ). Consider another situation with multimodal local conveyance. So, we do not consider variable (k), and model the situation of the increase of transportation tariff. Results of the modelling are presented at the table (Table 3). Table 3. Influence of factors on the objective function for multimodal local conveyance. Factor

Planned value, 0

Actual value, 1

Difference (1–0)

IMA

g

2000

2100

100

210

y

4

4

0

b

0.5

0.55

0.05

410

CT

4000

4620

620

620

0

From the Table 3 we can see, that the slight increase of the coefficient devoted to usage of environmentally friendly vehicles may significantly (even more than transportation tariff increase) influence total costs devoted to voyage. As we can see from both tables (Table 2 and Table 3) the analytical approach helps to acquire very high level of accuracy for calculations:     (10) international = CT  − fCT  = |−1696| − |−1704| = 8.     local = CT  − fCT  = |620| − |620| = 0.

(11)

The error between planned and actual values in both cases doesn’t exceed 0.5% (see Eqs. 10–11), so accuracy of calculations is 95%–100% respectively.

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3.2 Combined Method Based on Linear Programming Transport logistics includes a numerous basic-level operations (i.e. transparent impartible actions within supply chain), such as order fulfillment, cargo loading, cargo unloading, vehicle routing, storage on vehicle, order labeling, packaging and others. When applying IMA it is assumed that each of the operations has its own value and target. When applying linear programming, it is assumed that there is one target for all the operations. In such case efficiency assessment process is firstly based on defining relative error (Pij ) which is calculated as a difference between target result and actual value. For example, in order to reduce CO2 emissions and increase overall supply chain efficiency cargo loading should take no more than 0.5 h (t plan ). In fact, transportation vehicle was loaded 1.1 h (t fact ) due to disintegration between zones: Pij = pplanij − pfactij

(12)

where i – ordinal number of an indicator; j – ordinal number of a logistics operation; t planj – planned duration of the j-operation; t factj – factual duration of the j-operation. By calculating relative errors for all logistics operations, let us define the accumulated error: P =

n k  

Pij

(13)

i=1 j=1

where k – amount of indicators considered; n – amount of logistics operations considered. Consider two basic indicators – time and costs for completing certain logistics operations related to transportation process. Indicator ‘Time’. There are some logistics operations x j m X, where j = 1,…,n. t planj – planned duration of the j-operation, where t planj m Tplan , j = 1,…,n; t factj – a factual duration of the j-operation, where t factj m Tfact , j = 1,…,n. Consider restrictions for indicator ‘time’: tplanj − tfactj ≥ 0, Then the objective function will be as follows: f (x, t) = P ⇒ min. Indicator ‘Costs’. As with the previous indicator, for the second indicator ‘costs’: cplanj – planned costs for completing the j-operation, where cplanj m Cplan , j = 1,…,n; cfactj – factual costs for completing the j-operation, where cfactj m Cfact , j = 1,…,n; Consider restrictions for indicator ‘costs’: cplanj − cfactj ≥ 0, Then the objective function will be as follows: f (x, c) = P ⇒ min. In the same way other relevant indicators might be presented for further analysis. Linear programming is suitable for basic operations of transport logistics with transparent indicators: time, costs, assets, etc., i.e. for determined systems. While IMA is recommended to use for stochastic ones, when there is a high uncertainty level, and management decisions are taken on tactical or strategic levels (see Table 1). To validate the model, we simulated a random transportation process with Monte Carlo method (Poisson distribution had been taken, random walk is 100) and take average

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experts’ marks (EM ij ) on reliability for completing certain operation according to the plan. Where 0 – a lowest value, 1 is a highest value of an experts’ mark. Thus we can obtain relative reliability for each of the logistics operation: RRij =

EMij , Pij

(14)

where EM ij – experts’ mark of the i-indicator for the j-logistics operation. Then average reliability will be as follows: RR =

k n   RRij j=1

(15)

n

i

For example, RR of 0.4 demonstrates that in 40 cases out of 100, operation would be completed in time and with planned costs. Average reliability combines both empirical and analytical approaches. Note, that efficiency assessment process based only on ‘error’ analysis (Pij ) wouldn’t be informative in that case, because it describes general deviation of values for each operation considered. While including experts’ marks to calculations (via RRij ) changes the final results of analysis significantly. The combined method assumes transformation of experts’ assessment to analytics (Table 4). Table 4. Efficiency assessment of indicators ‘time’ and ‘costs’ with combined method. Logistics operation

Planned factual (modeled) Error, (Pjj ) Experts’ mark (EMij ) RRij

Indicator ‘time’, hours Cargo loading Delivery Cargo unloading Invoice statement Total sum

2

2.2

0.2

0.1

0.5

10

11.2

1.2

0.8

0.67

2

3.1

1.1

0.6

0.55

1

2.2

1.2

0.5

0.42

15

18.7

3.7

–

0.54

Indicator ‘costs’, $ Cargo loading Delivery Cargo unloading Invoice statement Total sum

250

250.3

0.3

0.3

1

1000

1000.2

0.2

0.1

0.5

250

250.1

0.1

0.1

1

100

101.1

1.1

0.7

0.64

1600

1601.7

1.7

–

0.79

Let us compare the results obtained for indicator ‘costs’ with IMA and combined method. Take from the Table 4, costs for delivery and costs for invoice statement: they are tending to exceed planned values (0.5 and 0.64 of RRij respectively). Looking at the Table 2 and Table 3 it is hard to identify the exact moment of causing costs increase (cargo loading, unloading, delivery, etc.), but at the same time it is possible to see the

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influence of costs reduction (200 $ reduction leads to 328 decrease of transportation costs) and costs increase (100 $ enlargement of tariff g brings extra 210 costs increase). 3.3 Application of Binominal Distribution for Approximation of Assessment Results As it was mentioned in the article, some of performance indicators are difficult to analyze because of their relative nature. When considering these indicators it is assumed to state their presence or absence in logistics system. For example, transport infrastructure, distribution centers, warehouses of certain class, customs throughputs, etc. For these indicators it is proposed to use binomial distribution in order to approximate final values. Let us define indictor ‘customs throughputs’ as a random variable X depending on random events x j . Than random variable X may have one of the following values: x 1 , x 2 , …, x j , where j = 1, …, n. Each of the value occurs with correlated probability. Running Bernoulli experiment with big number of experiments, a good approximation tool is acquired. As this method is well-studied and widely used we wouldn’t describe all the steps of Bernoulli experiment via MsExcel, and emphasize the importance of using binominal distribution only for relative indicators that are difficult to describe in particular values/numbers. The next stage of the efficiency assessment system proposed by the authors in Sect. 2.2 is Gap-analysis. It is used to define the difference between current state and desirable positions [23, 24]. Gap-analysis, conducted after having applied IMA, combined method based on linear programming and relative assessment based on binominal distribution, is focused on two main tasks. The first one – to compare current or planned performance with expected one for different types of indicators. Secondly – come to synthesis after performance structure analysis. Making the analysis of literature, statistics and business cases [25–27], we come to conclusion that it is beneficially to have the strong efficiency assessment system to improve current management and logistics models toward upcoming trends, such as sustainable logistics in correlation with analytical tools.

4 Conclusion and Recommendations Logistics nowadays is being under tough pressure for more sustainable solutions and reliable supply chains. That requires efficient management style with focus on customers, social and environmental issues in consideration with economic resilience. Finding a balance between business targets and overall environmental issues is a big challenge. In order to deal with it a comprehensive approach should be used. In the article authors propose to use the analytical approach to one of the key logistics management challenges – efficiency assessment process. Assessment of transport activity (as the most assets-taking one) gets special perspectives: companies may benefit significantly from a precise efficiency assessment system because it helps to improve management base, makes communications easier, contributes to research and development, and gives ability for comparing company’s goals and results with competitors.

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The proposed approach implies three different attitudes to assessment: integral method of analysis for basic indicators, combined method based on linear programming and experts’ marks for more generalized indicators (costs and time are taken as an example) and approximation with binominal distribution for relative ones. In our opinion such multidimensional attitude might be interesting both from scientific and applied perspectives. Despite the precise inspection of issues related to efficiency assessment for targeting sustainable development, there is still a perspective for future investigation. For example, warehouse logistics, purchasing, inventory management and other logistics functions might be considered in the same way as transportation-related operations. There are several sub questions that are also supposed to be of a high importance and might be considered as a perspective direction for future research: number of performance indicators in sustainable supply chain; ranking according to their importance and contribution to environment and society; correlation of the best environmental practices with higher customer loyalty and desirable logistics targets.

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12. Karakikes, I., Nathanail, E., Savrasovs, M.: Techniques for smart urban logistics solutions’ simulation: a systematic review. In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds.) RelStat 2018. LNNS, vol. 68, pp. 551–561. Springer, Cham (2019). https://doi.org/10.1007/ 978-3-030-12450-2_53 13. Lavrikova, Y., Buchinskaia, O.N., Wegner-Kozlova, E.O.: Greening of regional economic systems within the framework of sustainable development goals. Econ. Reg. 17(4), 1110–1122 (2021). https://doi.org/10.17059/ekon.reg.2021-4-5 14. Malnaca, K., Gorobetz, M., Yatskiv (Jackiva), I., Korneyev, A.: Decision-making process for choosing technology of diesel bus conversion into electric bus. In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds.) RelStat 2018. LNNS, vol. 68, pp. 91–102. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12450-2_9 15. Dong, B., Christiansen, M., Fagerholt, K., Chandra, S.: Design of a sustainable maritime multi-modal distribution network – Case study from automotive logistics. Trans. Res. Part E: Logistics Trans. Rev. 143, 1–15 (2020). https://doi.org/10.1016/j.tre.2020.102086 16. Olaniyi, O., Prause, G.: Assessment of SECA-related administrative burden in the baltic sea region. In: Abstracts of the 18th International Conference on Reliability and Statistics in Transportation and Communication, RelStat’18, 17–20 Oct 2018, p. 120. TSI, Latvia, Riga (2018) 17. Strimovskaya, A., Bazhina, D.: Trends and challenges of distribution logistics. In: Abstract of the 17th International Conference on Logistics: Modern Trends and Tendencies. 12–13 April 2018, pp. 298–301. Makarov University, Russian Federation, Saint-Petersburg (2018) 18. Manzini, M., Mariotti, M.: Sequentially rationalizable choice. Am. Econ. Rev. 97(5), 1824– 1839 (2007). https://doi.org/10.1257/aer.97.5.1824 19. Axsäter, S.: Inventory control, 2nd edn. Springer, U.S., NY (2006) 20. Saaty, T.: Neurons the decision makers, Part 1: the firing function of a single neuron. Neural Netw. 86, 102–114 (2017). https://doi.org/10.1016/j.neunet.2016.04.003 21. Strimovskaya, A.: Analiticheskaya ocenka klyuchevyh pokazatelej transportirovki dlya mul’timodal’nyh perevozok. Vestnik Astrahanskogo gosudarstvennogo tekhnicheskogo universiteta, Ekonomika 1, 84–92 (2017). https://doi.org/10.24143/2073-5537-2017-1-84-92 22. Chen, G., Yang, D., Liu, Y., Guo, H.: System reliability analyses of static and dynamic structures via direct probability integral method. Comput. Methods Appl. Mech. Eng. 388, 114262 (2022). https://doi.org/10.1016/j.cma.2021.114262 23. Karenets, S.: Gap metod opredeleniya riska. Bulliten’ innovazionnyh technologiy 4(20), 27– 29 (2021) 24. Lahlou, I., Motaki, N., Sarsri, D., Hanane, L.: Fit-gap analysis: pre-fit-gap analysis recommendations and decision support model. Int. J. Adv. Comput. Sci. Appl. 13(7), 391–406 (2022). https://doi.org/10.14569/IJACSA.2022.0130749 25. Zhu, L., Dawei, H.: Sustainable logistics network modeling for enterprise supply chain. Math. Probl. Eng. 2017, 1–11 (2017). https://doi.org/10.1155/2017/9897850 26. Dormady, N., Roa-Henriquez, A., Rose, A.: Economic resilience of the firm: a production theory approach. Int. J. Prod. Econ. 208, 446–460 (2019) 27. Trojahn, S.: Logistics strategies for resource supply chains. Trans. Telecommun. 19(3), 244– 252 (2018). https://doi.org/10.2478/ttj-2018-0021

Smart Technology

Smart Process Observer for Crane Automation Daniel Sopauschke(B)

, Erik Trostmann, and Klaus Richter

Fraunhofer Institute for Factory Operation and Automation, Sandtorstr. 22, 39106 Magdeburg, Germany {daniel.sopauschke,erik.trostmann, klaus.richter}@iff.fraunhofer.de

Abstract. A method of automated, noncontact analysis is presented, which scans a process crane’s work area fully extrinsically with special 3D LiDAR sensors and analyzes its motion dynamics in real time. Rule- and AI-based algorithms that interpret high-quality point cloud scans have been developed, thus making it possible to evaluate a process crane’s specific handling operations reliably. Existing CAD models of the crane assemblies are automatically fitted into the point cloud for the central process of fused data analysis. The workflow starts with the localization of the loading beam’s cables to estimate its initial orientation and position. The CAD models of the lifting beam and all other lifting system components are successively fitted into the point cloud exactly with the aid of local registration. Swivel joint design constraints are factored into the assessment. The lifting operation is displayed in a VR model automatically receiving all component orientations and positions and the load every second. The crane operator can view the current situation from defined perspectives and additionally receives information on crane component position, spacing and the load, which is needed to control the crane. Keywords: Crane automation · LiDAR · Functional safety · Rule-based algorithm · Digital twin · Assistance system

1 Motivation Process cranes are custom-designed and manufactured equipment. Limited visibility and large parts often make it difficult for crane operators to attach loads to the load hook securely. Lifting system components can always jam together in the harsh process environment, causing dangerous, unstable lifting situations. Technical monitoring of an automated Industry 4.0 cranes’ lifting process or hooks is urgently needed to ensure that heavy or bulky loads are attached safely and efficiently. In the future, the evaluation of individual components’ speeds and acceleration as they move toward the lifting point of the load should allow crane operators to take preventive action during lifting to avert dangerous situations entirely.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 177–190, 2023. https://doi.org/10.1007/978-3-031-26655-3_16

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Focused LiDAR sensors with overlapping smaller measurement areas can be used very efficiently to classify and identify crane parts in order to track, analyze and support crane operators when picking up loads. Various point clouds generated by LiDAR sensors represent the workspace jointly with overlapping areas to enable the subsequent process analysis. To achieve the given objective of a safe and smart process observer, recorded positions and orientations of the individual crane components derived from real LiDAR scans have to correspond to the theoretical model of a mechanical equilibrium during load lifting. In terms of functional safety, this provides another independent method for confirming correct lifting in addition to pure analysis of the crane hook. The dynamic process data are processed as a digital twin to automate the crane. In this real-time model the three-dimensional lifting system has to reproduce the handling process at a high update rate.

2 Related Work LiDAR technology [1] is getting increasingly popular and is used in a variety of fields such as surveying, geography or geodesy. Due to its fast generation of dense point clouds of the surroundings, LiDAR will be used in autonomous vehicles too. For this reason, the automotive industry will be a leading technology driver for robust and less expensive LiDAR sensors in the next years [2]. 360° LiDAR sensors have been applied to the field of supporting specific crane operations already. [3] presents a system for tracking a loader crane operator. Similarly [4] developed a system to track and detect the surroundings of a loader crane, including human bodies and environmental elements. [5] created a system to identify the parts of construction yard cranes and their surroundings in order to detect and avoid collisions between these objects. To date, however, focused LiDAR sensors with overlapping measurement areas have not been used to identify crane parts in order to track, analyze and support crane operators in picking up loads. After point cloud datasets have been captured by the LiDAR sensors and fused to an overlapping point cloud, the CAD models of the different crane parts have to be detected and fitted into the cloud. The process of fitting is commonly called registration. The registration is usually performed with the Iterative Closest Point (ICP) algorithm [6, 7]. This algorithm uses a nearest neighbor search strategy to identify potential corresponding point pairs between the point cloud and the CAD model. From these pairs, a rigid transformation can be computed [8], which reduces the distances between the correspondences. This two-step process is repeated until it converges to a local optimum.

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It is essential for a real-time analysis of the crane to perform the ICP efficiently. Therefore, a fast and spatial filtering technique for the point cloud is required. This allows to accelerate the costly nearest neighbor searches due to a reduction of the search space. The most commonly employed data structures for this purpose are the Octree [9, 10] and the k-d tree [11, 12]. Both use a recursive, spatial subdivision method to significantly reduce the number of candidates for the nearest neighbor. In our work we focused on the k-d tree, since it enables very efficient nearest neighbor searches [13] and can be implemented using a favorable memory layout [14], which is utilized in our approach to reduce the overall memory consumption and accelerate certain operations. There exist numerous extensions to the naïve ICP algorithm, which increase the chance of reaching the global optimum by improving the matching procedure. The correspondence detection can be extended from simple geometric distances to more sophisticated compatibility measurements [15], that take additional point cloud features into account. Common choices are colors [16], intensity values obtained from 3D-scanners [17] or surface features and contour lines [18]. Furthermore, normal vectors of the sampled surface can be used to help avoiding local optima, where local surface orientations are not compatible [19].

3 Process Observer 3.1 System Setup Our monitoring system uses four Livox Horizon©1 LiDAR sensors to continuously capture the scene. These sensors can capture up to 240,000 points per second each, using a non-repetitive scanning pattern which results in an increasing coverage of the measured scene over time. Due to their focused angular field of view and long detection range each sensor can capture a significant amount of the relevant area, reducing the number of required sensors to capture enough points to detect and track all crane parts. To achieve a good coverage of all sides of the relevant parts, the four sensors are mounted in each of the four corners of the crane bridge, roughly facing towards each other. This ensures that points are generated on multiple sides, which results in more stable and accurate detections and registrations afterwards. As the sensors are mounted directly to the moving crane bridge, they move together with the crane, simplifying the data analysis, since the position of the crane itself does not need to be tracked to be able to analyze the point clouds and extract the relevant parts. 3.2 System Calibration and Coordinate System The detection process needs to fuse the data of all four sensors into a single point cloud. Hence the relative alignment of the sensors has to be determined to compute a transformation matrix for each one, which converts the different coordinates into one common coordinate system.

1 https://www.livoxtech.com/horizon, accessed 7 July 2022.

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A short recording of the crane being located in an unmoving resting position is captured. Afterwards the point clouds are manually transformed into a rough alignment, using one of them as the reference. This coarse alignment is further improved by an automatic registration step, using the previous transformation as a starting point for the ICP algorithm. Special care has to be taken when applying the ICP, since the different point clouds only overlap in some areas and the ICP naïvely tries to minimize the overall distances of the point clouds. We therefore restrict the maximum distance in which a neighbor might be found to restrain the ICP matching and subsequent alignment to areas, which are already close together. Additionally, the maximum distance is reduced further throughout the iterative progress. After the ICP has been applied, the transformation matrices for all four sensors into a common, fused-sensors, coordinate system are known. This coordinate system is however not the final one, as it does not interact well with the following analysis steps. These make much use of regions of interest (ROI), axis aligned bounding boxes that are used to extract a smaller portion of the whole point cloud to simplify the analysis. The current common coordinate system is not necessarily aligned to the global axis, complicating the definition of the ROIs. Hence the fused-sensors coordinate system is further transformed. Based on the determination of reference planes, covering the three spatial axes, a transformation is computed that aligns the fused point cloud with the global axis in the following way (see Fig. 1): The z-axis points upwards, perpendicular to the ground plane. The x-axis points along the main movement direction of the traverse and the y-axis points parallel to the long traverse extent direction. Such an alignment allows for a simpler definition of the ROIs. Lastly, the now axis aligned fused-sensors coordinate system is transformed one more time into the final crane coordinate system. This ensures that all coordinates and detections are reported in relation to a known crane origin point. This simplifies the definition of the ROIs even more, as they can now be derived from the construction drawing of the crane. Since the crane is moving together with the sensors, the coordinate systems and ROIs do not need to be updated. The transformation from the intermediate fused-sensors coordinate system to the final one is computed by detecting some reference planes, that are visible on all possible crane positions. Since the crane coordinate system shares the axes orientation with the intermediate one, the final transformation is reduced to a simple translation. This last calibration step is performed continuously throughout the detection. This is necessary since the crane itself was observed to deform and bend slightly when a heavy load was lifted. As the sensors are mounted to the crane, they would change their orientation ever so slightly too, causing the fused point clouds to be misaligned.

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3.3 Crane Detection

Fig. 1. Example scene: The point clouds captured by the individual sensors are color coded. A coordinate system is added for reference. The ropes, the front side of the traverse and the hooks can be seen in the center of the image.

The detection process of the crane parts consists of three consecutive stages. First the ropes are detected in the scene. The location of the traverse is performed in the second step, which can use the results of the first one, since it is known that the traverse is placed under the ropes. The same applies to the third step, the detection of the hooks. Their relative offset in relation to the traverse is known too and can be used as an initial guess of their position in the scene. Figure 1 illustrates an exemplary scene during the processing. The ropes can be seen by all four sensors, due to their mounting, orientation and the current crane position. Since the traverse is moved all the way back in its x-axis, it can be seen by only two of the sensors. This is however enough to perform a detection at this position, since all relevant parts are visible. As soon as the traverse moves forwards, all sensors will be able to generate points on it, improving the detection quality in the important working areas during the load pick up. In the following we will give a more detailed explanation of the detection process of the individual parts and the involved algorithms and techniques. Detection of the Ropes. The detection algorithm for the ropes uses the fact, that the ropes can only be located inside a narrow region inside the point cloud. Figure 2 shows the ROIs used for their detection and the contained points from Fig. 1. Since these regions might also contain erroneous measurement points or temporary, smaller objects, the following preprocessing is applied to determine the position of the ropes: To handle artifacts or smaller objects inside the ROIs, a region growing algorithm is applied to the point cloud [20]. This algorithm splits the point clouds into connected components based on a maximum distance between neighboring points. A k-d tree is used to speed up the necessary computations of nearest neighbors. Since it is known

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a-priori that the ropes are always present inside the ROIs and make up the majority of the points, only the largest of these connected components gets preserved while the points from the other components are deleted.

Fig. 2. The regions of interests used for the detection of the left and right rope and the detected center lines.

Next a cylinder is fitted into the remaining points using the gaussian sum of squared error as the minimization metric. Since it is known that the ropes are more or less aligned with the z-axis, the direction of the cylinder is fixed during the computations, which improves the performance and stability. Even if the traverse oscillates in x- or y-axis direction, the angle of the ropes remains very small and is mostly lost in the general noise of the point clouds, since the individual ropes are very thin. Furthermore, the subsequent processing only requires a general position of the ropes to continue the detection of the other parts. The detection results of the ropes can be verified by comparing values like x-y position or computed diameter of the cylinder to the values of the former detections and to expected values. This allows to recognize missing or wrong detections and adapt the further pipeline to this case. Detection of the Traverse. The region of interest used for the detection of the traverse has to be significantly larger than the narrow one used for the ropes, since the range of possible traverse locations, especially in the x- and z-direction, is much larger. It is therefore highly likely that more objects than just the traverse might be visible inside this ROI. To achieve a performant and stable detection, the position of the traverse is guessed based on a-priori knowledge and the utilization of the results of the previous step. Afterwards the guess is refined through a registration. It is known that the traverse has to be located below the ropes. Therefore, as a first step, all points inside the traverse detection ROI, that are closer than a given maximum distance to the center lines of the ropes, are selected. Figure 3 illustrates this process. The selected points near the rope center lines will contain different parts of the point cloud. In

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addition to points from the ropes themselves there are also points from the cable guide and traverse present. Furthermore, there might exist points from objects below the traverse, which happen to be located inside the ROI of the traverse too. To separate these parts, a second region growing is performed. Since the rope, cable guide and traverse are in close proximity of one another, they will form a single and large connected component. After all components have been identified, the one with the largest average z-axis position is chosen. Next, the point with the lowest z-coordinate of this component is determined. This point ideally belongs to the upper part of the traverse, below the cable guide. Since the relative offset of the cable guide on the traverse CAD model is known, an initial guess for the position of the traverse can be computed. The resulting guess is shown on the left side of Fig. 3. Based on this initial guess, the ICP is used to refine the position.

Fig. 3. The points inside the region of interest for the traverse detection; left side: initial guess of the traverse position based on the right rope’s position; right side: traverse after registration.

To speed up the required nearest neighbor searches, a k-d tree is constructed from the CAD model of the traverse. The tree uses the same layout as described in [14], but storing triangles instead of points. Since the CAD model of the traverse does not change during the processing, the k-d tree can be precomputed, further reducing the runtime during the registrations. We employ a modified version of the ICP for this computation, that takes the normal vectors of the points into account. This ICP variation only considers candidate points as valid nearest neighbors if their normal vector is compatible to the one from the CAD model. The compatibility is measured as the dot product between the two normal vectors. Only neighboring pairs whose dot product is larger than a given threshold are used for the alignment computations. This approach ensures that the points are transformed to the correct side of the CAD model. This is especially relevant in situations where the traverse is only seen from one side due to its position and the location of the sensors. The normal vectors of the point cloud are computed by fitting least-squares planes into the local neighborhood of the points and using the normal of this plane [21]. The resulting normal vectors, however, are not aligned uniformly due to a sign ambiguity inside the computations. There exist sophisticated methods to overcome this challenge

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(see [22–24]). We can use a simpler version in our case, since we know the locations of the sensors. The sensors can only have generated points on surfaces facing towards them. Therefore, a simple dot product check between the computed normal vector and the direction from the point towards the sensor is enough to align all normal vectors properly. The result of the registration can be seen on the right side of Fig. 3. The offset caused by the initial guess was corrected by the registration.

Fig. 4. The points inside the region of interests for the hooks; left side: initial guess of the hook’s positions under the traverse, right side: hooks after registration.

The procedure described above only applies if the ropes were detected in the step before. Otherwise the initial position of the traverse is guessed through an extrapolation based on an its last detected positions and corresponding time stamps. This approach is however not as accurate, since the track of the traverse does not describe a simple linear motion in most cases. Detection of the Hooks. Detecting the hooks is very similar to the procedure used for the traverse detection. The relative positions of the hooks in relation to the traverse are known a-priori. The initial guess for the hooks positions is thus derived from the traverse position. If no traverse has been detected, the hook positions are guessed based on their last locations. After the initial positions have been derived, the registration of the hooks is performed in the same way as was done for the traverse. Figure 4 shows the starting point and registration result. Again, the adapted version of the ICP with compatible normal is used to reduce false neighborhood detections and to improve the registration quality. Since the hooks are narrower, a misregistration towards the wrong side would be more likely. This is particularly true in the border areas of the observed space, where most likely only two of the sensors are able to generate points on the hooks. As a result of the alignment of the cameras and the hook positions, the generated points will be located on the back and partially lateral sides of the hooks. Due to the requirement of matching normal vectors, a registration is possible in this challenging scenario too.

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Fig. 5. Example of the resulting visualization. The detected crane parts and their locations and orientations are displayed to support the crane operator.

Visualization of the Results. The results of the detection are used to create a digital twin of the actual crane, capturing its key components and their parameters. This digital twin is visualized in a simplified real-time environment for the crane operator. Figure 5 shows a screenshot from this visualization. Only the CAD models are displayed at their detected positions and orientations to avoid overwhelming the crane operator with rapidly changing point clouds. In addition to the models, some of their key parameters are displayed too. They allow the operator to quickly assess the state of the crane.

4 Results We tested our approach on a number of recorded scenes of the crane performing a variety of its common tasks. Each scene had a duration between one and four minutes. All tests were performed on a Windows PC equipped with an Intel-i9900k processor and 64GB of RAM. As the four sensors continuously capture and stream small amounts of points, the points need to be collected over some period of time before starting the detection routine. This period of time is commonly known as integration time. Selecting a long integration time results in a large number of collected points and a dense coverage of all surfaces. However, it can also lead to the introduction of significant motion blur due to objects moving during the integration. For our tests the integration time was set to 500 ms. This value showed the best overall results with regards to a sufficient point cloud density of around 500,000 points and a reduced motion blur during fast movements of the crane.

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Fig. 6. Position of the traverse in x- and z-axis during the scene. The approach of the target (1), the pick-up (2) and the drop off (3) are clearly visible.

The intermediate results of the detection procedure were already presented in Figure 3 and Fig. 4. The developed algorithms were able to detect the ropes and traverse in all of our example scenarios. The detection of the hooks was possible in most situations too. In some cases, other objects in the scene caused a shadowing on the hooks and subsequently the generation of too few points to perform a stable registration. The processing of a single detection cycle took around 580 ms. The majority of the time was taken up by the registration steps for the traverse and the two hooks, each requiring around 120 ms. We also looked in more detail into the graphs of the detected positions and the possibilities to infer further information about the crane from them. Figure 6 shows the detected positions of the traverse in x- and z-direction over the time of one scene. The different movements of the crane can be derived from the charts. The crane started with its traverse moved all the way back. In the time span marked with (1) the traverse is moved forward and lowered to pick up the load (see Fig. 1 for the definition of the coordinate axis). Afterwards, up to 93 s in time span (2), the traverse is lifted again. This is followed by a fast backward and forward motion at the start of time span (3), in which the crane was moved to the side. Since the coordinate system is defined relative to the crane, this sideways motion is not directly detectable in the graphs. Next the traverse is lowered again to drop off the load. Finally, after time span (3), the traverse is moved backwards and half way up again, returning to its starting position. The detection algorithm showed a good performance in this case, since the resulting positions describe a smooth curve without any significant outliers or large, unrealistic spikes.

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Fig. 7. Position of the traverse in y-direction during the scene. The oscillation becomes smaller during the approach of the target. The large spike is caused by the inertia of the traverse during a sideways motion of the crane.

Figure 7 shows the tracked y-position of the traverse in the same scene. An initial oscillation of the traverse is clearly visible. This oscillation is the result of the previous, not captured sideways movement of the crane, resulting in resonating traverse movement due to the inertia of the system. The oscillations are damped during the following approach of the load. After the load has been picked up and the whole crane has moved sideways a large spike can be seen. This spike is caused by the inertia of the traverse, resisting the sideways motion of the crane (lower arc) in the beginning and continuing its motion after the crane has already stopped its movement again (upper arc). The oscillation is damped again when the load is dropped off and returns with the final sideways motion of the crane in this scene. Figure 8 displays the pitch angle, the rotation around the y-axis, of one of the hooks in a different scene. During the approach of traverse and target, the angle fluctuates significantly. This is caused by the non-linear motion of the traverse and inertias of the different parts of the system. However, as soon as the hook has picked up its load, the angle stabilizes at around 5°. This specific angle is determined by the construction of the hook and leads to a load balancing of the system. The detection of this equilibrium through the hook’s angles can be used to check whether the load was hooked up correctly. A wrongly hooked load would result in a significantly different pitch angle, as the hook has to tilt differently, to reach the load balancing again.

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Fig. 8. Deflection of the crane hook (pitch angle) over time in a different scene. The value settles at around 5° when the load is picked up. This specific value is determined by the construction of the hook.

The detection process of the hooks turned out not be as stable as the one of the ropes and traverse. The general positions and orientations of the hooks were detected in all cases, but the determined coordinates and angles did contain more noise than the ones for the ropes and traverse. The two main reasons are found in the shadowing of the hooks by other objects, resulting in fewer points, and in the faster movement of the hooks in general, causing more motion blur in the point cloud. As a result, the ICP had some margins during the fitting of the CAD model of the hook. This explains the two downward spikes to 2° in the pitch angle in Fig. 8 at 75 s and 175 s.

5 Summary In this article we presented a novel method for the automatic detection, registration and tracking of crane elements in a point cloud stream captured by four LiDAR sensors. After a calibration and alignment step of the sensors, the crane parts are identified one after another by exploiting known relative positions and offsets between them. An initial guess of their location is further refined by an automatic registration process, using a modified variant of the ICP algorithm. The analysis of various example scenarios showed the stability of our approach and the resulting opportunities for crane operator support as well as possible automations. A parallelized and efficient implementation of the algorithms and data structures permits a fast update rate of the detections. In further research we would like to expand the detection to more elements such as the surroundings of the crane, to improve the digital twin model and derive additional state information. Furthermore, known mechanical restrictions of the degrees of freedom for the orientation of different crane parts might be used to restrain and accelerate the registrations further and to detect false detections or registrations more easily. Finally, the possible derivation of control commands to the crane from the detected positions and orientations could be examined.

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Acknowledgements. Part of the developments and evaluations described in the paper was carried out in the project ‘Pro-Kran’ funded by the Federal State of Saxony Anhalt and the European Commission within the EFRE and REACT-EU program. Funding reference: FKZ 2204/00014.

References 1. Danzl, R., et al.: Leitfaden zur optischen 3D-Messtechnik. Fraunhofer Verlag (2022) 2. Goelles, T., et al.: MOLISENS: a modular MObile LIdar SENsor System to exploit the potential of automotive lidar for geoscientific applications. Geoscientific Instrumentation. Methods Data Syst. Discuss. 1–22 (2022). https://doi.org/10.5194/gi-2022-3 3. Mi˛adlicki, K., Saków, M.: LiDAR based system for tracking loader crane operator. In: Trojanowska, J., Ciszak, O., Machado, J.M., Pavlenko, I. (eds.) MANUFACTURING 2019. LNME, pp. 406–421. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18715-6_34 4. Miadlicki, K., Pajor, M., Sakow, M.: Loader crane working area monitoring system based on LIDAR scanner. In: Hamrol, A., Ciszak, O., Legutko, S., Jurczyk, M. (eds.) Advances in Manufacturing. LNME, pp. 465–474. Springer, Cham (2018). https://doi.org/10.1007/978-3319-68619-6_45 5. Jeong, H., Hong, H., Park, G., Won, M., Kim, M., Yu, H.: Point cloud segmentation of crane parts using dynamic graph CNN for crane collision avoidance. JCSE 13, 99–106 (2019). https://doi.org/10.5626/JCSE.2019.13.3.99 6. Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: SPIE pp. 586–606 (1992). https://doi.org/10.1117/12.57955 7. Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vis. Comput. 10, 145–155 (1992). https://doi.org/10.1016/0262-8856(92)90066-c 8. Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Cryst. A 32, 922–923 (1976). https://doi.org/10.1107/S0567739476001873 9. Meagher, D.: Geometric modeling using octree encoding. Comput. Graphics Image Process. 19, 85 (1982). https://doi.org/10.1016/0146-664x(82)90128-9 10. Schütz, M., Ohrhallinger, S., Wimmer, M.: Fast out-of-core octree generation for massive point clouds. Comput, Graphics Forum 39, 155–167 (2020). https://doi.org/10.1111/cgf. 14134 11. Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18, 509–517 (1975). https://doi.org/10.1145/361002.361007 12. Brown, R.A.: Building a Balanced k-d Tree in O(kn log n) Time. arXiv (2014) 13. Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3, 209–226 (1977). https://doi.org/10.1145/ 355744.355745 14. Sopauschke, D., Teutsch, C., Trostmann, E., Berndt, D.: A parallel memory efficient outlier detection algorithm for large unstructured point clouds. In: SPIE, pp. 432–444 (2021). https:// doi.org/10.1117/12.2592299 15. Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings Third International Conference on 3-D Digital Imaging and Modeling, pp. 145–152. Quebec City, QC, Canada (2001). https://doi.org/10.1109/IM.2001.924423 16. Color Supported Generalized-ICP: In: Proceedings of the 9th International Conference on Computer Vision Theory and Applications. SCITEPRESS– Science and and Technology Publications (2014). https://doi.org/10.5220/0004692805920599 17. Godin, G., Rioux, M., Baribeau, R.: Three-dimensional registration using range and intensity information. In: SPIE, pp. 279–290 (1994). https://doi.org/10.1117/12.189139

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Analysis of Air Pollution Monitoring System in Lithuania Marius Mažeika1(B) and Darius Juodvalkis1,2 1 Kaunas University of Applied Engineering Sciences, Tvirtov˙es al. 35, 50155 Kaunas,

Lithuania {marius.mazeika,darius.juodvalkis}@edu.ktk.lt 2 Kaunas University of Technology, Student˛u g. 56–232, Kaunas, Lithuania [email protected]

Abstract. According to the European Environment Agency data, air pollution is a major cause of early human death due to environmental factors. It is quite clear that this problem is most topical in cities, which have many sources of pollution (industry and transport), and are densely populated. Industry and transport are the main sources of pollution in cities. The transport sector currently accounts for around 40–50% of NOx and 10–15% of PM emissions. When choosing the location of air quality monitoring stations, it is important to take into account many factors, which depend on the purpose of the measurements (station purpose), the source of pollutants and other environmental factors. When installing industrial, urban and rural background air quality monitoring stations, the factors that determine the optimal location of the station are usually clear and unchanging. A bigger problem is with the selection of air quality monitoring station location, because they are designed to measure the impact of transport pollution on ambient air quality, as traffic intensity, congestion and changes need to be taken into account. In total 18 state air quality monitoring stations are installed in Lithuania, 5 of which are designed to monitor the impact of transport pollution on ambient air quality. Comparing the number of air quality monitoring stations in Lithuania to other Western European countries, it is possible to single out the insufficient number of these stations and some air quality monitoring stations are located in sub-optimal locations. The purpose of this article is to perform the analysis and relation of the locations and recorded data of Lithuanian stationary state air quality monitoring stations. The article analyses the layout of Lithuanian air quality monitoring stations, their density and recorded data. By analysing the data of Lithuanian air quality stations, it is determined whether the stations are currently installed in optimal locations. The number, density, location and recorded data of air quality monitoring stations in Lithuania are compared to examples from other countries. Keywords: Air quality · Transport pollution · Monitoring station

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 191–200, 2023. https://doi.org/10.1007/978-3-031-26655-3_17

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1 Introduction According to the European Environment Agency, air pollution is the main cause of premature human death due to environmental factors. It is obvious that this problem is the most acute in cities, as cities have many sources of pollution (industry and transport) and are populated with the largest number of people. Industry and transport are the main pollutants in cities. Currently, the transport sector accounts for approximately 40–50% of NOx and 10–15% of PM emissions [1]. Automobile engines are constantly being improved by adapting them to tougher requirements in order to reduce pollutant emissions. However, the ever-decreasing car emissions are offset by the growth in the number of vehicles, and therefore the pollution of the transport sector and its share in the total amount of pollution remains more or less constant. Accumulation or dispersion of pollutants in the air depends greatly on meteorological conditions. Meteorological data are essential when it is necessary to assess the distribution of air pollutant concentration in space as well as the impact of economic or other polluting activities on air pollution, to design various scenarios in order to determine the effectiveness of the expected measures or to evaluate the air quality by simulation in places where there are no possibilities for air quality measurement.

2 Analysis of Current Situation In Lithuania, the network of air quality monitoring stations was developed in 2003, following the implementation of the EU-financed PHARE Twinning project: according to the requirements of the EU directive, it was calculated how many air monitoring stations there must be in Lithuania according to the location of pollution sources and the amount of pollutants emitted into the environment [2]. Currently, there are eighteen stationary ambient air quality monitoring stations in Lithuania that are state-owned [3]. The data of state-owned ambient air quality monitoring stations is recorded by the Environmental Protection Agency. The purpose of state-owned air quality monitoring stations is different: • five stations are installed in places with high traffic intensity and their purpose is to measure the impact of vehicle pollution on air quality. • four stations are located close to large industrial facilities and they measure the impact of pollution from these facilities on air quality; • five stations are urban background; they are installed in cities away from transport roads and industrial facilities; • three stations are rural background, they are installed in non-urban areas and away from transport roads and industrial facilities. When choosing the installation locations for air quality monitoring stations, it is important to take into account many factors that depend on the purpose of the measurements (the purpose of the station), the source of pollutants and other environmental factors. When installing industrial, urban and rural background air quality monitoring stations, the factors that determine the optimal location of the station are usually clear and

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unchanging. There is a bigger problem with air quality monitoring stations, which are designed to measure the impact of transport pollution on ambient air quality by selecting the installation location, because it is necessary to take into account the intensity of traffic, traffic congestion and its changes. 2.1 Air Quality Monitoring in Cities One state-owned monitoring station for the effect of transport pollution on ambient air quality has been installed in Vilnius city (Fig. 1), which is located in Zirmunai near Kareiviu street (coordinates 54°42 55 N 25°17 21 E). There is high traffic intensity on Kareiviu street and traffic jams often form here.

Fig. 1. Air quality monitoring station on Kareiviu street in Vilnius city.

There is one state-owned monitoring station for the effect of transport pollution on ambient air quality in Kaunas city (Fig. 2), which is located in Petrasiunai near R. Kalanta street (coordinates 54°53 42 N 23°59 10 E). R. Kalanta street has an average traffic intensity and traffic jams rarely occur here. There are also several industrial facilities on R. Kalanta street, which emit certain pollution components and can have a significant effect on the overall ambient air pollution.

Fig. 2. Air quality monitoring station on R. Kalanta street in Kaunas city.

In Klaipeda city, two state-owned monitoring stations for the effect of transport pollution on air quality are installed, one of them is located near Bangu street (coordinates

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55°42 27.5 N 21°08 28.3 E). Low traffic intensity is recorded on Bangu street, but the city centre is not far from this place. The second air quality monitoring station in Klaipeda city, which records the effect of traffic pollution, is located near Silute highway (coordinates 55°41 24.0 N 21°10 44.9 E). There is high traffic intensity on Silute highway and traffic jams often form here during rush hours. In Siauliai city, one state-owned monitoring station for the effect of transport pollution on air quality is installed, which is located at the intersection of Zemaite, J. Basanavicius streets and Ausra avenue (coordinates 55°56 16.1 N 23°18 29.8 E). This place in Siauliai city is not characterized by high traffic intensity, but it is close to the city centre and traffic jams form here during rush hours. 2.2 Monitoring of Pollution from Industrial Facilities In order to properly regulate the amount of pollutants entering the air and develop conditions for controlling the air quality, it is important to have accurate information about the air quality in various places. There are several large industrial facilities in Lithuania, which emit large amounts of pollutants into the environment and have a significant impact on the air quality. Most often, in order to assess the impact of these pollutant sources, state-owned air quality monitoring stations are installed not far from them. The effect of industrial facilities on air quality is measured in Mazeikiai, where the only crude oil refinery company in the Baltic States operated by AB “Orlen Lietuva” operates [4]. The design capacity of AB “ORLEN Lietuva” refinery is 10 million tons of crude oil per year. State-owned air quality monitoring station (Fig. 3) is located in Mazeikiai city near Gabija path (coordinates 56°18 34.5 N 22°19 53.2 E).

Fig. 3. Air quality monitoring station in Mazeikiai city.

AB “ORLEN Lietuva” pays special attention to environmental protection. Despite the fact that the company allocates a lot of funds to the implementation of nature protection measures, closely cooperates with Lithuanian and international companies in developing ecological programs, the crude oil refinery still emits certain pollutant emissions. AB “Orlen Lietuva” crude oil refinery is very close to the border of Latvia, so the impact of this industrial facility on air quality is inevitable in neighbouring Latvia as well. The nearest cities in Latvia are Kalni and Ezere, which have less than a thousand permanent residents [5].

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Near Jonava city, where in 2021 according to the data, there were about 27 thousand population, a large company AB “Achema” operates. AB “Achema” is a leading producer of nitrogen fertilizers and chemical products in Lithuania and the Baltic states [6]. The company’s activities undoubtedly have an effect on the ambient air quality, which is why a stationary state-owned air quality monitoring station has been installed in Jonava city. This station is located close to Jonava city center and is about 2.5 km from the company territory (Fig. 4). AB “Achema” is constantly monitoring and evaluating its activities that may have environmental effects. Reliability and control of organic situation of AB “Achema”, a company producing chemical products, are the most important company’s priorities in the field of environmental protection. One of the main goals of the company’s activities is the improvement of the state of the environment, the management of waste generated in the production process, and the application of pollution prevention measures in order to reduce the company’s environmental impact.

Fig. 4. Air quality monitoring station in Jonava city.

AB “Achema” implements environmental monitoring programme. The monitoring of the effect on the ambient air, controlling concentrations of ammonia, nitrogen dioxide and sulphur dioxide outside the company’s territory, determines the impact of the discharge of pollutants discharged with surface water into the Neris River on the river water. In Kedainiai city there is also a large fertilizer plant AB “Lifosa”. AB “Lifosa” is one of the largest and most modern fertilizer manufacturers in Europe. The company mainly produces phosphate fertilizers for soil enrichment and fertility. In order to contribute to reducing the air pollution, AB “Lifosa” is modernizing the plant by installing environmentally sustainable and resource-efficient equipment and implementing smart solutions [7]. Despite the company’s efforts in the field of environmental protection, the impact of pollutants emitted during production processes on the ambient air quality still exists and it is monitored at the state-owned air quality monitoring station in Kedainiai, Rasos street (coordinates 55°16 48.02 N 23°57 28.49 E). The distance from this station to AB “Lifosa” is about 3 km. One more place for monitoring the impact of industrial pollution on ambient air quality is in Vilnius city. There are many industrial facilities of various sizes in Vilnius. This station is located near Savanoriu avenue in Vilnius (coordinates 54°40 23.48 N 25°14 58.29 E).

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2.3 Monitoring of Air Pollution in Other Places The system of background air monitoring stations makes it possible to analyse the air pollution brought from other countries, the general – background – pollution level of the country’s air basin, its changes and the factors that determine them. In 2022, 5 urban and 4 rural background air monitoring stations were operating in Lithuania. Urban background air quality monitoring stations are installed away from transport arteries and industrial facilities and in densely populated areas. These stations are located in the northern part of Lithuania in Akmene city, in Kaunas municipality – in Noreikiskes settlement in Rapsu street (coordinates 54°53 0 N 23°50 9 E) and two stations are located in Vilnius city – one in the old town and one in Lazdynai microdistrict. Noreikiskes station is located away from the Kaunas city center, in an actively developing area of dwelling houses, but not far from the Via Baltica E67 road, where there is always high traffic intensity (Fig. 5).

Fig. 5. Air quality monitoring station in Noreikiskes.

Rural background state-owned air monitoring stations are installed in areas that are far from any sources of pollution and are designed to monitor background air pollution and the possible impact of pollutant flows from other European regions on Lithuanian air quality. Such stations are installed in Neringa, Telsiai County in Plunge District, in Dzukija in Varena District and in Aukstaitija in Utena County.

3 Result Analysis Analysing the network of transport pollution monitoring stations in Europe (Fig. 6), it is noticeable that they are located closer to big cities and along main roads, where there is intense traffic and possible congestion. Analysing the network of industrial pollution monitoring stations in Europe [8], their arrangement is less frequent than that of transport or background (Fig. 6) monitoring stations. A higher concentration of these stations is located closer to cities famous for industry. The network of background pollution monitoring stations in Europe (Fig. 6) is the densest (compared to transport and industry) [8]. This can be explained by the fact that there are many places where there are many sources of pollution, and in order to isolate them, continuous air quality measurements and data analysis are needed, which helps to protect the environment and human health. Lithuania stands far behind Western Europe in terms of the network of stations, and this area needs to be improved. The Lithuanian Department of Statistics [9] regularly

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Fig. 6. Network of air pollution monitoring stations in Europe: a – transport purpose; b – industrial purpose; c – background purpose [8].

provides information on data recorded at air quality monitoring stations. According to the World Health Organization, particulate matter is the most harmful air pollutant for human health. PM10 can penetrate deep into the lungs, and even more harmful to health is PM2.5 , which can penetrate the lung barrier and enter the blood system. Chronic exposure to particles increases the risk of developing cardiovascular and respiratory diseases, lung cancer [10]. Data from air quality monitoring stations on the amount of particulate matter in the air are presented in Fig. 7. 40 35 30 25 20 15 10 5 0

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Fig. 7. Average annual maximum concentration of PM10 particulate matter recorded in cities, µg/m3 in 2020.

Sulphur dioxide is another harmful pollutant of the ambient air. It’s a colourless gas derived from the combustion of sulphur-containing fossil fuels. Sulphur dioxide is a significant air pollutant in many parts of the world. Oxidation of sulphur dioxide at the surface of particles in the presence of metallic catalysts leads to the formation of sulphurous and sulphuric acids. It causes coughing and mucus secretion and aggravates conditions such as asthma and chronic bronchitis [10]. The average annual maximum concentration of sulphur dioxide recorded in cities is presented in Fig. 8. Another pollutant recorded at air pollution monitoring stations is nitrogen dioxide. Many variants of nitrogen oxide exist, and the air pollutant variant is one of the significant impacts on humans. Nitrogen dioxide is a reddish-brown gas with a characteristic pungent odour. It’s a vital oxidant gas that reacts with water to produce nitric acid and nitric oxide.

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10.4

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Fig. 8. Average annual maximum concentration of sulphur dioxide recorded in cities, µg/m3 in 2020.

Elevated levels of nitrogen dioxide can cause damage to the human respiratory tract and increase a person’s vulnerability to, and the severity of, respiratory infections and asthma. Long-term exposure to high levels of nitrogen dioxide can cause chronic lung disease [10]. The average annual maximum concentration of nitrogen dioxide recorded in cities is presented in Fig. 9. 40 30 20 10 0

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Fig. 9. Average annual maximum concentration of nitrogen dioxide recorded in cities, µg/m3 in 2020.

Analysing the data of the air quality monitoring stations located in Lithuania (Fig. 7, 8 and 9), it can be noticed that the data of the stations are quite different, which shows that there may be a larger number of stations, which would allow for a more detailed analysis of the sources of pollution and the dependence of the amount of pollutants on external factors. The long-term data records of all state-owned air monitoring stations installed in Lithuania are publicly available [11]. The major industrial facilities of Lithuania, which are located near Jonava, Kedainiai, Akmene and Mazeikiai, emit certain pollutant emissions into the air. The data on the amount of particulate matter (PM10 ) in the air recorded on different days in 2022 from industrial pollution air monitoring stations are presented in Fig. 10. The permissible concentration of particulate matter is 50 µg/m3 . As it is seen in Fig. 10, the concentration of particulate matter is constantly changing, but only twice this year

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and for a short period of time in certain places, it exceeded the permissible maximum rate.

Fig. 10. Amount of particulate matter recorded by industrial pollution air monitoring stations in 2022

The concentration of nitrogen dioxide in the air recorded on different days of 2022 by the state-owned air monitoring stations installed near the industrial facilities is presented in Fig. 11.

Fig. 11. Amount of nitrogen dioxide recorded by industrial pollution air monitoring stations in 2022.

Particulate matter (PM10 ) is not the main pollutant emitted by some industrial facilities. Lithuanian industrial facilities, such as AB “Achema”, AB “Lifosa” and AB “ORLEN Lietuva”, emit more nitrogen dioxide due to the processes taking place in them [12]. Due to the prices of energy resources and the problems that have arisen in the areas of supply and production realisation, AB “Lifosa” did not carry out the production this year from mid-April to 1 August, and AB “Achema” stopped production from September 1. These periods are presented in Fig. 11 A and B zones. Analysing the data presented in Fig. 11, it can be observed that the concentration of nitrogen dioxide in the air near the industrial facilities is changing, but this year it never exceeded the permissible rate (200 µg/m3 ). Analysing the data in periods A and B, it can be observed that in

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the absence of production in industrial facilities, i.e., as they did not emit pollutants, air monitoring stations installed in nearby cities did not record any significant reductions in nitrogen dioxide.

4 Conclusion 1. When choosing the location of air quality monitoring stations, the following factors are taken into account: the purpose of measurements, the source of pollutants, the number of population and the intensity of traffic flows. 2. There are three types of air pollution monitoring stations in European countries: transport pollution, industrial pollution and background pollution, which are installed depending on the aim of obtaining data or finding out the prevailing sources of pollution. 3. In the European Union countries the methodology for arranging the network of air pollution monitoring stations is similar, but the density of the stations is different. From the data presented, it can be seen that the number of these stations in the Baltic countries is significantly smaller and insufficient for accurate data analysis. 4. According to the data of stationary stations in Lithuania, it is difficult to identify the real sources of pollution (transport, industry, household), so the number and layout of these stations must be reviewed and increased. Industrial facilities that do not perform production during a certain period of time do not have a significant impact on changes in air quality.

References 1. Shaw, S., Van, B.: Nitrogen Oxide (NOx ) emissions as an indicator for sustainability. Environ. Sustain. Ind. 15, 100188 (2022) 2. Valstybinio audito ataskaita: Aplinkos oro b¯ukl˙es vertinimas Nr. VAE-6 (2022) 3. Environmental Protection Agency. https://aaa.lrv.lt/lt/veiklos-sritys/oras/oro-monitoringovietos/stoteliu-tinklas-lietuvoje. Last accessed 21 Jul 2022 4. AB “Orlen” Homepage: https://www.orlenlietuva.lt/EN/Company/OL/Pages/Refinery.aspx. Last accessed 24 Jul 2022 5. CSB Republic of Latvia Homepage: https://www.csp.gov.lv/en. Last accessed 24 Jul 2022 6. AB “Achema” Homepage. https://www.achema.lt/introduction. Last accessed 28 Jul 2022 7. AB “Lifosa” Homepage. https://www.lifosa.com/en/social-responsibility/safety/environme ntal-protection/77. Last accessed 25 Jul 2022 8. EEA Homepage: https://www.eea.europa.eu/data-and-maps/dashboards/air-quality-statis tics-expert-viewer. Last accessed 25 Jul 2022 9. Statistic Lithuania: Lietuvos aplinka, žem˙es u¯ kis ir energetika (2021) 10. Mazedul K.: Technical Report: Portable Air Quality Monitor (2022) 11. Environmental Protection Agency: https://aaa.lrv.lt/lt/veiklos-sritys/oras/oro-kokybes-statis tika-ir-duomenys. Last accessed 24 Jul 2022 12. Jakuˇcionyt˙e, M., Žaltauskait˙e, J., Sujetovien˙e, G.: Passive lichenoindication as a tool for evaluation of air quality in the environment of a fertiliser plant. Env. Res. Eng. Manag. 72(4), 37–44 (2016)

Piezoelectric Films Application for Vibration Diagnostics Aleksey Mironov(B) , Pavel Doronkin , Aleksejs Safonovs , and Vitalijs Kuzmickis D un D Centrs, 12-16, Jasmuizas street, Riga, Latvia [email protected]

Abstract. The article shows the actuality of piezoelectric films application for vibration diagnostic techniques that are widely used for condition monitoring of operating machines and structures in industry, transport and energy. The application of vibration diagnostics, in particular for structural monitoring, is constrained by high costs and complexity of the measurement systems, especially accelerometers. As an alternative, the use of piezoelectric film sensors is considered that cost is cheaper and size is less than strain gauge. The use of such sensors avoids calibration on the object, power supply from an additional current source, and dramatically reduce size, weight and costs of the sensor network. The properties of piezoelectric films are analyzed in comparison with strain gauges and the benefits are considered. The compatibility of piezo films with existing methods, hard and software of monitoring systems is considered. To determine the relationship between the signal generated by the piezoelectric film and parameters of vibrations, the experimental stand was developed, the studies were conducted, and the dependencies were revealed. The relationship between the signal measured by piezoelectric film and the body vibration was theoretically established and experimentally verified. The curvature of neutral line and vibration velocity (orthogonal to surface) of a vibrated body are the parameters that correspond to the piezo film’s signal. Keywords: Piezo-electric films · Vibration diagnostics · Structural monitoring

1 Introduction Condition monitoring of complex objects containing combine operating machines and structures is an actual problem in many branches of industry, transport and energy. Such issues are most pronounced in aviation, where all aircraft aggregates need health monitoring. Actual board systems allow monitoring partly the engines and transmission, while other machines and structures are out of control in-flight. Most of aircraft’s structural units and operating machines (electric motors, pumps, fans, etc.) may be surveyed applying the non-destructive techniques (NDT). However, these techniques, like ultrasound, eddy current, X-ray, etc. [1–3] are available at technical maintenance or repair stops only. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 201–212, 2023. https://doi.org/10.1007/978-3-031-26655-3_18

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Between such stops the health of above structure’s and machine’s remains unobservable for a long time while some latent defects may grow up and raise risk of accident. The problems of flight safety and the rising maintenance costs expand the requirements for the monitoring system. The gap between the capabilities of actual systems from one side and expanding requirements stimulates the need for board condition monitoring system of the entire aircraft. However, attempts to create the inclusive [4] structural health monitoring system (SHM) run into the obstacles as high costs and bulkiness. Methods of vibration diagnostics [5] are potentially the most effective for condition monitoring of machines and structures of the operating objects. These methods consider oscillations as a response of the object to excitation caused by its functioning. As the excitation corresponds to machine operation it has the main importance for diagnostics of rotating machinery. The ratio between components of wide-frequency vibration may define the key diagnostic parameters of machine’s condition. The dynamic response to excitation plays a main role for structural monitoring, as this response is determined by the modal properties of the structure. Operational Modal Analysis (OMA) methods developed in recent decades allow determining the modal properties of a working structure without stopping it [6], in contrast to NDT. The areas of the vibration diagnostics techniques for SHM application expand and cover not only aviation [7], but also an industry [8], power engineering [9] and a ground transport [10]. Typically, the diagnostic system uses vibration signals of piezoelectric transducers (accelerometers) as input data. The high cost of accelerometers (from hundreds to thousands of euros per piece) is caused by the complexity of manufacturing and calibration. For an aircraft engine, which is expensive in manufacture and operation, the use of 1–2 such accelerometers is paid off. For SHM using OMA the hundreds sensors are required that makes economically unreasonable the monitoring system with accelerometers. For example, the board system built for modal identification of the entire airliner [4] included few hundreds accelerometers. Its cost (without wiring and measuring equipment) probably exceeds a million euros that is too expensive for serially manufactured aircrafts. More budget solutions are also desired for vibration diagnostics of low-cost and mass-produced machines, such as electric motors, pumps, etc. Alternatively, the simple and cheap film sensors can be used to measure vibration signals. When a solid body vibrates, its surface deforms, so the film sensor placed on surface generates a signal associated with the vibration. The film sensors like wire strain gauges and more modern piezoelectric film sensors are known. The strain gauges are widely used for structures testing and in load measurements, the piezoelectric film sensors (piezo films) are applied in tactile interfaces of computer systems, alarm and security systems, etc. The mass and dimensions of film sensors are negligible, so they practically do not affect the modal properties of the object and do not clutter it up. The ability to automate the film sensor production cause the particular interest for application in structural monitoring of serially produced objects. For this purpose, the sensor clusters printed on pre-formed flexible skin can be used. With these advantages, the film sensors, if used instead of accelerometers, can contribute to the promotion of vibration monitoring. This paper considers the basics of film sensors application for vibration diagnostic systems of operating objects.

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The relationship between the parameters of surface strains and acceleration of the solid body was considered in [11] within the hypothesis framework of the pure bending of the solid-state beam assuming its similarity to limited areas of a deformed beam. Based on the study it is resumed that in some areas of the deformed beam the deformation of the surface layer was proportional to second derivative of neutral line displacement. Based on the above, we can assume that to solve the diagnostic problem the parameters of dynamic deformations can be used instead of vibration parameters. For rotating machines, the parameters of dynamic deformations may be used for most typical maintenance tasks, like identifying of the imbalance source or balancing. For health monitoring of shell or frame structures the strain signals can be more informative for the techniques that apply modal parameters computation. In order to replace accelerometers with deformation sensors, it is necessary to select the optimal type of sensors taking into account the conditions of its application. On the one hand, the choice must consider economic, technical, technological and other factors. On the other hand, the type of sensors selected must be maximally compatible with already existing methods of data processing, the measuring equipment and the software tools. For this purpose the relation between the film sensor signal and physical parameters of vibration must be defined.

2 Cost-Effective Sensors for Monitoring Systems The oscillations of a solid body cause deformations of the surface fibers subjected to tension-compression strains. The strain gauge and the piezoelectric film sensor may be used to measure these strains. The electrical signals of these sensors are differently related to the deformations of the surface. The stretching conductive element of a strain gauge (Fig. 1a) lengthens and its cross-section decreases, growing the resistance. Opposite, the compressing element decreases the resistance. The current from an external source varies inversely with the resistance, reflecting the time-varying deformations of the surface. The measured strain gauge signal can be calibrated in units of voltage or strain. The deformable piezoelectric film (Fig. 1c) generates its own charge that corresponds to stretching. The typical piezoelectric film sensor has few tenths μm thick piezoelectric PVDF polymer film with screen-printed silver ink electrodes, laminated to a polyester substrate [12].

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Fig. 1. Sensors and its signals: strain gauge (a, b), piezoelectric sensor (c, d).

Different principles of electrical signal formation cause different properties of the sensors. Table 1 shows a comparison of sensors properties in regards to application in OMA based SHM system. Table 1. Comparison of the strain gauge and piezoelectric sensors properties. Sensor type

Strain gauge

Piezoelectric film sensor

Signal origin

Change in electrical resistance Electric charge generation

Current source

Needed

No need

Balancing

Needed

No need

Ambient temperature

Affects

Affects

Influence of the stress state (static component) on the signal

The output signal changes in proportion to static and dynamic stress

The output signal changes in proportion to dynamic stress [13]

Use in the built-in SHM system

For systems evaluating static strains

For systems evaluating dynamic strains

Elastic clusters formation availability

Limited

Possible

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Comparing the two kinds of film sensors in terms of OMA applications, the advantages of the piezoelectric sensor are obvious. The piezo-film sensor generates electric charge, unlike a strain gauge, so it does not require a current source. To measure the dynamic component of the signal, the strain gauge must be balanced after installation, and also if the constant component changes. When forming a multipoint measurement system, the need to balance each sensor is the biggest disadvantage of strain gauges. The piezo film generates a dynamic component only so, it has the decisive advantage for OMA aimed multipoint measurements. Changes in temperature affect the conversion coefficients of both types of sensors. When the ambient temperature changes equally for the whole object, all sensors installed on it change the conversion coefficients in the same way. If the object is heated unevenly, the sensitivity of the sensors located in different zones changes differently. Therefore, applying both kinds of sensors, it is necessary to consider the temperature variation [14]. The static stress changes conversion coefficient of both sensor kinds but it affect differently the signals of sensors. When a stress-strained state of the structure changes, both constant and dynamic components of the strain gauge signal change (Fig. 1b). The piezo film signal in such case changes the amplitude (Fig. 1d) only [13] as it does not measure the constant component. Practically speaking, the lowest frequency that piezo film measures is about 0.001 Hz [15]. Both types of film sensors can be used for embedding in composite structures, forming so-called smart materials. For this purpose, the sensors and their wires may be glued to the surface of the base structure and then coated with a protective layer. Embedding in a structure is followed by nuances that must be taken into account. At first, the embedding of film sensors in the structure creates local stress concentrators [16], and this must be taken into account in the strength estimation of the “smart” structure. The second, when the protective layer hardens, the sensors undergo deformation and their conversion coefficients change, while strain gauges also get a constant component of the signal. At significant static strains, this property of strain gauges complicates the measurement procedure, because there is a need for additional balancing of them after embedding. It should be noted, the same variation in conversion coefficients of piezo films is insignificant for OMA based SHM because it uses a normalized scale of modal diagnostic parameters. Piezo film manufacturing technology offers an important advantage allowing the printing of sensor clusters. Such clusters, comprising dozens or hundreds of sensors along with printed leads, can be formed as an elastic unit with a complex shape that corresponds to the surface of an object. The cluster sections of the sensor network covering the interior or exterior of an object simplify automation and cost reduction of standard monitoring systems for typical constructions. Thus, film sensors provide signals, whose frequency structure is defined by vibrations of the object. The influence of static deformations and temperature affects the conversion coefficients of the sensors that must be taken into account in the diagnostic algorithms. Both types of sensors can be integrated into the structure. However, piezo films are preferable because they do not require an external current source or balancing and have advantages in further automation.

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3 Piezo Films Applicability The opportunities and rates of piezo films application depends on its compatibility with existing or planned monitoring systems. 3.1 Measurement Compatibility In terms of measurement, piezoelectric film sensors are compatible with typical equipment used for measurement of accelerometer signals. Such equipment is widely used both in laboratory conditions [17] and in on-board measurement systems [18]. Such compatibility means the OMA based SHM systems will be able to use existing or modified types of measuring equipment. Leading manufacturers specify for commercially available piezo films the conversion range (parameter g31- Piezo Stress constant) from 0.05 to 0.28 Vm/N (V/m/N/m2 ), depending on the sensor type [12]. Since there is no individual calibration and the conversion coefficient varies ± 20% [12], piezo-film sensors are not used for stress measurements. But for advance diagnostic techniques that use comparative estimates and parameters in normalized scale the piezo films are quite suitable. Thus, the Vibration Passport (VP) technology [19] does not measure vibration values in terms of velocity or acceleration. For operating machines the algorithms of Vibration Passport (VP) estimate condition of bearings, pumps or fan stages using the ratios of vibration components [19]. To monitor operating structures, the Modal Passport (MP) technology based on OMA [20] identifies defects evaluating the change of modal parameters in the normalized scale. Regardless of conversion differences between the sensors of the structure, the relationship between the signals remains unchanged until the state of the structure changes. Thus, the existing equipment is usable for signal measurement of piezo film sensors and ± 20% scatter between sensors does not hinder its application. 3.2 Methodical Compatibility The operating monitoring systems and those under development use vibration signals as input. To apply the most of its hardware and software the relation between the piezo film signal and vibrations must be defined. The piezo film signal corresponds to local strains (stretching) of surface fibers but the typical vibration transducer measures some physical parameter of the body oscillations. Let’s consider this relationship for structural monitoring tasks. The OMA methodology and software evaluates the modal parameters of the structure using its geometric model. The nodes layout in a model corresponds to spatial location of the sensors on the real structure. The signal of each sensor in the model is displayed as a vector, applied at the node (related to sensor’s location) and orthogonally directed to the surface area. The electric voltage of the piezofilm signal is proportional to the strains (surface deformations) caused by structural vibrations. The relation between them depends on what vibration parameter the piezo film signal reflects. To analyze above relation the beam oscillations model is considered. The film on beam surface occurs simultaneously

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in bending and stretching deformations. Stretching is characterized by the change of piezofilm length ε, and bending that characterized by the curvature 1/ρ of the beam neutral line (Fig. 2). The piezo film with a singular width and length X generates electric charge that determines the voltage U measured at the contacts. The momentary voltage U(t) is considered as the sum of both dynamic deformations.

Fig. 2. Model layout of the piezo film and vectors.

If to divide the length X of the piezo film into elementary sections dx and to integrate the elementary deformations εi (t), 1/ρi (t), the voltage U at the sensor contacts will correspond to the strain integral along the sensor length. X

X

U = kε ∫ εi (x)dx + kρ ∫[1/ρi (x)]dx,

(1)

where kε and kρ are the conversion coefficients of tensile strain and bending strain. In [11], under assumption of pure bending, it was justified that deformation εs of the elementary surface area of an oscillating solid body is proportional to the curvature of its neutral line ρ1 and the 2nd derivative of its normal displacement y. h 1 εs = yi  · ; = y , 2 ρ

(2)

2

where y = ddx2y – is the second derivative of the normal displacement of the elementary surface area along the length of the piezofilm; h/2 – distance from the neutral axis of the beam to the surface. Substituting (2) into the expression (1), we obtain that the momentary voltage U within the assumptions is proportional to the integral of elementary accelerations along the length X of the piezo film and to coefficients inherent to the curvature of the neutral line and stretching of the film.   X  X  h X  h U = kε ∫ yi (x)dx + kρ ∫ yi (x)dx = kε + kρ ∫ yi (x)dx. (3) 2 2 By integrating the expression (3) and noting the 1st derivative of normal displacement  yi (x) is the normal velocity vX integrated along the film, we have.     h h  U = kε + kρ yi (x) ≈ kε + kρ vX . (4) 2 2

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As normal velocity of oscillating body is time variable   h U (t) ≈ kε + kρ vX (t). 2

(5)

Expression (5) shows the voltage at the sensor contacts depends on the velocity normal to  averaged over the length of the sensor. The proportionality coefficient  the surface kε 2h + kρ reflects the influence of conversion coefficients for tensile strain kε (taking into account the distance h to a neutral line of the structure) and bending strain kρ . Thus, the amplitude of the signal generated by the piezo-film (within the made assumptions) can be considered as proportional to velocity in the point of film installation. The vector of vibration velocity V (Fig. 2) is directed along the perpendicular to the surface area, covered by the film. The signal amplitude depends of conversion coefficients and is subject of influencing factors, like temperature, constant stress, etc. The vector interpretation introduced in this way allows using the piezo films with the measurement units and software products applicable for vibration signals. For instance, the commercial software Vibropassport™ can use piezo films for vibration diagnostics of rotor machines and the software platform ARTeMIS™ - for structural monitoring based on OMA methods.

4 Experimental Verification To verify the relationship between the signals of dynamically stretched piezo film and vibrations, the study was carried out using a special experimental stand (Fig. 3). The stand contains the testing head that converts vertical vibrations (actuated by the shaker) to stretching deformations of the piezo film. The testing head provides only tensile deformations of the film instead of bending ones used in [13].

Fig. 3. Experimental stand for the piezoelectric film sensor properties studying.

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The piezo film has pre-stretching when installed in clamps to avoid its stability loss in operation. The head comprises a flexible plate (1) on which two clamps (2) are on opposite sides of the rod (3) attachment. The sensitive element of the piezoelectric sensor (5) is fixed in the clamps, which act as inertial masses. In the reciprocating motion of the rod, the flexible plate performs bending oscillations under the influence of inertial masses (clamps and accelerometers). In vibrational motion the clamps move in the vertical direction Z and turn around the Y axis, stretching or compressing the film. When the rod oscillates, the distance between the clamps changes cyclically, resulting in periodically varying deformations of the piezo film. The spatial vectors of clamps motion are measured by 3-axial accelerometers 6 installed next to the clamps. The X axis projection of clamps motion mainly determines deformations of the piezo film. The small asymmetry and the difference of the inertial masses of the right and left parts of the head causes the difference of the vertical motions (along Z axis) of the clamps and small additional deformations of the film. The frequency vibration (159.9 Hz) of the shaker defines the frequency of tested film signal. The multichannel input unit 3053B-120 (B&K) measure signals of the piezo film and the accelerometers. The LabShop software modules allow recording of measured signals and the data development. The data processing stage considers two basic stages: the integration of accelerometer signals and the enhancement of all signals. Integration of the time variable acceleration signal provides the velocity and double integration – the displacement of the clamps. The signal enhancement considers the synchronous averaging procedure of vibration and strain data and reduces measurement errors. The time interval of signal enhancement corresponds to the period of oscillation. With time recording length 60 s and above mentioned frequency, the applied data processing technique provides more than 9,500 averages. The result of data processing is the phase sweep of the signal amplitude over a period of one oscillation. To analyze the relation between the piezo film stretch and the clamps motion the parameters of relative movement between clamps (displacement, velocity and acceleration) are calculated. The phase sweep of film signal characterizing the stretch of piezo film is compared with the sweep of the vibration parameter. The mutual displacement of the clamps is the phase variable distance and the computation procedure of this distance is described in Eqs. (6, 7, 8). The same procedure is used for computation of velocity and acceleration. The distance between clamps S12 (ϕ) is determined as the sum of displacements measured by both accelerometers along X axis and the additional displacement along Z z (ϕ) arising from the difference of clamps displacement. axis S12 z S12 (ϕ) = S1x (ϕ) + S2x (ϕ) + S12 (ϕ),

(6)

     z S12 (ϕ) = S2z (ϕ) − S1z (ϕ) ∗ sin atan S2z (ϕ) − S1z (ϕ)/La ∗ Lp /2La

(7)

where

S1x (ϕ), S2x (ϕ) – values of X displacement of accelerometers 1 and 2 in phase; S2z (ϕ), S1z (ϕ) – values of Z-axis displacement of accelerometers 1 and 2 in phase ϕ; La – distance between accelerometers; Lp –length of the active part of the piezofilm.

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Substituting (7) into (6), the resulting expression for the mutual displacement between clamps is      S12 (ϕ) = S1x (ϕ) + S2x (ϕ) + S2z (ϕ) − S1z (ϕ) ∗ sin atan S2z (ϕ) − S1z (ϕ)/La ∗ Lp /2La (8) The mutual velocity and acceleration of the clamps were calculated in the same way. The sweep of the piezo film U (ϕ) signal was compared with sweeps of mutual displacement, velocity and acceleration of the clamps S12 (ϕ). Comparing these sweeps its similarity and the phase matching were in the interest.

5 Discussions The samples of the signal sweeps computed from experimental data are presented in Fig. 4. The sweep of phase variable distance between the clamps, which varies in the range of (−2… + 2.2 μm) within one period, is presented on Fig. 4a.

Fig. 4. Sweep over a period: mutual displacement of the clamps (a), vibration velocity and piezofilm voltage comparison (b).

This variable distance corresponds to the stretch variation of the piezo film within the oscillation period. The sweep of the piezo film signal was compared with the sweeps of the distance (mutual displacement), as well as the sweeps of its velocity and acceleration. As it evident from Fig. 4b, the piezo film signal (red) coincides in phase with the velocity of mutual displacement of the clamps (blue). This matching confirms that the signal of vibrating film is closely related to the vibration velocity. The sweeps of other vibration parameters are shifted in relation to the sweep of piezo film signal. The sweep of the distance was a quarter of a period behind the piezo signal sweep, and the sweep of acceleration was a quarter ahead of it. Thus, the result of the experimental research confirmed the assumption made in Sect. 3.2 about the relationship between the signal of the strained piezo film and the vibration velocity. Alongside the main objective, this research contributed to the development of the technique for calibrating piezo films to tensile strains. The technique considers the measurement of both the piezo film signal and the vibration velocity as it was described

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in Sect. 4. For example, in the given experiment the strain-to-vibration rate (conversion coefficient) was 1.05 V/mms−1 . For fixed excitation frequency and impedance of measurement channel, a normalized piezo-film conversion factor can be calculated that indicates the voltage generated per 1% of film stretch. For instance, the calculated value of 0.0035 V/% means that for one percent of the film extension the piezo film generates 0.0035 V.

6 Conclusions The study has demonstrated the possibility of using piezo electric film as the sensor for vibration diagnostic purposes. The possibility of replacing accelerometers with piezoelectric film sensors due to the relationship between the piezo-film signal and the vibration parameters is justified. In depends of application the relation to vibration velocity or the curvature of the neutral line (of the solid body) may be used. The matching between piezo film signal and vibration velocity was experimentally verified. The defined relation allows to apply piezo films for most typical tasks in vibration diagnostics of operating machines, like vibration source identification or for balancing. The low-cost sensors make it possible to equip mass-produced machines with inexpensive monitoring systems arising safety and reducing operating costs. The relation to the curvature is important for SHM applications, where the number of required sensors can be counted in hundreds. Simple and cheap sensors allow expanding of monitoring systems application for serial operating objects with high maintenance costs, like aircrafts or wind generators. The technique of piezo films calibration was developed that expands areas of its application for vibration monitoring and diagnostics. Acknowledgement. This research was funded by the European Regional Development Fund Project No. 1.1.1.1/20/A/016 “A prototype of typical structural health monitoring system of operating objects for condition based maintenance.”

References 1. Speckmann, H.: Structural health monitoring with smart sensors approach to a new NDI Method. In: Proceedings SPIE Conference on Smart Structures and Materials and NDE for Health Monitoring and Diagnostics, San Diego, California, pp. 17–21, March (2002) 2. Ihn, J-B., Chang, F-K., Speckmann, H.: Built-in diagnostics for monitoring crack growth in aircraft structures. In: Proceedings of 4th Internat Conference on Damage Assessment of Structures (DAMAS) Cardiff/Wales, Key Engineering Materials, vols. 204–205, pp. 299–308 Trans Tech Publications (2001) 3. Speckmann, H., Henrich, R.: Structural health monitoring (SHM) – overview on technologies under development. In: Proceedings of the World Conference on NDT, Montreal (2004) 4. Jelicic, G., Schwochow, J., Govers, Y., Sinske, J., Buchbach, R., Springer, J.: Online monitoring of aircraft modal parameters during flight test based on permanent output-only modal analysis. In: 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (2017). https://doi.org/10.2514/6.2017-1825

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5. Tiboni, M., Remino, C., Bussola, R., Amici, C.: A review on vibration-based condition monitoring of rotating machinery. Appl. Sci. 12(3), 972 (2022). https://doi.org/10.3390/app120 30972 6. Structural Vibration Solutions A/S. Homepage: https://svibs.com/. ARTeMIS Modal Structural Vibration Solutions. Accessed 16 Jan 2022 7. Chen, D., Wang, X., Zhao, J.: Aircraft maintenance decision system based on real-time condition monitoring. Procedia Eng. 29, 765–769 (2012). https://doi.org/10.1016/j.proeng.2012. 01.038 8. Eltabach, M., Sieg-Zieba, S., Song, G., Li, Z., Bellemain, P., et al.: Vibration condition monitoring in a paper industrial plant: supreme project. In: 13th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Paris, France. hal-01399036, October 2016 9. Mauricio, A., Qi, J., Gryllias, K.: Vibration-based condition monitoring of wind turbine gearboxes based on cyclostationary analysis. ASME. J. Eng. Gas Turbines Power. March 2019 141(3), 031026 (2018). https://doi.org/10.1115/1.4041114 10. Kostrzewski, M., Melnik, R.: Condition monitoring of rail transport systems: a bibliometric performance analysis and systematic literature review. Sensors (Basel, Switz.) 21(14), 4710 (2021). https://doi.org/10.3390/s21144710 11. Mironov, A., Priklonskiy, A., Mironovs, D., Doronkin, P.: Application of deformation sensors for structural health monitoring of transport vehicleslecture notes in networks and systems, vol. 117, pp. 162-175 (2020) 12. TE Connectivity Homepage. https://www.te.com/usa-en/product-CAT-PFS0006.html. Accessed 21 June 2022 13. Gusarov, B., Gusarova, E., Viala, B., Gimeno, L., Cugat, O.: PVDF piezoelectric voltage coefficient in situ measurements as a function of applied stress. J. Appl. Polym. Sci. 133 (2016). https://doi.org/10.1002/APP.43248 14. Sherman, J.D., Elloian, J., Jadwiszczak, J., Shepard, K.L.: On the temperature dependence of the piezoelectric response of prepoled poly(vinylidene fluoride) films. https://doi.org/10. 1021/acsapm.0c00902 15. Piezo Film Sensors Technical Manual, Images SI Inc. Sensor Products Division, Staten Island, NY 10312. Images SI Inc Homepage. http://www.imagesco.com 16. Konka, H.P.: Embedded piezoelectric fiber composite sensors for applications in composite structures. LSU doctoral dissertations, 1983 (2011). https://digitalcommons.lsu.edu/gradsc hool_dissertations/1983 17. HOTTINGER BRÜEL & KJÆR A/S Homepage. https://www.bksv.com/en/instruments/. daq-data-acquisition/lan-xi-daq-system. Accessed 21 June 2022 18. Meggitt PLC Homepage. https://www.meggitt.com/products-services/engine-health-and-vib ration-monitoring. Accessed 21 June 2022 19. Mironov, A., Doronkin, P.: Advanced vibration diagnostic system for aviation jet engine. In: Kabashkin, I., et al., (eds.) Reliability and Statistics in Transportation and Communication. RelStat 2021. LNCS, vol. 410, pp.171–185. Springer, Cham. (2022). https://doi.org/10.1007/ 978-3-030-96196-1_16 20. Mironov, A., Doronkin, P.: The demonstrator of helicopter structural health monitoring technique. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2020. LNCS, vol. 195, pp. 213–226. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68476-1_20

Autonomous Mobile Robot Study in the Context of Maintenance 4.0 Robert Giel

and Alicja D˛abrowska(B)

Wroclaw University of Science and Technology, Wroclaw, Poland [email protected]

Abstract. The popularization of technical objects’ autonomy in various transport branches is reflected in research on autonomous cars, drones, airplanes, ships, trains or mobile robots. Studies on autonomous mobile robots (AMR) mainly focus on navigation, localization, path planning and obstacle detection problems. These are the challenges of performing the defined tasks, i.e., reliable operation. In addition to these challenges, maintenance is an important issue rarely considered in the literature. One can find multiple papers concerning battery management, but there is definitely a lack of papers dedicated to failure consideration in the form of predictive maintenance applications. AMRs are highly complex objects with a serial reliability structure. Corrective and preventive maintenance strategies, used for many years in various objects and systems, are insufficient for such objects. New possibilities for maintenance are provided by the dynamic development of the Industry 4.0 concept. Along with it, the term Maintenance 4.0 has emerged. It assumes mainly the prediction of failures using sensors and machine learning algorithms. Considering AMR in the context of Maintenance 4.0 represents a new challenge not addressed in the literature to this time. The objective of this paper is twofold. Firstly, we would like to present a brief summary of the predictive maintenance methods available in the literature. Secondly, we would like to consider the AMR study for Maintenance 4.0 purposes. A general scheme of predictive maintenance method for AMR is introduced. Initial steps were taken on the example of chosen AMR. Keywords: Maintenance 4.0 · Autonomous mobile robot · Predictive maintenance · Signal processing

1 Introduction The rapid development of autonomous transport has been observed over the past few years. Research on autonomous drones, cars, ships, trains or mobile robots is ongoing. Each transport branch in the context of implemented autonomy generates similar (at the high level of generality) safety and reliability challenges. However, these challenges must be considered with reference to individual modes of transport due to the specifics of their operational conditions. Our research focuses on autonomous mobile robots (AMR) characterized by the ability to work without human involvement and adaptation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 213–222, 2023. https://doi.org/10.1007/978-3-031-26655-3_19

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of performed tasks to changing environmental conditions (e.g., reacting to dynamic obstacles). AMRs can be identified as one of the most popular solutions for autonomous internal transport. It is confirmed by, among others, from a commercial point of view, the presence of hundreds of suppliers for this product type [1] and from a scientific point of view – numerous publications (over one thousand publications in the Web of Science database and almost forty thousand positions in Google Scholar database). In the scientific field, the vast majority of recent studies focus on operational issues. Therefore, many solutions can be found related to problems such as navigation [2, 3], localization [4], path planning [5] or obstacle detection [6]. Meanwhile, a crucial group of issues related to AMR maintenance is rarely considered. One can find a few works devoted to battery management [7–9]. Furthermore, some studies address the challenge of fault detection in the form of fault tolerance principles integration into AMR’s real-time control architecture [10], self-diagnosis AMR internal state sensory system in the context of motion planning [11] or fault detection and isolation method for AMR’s fault-tolerant navigation. However, there is a noticeable lack of papers devoted to the maintenance strategies of AMR. There are performance requirements on the one hand and cost reduction necessities on the other. All of this is reflected in the efforts to maximize fault-free operation time and eliminate the unexpected failures of AMR. This determines the need to implement maintenance solutions that give better results than the corrective and preventive maintenance strategies used so far for many years in different systems. The latest solutions in this area have been developed in line with the development of the Industry 4.0 concept under the separate term Maintenance 4.0. This new maintenance paradigm proposes a set of techniques for monitoring the condition of technical objects. It is aimed at predicting failures through real-time analytics and machine learning [12]. Thus, Industry 4.0 technologies are applied here in the design of self-learning systems for predicting failures, diagnosing and triggering maintenance actions [13]. To sum up, Maintenance 4.0 has a two-fold role. On the one side, it can estimate the probability of correct operation up to a certain point in time. On the other side, it can estimate the so-called RUL (Remaining Useful Life) until failure occurs [14]. Latest summaries of predictive maintenance papers can be found in e.g. [15–18]. These and similar studies indicate that predictive maintenance methods available in the literature are dedicated to manufacturing equipment, objects from the automotive sector, power systems, and the oil and gas industry. More often, methods are dedicated to selected elements of complex technical objects, such as gears, batteries or motors. In the field of AMR research for industrial applications, it is difficult to find methods that comply with the Maintenance 4.0 concept. This indicates a research gap. However, in the field of robotics, one can find a few studies concerning predictive maintenance. Table 1 summarizes selected papers from the robotics domain whose results could be used in developing a predictive maintenance method for AMR. There are few studies available in the literature on predictive maintenance in the field of robotics. Some research on industrial robots and autonomous vehicles can be found among them. However, it is challenging to apply these results to AMR. The most comparable studies to ours involve a cleaning robot [19] and a wafer transport robot

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[23]. Unfortunately, they are based on only one type of signals, respectively vibration and acceleration. Thus, they require significant development. Table 1. Summary of selected predictive maintenance studies from robotics domain. Ref

Purpose of the study

Technical object

Sensors

Input data

Output data

[19] Identification of performance degradation and operational safety

Autonomous mobile cleaning robot

IMU

Vibration signals

Vibration class prediction

[20] Vehicle sensors monitoring in terms of fault detection, isolation, identification and prediction

A multi-sensor system, e.g. in an autonomous car

Accelerator pedal, steering wheel angle and brake pressure

Accelerator pedal sensor deflection in %, steering wheel angle, brake pressure

Fault class prediction, sensor health forecast

[21] Identification of robot’s health degradation

Industrial robot Robot’s built-in sensors

Resolver angle, speed, torque

Fault class detection, RUL

Lateral, longitudinal, angular, and curvature residues, RTK and number of satellites

Fault class prediction, predicted future states over a predefined time horizon

Acceleration

State type prediction (one normal and three abnormal)

[22] Fault detection, Autonomous diagnosis and vehicle (car) prognosis of faults related with the lateral and longitudinal controllers, the GPS sensor and localization algorithm [23] Diagnosis of abnormal robot state

Lateral and longitudinal controllers, built-in sensors

Wafer transport Acceleration robot sensor

The literature review showed a lack of a complex predictive maintenance method dedicated to AMR for industrial applications. One can find methods developed for individual AMR components or based on only one signal type, but this is insufficient. It is necessary to consider such objects comprehensively. Therefore, our efforts are aimed at developing a method for predictive maintenance of AMR that will fill the identified research gap. The paper is organized as follows. Section 1 places AMR maintenance in the context of Industry 4.0 and provides a brief summary of predictive maintenance studies. In Sect. 2

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we have generally described the proposed predictive maintenance method for AMRs. Section 3 shows initial actions consideration based on chosen AMR. The last section summarizes the entire paper and provides directions for future work.

2 Predictive Maintenance Method for AMR Regarding the identified research gap, our current research aims to develop a four-stage method for predictive maintenance of AMR (see Fig. 1). I. INITIAL ACTIONS AMR characterization and decomposition AMR elements/subsystems analysis sensor selection for AMR monitoring

II. DATA COLLECTION measurement system design sensor placement on the AMR measurement system final testing sensors' signals acquisition

III. MODEL DEVELOPMENT data preprocessing feature extraction ML algorithms implementation model verification

IV. MAINTENANCE ACTIONS PLAN Fig. 1. A general scheme of the predictive maintenance method for AMR.

One should note that the presented method scheme is characterized by a high level of generality and represents a preliminary concept. Moreover, this paper focuses on Stage I. Hence it is most extensively discussed and considered with the example of the selected AMR in Sect. 3. Other stages will be the subject of further publications. Stage I – Initial Actions Initial actions focus on the characterization and decomposition of the AMR. This is aimed at the later analysis of separated subsystems and elements, which will help reduce the number of monitoring points. Initial actions also include the selection of sensors. Table 2 shows the recommendations and proposed supporting methods for performing the specified initial actions.

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Table 2. Recommendations and supporting methods for the initial actions of Stage I. Initial action

Recommendations

Supporting methods

AMR characterization

• Providing technical • Overview of technical specifications (max speed, documentation weight, dimensions, charging • Brainstorming, interviews, surveys and other similar time, max load) • Indication of the application methods employing expert area with the characteristics of opinion the environmental conditions (temperature, humidity, human presence, unevenness of the surface) • Determination of operation and maintenance states

AMR decomposition

• Performing multiple decompositions with the distinction of subsystems and their elements • Formulating assumptions about the number of decomposition levels

• Overview of technical documentation • Brainstorming, interviews, surveys and other similar methods employing expert opinion

AMR’s elements analysis • Historical data analysis (if available) • Determination of reliability structure • Determination of evaluation criteria for elements (e.g., failure frequency, criticality, maintenance costs)

• MCDM (multi-criteria decision making methods) e.g., FMEA, AHP, TOPSIS

Sensor selection

• Literature review • Overview of sensors’ technical specifications • MCDM

• Selection of parameters for monitoring • Determine the number and types of sensors for each of the listed parameters

For the first two types of actions, relying on technical documentation and using expert knowledge is recommended. For the last two, on the other hand, it is advisable to use MCDM (multi-criteria decision making) methods because of the need for multi-criteria evaluation. Stage II – Data Collection Data collection is a key step that determines the correct performance of the predictive model being developed in the next stage. In the data collection stage, except for the final process of acquiring signals from the sensors, activities should be undertaken in the form of: designing the measurement system (concept, construction, testing the correctness of operation without the participation of AMR), placing sensors on selected elements of AMR and conducting performance tests of data collection. When

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designing a measurement system, it is worth paying special attention to the frequency of acquiring and saving data, as well as the easiness of later access to it. Stage III – Model Development A predictive model should be developed based on the data acquired in Stage II. However, the raw data from Stage II is unsuitable for direct use in the model. For this reason, the first action is to prepare them for use in the model, that is, to perform the so-called data preprocessing. The issues to be considered here are mainly missing and/or outlier data, noise, time-domain preprocessing, frequencydomain preprocessing, and time-frequency preprocessing. From the preprocessed data, depending on the domain considered, it is possible to extract features such as mean in the time-domain, peak frequencies in the frequency domain or spectral entropy in the time-frequency domain. Extracted features are input to the machine learning model. The use of machine learning algorithms (these will be classifier algorithms in this case) enables us to finally detect the AMR abnormal state and its cause on the one hand and predict the RUL the other. The verified model is ready to be implemented as the basis for maintenance decisions. Stage IV – Maintenance Actions Plan Output data of the developed model form the basis for planning maintenance activities. It may be necessary to take maintenance actions immediately or to implement them after a predetermined time. This time is based on the Remaining Useful Life estimated by the model.

3 Case Study – Stage I Consideration The object we have chosen for the study is AMR, which is the result of a completed project co-financed by the National Centre for Research and Development entitled ‘Robotic system of intelligent internal transport’. Its characteristics are included in Table 3. Table 3. AMR characterization. Attribute group

Description

Technical specification

Dimensions: 1,200 × 800 × 320 mm Weight: 350 kg Max speed: 1.1 m/s Max load: 1,000 kg Full load operating time: 5 h Charging time: 3 h

Application area

Indoor only, closed warehouse Humidity: 10–95% Temperature: 5–40 °C Allowed workspace shared with people (continued)

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Table 3. (continued) Attribute group

Description

Operation states

Driving empty Driving loaded Loading Unloading Task waiting (during planned operation time) Launch waiting

Maintenance states

Awaiting planned maintenance actions (before failure) Awaiting unplanned maintenance actions (after failure) Charging Repair

The mobile robot under study is a wheeled robot with the ability to pick up a europallet directly from the ground using retractable forks. Six operation states and four maintenance states have been defined for it. A simplified two-level decomposition of the robot is shown in Fig. 2. Six key subsystems are distinguished at the first level of decomposition, and elements (second level of decomposition) are indicated for each of them. Some subsystems have been omitted and not all elements have been indicated within subsystems for simplification purposes. AMR

CONTROL computer

POWER

DRIVETRAIN

COMMUNICATION

LIFTING

battery LiFePO4 24V/210Ah

4x mecanum wheels other elements

Wi-Fi module

forks other

SENSING exteroceptive sensors proprioceptive sensors

Fig. 2. Simplified two-level AMR decomposition.

It is recommended to start the AMR element analysis with an overview of historical data. Unfortunately, this type of data is very difficult to obtain. The only information included in AMR’s technical specifications is about the length of continuous operation time without a battery charge. For this reason, the analysis of AMR elements should rely heavily on expert opinion. The mentioned experts must be an interdisciplinary team from the fields of robotics, electronics, construction and reliability engineering. The listed fields are the necessary minimum. In addition, experts should be characterized by several years of working experience in their field. Beyond expert opinion, a literature review can be conducted supportively. Limitations on the length of the article make it impossible to present the entire analysis of AMR elements that has been carried out. For this reason, we only briefly

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present the results of the analysis. An integral part of the element analysis is determining the AMR reliability structure. From the separated subsystems’ perspective (see Fig. 2), we are dealing with a serial reliability structure. Failure of any of the subsystems results in the AMR going into an inoperable state. Additionally, expert opinions indicate that special attention should be paid to the following subsystems and/or elements of the AMR under study: lifting subsystem (for k position and motor), drivetrain subsystem (wheels’ motors), AMR’s body, AMR interior and power subsystem (electrical power lines). The last step of the initial actions includes the sensor selection. A framework for this matter can be found in e.g. [24]. Based on the literature review and expert opinions, monitoring such signals as vibration, temperature, noise, electric current and pressure force was necessary. Table 4 summarizes the results of the performed sensor selection process. Table 4. Summary of the performed sensor selection process. Parameter

Sensor

Number of sensors

AMR’s element

Temperature

Temperature sensor

Six

Drivetrain and lifting subsystems

Vibrations

IMU

One

AMR’s body, lifting subsystem, AMR interior

Noise

Sound sensor

One

Lifting subsystem, AMR interior

Fork bend

Distance sensor

Two (one per fork)

Lifting subsystem

Electric current Electric current sensor Four Pressure force

Tensometer

Power subsystem

Two (four for forks and Lifting subsystem and four for AMR top) upper body

The parameters presented in the table will have to be referenced (in further steps of the method beyond the scope of the article) to each of the defined states of operation and maintenance. It should be noted here that this is a preliminary selection of sensors. Their integration in the form of a measurement system and their placement on the AMR will be the subject of stage II of the method (see Fig. 1).

4 Summary and Conclusions The article briefly summarizes the current work on Maintenance 4.0 and presents the initial concept of the predictive maintenance method for AMR. In the field of robotics, predictive maintenance solutions focus on industrial or cleaning robots as well as autonomous vehicles. Dedicated AMR solutions are lacking. The only such work on a wafer transport robot is based on using only one type of signal, which is insufficient. Based on the identified research gap, our study is the first attempt in the literature to consider AMR in the context of Maintenance 4.0 comprehensively. In our method, we

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propose four stages, i.e., initiating actions, data collection, model development and maintenance plan formulation. The article focuses on the first stage. Thus, reliability theory tools and methods are applied here only in the analysis of AMR elements (e.g., the use of FMEA). Their more extensive use will occur in the method’s further stages. Future steps will focus on detailed consideration of stages two, three and four of the method under development. Thus, different signal processing methods and various machine learning algorithms will be considered in particular. Ultimately, it will be possible to detect the abnormal state of the AMR and estimate the RUL.

References 1. Fragapane, G., de Koster, R., Sgarbossa, F., Strandhagen, J.O.: Planning and control of autonomous mobile robots for intralogistics: literature review and research agenda. Eur. J. Oper. Res. 294(2), 405–426 (2021) 2. Gul, F., Rahiman, W., Nazli Alhady, S.S.: A comprehensive study for robot navigation techniques. Cogent Eng. 6(1), 1632046 (2019) 3. Tzafestas, S.G.: Mobile robot control and navigation: a global overview. J. Intell. Rob. Syst. 91(1), 35–58 (2018). https://doi.org/10.1007/s10846-018-0805-9 4. Panigrahi, P.K., Bisoy, S.K.: Localization strategies for autonomous mobile robots: a review. J. King Saud Univ. – Comput. Inf. Sci. 34(8), 6019–6039 (2021) 5. Sánchez-Ibáñez, J.R., Pérez-del-Pulgar, C.J., García-Cerezo, A.: Path planning for autonomous mobile robots: a review. Sensors 21(7898), 1–29 (2021) 6. Fraga-Lamas, P., Ramos, L., Mondéjar-Guerra, V., Fernández-Caramés, T.M.: A review on IoT deep learning UAV systems for autonomous obstacle detection and collision avoidance. Remote Sens. 11(18), 2144 (2019) 7. Rao, M.V.S., Shivakumar, M.: Overview of battery monitoring and recharging of autonomous mobile robot. Int. J. Recent Innov. Trends Comput. Commun. 6(5), 174–179 (2018) 8. Tomy, M., Lacerda, B., Hawes, N., Wyatt, J.L.: Battery charge scheduling in long-life autonomous mobile robots via multi-objective decision making under uncertainty. Robot. Auton. Syst. 133, 2–7 (2020) 9. Sreenivas Rao, M.V., Shivakumar, M.: IR based auto-recharging system for autonomous mobile robot. J. Robot. Control (JRC) 2(4), 244–251 (2021) 10. Crestani, D., Godary-Dejean, K., Lapierre, L.: Enhancing fault tolerance of autonomous mobile robots. Robot. Auton. Syst. 68, 140–155 (2015) 11. Kawabata, K., Okina, S., Fujii, T., Asama, H.: A system for self-diagnosis of an autonomous mobile robot using an internal state sensory system: Fault detection and coping with the internal condition. Adv. Robot. 17(9), 925–950 (2003) 12. Jasiulewicz-Kaczmarek, M., Legutko, S., Kluk, P.: Maintenance 4.0 technologies - new opportunities for sustainability driven maintenance. Manag. Prod. Eng. Rev. 11(2), 74–87 (2020) 13. Kumar, U., Galar, D.: Maintenance in the Era of industry 4.0: issues and challenges. In: Kapur, P.K., Kumar, U., Verma, A.K. (eds.) Quality, IT and Business Operations. SPBE, pp. 231–250. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5577-5_19 14. Jasiulewicz-Kaczmarek, M., Gola, A.: Maintenance 4.0 technologies for sustainable manufacturing - an overview. IFAC-PapersOnLine 52(10), 91–96 (2019) 15. Arena, F., Collotta, M., Luca, L., Ruggieri, M., Termine, F.G.: Predictive maintenance in the automotive sector: a literature review. Math. Comput. Appl. 27(1), 2 (2021)

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16. Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcalá, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019) 17. Zonta, T., da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P.: Predictive maintenance in the industry 4.0: a systematic literature review. Comput. Ind. Eng. 150, 106889 (2020) 18. Pech, M., Vrchota, J., Bednáˇr, J.: Predictive maintenance and intelligent sensors in smart factory: review. Sensors 21(4), 1–39 (2021) 19. Pookkuttath, S., Elara, M.R., Sivanantham, V., Ramalingam, B.: Ai-enabled predictive maintenance framework for autonomous mobile cleaning robots. Sensors 22(1), 13 (2022) 20. Safavi, S., Safavi, M.A., Hamid, H., Fallah, S.: Multi-sensor fault detection, identification, isolation and health forecasting for autonomous vehicles. Sensors 21(7), 1–23 (2021) 21. Izagirre, U., Andonegui, I., Landa-Torres, I., Zurutuza, U.: A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines. Rob. Comput. Integr. Manuf. 74(2021), 102287 (2022) 22. Gomes, I.P., Wolf, D.F.: Health monitoring system for autonomous vehicles using dynamic Bayesian networks for diagnosis and prognosis. J. Intell. Rob. Syst. 101(1), 1–21 (2020). https://doi.org/10.1007/s10846-020-01293-y 23. Yoo, J.H., Park, Y.K., Han, S.S.: Predictive maintenance system for wafer transport robot using k-means algorithm and neural network model. Electronics 11(9), 1324 (2022) 24. Kulkarni, A., Terpenny, J., Prabhu, V.: Sensor selection framework for designing fault diagnostics system. Sensors 21(19), 1–17 (2021)

Improvement of the Feature Selection Method for Network Attacks Classification Using Machine Learning in Digital Forensics Boriss Misnevs(B)

, Aleksandr Krivchenkov , and Alexander Grakovski

Transport and Telecommunication Institute, Lomonosova str. 1, Riga, Latvia {bfm,aak,avg}@tsi.lv

Abstract. The study considers transformations of the original data set obtained by capturing network traffic for effective detection of network anomalies by machine learning methods. The field of research is the selection of a reduced subset of the “features” of the data set but providing the construction of an effective classifier. Several modifications of the previously presented AHP-like feature selection method were tested. This algorithm allows us to get the minimum necessary set of features related to network attack events. The accuracy of detecting attacks using the proposed method and its modifications has been verified in a series of numerical experiments. Keywords: Network attack detection · Machine learning · Feature selection · AHP-like method

1 Introduction The article discusses several security mechanisms for detecting cyber attacks in digital forensics. They are usually classified as either anomaly-based or signature-based. Signature-based methods look for patterns (signatures) in network traffic data and generate alarms if the patterns match known attack behavior. Anomaly-based methods evaluate the normal behavior of the system and generate alarms when the deviation from the norm exceeds a certain threshold. In practice, most used intrusion detection/prevention systems (IDS/IPS) are based on signatures (for example, SNORT [1]). However, most of the research in recent years has focused on anomaly-oriented systems. Their review can be found in [2]. Four important types of anomaly detection methods are clustering, classification, information theory, and statistical analysis. In this article, we will focus on machine learning and deep learning (ML/DL) methods for network attack detection. These methods are compatible with the anomaly-based approach [3]. Anomaly-based attack diagnostics are handled by a supervised ML/DL learning approach that implements a classification problem. The training data consists of sets of samples (observations) for training the algorithm. Each sample consists of an input © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 223–231, 2023. https://doi.org/10.1007/978-3-031-26655-3_20

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object, usually a vector of variables (also called functions), and the desired output object (a vector of labels). Classification algorithms process the training data and generate a general rule that can be used to match (label) new input vectors. Datasets for further classification in IDS are formed based on the capture of network traffic. This raw packet capture creates large files, and therefore they are processed accordingly to reduce the size of the data. At this stage (Fig. 1), features are extracted and calculated (“Pcap” and “CSV” are file extensions; “Argus” and “Bro-IDS” are utility names). Tables are created with observations (records) in rows and features in columns. After this step, the size and complexity of the data are greatly reduced.

Fig. 1. Features creation and data processing for ML/DL methods application in IDS [2].

As you might expect, extremely high-dimensional data created problems for ML/DL learning methods. A learning model with many features tends to be overfitted. This also leads to performance degradation. To solve this dimensional problem, reduction methods have been considered, which is an important line of research in the field of ML/DL. The feature selection approach is a widely used data preparation and dimensionality reduction technique. It aims to select a smaller subset of relevant features from the original ones according to certain evaluation criteria. This generally results in lower computational costs, higher training accuracy for classification, and better interpretability of the results. In our article [2] the novel method for feature selection AHP-like was proposed and tested for use in the artificial neural network (ANN) classifier method. In the presented research, we discuss the attempts to improve the efficiency of classifications by a combination of important features, saving the reduced number of them. For the estimations of features reduction influence on classification efficiency, the supervised - classification methods k-NN (ML method) and ANN (DL method) were applied to well-known public data sets NSL–KDD, and UNSW-NB15 [5] (see Table 4).

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2 Features Selection and Their Number Reduction In practice, many feature selection methods have been developed, classified, and used. A survey of this problem is considered in [2] and [4]. As shown in [4], there is no best feature selection method, no best set of input variables, and no universally best machine learning algorithm. Therefore, in practice, it remains to find out what works best for a particular problem through careful experimentation. The AHP-like technique for identifying the most important features proposed and described in [2] makes it possible to structure this process by a hierarchy of features construction according to the degree of their connection with DoS attack events (Fig. 2 for NSL-KDD).

Fig. 2. Hierarchy of features for the NSL-KDD dataset according to the AHP importance weights estimate [2].

Building a features hierarchy it is opened the possibility to reduce the number of features to their only “important” list. The lists of “most meaningful” features for “DoS” attacks records and “Normal” records for the next binary classifying for both data set NSL-KDD and UNSW-NB15 are presented in Table 1.

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Table 1. The most important features for detecting DoS attacks and “Normal” records in the NSL-KDD and UNSW-NB15 datasets received the AHP-like method [2].

Status NORMAL

DoS Attack

NSL-KDD No

Features

UNSW-NB15

Correlation AHP weight

Features

Correlation AHP weight

1 ‘same_srv_rate’

0.8082

0.0623

'udp_vec'

0.1507

2 ‘dst_host_count’

0.4768

0.0458

'sttl'

0.0990

0.0376

3 ‘udp_vec’ 4 ‘tcp_vec’

0.2330 0.2018

0.0302 0.0277

'ct_dst_ltm' 'dur'

0.0887 0.0516

0.0343 0.0213

NSL-KDD No

Features

1 2 3 4 5

'same_srv_rate' 'srv_rerror_rate' 'dst_host_diff_srv_rate' 'udp_vec' 'ip_vec'

UNSW-NB15

Correlation AHP weight

0.7519 0.2535 0.2429 0.2172 0.2005

0.0526

0.0589 0.0301 0.0294 0.0275 0.0263

Features

'sttl' 'ct_dst_sport_ltm' 'dns_vec' 'FIN_vec' 'dtcpb'

Correlation AHP weight

0.5042 0.3937 0.3639 0.3174 0.2829

0.0509 0.0424 0.0400 0.0361 0.0331

Table 2. Ranking features using Correlation and Information Gain coefficients. The most important features found by the AHP-like method. Ranked features for DoS due to correlation coefficients

Ranked features for Normal due to correlation coefficients

Ranked features for Normal due to Information Gain coefficients

0.80816 same_srv_rate 0.79194 dst_host_srv_serror_rate 0.78929 dst_host_serror_rate 0.78725 serror_rate 0.78657 srv_serror_rate 0.67120 dst_host_same_srv_rate 0.62169 count 0.60986 dst_host_srv_count 0.58071 logged_in 0.47684 dst_host_count 0.44016 http_vec 0.26822 srv_diff_host_rate 0.24241 dst_host_same_src_port_rate 0.23298 udp_vec 0.20790 dst_host_srv_diff_host_rate 0.20182 tcp_vec < 0.20

0.75191 same_srv_rate 0.72253 dst_host_srv_count 0.69380 dst_host_same_srv_rate 0.69017 logged_in 0.65498 dst_host_srv_serror_rate 0.65184 dst_host_serror_rate 0.65065 serror_rate 0.64828 srv_serror_rate 0.57644 count 0.56231 http_vec 0.37505 dst_host_count 0.25350 srv_rerror_rate 0.25343 dst_host_srv_rerror_rate 0.25339 rerror_rate 0.25256 dst_host_rerror_rate 0.24289 dst_host_diff_srv_rate 0.21718 udp_vec 0.20366 diff_srv_rate 0.20045 ip_vec < 0.20

0.51868 diff_srv_rate 0.50984 same_srv_rate 0.47591 dst_host_srv_count 0.43821 dst_host_same_srv_rate 0.41091 dst_host_diff_srv_rate 0.40596 dst_host_serror_rate 0.40475 logged_in 0.39806 dst_host_srv_serror_rate 0.39274 serror_rate 0.38358 count 0.37912 srv_serror_rate 0.27083 dst_host_srv_diff_host_rate 0.26585 http_vec 0.21954 dst_bytes 0.19803 dst_host_count 0.18887 dst_host_same_src_port_rate 0.14155 srv_diff_host_rate 0.09424 srv_count 0.08826 dst_host_srv_rerror_rate 0.05674 rerror_rate 0.05217 dst_host_rerror_rate 0.05156 srv_rerror_rate 0.04119 src_bytes 0.03717 udp_vec 0.03672 duration 0.03104 ip_vec

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The results of ranking the features of the NSL-KDD dataset by the correlation and the Information Gain methods are presented in Table 2. The features included in the list of the most important following the AHP-like solution occupy “original” positions in these hierarchies, which confirm the originality of AHP-like. According to Table 1, the use of the AHP-like method for these data sets reduces the number of features to 4, essential for determining DoS attacks, and to 5 for normal (non-attack) records. The number of features is 4 or 5, these are different conditions when using the AHP-like method. More “noisy” data gives more features required. As it was shown in [2] and in the section with results of classification experiments, Accuracy is slightly reduced. In this regard, it is necessary to check the possibility of increasing Accuracy while maintaining the achieved minimum number of features, that is, some modification of the proposed method.

3 Modification of Features Set Received in AHP-Like Method The data sets obtained in the process of capturing network traffic and extracting features have sufficient information redundancy. This fact makes it possible to significantly reduce the original set of features for its subsequent use in classification. At the same time, the Accuracy of the classification decreases slightly [2]. After applying the AHP-like method for two sufficiently different sets of network data, a significant reduction in the number of features was obtained, while the classification methods may also be different. Changes in Accuracy are displayed for experiments in rows 1–4, 10–13, 16–17, 20–21 of Table 4. The decrease in classification efficiency with a significant decrease in the number of features is quite insignificant. Subsequently, attempts were made to additionally process the data sets (after obtaining the minimum set of features) to improve the classification efficiency. The main idea is to form calculated “integral features” for analysis, each of which is formed by the main features (Table 1) and a set of secondary features (Fig. 2), associated with it by high values of mutual cross-correlation: m wik · ak = wi1 · a1 + wi2 · a2 + · · · + wim · am , (1) Ai = k=1

where i is the number of “ensemble”, determined by the number of main features (Fig. 2); wik are weight coefficients, determined by the values of mutual cross-correlation of ensemble members and the DoS attack or Normal status (for modification of the AHPlike mod 2 method), or mutual cross-correlations of the ensemble members and the main feature in the ensemble (for modification of the AHP-like mod 3 method). Since the mutual cross-correlation coefficients can be negative: −1 ≤ wi1 ≤ 1, to keep the values of a normalized integral feature A˜ i in the range of 0 ≤ A˜ i ≤ 1, its normalization should be performed at the level of 0.5:        ˜Ai = 1 + wi1 · a1 − 1 + wi2 · a2 − 1 + · · · + wim · am − 1 . (2) 2 2 2 2 Examples of weight coefficients for diagnosing DoS attacks in the NSL-KDD dataset are given in Table 3. Algorithm modifications AHP-like mod 2 (experiments No 5 and 6

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in Table 4) and AHP-like mod 3 (experiments No 7, 8, 18, 19 in Table 4) as a result also form datasets with 4 features. Modification of the AHP-like-PCA algorithm (experiment No 9 in Table 4) resulted in a data set with two features only. The transformation of the datasets was based on the Principal Components Analysis technique applied to the training dataset after AHP-like selection of four features. The first two PCA components were selected only. The dataset thus obtained, is applied for further training of ML/DL systems. Table 3. An example of weight coefficients for ensemble integrated features calculation for both modifications of AHP-like method. No

Features

Correlation to DoS

Weight (AHP-like mod 2)

Correlation to main feature

1

same_srv_rate

−0.8082

−0.12536

1.00000

0.14776

‘dst_host_same_srv_rate’

−0.6712

−0.10411

0.78897

0.11658

‘dst_host_srv_serror_rate’

0.7919

0.12284

−0.76532

−0.11309

‘serror_rate’

0.7873

0.12212

−0.76188

−0.11258

‘dst_host_serror_rate’

0.7893

0.12243

−0.76064

−0.11240

−0.75678

−0.11182

0.70540

0.10423

−0.62802

−0.09280

2

‘srv_serror_rate’

0.7866

0.12201

‘dst_host_srv_count’

−0.6099

−0.09461

‘count’

0.6217

0.09644

Weight (AHP-like mod 3)

‘logged_in’

−0.5807

−0.09008

0.60053

0.08874

dst_host_count

0.4768

0.29153

1.00000

0.40181

‘dst_host_srv_diff_host_rate’

−0.2079

−0.12712

−0.45587

−0.18317

‘srv_diff_host_rate’

−0.2682

−0.16399

−0.36005

−0.14467

‘http_vec’

−0.4402

−0.26915

−0.35979

−0.14457

‘dst_host_same_src_port_rate’

−0.2424

−0.14821

−0.31301

−0.12577

3

udp_vec

−0.23297

1.00000

1.00000

1.00000

4

tcp_vec

0.20181

1.00000

1.00000

1.00000

4 Experimental Results for Different Features Selection Methods In this section, we have presented the most interesting results of our numerical experiments with the NSL-KDD and UNSW-NB15 datasets. The classification efficiency of all experiments was characterized by such parameters as Accuracy and Area Under Curve (AUC) (corresponding columns in Table 4). The binary classification was investigated in all our experiments. The efficiency of such classification may significantly depend on the ratio of the two types of records. The NSL-KDD and UNSW-NB15 datasets differ significantly in these ratios:

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• for NSL-KDD: DoS/All - 37%, Normal/All - 53%, • for UNSW-NB15: DoS/All - 5%, Normal/All - 95%. For the features selection and reduction, the next methods were used: the novel AHP-like method [2] and its modifications; Principal Components Analysis (PCA) [6]; Information Gain method [7]. For the classifier we have used: Artificial Neural Network (ANN method of DL); k-Nearest Neighbors (k-NN method of ML), in our calculation k = 3. As it is clearly visible from Table 4 it is a weak Accuracy dependency on the different modification of AHP-like method. Table 4. Results of classification for different features selection methods and different applied classifiers. Data set

Classified events (quantity in training and testing sets)

Number of the experiment

Number of features

NLS-KDD

DoS 45927/80046 5741/16803

1

42

2

4

3

42

4

4

AHP-like

k-NN

0.956

0.946

5

4 mod 2

AHP-like mod 2

ANN

0.890

0.909

6

4 mod 2

AHP-like mod 2

k-NN

0.944

0.940

7

4 mod 3

AHP-like mod 3

ANN

0.925

0.918

8

4 mod 3

AHP-like mod 3

k-NN

0.944

0.938

9

2 PCA

AHP-like PCA

k-NN

0.802

0.780

10

42

ANN

0.76

0.833

11

5

ANN

0.74

0.881

12

42

k-NN

0.764

0.829

13

5

AHP-like

k-NN

0.740

0.797

14

5bis

Information Gain

k-NN

0.729

0.751

15

5bis

Information Gain

SVM

0.689

0.724

16

48

ANN

0.93

0.911

17

4

AHP-like

ANN

0.93

0.873

18

4 mod 3

AHP-like mod 3

ANN

0.93

0.851

Normal 67343/58630 9711/12833

UNSW-NB15

DoS 4089/78244 12264/163077

Feature reduction method

AHP-like

AHP-like

Classifier

Accuracy

AUC area

ANN

0.95

0.986

ANN

0.92

0.956

k-NN

0.962

0.963

(continued)

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B. Misnevs et al. Table 4. (continued)

Data set

Classified events (quantity in training and testing sets)

Normal

Number of the experiment

Number of features

Feature reduction method

Classifier

Accuracy

AUC area

19

4 mod 3

AHP-like mod 3

k-NN

0.924

0.851

ANN

0.89

0.982

ANN

0.85

0.943

20

48

21

5

AHP-like

Fig. 3. Distribution of records DoS (red) and Normal (blue) for the features X (dst_host_count) and Y (same_srv_rate) for NSL-KDD dataset.

Minimized number of the features in sets in our experiments was in the range of 2–5. The influence on the classification efficiency of the feature number increases was discussed in [8] and as it was shown demonstrate no sufficient increase in efficiency. In this regard, we can talk about some relative stability of the method and the existence of a certain limit of accuracy, which is achieved on a reduced set. The existence of such an Accuracy limit can be qualitatively viewed in Fig. 3 wherein the range of blue crosses has incorporated the ranges of red crosses and due to this, the ranges can’t be highlighted without considering an additional axis for some additional feature. On the other hand, the presence of such inclusions (Fig. 3) can be interpreted by the potential presence of a small number of “artificial” records, specially entered into the dataset to “noise” the data and actually limit the accuracy of attack detection.

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5 Conclusions The present research describes studies of novel AHP-like features reduction methods [2] and their modifications. Using different experiments based on NSL-KDD and UNSWNB15 datasets feature reduction problem is investigated for use in ML/DL classification approaches. The investigated variants of modification for the main AHP-like method do not achieve an increase in Accuracy of subsequent classification. This is true for situations where the number of features in their reduced set remains the same or will decrease. In this study of binary classification, we analyzed the possibility of modifying the AHP-like feature selection algorithm, which would improve the classification performance of the selected features while maintaining or reducing their number. Our hypothesis was that such an improvement can be obtained. Based on the experiments performed, we concluded that the expected improvement is not achieved, which also indicates the relative “stability” of the AHP-like method. As a generalizing conclusion, we can say that for NSL-KDD and UNSW-NB15 datasets showed the effectiveness of reducing the number of features. The subset of features always contains a certain limit of their number. These selected features ensure the highest possible Accuracy in detecting the records of the appropriate type. It is experimentally confirmed. This AHP-like method can be proposed for use with other datasets.

References 1. SNORT. Source: Project “Snort”. https://www.snort.org/. Accessed 17 Aug 2022 2. Grakovski, A., Krivchenkov, A., Misnevs, B.: Feature selection method for ML/DL classification of network attacks in digital forensics. Transp. Telecommun. J. 23(2), 131–141 (2022) 3. Ahmad, B., Jian, W., Ali, Z.A.: Role of machine learning and data mining in internet security: standing state with future directions. J. Comput. Netw. Commun. 2018, 6383145, (2018). https://doi.org/10.1155/2018/6383145 4. Brownlee, J.: How to Choose a Feature Selection Method for Machine Learning. In: Machine Learning Mastery. https://machinelearningmastery.com/feature-selection-with-realand-categorical-data/. Accessed 04 Feb 2022 5. NSL-KDD and UNSW-NB15 datasets, CSV files. https://drive.google.com/drive/folders/1y6 vNHhFo9TegDES4UegqwBe_YkxMvfp9?usp=sharing. Accessed 20 Aug 2022 6. Smith, L.: A tutorial on Principal Components Analysis. Technical report OUCS-2002–12. Department of Computer Science, University of Otago, 26 February (2002) 7. Azhagusundari, B., Thanamani, A.: Feature Selection based on Information Gain, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2(2) (2013). ISSN: 2278–3075 8. Krivchenkov, A., Misnevs, B., Grakovski, A.: Structural analysis of the NSL-KDD data sets for solving the problem of attacks detection using ML/DL methods. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) Reliability and Statistics in Transportation and Communication. RelStat 2021. Lecture Notes in Networks and Systems, vol. 410. Springer, Cham (2022). https://doi. org/10.1007/978-3-030-96196-1_1

Optimisation of Quay Crane Scheduling Problem at the Port of Algeria Hizia Amani(B) , Linda Bouyaya, Rachid Chaib, Fatma Zohra Djekrif, and Mouna Aizi Transport Engineering Department, LITE Laboratory, University of Constantine 01, Constantine, Algeria [email protected]

Abstract. More than 90% of world trade is doing by global maritime transport, with over 90,000 ships. This has favored the evolution of containerized freight transport and this has greatly contributed to the development of intermodal transport. These handling operations require a sharp look and a well adapted speed of decision to achieve the shortest possible ship handling time, to ensure the good deployment of these operations while synchronising them with the overall architecture of the port. This work aims to discuss the Quay Crane Scheduling Problem (QCSP), the main objective is to minimize the loading/ unloading time of the containers and therefore reduce the waiting time of the ships in the terminals, with the port of Bejaia in Algeria- as a concrete application, using mixed integer linear programming and genetic algorithm. Keywords: Maritime transport · Containers · Handling operations · Quay cranes · MILP and genetic algorithm

1 Introduction It is obvious that seaports have accompanied the process of globalization of the market economy for several centuries. However, the increased changes in traffic patterns over the last decades, the growth and increasing specialization of trade, the strengthening of global operators, have meant that seaports have been undergoing profound changes for some time, both in their relationship to the markets and territories they serve and in their modes of organisation and governance. Seaports are rarely studied in isolation: they owe this to the importance of their strategic role for the global, national and regional economies, and consequently to the major share of activities they draw from their external environment. Therefore, they must be able to ensure a fast unloading/loading cycle. Among the problems encountered is the scheduling of quay cranes, which is one of the main problems in seaports. Its purpose is to minimise the berthing time of a ship during the unloading/loading process, which is the objective of our study. As a case study, we have taken the port of Bejaia in Algeria.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 232–241, 2023. https://doi.org/10.1007/978-3-031-26655-3_21

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2 Presentation of the Field of Study Located in the centre of the country, in the heart of the Mediterranean in the north of the African continent, the Port of Bejaia occupies a strategic geographical position. It serves a large and extensive hinterland. The city, the port and the container terminal of Bejaia are thus connected to all the roads of the country, to the railways and to the proximity of an international airport. Berthing quay: • • • •

Length: 500 m. Depth: 12 m. Basin area: 60 ha. Number of berth: 04.

The container terminal of Bejaia port has two gantry cranes (panamax) and two mobile harbor cranes (Fig. 1) for unloading /loading containers [13].

Fig. 1. Gantry and mobile crane-bejaia port.

3 Related Works The studies that have addressed the crane scheduling problem are different; some have addressed this problem with berth scheduling [6], and others with internal container storage [4, 8, 12]. The resolution of these studies is done by exact and approximate methods with the aim of minimising the time of unloading ship. [1, 2, 7, 12] authors used exact method (mixed integer programming, Branch and Bound method) and approximate methods (genetic algorithm, Particle Swarm Optimization). [3, 10], they used only exact method and [4–6, 8, 9, 11] used only approximate methods.

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In this work, we have studied the problem of crane scheduling, considering that unloading containers will be done by only one gantry crane and one mobile crane. The objective is to determine an optimal sequence of unloading tasks through the possible balance that can be achieved between the unloading times provided by the two types of cranes. We used integer programming as an exact method, and proposed a genetic algorithm as an approximate method to solve the problem.

4 Description of Problem A container ship consists of a set of bays. Each bay contains a number of containers. The unloading operation will be done by one quay crane (gantry) and one mobile crane knowing that each crane has a different unloading speed. It is assumed that gantry crane can handle 20 C/H (container/hour) and mobile crane can handle 10 C/H, so the unloading time of a single container will be 3 and 6 (min) respectively. The problem lies in the affectation of the two cranes to bays with the aim of achieving a minimum unloading time. Both cranes work simultaneously, so our objective is to balance the unloading time between the two cranes. For 2 cranes and 4 bays; the following graph (Fig. 2) presents the problem:

Fig. 2. Presentation of the problem by a graph.

The unloading possibilities for each crane are presented in the Table 1. Table 1. Unloading possibilities for each crane. One bay

Two bays

Three bays

1, 2, 3 or 4 1 + 2, 1 + 3, 1 + 4, 2 + 3, 2 + 4 or 3 + 1 + 2 + 3, 2 + 3 + 4, 3 + 4 + 1 or 1 + 4 2+4

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4.1 Assumptions • Only one container ship is considered. It consists of (n) bays. Each bay contains a number of containers. • One gantry and one mobile crane are considered. They move freely. • Each crane can move to another bay only after finishing the unloading of a previous bay, (each bay is unloaded by only one crane). • The travel time of two cranes is not included. 4.2 Mathematical Formulation Data i: bay index ∀ i = 1,…. n Vi: number of containers in each bay. Ut1: unloading time of one container by gantry crane. Ut2: unloading time of a container by mobile crane. Decision Variables Zi ∈ {0, 1}= 1 : if the bay canbe unloaded by gantry crane. 0 : otherwise. Wi ∈ {0, 1}= 1 : if the bay can be unloaded by mobile crane. 0 : otherwise. The Objective Function   n n       ZiVi − Ut2 WiVi Min(f ) = Ut1   i=1

(1)

i=1

Constraint Zi + Wi = 1

∀i = 1, . . . n,

(2)

(1): the objective function is to minimise the difference between the times taken to unload the bays by the two cranes. (2): each bay must be unloaded by only one crane. 4.3 Resolution In this work, two methods are applied to solve the problem. The mixed integer programming solved by LINGO solver, and a genetic algorithm developed in PYTHON.

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Numerical Example of Our Problem Number of bays: 3 Number of containers in each bay: 18, 12, 16. Ut1 and Ut2: 3 and 6 min. FUT: final unloading time. With 2 cranes [1 = gantry crane, 2 = mobile crane] we get: 23 possibilities (8P) (Table 2). Table 2. Possibilities for 2 cranes and 3 bays. P1

P2

P3

P4

P5

P6

P7

P8

Z1

1

1

0

0

1

0

1

0

Z2

0

1

0

1

0

1

1

0

Z3

0

0

1

1

1

0

1

0

W1

0

0

1

1

0

1

0

1

W2

1

0

1

0

1

0

0

1

W3

1

1

0

0

0

1

0

1

Table 3. Final unloading time for each possibility. P

Crane 1(min)

Crane 2(min)

Min (f)

FUT (min)

122 112

3 * 18 = 54

6 * (12 + 16) = 168

114

168

3 * (18 + 12) = 90

6 * 16 = 96

6

96

221

3 * 16 = 48

6 * (18 + 12) = 180

132

180

211

3 * (12 + 16) = 84

6 * 18 = 108

24

108

121

3 * (18 + 16) = 102

6 * 12 = 72

30

102

212

3 * 12 = 36

6 * (18 + 16) = 204

168

204

111

3 * (18 + 12 + 16) = 138

0

138

138

222

0

6 * (18 + 12 + 16) = 276

276

276

The optimal solution is: Min (f) = 6(min) and final unloading time = 96 min (Table 3).

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Genetic Algorithm Genetic algorithms are optimisation approaches that use techniques derived from genetic science and natural evolution: selection, mutation and crossing [14]. Thus, in the vocabulary of genetic algorithms the environment refers to the search space that defines the set of possible configurations of the parameters of the function to be optimised. An individual in this environment is a possible configuration of the parameters. A set of individuals forms a population. Each individual can be represented by a chromosome which is composed of a chain of genes containing the genetic characteristics of that individual. The gene being the elementary part of a chromosome represents a particular trait or function. The capacity of an individual to adapt to the environment is materialised by measuring the performance of the individual through the function to be optimised (or fitness function) [15]. Genetic Algorithm Steps The Selection Operator Selection is the first phase of population renewal. It allows the selection of individuals who will give birth to the offspring of a generation [16]. The Crossover Operator Crossover is a genetic operator allowing children to inherit part of the first parent and part of the second parent. The Mutation Operator The mutation operator is applied to individuals with a low probability. Mutation consists of modifying the value(s) of one or more randomly selected genes of an individual. This operator favours diversification in the evolution of individuals [17].

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Example of Our Problem 1= gantry crane 2= mobile crane

Bays: B1…B8

Parent X B1

B2

B3

B4

B5

B6

B7

B8

1

1

1

2

1

2

2

2

B1

B2

B3

B4

B5

B6

B7

B8

1

2

2

2

1

1

2

1

1

1

1

2

1

2

2

2

1

2

2

2

1

1

2

1

1

1

1

2

1

1

2

1

1

2

2

2

1

2

2

2

Parent Y

One-point crossover

Two children

Mutation Random position 1

1

1

2

1

1

2

1

1

2

1

2

1

1

1

1

5 Results 5.1 LINGO Solver Result LINGO is a software tool designed to efficiently build and solve linear, nonlinear, and integer optimization models. We have created a simple lingo model to solve the problem. The number of bays varies between 3 and 9. Number of containers for each bay: 36, 42, 38, 44, 35, 32, 40, 44, 39. For 3 bays; the number of containers in each bay is: 36, 42, and 38. For 5 bays; the number of containers in each bay is: 36, 42, 38, 44, and 35. The results are presented in the Table 4.

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5.2 Genetic Algorithm Result The genetic algorithm is coded in python version 2.7. Python is an interpreted objectoriented, high-level programming language with dynamic semantics. We have organized the genetic algorithm as follows: • • • • •

The generation of individuals is random. The fitness function = Min (f) One-point crossover. Mutation is random. Population of 100 individuals, and number of iterations: 400. The results of the genetic algorithm are presented in the Table 5. Table 4. Lingo solver results.

Zi

Wi

Zi

Wi

Zi

Wi

Zi

Wi

Zi

Wi

Zi

Wi

Zi

Wi

1

0

1

0

0

1

1

0

1

0

0

1

1

0

1

0

1

0

1

0

1

0

0

1

1

0

1

0

0

1

1

0

1

0

1

0

1

0

1

0

0

1

0

1

1

0

0

1

0

1

1

0

1

0

0

1

1

0

1

0

0

1

1

0

0

1

1

0

0

1

1

0

1

0

1

0

0

1

1

0

1

0

0

1

3 Bays f=6

4 Bays f = 84

5 Bays f = 54

6 Bays f=3

7 Bays f = 27

8 Bays f=6

9 Bays f=3

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Number of bays

Crane 1(min)

Crane 2 (min)

f (min)

Crane Order

3

234

228

6

4

348

264

84

[1, 1, 1, 2]

5

372

426

54

[2, 1, 1, 1, 2]

6

453

456

3

7

543

516

27

8

624

618

6

[2, 1, 1, 1, 2, 2, 1, 1]

9

699

702

3

[1, 1, 2, 1, 1, 1, 2, 1, 1]

[1, 1, 2]

[1, 1, 1, 2, 1, 2] [1, 2, 1, 2, 1, 1, 1]

5.3 Discussions The results obtained by the LINGO solver, the genetic algorithm (GA): are the same with an execution time < 1(s) for (GA) and 2.86 (s) 1.64 (s) 1.14 (s) 0.45 (s) 0.14 (s) 0.12 (s) 0.8 (s) for the LINGO solver. With the genetic algorithm the recorded results are after 3, 4 runs and sometimes the same solution (value of f ) can take different assignments of the cranes as long as they can move freely. It is clear that the number of containers in each bay and the unloading speed of each crane influence the affectation of bays to the crane; the gantry crane took in each example (3 bays to 9 bays) a greater number of bays than the mobile crane.

6 Conclusion In this work, we addressed the problem of scheduling crane in the container terminal of the port of Bejaia which has two gantry cranes and two mobile cranes, then we considered that each bay is like a task that it will be executed by the gantry or mobile crane and never by both, so only one constraint is established, and facilitated the resolution of the problem. The expected work could have: • A scheduling of only gantry cranes (a constraint will be added that prevents interference between the cranes). • A schedule that allows each bay to be unloaded by more than one crane (not at the same time); the results will be more efficient. • Other meta-heuristics to solve the problem and compare them with the genetic algorithm.

References 1. Skaf, A., Lamrous, S., Hammoudan, Z., Manier, M.A.: Solving methods for the quay crane scheduling problem at port of tripoli-lebanon. RAIRO- Oper. Res. 55, 115–133 (2021). Hal03221871

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2. Skaf, A., Lamrous, S., Hammoudan, Z., Manier, M.A.: Exact method for single vessel and multiple quay cranes to solve scheduling problem at port of Tripoli-Lebanon. In: International Conference on Industrial Engineering and Engineering Management .DEC BANGKOK Thailand (2018). Hal-02991560 3. Al-Dhaheri, N., Diabat, A.: The quay crane scheduling problem. J. Manuf. Syst. 36, 87–94 (2015) 4. Said, G.A., El-Horbaty. E.S.: An optimization methodology for container handling using genetic algorithm. Procedia Comput. Sci. 65, 662 – 671 (2015) 5. Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Hybrid evolutionary computation methods for quay crane scheduling problems. Comput. Oper. Res. 40, 2083–2093 (2013) 6. Fu, Q., Cai, C.: Berth and quay crane scheduling optimization based on improved genetic algorithm. In: China Automation Congress (CAC) 978–1–6654–2647–3/21/$31.00. IEEE (2021). https://doi.org/10.1109/CAC53003.9727945 7. Tang, L., Zhao, J., Liu, J.: Modeling and solution of the joint quay crane and truck scheduling Problem. Eur. J. Oper. Res. 236, 978–990 (2014) 8. Hop, D.C., Hop, N.V., Anh, M.T.T.: Adaptive particle swarm optimization for integrated quay crane and yard truck scheduling problem. Comput. Ind. Eng. 153, 107075 (2021) 9. Chen, L., Bostel, N., Dejax, P., Cai, J., Xi, L.: A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal. Eur. J. Oper. Res. 181, 40–58 (2017) 10. Dkhil, H., Yassine, A., Chabchoub, H.: Optimization of container handling systems in automated maritime terminal. Studies in Computational Intelligence. DOI: https://doi.org/10. 1007/978-3-642-34300-1-29 11. Msakni, M.K., Al –Salem, M., Rabadi, G., Kotachi, M., Diabat, A.: Quay crane scheduling problem with vessel stability. Transp. Res. 30, 60–69 (2018) 12. Jonker, T., Duinkerken, M.B., Yorke-Smith, N., de Waal, A., Negenborn, R.R.: Coordinated optimization of equipment operations in a container terminal. Flex. Serv. Manuf. J. 33(2), 281–311 (2019). https://doi.org/10.1007/s10696-019-09366-3 13. https://bejaiamed.com/ 14. Chouchani, I.: Use of a genetic algorithm for web service composition. University of Quebec at Montreal. (2010). https://archipel.uqam.ca/3381/1/M11483.pdf 15. Yachb, K.: Towards a contribution to maritime freight transport: optimization of container placement in a seaport. University of Oran 1(Computer laboratory Oran 1) (2017). https://the ses.univ-oran1.dz/document/15201711t.pdf 16. Ndeye, F.N.: Optimization algorithms for solving container storage in a port terminal. University of Havre (2015). https://tel.archives-ouvertes.fr/tel-01255365/document 17. Ayachi Hajjem, I.: Advanced optimisation techniques for solving the container storage problem in a port. Central School of Lille (2012). https://tel.archives-ouvertes.fr/tel-01266169/ document

Scripting Complex Events and Behaviors in Computer Simulation of a Security Monitored Area Jarosław Sugier(B) Department of Computer Engineering, Wrocław University of Science and Technology, Janiszewskiego 11/17 Z, Str. 50-372, Wrocław, Poland [email protected]

Abstract. The paper presents a method for definition of simulation scenarios which was developed in AvatarTraffic – a computer simulator for modelling and real-time monitoring environments like underground metro stations or airport halls, populated with avatars who, apart from typical commuting, can reply prescribed situations and react to actions taken by the operator of the system. The scenarios are expressed in textual form in a JSON configuration file as a part of scene definition and an original method used for modelling and then describing a scenario in this format is the focus of this work. The paper also explains particular mechanisms which are used to compile the description so that it can be executed by the simulation engine and presents a resultant command language which is offered to a security expert whose task is to plan a sequence of events and actions during the simulation. Keywords: Simulator · Scenario scripting · State machine · Unity engine

1 Introduction Recent advances in computer simulation and visualization techniques combined with new Artificial Intelligence methods of modelling human behavior made artificially simulated scenes populated with computer controlled human figures – avatars – very close to what can be observed in the real world. This work presents AvatarTraffic – a complete computer simulator capable of modelling in real time 3D environments like, for example, an underground metro station or an airport hall, populated with dozens or hundreds of avatars walking along prearranged routes and replaying some specific scenario. In particular, it concentrates on an original technique developed for scripting complex events and actions taking place on the scene which make up such a scenario. 1.1 Motivation Offering complete control and configurability, realistic simulators can nowadays validate regular operation of any surveillance system as well as reproduce diverse unusual and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 242–251, 2023. https://doi.org/10.1007/978-3-031-26655-3_22

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rare situations or accidents. With these capabilities, they are used e.g. for testing technical setup of monitoring devices (configuration of cameras, location of sensors, etc.) or for practical verification of safety procedures prepared for cases of hazardous events which otherwise would be difficult to stage in practice (accidents, crashes, fires, etc.). Another important area of their application has been becoming very popular especially in recent years: they are used to generate massive volumes of training data for Machine Learning algorithms which could support a monitoring system e.g. in detection of malicious behaviors or in early recognition of abnormal operational conditions. A simulator is not only capable of generating very large amounts of data required in ML training but also – last not least – eliminates the need for real-life video recording of people traffic which would e.g. violate various GDPR requirements (anonymization of records, etc.). The particular aspect of a simulator which is the topic of this paper – scripting sequences of events and actions which should take place during the simulation, prepared by security experts – is an essential part of any product of this type. Some early classification of solutions in this area can be found in [1] although this review concentrates on scripting languages available for generic extensions of popular game engines into serious games applications and does not focus only on the task of scenario scripting. An innovative solution of such kind is proposed in [11] where the authors developed a standalone graphical editor for custom scenario editing which was based on a concept of causality graph. An example application was used for preparation of scenarios for training a safety supervisor at an oil drilling site and used an environment based on Unreal 3 engine. A platform for fast development of scenario-based serious games which enable complex cognitive skills acquisition is presented in [5], with a custom language proposed to express conditions and action making up the scenario flow. A methodology for preparing a serious game for training professionals is described in [3] where a scenario is developed in relation with pedagogy tree representing expected educational effects. Finally, in [2] the authors describe a scripting language for complete control of the simulation character’s behavior, including reactions to situations on the scene. With the scenario being represented in a graph of scenes, the language also enables supervisor’s interventions in real time of the simulation. It should be noted that in the common jargon of simulation or game developers the term “scripting” is first of all used for a low-level method for programming behavior of a game object which is in opposition to “visual” configuring its properties through a graphical interface. Such programming is a foundation in any game development platform and uses either general purpose languages – like C# in Unity ([9]) or C + + in Unreal Engine ([10]) – or a custom interpreter. In contrast, the paper deals with a situation when the user – configurator of the simulator who is more a security expert rather than a programmer – describes specific scenario of events and actions along with actors – avatars – who will replay them. Such a description is a part of the simulator input expressed in some notation and – as a “scenario scripting” – is the subject of this work. The rest of the paper is organized as follows. In the next section the AvatarTraffic simulator is briefly introduced. The proposed method for scenario scripting is the subject of chapter 3, while conclusions summarize the paper in chapter 4.

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2 The AvatarTraffic Simulator 2.1 Organization and Interfacing The simulator has been developed on the Unity 3D platform ([6, 8]) and is prepared to be run as a standalone application which loads a configuration file, starts simulation of a scenario (which was a part of the configuration), and communicates with the monitoring system with NATS messages and video streams. Figure 1 presents organization of a typical setup in which the simulator is integrated with a real security monitoring system. The figure represents an optional situation where the simulation is supervised by an operator (the trainer) who can e.g. trigger different variants of the scenario – but fully automatic execution of the scripts is also possible. Apart from live video feeds from virtual monitoring cameras, AvatarTraffic communicates bi-directionally with the system through the NATS protocol sending statuses of sensors located in the scene (doors, motion detectors, card authorization points, etc.), receiving commands for moving the virtual cameras (Pan-Tilt-Zoom), sending messages generated by scenario actors in response to situations occurring during the simulation, or receiving reactions (commands) from the trainee. Using identical network interfaces and transmission protocols as the physical components, AvatarTraffic communication remains technically indistinguishable from the one generated by real devices. Security Monitoring System

Operator (trainer)

AvatarTraffic

Security staff (trainee)

Fig. 1. AvatarTraffic and security monitoring system.

Configuration of the scene is prepared in a form a JSON file and includes scene geometry (walls, platforms, etc.), specific assets for its visualization (light sources, cameras), human individuals moving within (avatars representing actors), and lastly – specification of the scenario itself in the form of a collection of state machines, which will be the topic of the next chapter. It should be noted that the presented prototype was developed first of all for realistic modelling of crowd traffic on a 3D scene. Photorealistic visualization was of second importance as this aspect can be later improved with standard tools and techniques of the Unity platform.

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2.2 Simulation Assets Visualization and controlling movement of a walking human character in a computer simulation is not trivial; the solution used in AvatarTraffic was presented in [7] and is based on resources offered by the Unity platform: 3D models of a humanoid figure and dedicated algorithms for implementing character relocation – navigation – on the scene. The latter are based on the NavAgent component which can plan movement of an avatar to a given destination point, taking into account static topology of the scene as well as dynamic obstacles appearing at runtime (e.g. other avatars). The NavAgent mechanism was the foundation of the two basic assets: Avatar (a single humanoid character) and PathGenerator (a generator of avatars following the same path). As it soon turned out in practice (and which was the main subject of study in [7]), the NavAgent could not automatically deal with more complex situations when many avatars were trying to access the same object or area. For this purpose, three special game objects were introduced: Queue, Door and WatingArea. A more detailed description of these assets can be found in [7].

Fig. 2. A platform of a train station simulated in AvatarTraffic with different assets ([7]).

A sample scene which uses all these assets is presented in Fig. 2. On a platform of a train station individual avatars represent security personnel, path generators create streams of passengers walking to/from a train, queues are used to model platform main entrance and exit, additional doors are provided for entry of authorized staff taking part in a simulated scenario, and waiting areas control behavior of passengers awaiting on the platform.

3 Scenario Scripting 3.1 Finite State Machines and JSON Description A general assumption about the AvatarTraffic engine was that construction of the scene and the simulation scenario, whatever complex and involved the latter could be, are both

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stored in structured text fields in a JSON format. This created a problem how to express a scenario – a timed sequence of events and actions happening on the simulated scene – in a way which would take into account the whole variety of involved entities (game assets being both subjects and objects of operations), their states, conditions and actions, yet at the same time would be efficiently defined in the form of text fields and, after being loaded by the application, could be executed by the Unity engine. The selected solution was based on a simple, elementary and well-known concept of a deterministic Finite State Machine (FSM, [4]). The machines appear as Script objects in the configuration input file and their operation – i.e. testing transition conditions, switching between states and generating output in form of commands sent to game objects – represents progress of the scenario. The AvatarTraffic’s configuration file can include any number of Script objects which are identified by unique names; their order is irrelevant as all operate as peers, without distinguishing special ones – it is up to the user if he or she needs to designate some machine as the main supervisor because it is possible for the FSMs to test each other’s state and in this way to synchronize (and possibly subordinate) their operation. Specification of every Script is just a list of its states. Every state is defined with the following fields: – – – –

name: must be unique within the FSM; onEntry: operations to be performed immediately after entering the state; transitions: list of transitions from a given state; postExit: operations to be performed immediately after exiting the state (i.e. when the current state has already changed to the next one).

The first state on the list is considered as the initial one; with this only exception their order is insignificant. Each element in the state’s transitions list defines one transition between them (an arc in the graph of the machine) and is specified by three fields: – condition: Boolean condition which triggers this transition; – switchToState: the name of the target state, as in the name field; – onTransition: operations to be performed at the moment of transition (i.e. before the state changes its value). Of the above fields: name and switchToState are simple identifiers, onEntry, postExit and onTransition are strings which denote commands sent to game objects (like C# void methods), while condition represents a boolean expression used to test state of game objects (equivalent to C# methods returning bool value). These are all rules which define semantics of FSM specification and its relation with simulation objects of the virtual environment. The field onEntry represents FSM output of Moore type (i.e. depending only on current state), whereas onTransition – Mealy type (i.e. depending both on current state and input values). The field postExit is redundant as compared to the strict model from automata theory and was added just to gather in one place Mealy outputs common to all transitions leaving particular state (which is often convenient in practice).

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Initialization of every FSM is done before simulation starts and consists in accepting the first state on the list as the current one and executing its onEntry field. After initialization, operation of the FSM is based on checking the transition conditions for the current state (in each simulation frame of the Unity engine) and performing the transition for the first condition which evaluates to true, along with execution of respective onEntry and postExit operations. Conditions are tested in order of appearance on the transitions list and, because they do not have to be disjoint, their order can be significant. An empty list of transitions makes the state a terminal one from which the machine will not exit until the scene is reloaded by the simulator. An empty condition is equivalent to true; such unconditional transitions are used for states in which FSM should remain for one simulation frame, implementing “nearly-immediate” sequences of operations which should remain synchronized between multiple FSMs. 3.2 Compilation and Execution Specification of state machines presented in the previous section requires expressing their operations and conditions in the format of a JSON fields, i.e. as a plain text strings. In order to avoid creating a new description language, it has been decided that these strings will be treated as fragments of C# code which would be compiled at scene initialization (after loading of the configuration file) and then invoked when the conditions or operations need to be executed. Compilation of the FSM operations takes place immediately before starting the actual simulation and is controlled by instances of a ScriptExecutor class which are created for each script loaded from the config. The procedure first creates a source code with strings from the JSON fields, compiles it and then retrieves delegates to all compiled functions for later invocations. The source code is created using a template presented in Listing 1: a special static class is created for this purpose and the FSM operations and conditions are transformed into static methods returning void or bool (to the total of two operations per each state plus two operations and one condition per each transition). The compilation is done with the Mono environment which is distributed for this purpose together the simulator. After the compilation to a memory DLL the ScriptExecutor retrieves and stores delegates to the methods and is ready for simulation of the FSM. 3.3 The Commands As it was seen in Listing 1, the static methods implementing the FSM operations are compiled with access only to the custom namespace Scenario (in particular they can use without prefix static methods of a class ScenarioObjectsInterface). Without linking to any other libraries (e.g. System or UnityEngine) this namespace alone is designed to provide a secure interface which controls what internal AvatarTraffic functionality can be accessed by the code loaded from the JSON file. A static class ScenarioObjectsInterface plays a crucial role in organization of this access as Fig. 3 illustrates. Its task is to keep dictionaries of the game objects of the scene (with references to all avatars, path generators, queues, doors, etc.) and to grant access to them upon request returning one of dedicated interfaces.

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For example, its method Avatar(string Name) finds an avatar object of a given name and yields access to it returning an interface IAvatar – which makes it the only way in which any scenario script can gain access to an individual avatar on the scene. A call to such a method in the JSON string looks simply as Avatar(“Name”) and should be followed by the name of the interface member. For example, a text “Avatar(“SGuard”).GoTo(2.5, 3.0)” is a command which sends an avatar named “SGuard” to a destination given with explicit coordinates. Most operations (as well as conditions) of the script written in the JSON specification begin with accessing an object in this way and then proceed to calling a specific method of its interface.

Because JSON fields are interpreted as bodies of C# methods, one field can hold multiple such commands separated with semicolons. Texts for condition fields, in turn, must evaluate to a logical expression: should invoke bool methods of the interface (e.g. Avatar(“Name”).IsIdle()) or should create such an expression with comparison operators (e.g. PathGen(“Name”).Count() > 5). Complex conditions must be created with standard C# logic operators &&, || and !. The access methods of the ScenarioObjectsInterface class together with definition of the interfaces are the core of the command language which is visible to and used by the author of the scenario. The list of the access methods includes: public public public public public public

static static static static static static

IAvatar Avatar(string Name); IPathGen PathGen(string Name); ILockedDoor Door(string Name); IQueue Queue(string Name); IWaitingArea WaitArea(string Name); IScript Script(string Name);

In every case identification of the requested object is by its name which is the first field in every JSON description. Having access to an object, operations which can be executed are briefly listed below:

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Fig. 3. Controlling access to the simulation objects available to the external FSM code.

– IAvatar allows to test state of the avatar (idle or walking along its path), direct it to a new waypoint or door/queue/waiting area, or read coordinates of its current destination; – IPathGen can return IAvatar interface of its n-th avatar, pause / resume generation, return the number of avatars currently on the path, or force generation of a given number of avatars by a paused object; – ILockedDoor and IQueue include just a method for directing an avatar to the respective object which has the same effect as calling Avatar(“NN”). GoTo(Door(“Name”)) or Avatar(“NN”).GoTo(Queue(“Name”)) – IWaitingArea offers a method for adding an avatar like the two interfaces above, but can also return the current number of avatars in the area and force to release all of them instantly; – IScript offers only one method: a test if the FSM is currently in a state of a given name; any other commands which would affect operation of the machines are not allowed as they could breach fundamentals of the scripting mechanism. In addition to communication with simulation objects, the ScenarioObjectsInterface provides also simple auxiliary resources: random number generators, handlers of the input from system console (of the computer running the simulator, i.e. accessible for the trainer), sending and receiving NATS messages, or ad hoc created timers which can be used for controlling timed transitions of the FSMs.

4 Conclusions The method for scenario description used in AvatarTraffic is based on two concepts: Finite State Machines and runtime compilation of the text as a C# code. Additionally,

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as the entire setup of the simulated scene is specified in the JSON format in a text file, the same method is used for serialization of the scenarios. Advantages of the FSMs applied to scenario representation are: relative simplicity of the concept (which does not require any experience with programming languages), natural parallelism in operation of their multiple instances and ease of textual specification in a structured JSON format. During development the FSMs concept confirmed to be a suitable choice also from practical point of view thanks to its flexibility and ease of implementation in the Unity’s C# environment. Alternatively, adoption of any dedicated scripting language would require an external parser or interpreter and interfacing with such a third-party resource would bring additional problems. Treating the JSON strings directly as C# code – the native language of the Unity engine – made experimenting with the set of available commands very easy because their modifications were simple adjustments in methods of an interface. This was a valuable advantage during development. On the other hand, the need to comply with C# syntax imposed a lot of limitations on allowable notation, most of which may seem awkward to a scenario author who is not a professional programmer (e.g. obligatory use of the object.method() syntax, logical operators in conditions written as ‘&&’, ‘||’ and ‘!’, etc.). Nevertheless, relying on the C# grammar eliminated a lot of burden which otherwise would have to be solved with custom interpreter as well as resulted in the highest performance of the compiled description. In the final result execution of a scenario in AvatarTraffic is based on granting a direct access to the game objects on the level of the C# code in the same way as is applied by the Unity engine in its internal scripting. This is a very powerful authorization which potentially enables external scripts to make use of all object properties and therefore a special mechanism based on C# namespaces/interfaces has been developed to ensure their secure and controlled behavior. Specification of a simulation scenario is a complicated task. While the concept of a state machine is a good instrument for modelling general outline in the flow of actions, the need to describe in this way vast amount of particular details makes the final specification in the JSON format not only long but also decomposed into tiny fragments dispersed over many individual fields. Working with such a specification is not easy for a security expert without IT background (an expected author of the scenario) so – as a direction for further development – a user-friendly visual editor supporting FSM design would be a very helpful auxiliary tool. Acknowledgements. The paper presents results of a research project supported by grant No. POIR.01.01.01–00-0235/17 awarded by the Polish National Centre for Research and Development (NCBR) within the framework of the Smart Growth Operational Programme 2014–2020 which was financed by the European Regional Development Fund (ERDF).

References 1. Anderson, E.F.: A classification of scripting systems for entertainment and serious computer games. In: 2011 Third International Conference on Games and Virtual Worlds for Serious Applications, pp. 47–54 (2011). https://doi.org/10.1109/VS-GAMES.2011.13

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2. Busetta, P., Robol, M., Calanca, P., Giorgini, P.: PRESTO script: scripting for serious games. In: AI & Games Symposium at AISB, pp. 18–22 (2017) 3. Duval, Y., Panzoli, D., Reymonet, A., Plantec, J.-Y., Thomas, J., Jessel, J.-P.: Serious games scenario modeling for non-experts. In: Proceedings of the 7th International Conference on Computer Supported Education – vol. 1, pp. 474–479. SCITEPRESS (2015). https://doi.org/ 10.5220/0005489904740479 4. Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation, 2nd edn. Pearson Education, Harlow (2000) 5. Slootmaker, A., Kurvers, H., Hummel, H., Koper, R.: Developing scenario-based serious games for complex cognitive skills acquisition: design, development and evaluation of the EMERGO platform. J. Univ. Comput. Sci. 20(4), 561–582 (2014). https://doi.org/10.3217/ jucs-020-04-0561 ´ 6. Sugier, J., Walkowiak, T., Mazurkiewicz, J., Sliwi´ nski, P., Helt, K.: Performance evaluation of event-driven software applied in monitoring systems. In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds.) RelStat 2018. LNNS, vol. 68, pp. 311–319. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12450-2_30 7. Sugier, J.: Modelling pedestrian behavior in a simulator for a security monitoring system. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) DepCoSRELCOMEX 2021. AISC, vol. 1389, pp. 425–436. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-76773-0_41 8. Unity Real-Time Development Platform. http://unity.com. Accessed 26 Dec 2021 9. Unity Technologies: Unity User Manual – Scripting. https://docs.unity3d.com/Manual/. ScriptingSection.html. Accessed July 2022 10. Unreal Engine: Programming and Scripting. https://docs.unrealengine.com/4.27/en-US/Pro grammingAndScripting. Accessed July 2022 11. Van Est, C., Poelman, R., Bidarra, R.: High-level scenario editing for serious games. In: Proceedings of the International Conference on Computer Graphics Theory and Applications (GRAPP-2011), pp. 339–346 (2011). https://doi.org/10.5220/0003374503390346

Avatar: A Telepresence System for the Participation in Remote Events Dietrich Trepnau1(B) and Klaus Richter2 1 Magdeburg University, 39106 Magdeburg, Germany

[email protected] 2 Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany

[email protected]

Abstract. A unique telepresence helium blimp drone for the purpose of participating in remote events is presented. The blimp is safe for humans, can be controlled remotely over the internet, and can provide a high-quality 4K video stream to multiple users at the same time. We show that a careful selection of upto-date hardware enables the blimp to run computationally expensive algorithms like SLAM and deep learning neural networks without relying on a local ground station to perform complex tasks. Additionally, we compare the performance of the ORB-SLAM2 algorithm when used with recent depth estimating neural networks in RGB-D mode to its performance when used with conventional passive stereo images. Our experiments show that the ORB-SLAM2 algorithm can be used with estimated depth images to reliably detect turns of the vehicle. However, in this case the algorithm also underestimates the covered distance for straight movements in one direction which results in very inaccurate computed positions. Keywords: Drone blimp · SLAM · Telepresence system · Deep neural networks · Depth estimation

1 Introduction The COVID-19 pandemic has been a hard challenge for the visitors of events. Virtual events allow for a safe participation and save time, but they do not allow interaction with other visitors, which is an important social aspect of events. This problem can be solved by telepresence systems that allow users to participate in remote events. For this purpose, we developed a helium-filled blimp drone which can safely navigate through rooms and can be controlled by a client application. The drone can provide a livestream of its camera in 4K resolution and can be controlled intuitively by the viewing direction of the user. Previous research of small blimps exists [1, 2], but because of weight constrains, their functionality is very limited. Only through recent advancements in the miniaturization of sensors and microprocessors is it now possible to equip them with powerful, but lightweight sensors such as the Luxonis OAK-D, and the required processing power to run sophisticated algorithms such as SLAM. This enables the drone to safely navigate and provide a satisfactory user experience of the event. To achieve this goal, we had to solve the following problems: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 252–258, 2023. https://doi.org/10.1007/978-3-031-26655-3_23

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• Reliable low-latency video streaming. • SLAM for the localization of the drone and mapping of the environment. • Adequate control of the blimp with few and small light-weight motors. Specifically, we use the ORB-SLAM2 algorithm [1] for localization of the blimp. ORB-SLAM2 can handle inputs from stereo images pairs as well as RGB-D data and we experimented with both of those modes. Since the Luxonis OAK-D only has stereo vision however, we experimented with deep neural network models that estimate the depth from monocular images for the RGB-D mode.

2 Related Work Quite a few different telepresence robots have been developed in the last few decades [2]. These robots attempt to give their operator the feeling of being somewhere else, usually by providing a video conference system. They usually move by using wheels which limits their mobility. Only a few flying telepresence robots have been developed by using drones [3] or blimps [4]. Since usual drones are considered a safety risk for humans at the location of the robot [5], we decided to also use a blimp. Blimps are softer than usual drones and therefore less harmful than drones in cases of collision. Some research on small scale blimps has been conducted in the past. For example, the authors of [6, 7] designed and constructed such small indoor blimps. The resulting blimps are not telepresence systems, but they give insights about common design challenges and problems of small blimps such as the difficulty of the exact placement of motors or the strict weight limitations. In [7] it is mentioned that it was challenging to find a camera that was light enough and, in both papers, a local ground station is used to receive the video image and perform all computationally expensive operations. Helium-filled blimps have an uplift force of around 1 g per liter helium and therefore the hardware on the blimps must be very light. Another problem of mobile robots in general is the safe navigation in unknown environments. Simultaneous Localization and Mapping (SLAM) algorithms have been heavily researched to solve this problem. SLAM algorithms attempt to construct a map of the current environment and at the same time localize the robot within this map [8]. An extensive list and comparison of different SLAM algorithms is given by [9]. In this paper we only use visual slam algorithms, which use visual data such as monocular camera images, stereo camera images, or RGB-D data as input.

3 System Overview Our full telepresence system consists of three parts: • The blimp. • A server application for communication.

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3.1 A Client Application 3.2 The Blimp Unlike any other blimp we attempted to design the blimp to be “human-like”. Therefore, our blimp has a narrow form, with the height of a human instead of the typical form of an airship as can be seen in Fig. 1. One of the main challenges of developing a blimp are the strict weight limitations. Our current prototype can lift 342 g and it is therefore important to choose lightweight components without compromising too much on performance. For this reason, we chose recently released hardware like the Luxonis OAK-D [10] as a camera and the Raspberry Zero 2 W [11] as the onboard computer of the blimp. After removing the metal case of the Luxonis OAK-D, it weights 18 g. It has 4K color camera and two 720p greyscale stereo cameras for depth perception. It also contains an IMU-Sensor and can provide a H.265 encoded video stream for streaming over the internet. Additionally, it contains a Myriad X VPU for execution of neural network models and several common image processing algorithms on the camera without burdening the onboard computer of the blimp. The Raspberry Zero 2 W weights 12 g and was chosen because it is the lightest microprocessor we could find with enough computational power to run the ORB-SLAM2 algorithm. For controlling the blimp, we use four small DC-motors. Two of them are mounted at the sides of the blimp for clockwise and counterclockwise rotation. The other two motors are mounted on the backside of the blimp. One motor is used to move the blimp forward and the last motor is used to move the blimp upwards. Furthermore, we use a Time-of-Flight distance sensor that is attached to the bottom of the blimp to determine its current height. When turning on the blimp it connects automatically to the internet via Wi-Fi and contacts a server. The software on the blimp is responsible for initializing the blimp, communication with the server, and controlling the motors. We implemented a few control functions that are always active even when there is no input from the pilot. We noticed that the blimp is easily influenced by wind which causes it to slowly rotate. Therefore, we detect such rotations by using the gyroscope of the camera and counteract to stabilize the blimp. At the same time this function helps to counteract the inertia of the blimp after the pilot rotates the blimp. Another automatic function of the blimp is to keep a specific height. The pilot can adjust the preferred height up to a value of 4 m. 3.3 Server Application To achieve the best possible latency, our first attempt was to establish a direct Peerto-Peer connection to the client. However, we experienced several connection issues with this approach due to badly configured networks and decided to set up a server that is always reachable over the internet instead. After authentication, the blimp starts sending its H.265 encoded video data to the server using the UDP protocol. The server application receives the video data and then redirects it to all active users in the session. One important advantage of using a server for re-streaming is that we can ensure that multiple users can watch the same video stream while still having relatively low latency. The bandwidth of the server is sufficient for streaming to multiple users while the Wi-Fi connection of the blimp does not have the required bandwidth to handle such a use-case.

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Fig. 1. First prototype of our “human-like” blimp.

Command signals from the pilot are likewise exchanged via the server but are send using the TCP protocol to avoid packet loss. 3.4 Client Application A client application can connect to the server and control each motor separately through predefined commands. All users that joined a session can receive and decode the encoded video data to display it for the user. Multiple users can join the same session at the same time to watch the same video stream. The client application can send yaw angle data to the blimp to support input devices that use the viewing direction of the pilot. For example, a VR-Headset could send such yaw angle data which is then used to rotate the blimp whenever the pilot moves his head to keep up with viewing direction of the pilot. The client application can also change the value that is used to hold a specific height.

4 SLAM We chose to run the ORB-SLAM2 algorithm on our blimp for localization of the blimp. The algorithm runs at approximately 2 fps on the Raspberry Zero 2 W which is fast enough for our purposes as the blimp usually moves slowly.

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The algorithm can run with monocular camera images, stereo camera images, or RGB-D data. Since our camera only has passive stereo vision, we use the stereo mode by default. However, we also experimented with deep learning networks that estimate the depth from monocular camera images. Due to the Myriad X VPU such deep learning models can run directly on the camera if they are converted to the right format first. A few pretrained models such as FastDepth [12] and MegaDepth [13] are even provided by the Luxonis community, but we found that they can be very inaccurate and are trained on low resolutions. Therefore, we decided to use the more accurate and recent Adabins [14] instead to estimate the depth of monocular images and then use the result for the RGB-D mode of ORB-SLAM2. We test the algorithm on the first three sequences of the KITTI dataset [15] since it contains rectified stereo pairs of RGB images that can be used for both modes of the ORB-SLAM2 algorithm. The authors of Adabins provide a pretrained model for the KITTI dataset with their official implementation [16] of Adabins that we use. We then compare the results of the stereo mode and the RGB-D mode to the ground truth positions. 4.1 Results We plotted the estimated positions of our experiment against the ground truth positions in Fig. 2. As can be seen in the figure, the results for the stereo mode are close to the ground truth for all three sequences. The plots of the estimated positions of the RGB-D mode also depict the general shape of the driven route, especially for the first and third sequence, but the computed positions are very inaccurate. The most accurate result for the RGB-D mode is for the first sequence with an average position error of 41.64 m. The worst result of the RGB-D mode is for the second sequence with an average position error of 1,159 m. In this sequence a car drives on a highway and only moves forward with a speed of around 1 m per frame. The SLAM algorithm however did only detect a forward movement of around 10 cm per frame in RGB-D mode which is why all computed positions are relatively close to each other. The third sequence has an average position error of 333.25 m and is another case in which the movement was underestimated by the SLAM algorithm but not as strongly as in the second sequence. In this sequence the shape of the plotted positions also looks very similar to the actual driven route, only scaled down. These results indicate that the RGB-D mode of ORB-SLAM2 with estimated depth images can detect turns and changes in orientation reliably but underestimates the covered distance in the case of straight movement in one direction.

5 Summary In this article we present our design of a unique blimp for the purpose of participating in remote events. The blimp can be remotely controlled and is safe for humans at the event site. We showed that a careful selection of up-to-date hardware enables the blimp to perform computationally expensive and complex tasks such as SLAM and deep learning while staying lightweight and without relying on a ground station. Additionally, we

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Fig. 2. Comparison of the computed SLAM positions (Blue) with the ground truth positions (Orange) for the first three sequences of the KITTI dataset. The stereo mode was used for images on the left side, while the RGB-D mode was used for images on the right side.

experimented with deep learning networks that estimate depth from monocular images and investigated if the estimated depth images can be used for the ORB-SLAM2 algorithm. The results of these experiments indicate that the estimated depth images can be used to detect turns and changes in orientation but are too inaccurate to compute the location of vehicle. For localization of the vehicle the stereo mode or real depth images should be used instead.

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In further research we would like to allow the pilot to communicate and interact with other participants at the event site and use the SLAM algorithm for automated flight and path finding.

References 1. Mur-Artal, R., Tardós, J.D.: Orb-slam2: An open-source slam system for monocular, stereo, and Rgb-D cameras. IEEE Trans. Rob. 33(5), 1255–1262 (2017) 2. Kristoffersson, A., Coradeschi, S., Loutfi, A.: A review of mobile robotic telepresence. Adv. Hum.-Comput. Interact. 2013, Article ID 902316 (2013). https://doi.org/10.1155/2013/ 902316 3. Zhang, X., Braley, S., Rubens, C., Merritt, T., Vertegaal, R.: LightBee: A self-levitating light field display for hologrammatic telepresence. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–10 (2019) 4. Tobita, H., Maruyama, S., Kuzi, T.: Floating avatar: telepresence system using blimps for communication and entertainment. In: CHI 2011 Extended Abstracts on Human Factors in Computing Systems, pp. 541–550 (2011) 5. Wojciechowska, A., Frey, J., Sass, S., Shafir, R., Cauchard, J.R.: Collocated human-drone interaction: Methodology and approach strategy. In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 172–181. IEEE, Daegu Republic of Korea (2019) 6. Van Asares, A., Ko, P.S., Minlay, J.S., Sarmiento, B.R., Chua, A.: Design of an unmanned aerial vehicle blimp for indoor applications. Int. J. Mech. Eng. Rob. Res. 8(1), 157 (2019) 7. Yao, N.-S., et al.: Autonomous flying blimp interaction with human in an indoor space. Front. Inf. Technol. Electron. Eng. 20(1), 45–59 (2019). https://doi.org/10.1631/FITEE.1800587 8. Yousif, K., Bab-Hadiashar, A., Hoseinnezhad, R.: An overview to visual odometry and visual SLAM: applications to mobile robotics. Intell. Ind. Syst. 1(4), 289–311 (2015) 9. KITTI SLAM Evaluation (2012). http://www.cvlibs.net/datasets/kitti/eval_odometry.php. Accessed 08 July 2022 10. Luxonis OAK-D. https://shop.luxonis.com/products/oak-d. Accessed 10 July 2022 11. Raspberry Zero 2 W. https://www.raspberrypi.com/products/raspberry-pi-zero-2-w/. Accessed 10 July 2022 12. Wofk, D., Ma, F., Yang, T.J., Karaman, S., Sze, V.: Fastdepth: fast monocular depth estimation on embedded systems. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 6101–6108. IEEE, Montreal (2019) 13. Li, Z., Snavely, N.: Megadepth: learning single-view depth prediction from internet photos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2041– 2050. IEEE, Salt Lake City (2018) 14. Farooq Bhat, S., Alhashim, I., Wonka, P.: AdaBins: depth estimation using adaptive bins. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4009–4018. IEEE, Nashville (2021) 15. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Rob. Res. 32(11), 1231–1237 (2013) 16. Official Adabins implementation. https://github.com/shariqfarooq123/AdaBins. Accessed 10 Aug 2022

Anomaly Detection for Predictive Maintenance on the Example of an Induced Draft of a Waste Power Plant Nicolas Dolle1(B)

, Christian Wilhelm1

, and Kirill Anikin2

1 P-A-Systems Faculty of Economics, Aalen University, 73430 Aalen, Germany

{nicolas.dolle,christian.wilhelm}@hs-aalen.de 2 Data Science Specialist, Schenkenberg Street 49, 73733 Esslingen, Germany

Abstract. Thanks to Artificial Intelligence algorithms, new digital systems that supervise machines in production or in power plants are more and more enabled to provide pattern recognitions realizing predictive maintenance workflows. Nevertheless, the requirements of predictive maintenance are still very challenging when it is applied on machines which are not completely new and do not consist of intelligent PLC-systems offering all required interfaces to sensors and actors out-of-the-box. That is why other embedded systems are needed to implement predictive maintenance systems for machines that are not controlled by a PLC. Furthermore, these systems usually do not have any internet connection at all. This results in two requirements for so called “retro-fitted” predictive maintenance solutions: (1) External hardware and sensors are needed; (2) The system must be offline-capable [1]. This paper focuses on the development of an offline-capable anomaly detection system analyzing the footprint of a waste incinerator power plant’s induced draft. The induced draft is responsible to drain the poisoned gas out of the power plant’s burning chamber. It is not a redundant component and a critical element of the smoke gas cleaning system. The goal of the footprint analysis in this paper is to find out if an external embedded system with sensors could be used to find anomalies in the behavior of the machine offering the possibility to estimate maintenance cycles as well as to predict dangerous incidents. Keywords: Anomaly detection · Machine learning · Isolation forest · Raspberry Pi · Gyroscope sensors

1 Introduction Today, retro fitting of older machines or machines without PLC is a very common process to digitalize the production or mechanical value chain. Usually, this procedure combines different technologies, such as edge computing, cloud computing, and additional sensors which are placed on the machine units [1]. This paper focuses on a retro fitting solution for an induced draft of a waste incinerator plant. The induced draft is placed at the end of the process chain of the incinerator plant. It © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 259–268, 2023. https://doi.org/10.1007/978-3-031-26655-3_24

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is responsible to drain out the smoke gas out of the burning chamber through the whole smoke gas cleaning system. As the system is not a redundant system, it is a critical component of the powerplant. Currently, this system is supervised only by normal lead-system values. These are electricity, engine speed, and simple vibration values of the front and back bearing wheels. The lead system provides one value each minute. Based on this sampling rate, the system can catch long-term trends. The goal of the anomaly detection system based on the raspberry edge device is to catch not only long-term trends but also short-term trends as well as incidents based on anomalies occurring in the data stream. Furthermore, it was applied to validate the long-term trends also with external sensors compared to the lead-system. The research subject is Machine Learning and Anomaly Detection, the research object is the induced draft of the waste incinerator power plant. Within this paper, the following research question has been answered: – Can a retro fitting solution with a raspberry edge device, sensors, and an anomaly detection system provide a reliable system for catching long-term trends, short-term trends, and prevention of incidents? To answer this question, the following tasks were solved: (1) Implementation of an embedded system using a raspberry Pi connected with gyroscope sensors; (2) Data Acquisition with an in-memory database; (3) Anomaly Detection System Implementation based on Machine Learning using edge computing [2]; (4) Model Optimization for continuous machine monitoring and incident prevention. This study proves the capability to apply predictive maintenance methodologies based on subsequently implemented hard- and software systems.

2 State of the Art Analysis Usually, most edge devices (in our case raspberry pi systems) are only used as a data handler, where the calculation and data storing will be done on an independent cloud system [3, 4] and the edge device just provides a “gateway” service [4]. If further logics are applied on the edge device, these are normally rule-based anomaly detections without AI, e.g. air pollution supervision [3], or myocardial infection [5]. There are also similar systems in research and economy which use Artificial Intelligence, such as: 1. Supervised learning with Convolutional Neural Networks: Observing sounds (diesel engine, voice, music instrument) offline capable with a prediction rate between 95.5 to 98.4 [6]. 2. Supervised learning with a K-Means cluster algorithm: Using a raspberry as a network behavior monitor by blocking Ips [7]. 3. Supervised learning with Convolutional Neural Networks: Disease detection of tomato leaves [8].

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All of the mentioned systems have a similarity. They all use a supervised learning approach. That means that they are trained in advance with a training dataset covering an exact amount of labeled data packages. For example, “good” and “bad” parts or other categories, which allows the algorithm to learn exactly the related patterns of each class. Additionally, trained models that are applied on a huge amount of data required long training and validation calculation times as well as a high computing power of the hardware. A raspberry pi computer does not provide a strong hardware basis for the application of complex AI models [5, 9]. Offline-capable solutions using Artificial Intelligence with heavy and complex models do normally need a high performing hardware basis which is heavily exaggerating the system abilities of a raspberry pi solution. These hardware systems normally cost a lot of money [10]. There is the alternative to equip the raspberry pi with an additional hardware stick for a performance upgrade, for example the Google Coral sticks extending computing capabilities [11]. In this research the team did focus on the usage of a cost-effective raspberry pi solution without any added hardware components. It has been tried to apply AI methods such as Machine Learning processes on the raspberry pi while acquiring and storing data to be just-in-time for the process. In literature it is shown that using a raspberry pi approach can compensate overall costs compared to a cloud approach which is leading to immense monetary efforts over the time. The following image represents the overall costs occurring using cloud functions compared to a raspberry solution. Furthermore, it is very energy efficient when it comes to a rollout to more than just one machine. As the provider of the test machine does have more than just one waste incineration power plant, also this aspect was considered [12]. Especially in our case, we generate around one gigabyte data per sensor per month – if we are not dropping any data. A raspberry has two sensors connected which produce two gigabyte data per month. This data can grow depending on the use case (e.g., two sensors per raspberry device, two raspberry devices per machine and 50 machines available, 2*2*50 = 200 gigabyte data per month). So, we get following the formula for data acquisition (Fig. 1): 1

Gigabyte Gigabyte ∗ x sensor ∗ y SensorPin ∗ z machine ≈ x ∗ y ∗ z month month

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Fig. 1. Costs of cloud services compared to raspberry pis [12].

3 Applied Methods For development of the unsupervised learning Anomaly Detection system an experiment has been done. The experiment was made using the lead-system data from the induced draft machine as well as the data from the applied external sensors (gyroscope sensors with X, Y, Z acceleration values). The experiment was done before and after the machine maintenance (revision) cycle to prove its capabilities and that the system is working. Before executing the experiments, the assumption, the authors made were: 1. The general data streams of the gyroscope sensors are the same before and after the revision. 2. The system can predict based on the gyroscope data if a maintenance of the induced draft is needed using the amount of anomalies occuring in the continuous data stream. 3. The long-term trends for maintenance timepoint prediction are also recognizable in the lead-system data from the internal sensors. The methodology to fulfill the experiments and examine the expected results followed a strict approach which is displayed in the following table (Table 1). Table 1. Methodology and executed steps during the experiment. No. Step

Description

1

Applying external sensors on the machine The gyroscope sensors have been applied on the machine

2

Prepare raspberry pi solution with database

The raspberry pi solution was connected to the sensors and the acquired data could be saved in an index-based database (influxDB) which saves the occuring data streams in the RAM (continued)

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Table 1. (continued) No. Step

Description

3

Collect data before revision

The footprint of the machine a few weeks before revision has been saved in the database

4

Collect data after revision

The footprint of the machine directly after the revision has been saved in the database

5

Create AI-model on raspberry pi and train An AI model with unsupervised learning model with data after revision capability has been trained with the data that has been acquired after the revision cycle. Here, the authors did chose an Isolation Forest Machine Learning algorithm that provides the required performances to be applied on the raspberry pi device [13]

6

Creating a just-in-time feedback interface for the user

To provide a User Interface for the user if an anomaly is detected, Grafana has been used. Grafana is a visualization system for time series data. The authors implemented the solution on the raspberry and connected a LCD-Monitor to the edge device solution

7

Supervise data stream

The data stream is continuously supervised by the anomaly detection algorithm. The algorithm checks the amount of anomalies occuring in the machine’s footprint and gives a visual alert on the User Interface

4 Results The created system can store up to three months of data in the influxDB in-memory database. The data handler on the system will collect up to 400 datapoints per second (400 Hz) and calculate the mean of 100 data points to reduce the amount of data stored in the RAM. So, the raspberry pi solution collects one data point per second and provides a 60-times higher sampling rate than the existing lead-system. The datapoints will be created by the sensors on the waste incinerator power plant’s induced drafts created vibrations. Then, the isolation forest model checks the data every second and will give feedback on the LCD via a green or a red light. Green light means “everything o.k.” and red light means “anomaly discovered”. The image illustrates the interaction of the system’s components (Fig. 2).

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Fig. 2. System setup.

The first results of the isolation forest model were as expected. Since 3D-data presentation methods were applied, the outlier data points were successfully recognized as anomalies (marked in red in the image below) (Fig. 3).

Fig. 3. Illustration of anomalies in 3D-graph.

As shown in the Fig. 4, the anomaly detection will be called directly after calculating the mean of 100 datapoints. This shortens the runtime significantly, because no connection to the database will be needed by the anomaly detection algorithm.

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Fig. 4. Code sample, collect data, call of anomaly detection.

The models for both sensors were trained with 0.2 contamination value, since it is assumed that directly after the revision, there shouldn’t be any problems with the machine. Adapting this model to the data collected before the revision we receive following outcome: – Sensor 1: 0.2% anomalies after the revision to 15.49% anomalies before the revision. – Sensor 2: 0.2% anomalies after the revision to 23.01% anomalies before the revision. The difference from Sensor 1 to Sensor 2 can be explained due to the placing. Sensor 1 is on the case (lower vibrations), sensor 2 on the crankshaft (higher vibrations) of the machine.

5 Analysis and Discussion Since the authors assume, that the data stream will be the same before and after the revision, they compared the statistics of both data sets for both sensors. Table 2. Mean and standard deviation of the sensors 1 and 2.

Mean

Standard deviation

Sensor 1 before revision

Sensor 1 after revision

Sensor 2 before revision

Sensor 2 after revision

x

3.292

2.994

−0.458

−0.458

y

0.378

−0.092

−1.208

−0.970

z

10.451

9.013

10.338

10.338

x

0.483

0.296

0.498

0.276

y

1.911

0.619

0.598

0.295

z

0.469

0.268

0.556

0.192

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As seen in Table 2, the standard deviation increased more than one hundred percent. Sensor 1 has slightly other mean values due to complications of putting the sensor back to the place it was before the revision. For the calculation of the models, the data before and after the revision of sensor 1 were normalized to the same level, meaning by setting the mean to the same level. This step was skipped at sensor 2 because it could be placed directly at the same location, and it did produce nearly the same footprint. Other differences of the machine’s footprint can be explained by component changes of the induced draft supplier. The comparison of this result with the internal data of the induced draft of a waste power plant bearer back and front values, brought nearly the same result. Only electricity and engine speed could not be validated with the external anomaly detection system. Table 3. Detected anomalies. % anomalies after revision (trained model)

% anomalies before revision

Vibration back bearing wheel

0.17

37.64

Vibration front bearing wheel

0.14

20.18

Electricity (Ampere)

0.19

0.07

Engine speed

0.19

0.02

Comparing Table 3 with the results from our two sensors, we can strengthen our assumption. Ampere and Engine speed were statistically checked and are correlating with each other. Usually, before the revision, the engine speed will be slowed down due to pollution of the machine. The reduction of the engine speed is due to safety issues before the maintenance cycle. The final validation of our approach and models can only be done after the next revision. Due to the comparison of the internal data and external data (our sensors), we can assume that the validation will be successful. The anomalies are growing over time to a similar status as if a maintenance would be required. This status can be described as a similar picture to the following number of anomalies: 15.49% for sensor 1 and 23.01% for sensor 2; before the next revision. If the anomalies grow slower over the time, we can advise the operators that the revision could be delayed to the point, the anomalies reached the target level. All in all, the authors could evaluate the experiments of developing an anomaly detection system for the induced draft of the waste incinerator power plant using a raspberry pi system as success. The system can supervise the machine’s data stream and detect anomalies occurring during the machine’s operation. It learns the normal “footprint” of the induced draft and can compare the behavior of the data stream continuously by analyzing each data point every second with the implemented isolation forest algorithm. The assumptions made at the beginning of the experiments were approved.

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Although the system has proven its abilities, the system needs to be updated with a second data base for long-term storing as the in-memory database will drop out after three months currently due to RAM limitations. Additionally, further research and validation is needed as there should be a second analysis done after two revisions of the induced draft. The next revision cycle helps to validate the system one more time as the exact timepoint of replacement of the new induced draft can be supervised. It is very important for the final proof of the developed system as a whole maintenance cycle should be supervised. That is also the reason for the needed upgrade in terms of the long-term database capabilities. Another important point is that only the isolation forest algorithm has been applied. It could be leading to the goal to evaluate also other algorithms for anomaly detection performance. Finally, it can be summarized that the experiment has been a success and the methodology has been proven, but further research should be conducted in terms of longer data stream supervision cycles and more technologies.

References 1. Al-Maeeni, S.S.H., Kuhnhen, C., Engel, B., Schiller, M.: Smart retrofitting of machine tools in the context of industry 4.0. In: 13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 369–374 (2020) 2. Murshed, M.G.S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., Hussain, F.: Machine learning at the network edge: a survey. ACM Comput. Surv. 54, 1–37 (2022). https:// doi.org/10.1145/3469029 3. Saha, A.K., et al.: A raspberry Pi controlled cloud based air and sound pollution monitoring system with temperature and humidity sensing. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 08–10 Jan 2018, pp. 607–611. IEEE (2018). https://doi.org/10.1109/CCWC.2018.8301660 4. Sajjan, V., Sharma, P.: Analysis of air pollution by using raspberry Pi-IoT. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 20–22 January 2021, pp. 178–183. IEEE (2021). https://doi.org/10.1109/ICICT50816.2021. 9358535 5. Nargundkar, S., Manage, P., Tigadi, A., Rudrappa, G., Chougula, B., Konnur, A.: A novel non invasive myocardial infarction vigilant system by using raspberry pi. In: 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 03–05 December 2020, pp. 170–174. IEEE (2020). https://doi.org/10.1109/ICISS49785.2020.9315917 6. Darington, M.J., Kumar, V.S.: In-situ detection of anomalies in the acoustic signal using neural network approach. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 06–08 July 2021, pp. 1–6. IEEE (2021). https://doi.org/10.1109/ICCCNT51525.2021.9579583 7. Sumanth, R., Bhanu, K.N.: Raspberry Pi Based Intrusion Detection System Using K-Means Clustering Algorithm. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 15–17 July 2020, pp. 221–229. IEEE (2020). https://doi.org/10.1109/ICIRCA48905.2020.9183177 8. Gonzalez-Huitron, V., León-Borges, J.A., Rodriguez-Mata, A.E., Amabilis-Sosa, L.E., Ramírez-Pereda, B., Rodriguez, H.: Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4. Comput. Electron. Agric. 181, 105951 (2021). https://doi.org/10.1016/j.compag.2020.105951

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9. Utomo, D., Hsiung, P.-A.: Anomaly detection at the IoT edge using deep learning. In: 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), Yilan, Taiwan, 20–22 May 2019, pp. 1–2. IEEE (2019). https://doi.org/10.1109/ICCE-TW46550.2019. 8991929 10. Candelario, K., Booth, C., St.Leger, A., Matthews, S.J.: Investigating a raspberry Pi cluster for detecting anomalies in the smart grid. In: 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 03–05 Nov 2017, pp. 1–4. IEEE (2017). https://doi.org/10.1109/URTC.2017.8284197 11. Sajun, A.R., Shapsough, S., Zualkernan, I., Dhaouadi, R.: Edge-based individualized anomaly detection in large-scale distributed solar farms. ICT Express 8, 174–178 (2022). https://doi. org/10.1016/j.icte.2021.12.011 12. Besimi, N., Çiço, B., Besimi, A., Shehu, V.: Using distributed raspberry PIs to enable lowcost energy-efficient machine learning algorithms for scientific articles recommendation. Microprocess. Microsyst. 78, 103252 (2020). https://doi.org/10.1016/j.micpro.2020.103252 13. Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 15–19 December 2008, pp. 413–422. IEEE (2008). https:// doi.org/10.1109/ICDM.2008.17

Development of a Reliable and Offline Capable Hard- and Software Technology for Long-Term Machine Data Acquisition, Data Storing and Data Exportation Nicolas Dolle1(B)

, Christian Wilhelm1

, and Kirill Anikin2

1 P-A-Systems Faculty of Economics, Aalen University, 73430 Aalen, Germany

{nicolas.dolle,christian.wilhelm}@hs-aalen.de 2 Data Science Specialist, Schenkenberg Street 49, 73733 Esslingen, Germany

Abstract. Internet of Things (IoT) and Industry 4.0 hard- and software components are complex systems of application in the production and machinery field. Newer systems often approach the machine operators with modern user interfaces and capabilities of exporting data, connection of the sensors to manufacturing execution systems (MES) and analysis functionalities on specific cloud systems. But there are enormous amounts of production plants and machine park operators that are not able to use modern technologies due to diverse reasons: (1) The machines used for production are old machines and not capable to apply modern IoT technologies; (2) The production plant does not have an internet connection; (3) The machines used are modern but simple machines without IoT components fulfilling easy but critical tasks. This paper focuses on the development of a technological solution enabling machine operators with the above-mentioned challenges to benefit from IoT components like data acquisition, long-term data storing and flexible data exportation, including the important requirement “offline-capability”. To solve these challenges, the research team was combining a soft- and hardware solution that can acquire data continuously at machines with different technology components. The following steps were executed: (1) Evaluation and implementation of a load-balanced data acquisition technology with open-source database technologies and long-term data storage capabilities; (2) Evaluation and implementation of intelligent and flexible data exportation possibilities with no internet connection. Keywords: Data acquisition · Data storage · Data exportation · Raspberry Pi · Offline-capable

1 Introduction Smaller and mid-sized enterprises (SMEs) from production industry use to have several machines that are already many years old. Nevertheless, these machines, even if they are written off completely, keep on working and producing products for sale. Especially © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 269–276, 2023. https://doi.org/10.1007/978-3-031-26655-3_25

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those machines which are written off already, drive the margin of a production SME significantly. However, those machines normally do not work with any Programmable Logic Controller (PLC), or the controller of the unit comes only with limited connectivity functionalities. So, the machines do work properly, but they do not provide modern Internet of Things (IoT) possibilities. This is also the reason why replacing working machines that are main margin drivers continuously is not an option due to high investment costs required for new production machines. That is why there are Retro-Fitting opportunities by placing external sensors and external IoT components on a machine. The authors of this paper do work together in a commercial project which is developing such an industrial Retro-Fitting solution for SMEs (called SensorPiN). It is a raspberry pi solution of industry which has been applied already successfully in different application fields always by using external sensors to acquire additional data enabling SMEs to also use IoT advantages like: Predictive maintenance, anomaly detection, pattern recognition and process optimization. Generally, the added IoT components (usually a device and external sensors) already can work online with normal internet connections like Wi-Fi, LAN, or mobile network access. However, the practical approach has been shown that there is an intense lack of connection possibilities in the production and machinery environment. Five examples of the application have been selected and tested to underline the need of further development in offline capabilities of data acquisition (Table 1). Table 1. Evaluated and tested application fields. Industry

Wi-Fi

Mobile broadband

Mobile phone network

LoRaWAN [6]

Brewery

No

3G

Yes

Possible

Waste power plant

No

No

No

Not possible

Hospital*

No, but available (problems due to GDPR)

5G

Yes

Not possible

Metal industry

No

3G

Yes

Possible

Recycling company

No

No

Yes

Possible

* Here, also a hospital (which is not a production company) has been considered as this organization

has additionally the challenge of keeping all data of patients or patient related data under closure.

Most of the industrial warehouses, productions and productive locations of SMEs have no available Wi-Fi- connection. Also, mobile connection does not solve the issue of connectivity as some of the production locations have such strong security regulations and shielded walls letting no signals through.

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There is a need for collecting and storing data completely offline. Even LoRaWAN would not work, only by using long cables with senders outside of the plant. Applying cables, also LAN connections would work but due to safety issues for the staff, it is not allowed to put long cables as potential sources of injuries into the production environment. This leads to the need of an offline capable data acquisition methodology using an edge device (here: Raspberry Pi) and external sensors. The solution must be able to save data continuously not running out of capacity as well as to be usable in an easy way. This paper focuses to answer the following research question: – Is there an easy and usable way to collect data with a Raspberry Pi based IoT device completely offline but still reliable in terms of duration and big data in a generalized way?

2 State of the Art Analysis Usually, it is common storing data acquired by an edge device and external sensors on cloud infrastructure services and not raspberry as edge device itself [1, 2]. However, using a raspberry pi can lower the overall costs compared to cloud services significantly. Furthermore, it is also more energy efficient [3]. But of course, there are also challenges that cannot be solved easily by an edge device and that are easier to display by using cloud infrastructures. Those are: Data storing capacities and AI model or other logic performances that work more efficient in environments with enough RAM, CPU and GPU available [4, 5]. Combining a raspberry pi together with an LTE mobile adapter as IoT device was firstly described in 2016 and is still up to date due to missing connectivity solutions in some working areas [6]. However, it does not solve the connectivity issues that have been faced during the practical experiences of the use cases in industry. Also, LoRaWAN is a possibility to transport information from the machine via the raspberry to an endpoint [7]. Usually, all five minutes, one value is sent. This frequency could be modified. Furthermore, the distance of LoRaWAN can reach up to 2.5 km in landscape areas and 1.3 km in urban areas [8]. The potentials of raspberry pi devices in the IoT environment is huge as the variety of raspberry applications in the IoT field reach from data collection, server provisioning up to a cloud gateway [9]. To save data continuously and offline-capable, Mahendra et. al. Use for example an USB stick directly connected to sensors via a simple hardware gateway owning just enough “logic” to save the data to the flash storage [10]. In any way, this would not be the right approach for the desired outcome of this research. Working with a USB stick provides good opportunities in extending the storage capacity of the raspberry pi solution. It is very important that the new approach of long-term data storing does not affect existing algorithms and analysis capabilities of the IoT device (such as anomaly detection and other AI analytics). This means that getting rid of the raspberry by just using a method to save the data on the flash drive is not an option. Furthermore, when supervising machines, the frequency of supervision is very critical in terms of what

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analysts can do with the acquired data afterwards. Too low frequencies do not catch certain patterns operators want to see while analyzing their machine’s behavior. That is why the authors are working with 400 Hz frequency of data acquisition. Using these technological specifications, the raspberry pi as edge device is required. For long-term data acquisition with 400 Hz frequency packaging (zipping) of the data packages is essential [11] since otherwise the data would take too much space [5] have. The authors have developed a solution strategy that involves a raspberry pi (SensorPiN – Model 4 with 8 GB RAM), 2 USB flash discs and sensors described in the following chapter. In addition to the described technologies, also other hardware solutions could be used for data storing. In the following table, all alternatives that could solve the problem which was proposed in the introduction are presented according to the use case needs: Table 2. Evaluated alternatives. Offline capability Costs

Easy usability Benefit

Raspberry Pi with YES 2 USB flash discs

LOW

YES

Fits to the object of research

Raspberry Pi LoRaWAN Connection

LOW

YES

Data is streamed directly

Raspberry Pi with NO LTE Connection

HIGH (especially YES for Germany the location of the study)

Data is streamed directly

Raspberry Pi with NO WiFi Connection

LOW

Data is streamed directly

NO

YES

Based on the table one can summarize that the Raspberry Pi solution combined with the two USB flash discs offers an easy, cost efficient and usable method to solve the described problem.

3 Applied Methods The authors have developed a solution strategy for the offline capable long-term data acquisition and storing requirements by using 2 USB flash discs. To collect and export data those two USB sticks provide a simple workflow enabling users in the production field to acquire data, save data and export data whenever desired. – A red USB Stick (Master USB) will act as master storage. This stick will never be removed. – A blue USB Stick (Slave USB) is the data supplier that can always be removed by the operators of the machine. While removing the stick no data will be lost. It can be removed during the normal production shift cycles.

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The following image illustrates the system’s architecture (Fig. 1):

Fig. 1. Architectural overview.

The Sensor runs with 400 Hz, so every 2.5 s 1,000 data points will be pushed to a.csv file. This way it is more resource efficient for the system than pushing data every 1/400 s. At 0:00 a script will start, collect the.csv file of the entire day, zip it and push it to the slave USB stick. Using 64 GB stick and around 1.2 GB data creating per day we can store 53 days on the master USB stick. After zipping with the factor ten, we can store 533 days on the slave stick (see Table 2). That means that the data which is acquired will be reduced by 90% in terms of its needed space. According to Shah and Sethi, space saving for normal gzip is considered by around 30%, but they compared the space saving for string data [11]. Since we use float data in this approach, higher compression rates could be achieved (Table 3). Table 3. Max. data storage for the slave stick per sensor. GB data per day Max. days of savings Max. GB Data size if unzipped Normal storage

~1.2

Storage with gzip ~0.12

53

63.6

533

63.69

639.6

The normal work shift time in Germany is scheduled from 08.00 until 16.00 o’clock. Because the system zips and saves the data each night, the user can remove the USB stick during the day having all data until midnight from the former days. The process is displayed in the following image (Fig. 2): To prevent failure due to power failures, there is a stats file on every USB stick, saving the timestamp (related day) of the data. If a day is missing on the slave flash drive, the master flash drive will zip and push it again. Currently, the comparing will only be done for the last 50 days. If data packages will be older than 50 days, they will be ignored. Even if a machine operator forgets to put back the slave USB stick, all information will be redundant available on the master USB. So, when the user puts the slave USB stick back in, every missing data will be transferred at night. This approach is securing that no data losses occur during the operation of the machine supervision.

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Fig. 2. Data compression process.

4 Analysis and Discussion The system provides intense possibilities so save more than 1 ½ years of data offline with a frequency of 400 Hz without removing the device or without need to export the data continuously in between. Nevertheless, there are a few limitations of this approach which must be considered: 1. Only mobile broadband, Wi-Fi and LoRaWAN were taken into account in this research. This is because the authors and the users who did provide access to the practical application were used to these technologies. Furthermore, these techniques were promising the most performant results. Bluetooth and other derivates were not evaluated since even LoRaWAN has problems with thick iron safety walls (for example those of the power plant environment). 2. For the data storing and data exportation only a.csv file has been chosen. The decision for this technological requirement has been done based on the need of developing a generalized solution and.csv can be read easily by external third-party systems of the users. All in all, it can be summarized that the solution provides a reliable system for offline data acquisition every user can work with not needing any education. However, the system is mainly made for SMEs as these companies face higher cost pressures in digital transformation than major enterprises who have more possibilities to invest in more modern machines. The solution offers a possibility for SMEs to digitalize their older machines using an IoT device, external sensors and two USB flash discs for continuous offline data acquisition and storing. Nevertheless, the limitations require further applied research in practical projects with the users of the system.

5 Conclusion Overall, the authors provide a long-term machine data acquisition tool based on a Raspberry Pi device for all data acquisition purposes. As a user, it is not necessary to interact

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with the database or the system itself, just the USB stick needs to be removed and put back if the data has been collected. Generally, one can say that the research to solve the problem based on a Raspberry Pi was successful as the solution provides all required functionalities mentioned in the challenge description of the introduction. The solution is usable for everyone and provides a very practical approach which is also additionally helping for the application in the industries of the mentioned use case scenarios. The goal was to implement a continuous running system without creating a GUI like Ni et al. did [12]. The reason for this was that the application fields are usually in a sensitive environment, where every screen could cause diversions (for example in the hospital and medical environment). Nevertheless, there is need for further research. The authors did not dive into other possibilities than a Raspberry Pi as hardware component. This is because the Raspberry Pi system has been mandatory during this research based on the use case providers from industry as cost effective solution. However, the authors recommend doing further research in terms of other hardware systems that could support the required functionality.

References 1. Sajjan, V., Sharma, P.: Analysis of air pollution by using raspberry Pi-IoT. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 20–22 January 2021, pp. 178–183. IEEE (2021). https://doi.org/10.1109/ICICT50816.2021. 9358535 2. Saha, A.K., et al.: A raspberry Pi controlled cloud based air and sound pollution monitoring system with temperature and humidity sensing. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 08–10 January 2018, pp. 607–611. IEEE (2018). https://doi.org/10.1109/CCWC.2018.8301660 3. Besimi, N., Çiço, B., Besimi, A., Shehu, V.: Using distributed raspberry PIs to enable lowcost energy-efficient machine learning algorithms for scientific articles recommendation. Microprocess. Microsyst. 78, 103252 (2020). https://doi.org/10.1016/j.micpro.2020.103252 4. Utomo, D., Hsiung, P.-A.: Anomaly detection at the IoT edge using deep learning. In: 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), YILAN, Taiwan, 20–22 May 2019, pp. 1–2. IEEE (2019). https://doi.org/10.1109/ICCE-TW46550. 2019.8991929 5. Darington, M.J., Kumar, V.S.: In-situ detection of anomalies in the acoustic signal using neural network approach. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 06–08 July 2021, pp. 1–6. IEEE (2021). https://doi.org/10.1109/ICCCNT51525.2021.9579583 6. Petrov, N., Dobrilovic, D., Kavali´c, M., Stanisavljev, S.: Examples of raspberry pi usage in internet of things. In: Proccedings of the ICAIIT2016, International conference on Applied Internet and Information Technologies, 3–4 June 2016, pp. 112–119. University “St. Kliment Ohridski” Bitola, Macedonia (2016). https://doi.org/10.20544/AIIT2016.15 7. de Carvalho Silva, J., Rodrigues, J.J.P.C., Alberti, A.M., Solic, P., Aquino, A.L.L.: Lo-RaWAN — a low power WAN protocol for internet of things: a review and opportunities. In: 2nd International Multidisciplinary Conference on Computer and Energy Science, pp. 1–6 (2017) 8. Petrariu, A.I., Lavric, A., Coca, E.: LoRaWAN gateway: design, implementation and test-ing in real environment. In: 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME), Cluj-Napoca, Romania, 23–26 Oct 2019, pp. 49–53. IEEE (2019). https://doi.org/10.1109/SIITME47687.2019.8990791

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9. Zhao, C.W., Jegatheesan, J., Loon, S.C.: Exploring IOT application using raspberry pi (2015) 10. Mahendra, O., Syamsi, D., Ramdan, A., Astrid, M.: Design and implementation of data storage system using USB flash drive in a microcontroller based data logger. In: 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), Bandung, Indonesia, 29–30 October 2015, pp. 58–62. IEEE (2015). https://doi.org/10.1109/ICACOMIT.2015.7440175 11. Shah, A., Sethi, M.: The improvised GZIP: a technique for real time lossless data compression. EAI Endorsed Trans. Context-Aware Syst. Appl. 6, e5–e8 (2019). https://doi.org/10.4108/eai. 1-10-2019.160599 12. Ni, T., Su, S., Zhou, L.: EDIT: easy-to-use bio-data extraction, integration and refreshing tools. In: First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2006), pp. 91–95 (2006)

CargoTube: Next Generation Sustainable Transportation by Hyperloop Technologies Walter Neu1,2(B) , Heiko Duin3 , Lukas Eschment1,2 , James Napier1,2 , Thomas Nobel4 , Stephan Wurst5 , and Thomas Schüning1,2 1 University of Applied Sciences Emden/Leer, Institute of Hyperloop Technology,

Constantiaplatz. 4, 26723 Emden, Germany [email protected] 2 University Oldenburg, Carl-Von-Ossietzky-Straße 11, 26129 Oldenburg, Germany 3 BIBA - Bremer Institut für Produktion und Logistik GmbH, Hochschulring 20, 28359 Bremen, Germany 4 to-be-now-logistics-research-gmbh, Friedrich-Wilhelm-Raasch-Straße 22, 28865 Lilienthal, Germany 5 BALance Technology Consulting GmbH, Contrescarpe 33, 28203 Bremen, Germany

Abstract. CargoTube is a new approach for cost-efficient Low-Pressure Tube Transport (LPTT) which uses established technologies and can be implemented quickly. This makes it an affordable alternative to developments such as complex conventional hyperloop systems. CargoTube is less costly to deploy and affords lower safety and infrastructure requirements. At the same time, it is complementary to and compatible with emerging hyperloop standards and technologies. The benefits of zero direct emission while still providing high-speed movement of goods with great potential for sustainably, linking urban areas, economic regions, and production facilities are obvious

. Keywords: Low-pressure tube transport · Production logistics · Green transport · Hyperloop · CargoTube

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 277–284, 2023. https://doi.org/10.1007/978-3-031-26655-3_26

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1 Greenhouse Gas Emissions and Energy Demand of Transportation CargoTube’s most important long-term impact will be the provision of climate neutral energy-efficient high-speed freight transport, thus making a major contribution to the required transition to a net greenhouse gas neutral EU economy by 2050 [1]. Additionally, societal benefits such as reduction of additional transport emissions and pollution, e.g. aerosols, particulates, and noise; better quality of life, and helping to tackle the ongoing shortage of personnel in the European logistics industry. The reduction of congestion in urban areas and the European TEN-T network are addressed as well. Especially in high-speed transportation, the air resistance makes up the largest energy consumption by far, i.e. a next generation train at 400 km/h results in more than 83% energy losses by air friction. These losses scale with the velocity cubed due to aero- and thermodynamics and cannot be recuperated by any means. Substantially minimizing the high-impact energy consumption due to air friction is therefore the key parameter for sustainability of future transport modes. The vision for 2050 is a zero-emission multimodal cargo transport network, with CargoTube offering high-speed (but sustainable) connection options, seamlessly integrated with other green modes such as electric barges, green railways, zero emission ships, and electric delivery vehicles. With a rise in transportation demand which is expected to grow an additional 60% by 2050 [2], emission reductions and energy savings have to be very significant. This is why new paths should be explored with approaches different to the incremental improvements made in traditional transportation today. An energy reduction of more than 90% with today’s transport modalities is not possible as a matter of fact. Air resistance cannot be avoided for any future vehicle iterations. Slowing down and reducing the velocity would be the only option to be substantially impacting these losses, being in stark contrast with today’s fast-moving world. Instead, low-pressure tube transports such as CargoTube propose a guided vehicle inside of low-pressure tubes at 1% to 1‰ of the atmospheric pressure, thus drastically reducing the air resistance and saving the largest waste of the energy consumed during high speed transportation.

2 Energy Savings/The CargoTube Approach A major problem with decarbonizing freight transport is the requirement for fast movement of goods. Currently high speed usually means high energy, which is challenging to supply without the use of fossil fuels or other unfavorable sources. LPTT is estimated to use up to 80–90% less energy than high-speed rail transportation. Furthermore, the remaining 10–20% of energy required for acceleration and operating demands such as vacuum pumps can be supplied by electricity, for which there is an increasing range of zero emission sources (the lower energy demand is making it more achievable to meet this requirement). This is supported by previous hyperloop research which shows e.g. that routes could be up to 10 times more energy efficient than electric cars and 50–60 times superior to air transport [3]. Additionally, the use of advanced logistics optimisation algorithms including AI approaches ensures that the network is used most efficiently, further reducing energy use.

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CargoTube is a technology variant of the Hyperloop concept, which is in discussion since 2013 and was proposed by Elon Musk primarily for energy-efficient passenger transport [4]. With CargoTube, goods are transported in a tube with low pressure environment, but at medium speeds in order to maximize energy efficiency. Hyperloop technology is based on two pillars to reduce energy consumption. Firstly, the movement takes place within a low-pressure tube and secondly, contactless levitation is envisaged to avoid friction (air friction and rolling friction) [5]. Hyperloop technology is currently being developed for the transport of people and goods and is expected to start with the first test applications in the next 10 years. Quantitative studies show that the majority of goods in Germany are delivered via truck [6]. Pallets are usually the predominant load carriers, which is why the concept for the vehicle structure and the required tubes were selected accordingly for this size. The goods carrier can be a Euro mesh box or, for example, a standard transport box from Industry stakeholders. The maximum dimensions of the transport volume of such a standardized box are e.g. 1,200 x 1,000 x 1,000 mm. The size of the tube is based on the currently possible standard dimensions of the suppliers, which are currently max. 66” (1,676 mm) outside diameter, in order to realise the accommodation of the transport vehicle with the freight boxes. The transport weight is initially based on the permissible weight of the transport boxes, which is 250 kg. The basic CargoTube concept is shown in Fig. 1.

Fig. 1. CargoTube engineering sketch; guiding system, standardized boxes and pod outline.

3 Environmental Benefits In addition to the reduction in energy demand and Greenhouse Gas emissions of LPTT systems such as CargoTube and Hyperloop, these new modes of transport offer the additional benefit of 24/7 operation with an automated system physically separated by the environment surrounding the tubes, so that outside interference is excluded. Furthermore, emissions such as light emissions at night and fine dust emissions are prevented, offering a much better integration in the surroundings compared with traditional transport modes (Fig. 2).

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Fig. 2. Benefits of Hyperloop and all low-pressure tube technologies.

4 Application of Artificial Intelligence and Digital Twin Technology For CargoTube the objective is not only to develop, demonstrate and validate the LPTT CargoTube hardware technology itself, but also to develop a virtual CargoTube network simulator and Digital Twin technology, with robotic handling simulators, airlock modelling, switching technology simulator, and Life Cycle Analysis (LCA) tools. To achieve this the technology must be validated and a digital twin with simulations and models enhanced by AI must be created. Requiring, development of advanced handling, airlock and dynamic switching concepts and designs as well as lab demonstrators. A Virtual CargoTube Network Prototype which can be used to simulate routes and networks, and is coupled to real-world data to enable Digital Twin capability is needed. A Robotic Smart Packing/Loading Simulator which increases intralogistics speed and loading/packing at logistics nodes supported by open source simulation capability must be developed. In addition, a LPTT Life Cycle Analyser & Simulation Framework to analyse the environmental footprint of proposed LPTT networks/operations as well as their socio-economic, climate, micro-economic and logistics related impacts is necessary. The goal is to demonstrate the CargoTube LPTT Technology in laboratory demonstrators to TRL4 to prove the technological feasibility. A further objective is to simulate and analyse full-scale complex CargoTube networks for major industrial applications, including e.g. medical/pharmaceutical distribution and freight/postal logistics. This will establish the logistical and operational feasibility and accurately quantify the benefits and performance characteristics, including a full life cycle analysis. These use cases could additionally show the much increased benefit and profitability of LPTT technologies in addition to proving their maturity. The core concept is based on existing technologies and components, albeit applied in an innovative and potentially very effective way.

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Therefore, the objectives are not dependent on third party development of new technology or capability. The strong emphasis on mutual support and validation between the lab demonstrators and the simulation capabilities increases confidence in the results. Innovative software components and algorithms are necessary, but these developments will be guided by physical measurements and industry stakeholder expertise. This combination facilitates thorough analysis of how CargoTube can benefit multimodal freight networks and be sustainably developed in a coordinated fashion.

5 Requirements for a CargoTube Digital Twin A CargoTube Digital Twin will allow complete CargoTube networks to be created in a virtual environment for simulation and modelling purposes. Key advancements include the ability to model switching from track to track and merging of pods and trains in the tube network, as well as loading and unloading activities. A further innovation will be the design of integration components which allow coupling of this virtual network to real- world data to create the possibility of a full Digital Twin of a test installation or ultimately a full operational network. The virtual network prototype for CargoTube should allow performance and configuration parameters to be set based on the laboratory tests being undertaken in CargoTube lab demonstrators and indeed in other ongoing research projects which may be capturing complementary data. This ability to accurately model virtual hyperloop vehicles, operating in a full network will provide new information on how a proposed network or test facility should be configured and sized. The inclusion of an open-source Robotic Smart Packing/Loading Simulator in CargoTube is innovative in two ways. First, the capability to model robotic packing and loading/unloading, and corresponding intralogistics operations in a tube cargo network has not been considered in this depth before. Furthermore, new mobile manipulation capabilities can be researched, including the possibility of increased use of AI for characterization and detection of unknown objects. Market research of industry stakeholders has identified that fast loading/unloading of hyperloop or LPTT cargo technology is vital to achieve many of the anticipated benefits. The high speed of the cargo transit would be of no use if it takes a long time to unload the cargo. Mobile manipulation technology has now matured to a level that can provide humanlike capability: lightweight, power-efficient, strong, fast, and dexterous robotic arms (including two arms working together). Robots will allow industry to achieve many unmet needs in the coming years, such as increasing throughput on the factory floor, performing picking operations in warehouses, etc. In CargoTube the application of this new capability to modularize logistics operations such as the pods and containers used in the tube cargo operation will be researched to develop new mechanisms and approaches for autonomous smart packing. Apart from the immediate application to the CargoTube concept these innovations will be relevant for many other logistics operations and will be supportive of the emerging Physical Internet paradigm for future autonomous logistics networks. A CargoTube Digital Twin will extend the capability of the network by providing a scheduling solution for tube cargo logistics that routes cargo through the network and

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optimises the consolidation of shipments based on the pod size (e.g. 5 pallets per pod) to maximise the utilisation of the capacity.

6 Pairing Digital Twin Technology with Measurements from Test Infrastructures A Virtual CargoTube Network Prototype that is used to simulate routes and test facilities can be coupled to and be enriched with real-world data. This allows virtual CargoTube networks to be built and simulated, using measurements from real physical test installations to accurately define and validate operational parameters/performance of the key components (switches, loading/unloading, pods, product handling, etc.). This approach provides a “best of both worlds” solution to developing an extensive understanding (and validation) of CargoTube’s potential, maximizing the quantity and quality of information and understanding obtained from the effort and budget expended. Real physical measurements are made to establish the performance of the technology components. The simulation capability uses these validated inputs to extrapolate and to create complete CargoTube networks, including transport and intralogistics aspects. The key point is that many such networks can be considered and modelled in this way, contemplating many technological configurations and geographical scenarios. A key advance is the capability to model the network aspects of LPTT cargo networks, and in particular the ability for the cargo vehicles in the tube to dynamically merge in and out of virtual trains by coupling and decoupling. When switching from one branch to another in the network it is important to avoid slowing down the individual car (to in turn avoid hampering other network traffic). By enabling a complex tube network to be modelled to this extent, it will be possible to develop detailed simulations of logistics flows through the tube network. For example, for a given tube network represented in the Virtual CargoTube Network Prototype, the Digital Twin will determine the preferred routings for each shipment. The detailed logistics planning software shows how specific industry logistics networks, companies and operations would use such a network. Again, many network configurations, and many use cases and industrial companies can be considered. For each network/configuration/scenario the important KPIs such as reduction in energy, pollution, cost, congestion, etc. can be calculated quickly. This also facilitates a productive and rapid iterative approach.

7 Cargo Testing To validate the technology, the University of Applied Sciences Emden/Leer is planning an initial full-scale demonstrator on the university campus to gather experience on loading and unloading, taking vacuum technology into account (see Fig. 3). A first tube section of 24 m length is under construction planned to be operational at a lowpressure down to 10 mbar to demonstrate selected key parameters (KPIs) of the new mode of transport. The goal is to establish a test environment at scale and advance the understanding of necessary cargo loading/unloading optimization. Furthermore, current

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small scale prototypes explore the technical backbone needed for the system to allow a hyperloop transport network to operate at high frequency. In order to achieve this goal, the demonstrator will be used to explore e.g. dynamic testing with different track/wheel configurations on the cargo vehicle and potentially also investigate magnetic levitation demonstrators. The airlocks in the demonstrator enable the measurement of volumetric measurements of the gas volume to be evacuated as well as the shape of the vehicle and cargo bay as well as the positioning of the vacuum port. These data sets will serve as an experimental basis for the digital twin technology to verify simulated data and to augment existing models thus enabling more accurate and realistic models and simulations. Laboratory results will allow for reliable evaluation of the scalability of the simulations and digital twin technology in an iterative fashion and be fed by a network of existing local supply chains or even larger networks of industrial promoters. In the following development step, a 200 m track is planned to determine the behaviour of the load under extreme acceleration and braking processes. This will provide further insights and prepare the planning and realisation of larger scale pilot systems.

Fig. 3. CargoTube planned test track with cargo pod.

8 Impact on Logistics CargoTube will make major advances towards realising low-energy high-speed freight transport. Novel application of existing technology components to create a low pressure tube transport (LPTT) solution, enabling a feasible zero-emission high-speed transport solution to come to market quickly with realistically achievable levels of investment in infrastructure. A “network focused” approach – the proposed LPTT technology works not only for point-to-point connections but also for much larger networks. This, combined with innovations in switching and merging control for pods provides much greater flexibility. Integration of robotic handling/packing and intralogistics automation into the end-toend CargoTube solution addresses the important issue of fast automatic loading of items into containers and pods. Explicit consideration of integration and synergy with other freight transport modes, particularly emerging green options, and detailed optimisation of these CargoTube-enhanced multimodal networks will improve freight flow and lower cost and environmental influence of logistics.

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Acknowledgements. The project ePIcenter has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 861584.

References 1. Tiseo, I.: Transportation emissions in the European Union - Statistics & Facts, Statista (2021). https://www.statista.com/topics/7968/transportation-emissions-in-the-eu/#dossierKeyfigures 2. EEA. Trends and projections in Europe 2020 - Tracking progress towards Europes climate and energy targets—European Environment Agency (2019). https://www.eea.europa.eu//publicati ons/trends-and-projections-in-europe-1 3. https://www.expatica.com/ch/lifestyle/sw-hyperloop-prototype-track-unveiled-in-switze rland-216832/ 4. Musk, E.: White paper Hyperloop Alpha. Los Angeles (2013) 5. Kirschen, P., Burnell, E.: Hyperloop System Optimization. Virgin Hyperloop, Los Angeles (2021) 6. Bundesamt, S.: Share of trucks in freight transport performance in Germany in the years from 2013 to 2024 (2022). https://de.statista.com/

Training Environment for Rare Events Learning a Feasibility Study ´ Przemysław Sliwi´ nski(B) , Jarosław Sugier , Jacek Mazurkiewicz , and Tomasz Walkowiak Wrocław University of Science and Technology, Wybrze˙ze Wyspia´nskiego 27, 50-370 Wrocław, Poland {przemyslaw.sliwinski,jaroslaw.sugier,jacek.mazurkiewicz, tomasz.walkowiak}@pwr.edu.pl

Abstract. Monitoring a city in general and its infrastructure and public transportation nodes (like metro/subway) in particular, is a part of the Teleste company activity. With a number of real-time video cameras counted in thousands, the system that in an intelligent way will support the situational awareness is a logical way to exploit the potential of the machine learning and machine vision achievements. In the paper we report and discuss applicability of the 3D scene generating tools (“engines”) to create a realistic, physically plausible and interactive training environment for simulation and generation of rare random events, interactive training scenarios with the help of the digital models of architecture and infrastructure artifacts, and of the crowd behavior. Keywords: GDPR policies · Rare events detection · Bayes’ formula · Reproducibility · Realistic simulations · 3D scenes and scenarios generators · Machine learning · Personnel training

1 Introduction We start with a textbook application of the Bayes’ formula in order to provide a formal background for the report. Let E be an unusual/rare event (e.g. an accident, a fire or a train failure) and let D be a (binary) detection decision made by a classifier (e.g. smart sensor or DCNN-based analytics located at the edge/in a computing cloud). Then: 1. P(E) is a probability that the event occurs (sometimes referred to as a prior probability). 2. P(D) is the overall probability that the classifier indicates that an event E occurs (whether it occurred or not). In the binary case it is given by a simple total probability formula P(D) = P(D|E)P(E) + P(D|¬E)P(¬E), where P(D|¬E) is a false positives probability (the detector signals the event when it has not occurred). Note that P(D|¬E) = 1 − P(¬D|¬E), where the latter probability is a true negative rate. 3. P(D|E) is a probability that a classifier correctly detects an unusual even if it occurs (a true positive rate). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 285–292, 2023. https://doi.org/10.1007/978-3-031-26655-3_27

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Finally, P(E|D) is a probability that the event E indeed occurred when the detector indicates so (and thus that the appropriate action should be started). Its value is of the main practical interest, and is given by the Bayes formula P(E|D) =

P(D|E)P(E) P(D|E)P(E) = . P(D) P(D|E)P(E) + P(D|¬E)P(¬E)

The formula is well known and widely applied. In particular, it gives a formal and sound explanation of the otherwise counter-intuitive poor behavior of “very good” classifiers when the detected events are very rare; see [1]. Example 1: Let a classifier be characterized by the true positive rate P(D|E) = 1 and the true negative rate P(¬D|¬E) = 1 − 1/100. That is, it always detects the event E when it occurs and generates false alarms “only once in a hundred times”, i.e., P(D|¬E) = 1/100. One can call such a classifier a good one, however, if the event E is very rare and occurs with probability P(E) = 1/1 000, say, then P(E|D) ∼ 9%, which means that only one in eleven alarms is not false. Example 2: To get one false alarm out of eleven (P(E|D) ∼ 90.9%), the classifier with two orders of magnitude smaller false positives probability, i.e. with P(D|¬E) = 1/10 000, has to be used.

Fig. 1. Human silhouettes counting in presence of smoke and obstacles (left: note false positive and false negative detections) based on the OpenPose library (see [2] and https://github.com/CMUPerceptual-Computing-Lab/openpose ).

Interestingly, the Bayes’ formula offers an immediate and relatively simple solution to that problem (Fig. 1). Solution 1: Assume we have a pair of different classifiers with P(D|¬E) = 1/100 (as in Example 1). If we apply the other when the former classifies an event as an alarm, then, by virtue of the Bayes formula (now with a prior probability P(E) = P(E|D) ∼ 9%), the true alarm probability increases tenfold, P(E|D) ∼ 90.9%, and matches the classifier

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from Example 2. Adding yet another classifier with P(D|¬E) = 1/100 will further improve the overall performance up to the level P(E|D) ∼ 99.9%. The rare events introduce an additional problem: the learning data representing such events are relatively scarce and their usage and availability (in a form of open datasets/benchmark) is ethically dubious and often restricted by law – especially in case when they are related to tragic or violent accidents. In the S-AWARE project we proposed the following. Solution 2: Instead of learning the analytics (or testing the available third-party solutions) using real pictures and video sequences, use “3D game engines” to create realistic, interactive and easily customizable simulations. In the remainder, we discuss applicability of this solution in detail.

2 Training Environment for Rare Events Learning Monitoring public transportation nodes (like subways) is, with a number of real-time video cameras counted in tens of thousands, a tempting playground for systems that in an intelligent way support the situational awareness of the operator and a natural way to take advantage of the machine learning and machine vision algorithms potential. In the note we report and discuss application of 3D scene generating tools (“game engines”) in development an end-to-end realistic and interactive training environment for simulation and generation of: • Rare and dangerous random events (e.g. a conflagration or installation fire, flood, criminal offences, acts of violence, terroristic attacks, explosions, or train malfunctions). • Interactive (situation dependent) sequences of user-defined events (e.g. SOPs (standard operating procedures), like evacuation in case of the aforementioned events, in particular: modeling the consequences of user decisions (e.g. opening or closing evacuation doors, sending security or fire service personnel). For modeling behavior of: • a crowd (a panic in narrow tunnels and damaged passages); • groups of people (fire brigades, security and medical squads); • a single person (non-authorized personnel intrusion, potentially fatal accidents, railway tracks trespassing).

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And for simulations of: • sensors (monitoring cameras, fire and smoke detectors) failures • presence of visual obstacles (fog, smoke, fire, multiple light sources) Two kinds of applications have been analyzed: • training the personnel, e.g.: – learning SOPs, verifying their effectiveness, – testing coordination, cooperation and communication. • training the machine learning algorithms, in particular, for – delivering realistic data representing rare and dangerous events and scenarios, – reliable (i.e. by reduction of false positives) detection of such events, – detecting people in dangerous zones, counting and tracking people. Note that these applications are intertwined: • Machine learning is used to collect, analyze and aggregate the data from (possibly thousands) sensors in order to support the decision making • The personnel’s decisions are used by the machine learning algorithms for their assessment The rationale behind such our approach is thus multifold: • GDPR (or any other privacy protection policies) significantly reduces the amount of data (especially in a form of video recordings) available for machine and personnel learning. • Statistical inference (especially in case of causal rather than correlative models) and machine learning require a large number of illustrative examples. Dangerous events are relatively rare and thus do not offer sufficient amount of data. • Machine learning algorithms are usually learned with the help of high-quality data (i.e. the video captured in good exposure conditions, with uniform and usually white light, without smoke, fog or fire obstructing/occluding a scene). Using only real data hampers their quality and dependability. • As mentioned earlier, learning with the help of real recordings can be morally and ethically questionable.

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Fig. 2. A 3D scene with deterministic (left) and random, governed by the Poisson law (right) lights.

3 Selected Topics Generating realistic scenes and scenarios is a multi-aspect challenge. Here we report those related to the visual fidelity of the resulting frames (Fig. 2). 3.1 Poisson Law Light Sources Frames (sequences of images) generated by the Unity 3D software (that was used in the project), and by the similar platform like e.g. Unreal Engine, are deterministic in the sense that each image pixel is determined by the lights, objects present in a scene, their relative locations and their surface properties. It is a convenient (and visually indistinguishable) approximation of the random nature of light (especially when the scenes are sufficiently bright and used due to simplicity. Nevertheless, from the machine vision and machine learning point of view, in the real object detection and classification tasks (like moving persons under varying lighting conditions) it seems pivotal to generate images as if they are captured by digital sensors. During the project development a simple conversion algorithm was proposed and examined: the frames generated by Unity 3D software were transformed into their randomly generated counterparts where the value of each original pixel was used as a parameter λ in the Poisson distribution; see [3, 4]. 3.2 Varying and Non-uniform Light Sources Standard images for object/person detection learning are usually of good quality: the objects and persons are well and uniformly lit and not obscured or occluded; see e.g. [5–9]. In turn, the lighting conditions in subway are rather specific:

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• There are no single and uniform light sources that have various spectral characteristics (LED, incandescent or fluorescent lamps, large displays). • Presence of reflections, shadows and moving light sources. The above conditions make a reliable object/person detection even more difficult. Considering the known (and rather common sense) assumption that machine learning algorithms will perform better if the learning sets include images of the same objects taken in various conditions, the simulated environment in the project was programmed to include such light sources as well (Fig. 3).

Fig. 3. From left to right: scene generated with decreasing realism. In particular, optical aberrations introduced by the CCTV optics and reflections and shadows are not present in the least realistic scene.

4 Recommendations The scenes and scenarios generated by the Unity 3D platform were find sufficient for both planned applications, that is for people training and machine learning. Not all problems posed by integration of the simulated environment with the real one for both people and machine learning purposes were solved, however, the experience gathered during the project led to the following recommendations related to the applicability of such environments (one immediate recommendation, derived immediately from application of the Bayes’ formula, was to suggest application of a battery of various classifiers instead of a single one).

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4.1 Integration with the Real S-AWARE System The software tools and protocols examined during (and for) the project were sufficient (for instance, both the remote procedure call and the streaming protocols, REST and NATS, respectively, were tested and approved) and able to mimic the real-life conditions and real-time events. Nevertheless, in as a long-term goal it was recommended to create “cyber-twins”, that is, the digital models of the existing subway architecture and infrastructure together with appropriate models of crowd behavior and customer’s SOPs (“standard operating procedures”). Such a move is initially rather expensive and risky because: • In order to implement and embed the customer-specific SOPs in the simulated environment, one has to translate them from “human language” into the formal one, with precisely defined actors, actions, data flows and state machines. • At the current state-of-the-art it is not possible to use such a written scenario to automatically create static and dynamic elements of scenes and actions. Therefore, each such scenario has to still be tailored and adapted. • Testing and validation of the scenarios and implemented SOPs, especially when they have options and loops, and depend on random events, is cumbersome and time consuming. In order to alleviate this risk, we recommended development of the testing tools that will formally (or using e.g. Monte Carlo methods) verify that all path in SOPs end up in an admissible and predicted by the procedure state. 4.2 Integration with Third-Party ML/MV Analytic Modules/Platforms Taking into account a rapid progress in the ML/MV field (new algorithms, new effective classifier implementations and growing hardware support) we recommended using an open architecture in which the third-party analytics are used as plug-ins in various configurations. Such an approach allows in particular for: • Verification and integration of customers’ preferred/trusted classifiers that can be available only through a secured access protocol. • Flexible licensing and software updating policies. Nevertheless, in spite of this rapid progress, we recommend using available algorithms in a “man-in-the-loop” configuration where they are only entitled to support awareness of the personnel and suggest possible solutions like: • The best evacuation routes. • Assistance in counting and tracking people. • Assessment of availability and readiness of security personnel. These application restrictions are motivated by the following known limitations of the ML/MC technologies: • There are no formally sound results that would explain effectiveness of the deep convolutional neural networks in object detection tasks.

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• There are no results that would explain the limits of the recursive/LSTM networks in tracking applications. • The only available performance assessments are based on benchmark data. Finally, the initiatives like Open AI (see [10]), which are aimed at delivering safe and verified ML/MV solutions can make the above reservations obsolete and irrelevant. Acknowledgements. The report was developed as a part of the POIR.01.01.01–00-0235/17 project entitled “S-AWARE – Advanced Intelligent Security System” and founded by European Regional Development Fund for Teleste Video Networks LLC in 2018–2020.

References 1. Lee, P.M.: Bayesian statistics. Oxford University Press, London (2012) 2. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: “Realtime multi-person 2D pose estimation using part affinity fields.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299 (2017) 3. Seitz, P., Albert, J.T. (eds.): Single-Photon Imaging, vol. 160. Springer, Cham (2011) ´ 4. Sliwi´ nski, P., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Helt, K.: Off-the-shelf convolution neural networks low-light object detection comparison. In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds.) RelStat 2018. LNNS, vol. 68, pp. 302–310. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12450-2_29 5. Saravanakumar, S., Vadivel, A., Saneem Ahmed, C.G.: Multiple human object tracking using background subtraction and shadow removal techniques. In: 2010 International Conference on Signal and Image Processing. IEEE (2010) 6. Joshi, Kinjal, A., Thakore, D.G.: A survey on moving object detection and tracking in video surveillance system. Int. J. Soft Comput. Eng. 2(3), 44–48 (2012) 7. Bai, L., Han, J., Yue, J.: Night Vision Processing and Understanding. Springer, Cham (2019) 8. Adjabi, I., et al.: A past, present, and future of face recognition: a review. Electronics 9, 1188 (2020) 9. Guo, R., Dai, Q., Hoiem, D.: Paired regions for shadow detection and removal. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2956–2967 (2012) 10. Rossi, F.: Beneficial and safe AI: academic, governamental, and private initiatives. Sistemi Intelligenti (2017)

Smart Transport and Mobility

Comparison Analysis Between Pneumatic and Airless Tires by Computational Modelling for Avoiding Road Traffic Accidents Mykola Karpenko(B)

, Olegas Prentkovskis , and Paulius Skaˇckauskas

Department of Mobile Machinery and Railway Transport, Vilnius Gediminas Technical University, Plytin˙es g. 27, 10105 Vilnius, Lithuania {mykola.karpenko,olegas.prentkovskis, paulius.skackauskas}@vilniustech.lt

Abstract. The work presents theoretical research on the pneumatic and airless tires based on the numerical modelling using the Finite Element Method (FEM) like alternative to experimental testing, since its time and economical values are more effective for a research connecting with a tire behaviour. The approach presented in this research of the numerical simulation sequence enables the engineer to determine efficiently dynamic properties and behaviour of vehicle airless tire at design stage. In the research numerical FEM modelling and comparing analysis between pneumatic (with different inflation pressure) and airless tire behaviour characteristics (tire deformation, tire components material stiffness and stress results etc.) under investigation. According to numerical simulation results and comparing analysis between pneumatic and airless tires behaviour characteristics was established that airless tire provide more safety using of a vehicle in order to avoid road traffic accidents by a tire puncture and blowout, flat tire problem and avoid complicate sensors installation. Keywords: Tire · Airless · Composite · Road traffic accidents · Numerical modelling

1 Introduction The development of automotive industry sector pushed a different types of vehicles on leading position of land goods transportation or peoples traveling. The massive increasing of peoples traveling by personal vehicles lead to increase the annual million kilometres travelled by vehicles and numbers of registered vehicles [1]. According to United National Economic Commission (UNEC) statistics [2] – the trends in road traffic accidents (RTAs) and fatalities’ in period 2009–2019 years is slowly decrease, shown in Fig. 1a. Between 2009 and 2019, the total number of fatalities in RTAs decreased by ~20% in the EU region. At the same time, World Health Organization (WHO) [3] statistic shown that the RTAs is on the leading place of deaths for a children and young people up to 29 years old. According to [4] research, between the 2015 and 2030 years expected © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 295–305, 2023. https://doi.org/10.1007/978-3-031-26655-3_28

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an up to $1.8 trillion losses costs (hospitalization, combined societal losses of labour etc.) due to collisions from RTAs. The COVID-19 pandemic in 2020 resulted in varying degrees of lockdowns and other restrictions in different EU member countries, comparing to previous years, see Fig. 1b shown on Germany example. While these restrictions reduced vehicle traffic to a greater or lesser degree, the impact on RTAs was more nuanced. This why was an insignificant decrease of facilely in RTAs by 2020 can be observed in different organizations statistics.

Fig. 1. UNEC statistics 2009–2019 [2]: a) road traffic fatalities by region (thousands); b) monthly road traffic fatalities in Germany in 2014–2020 period of time.

The main aim of this statistics is providing an information if an implements of new technologies or methods and rules help to avoid or decrease a RTAs. The researches in transport sector on the safety for drivers and pedestrians still one of the relevant aim for investigation according to [5] and one of the important direction for future of

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road transport sector. According to [6], at around half of RTAs was connected with a tire problems many of which are caused by punctured or blowout tires, especially is implement for electrical car, since their more loaded by own mass [7], in general by 30%. From another side, since a vehicle tires being the only a contact between the vehicle and the road surface (contact patch problem) [8], their conditions much influenced on a dynamic stability and safety, especially if tires is flat. According to [9], the main causes of tires blowout are severe surface wear and abnormal tire pressure, which is most likely to occur in the case of high-speed driving or sudden braking. Maintaining correct inflation pressure in tires helps to keep vehicle handling and braking at its best, as well as improving fuel efficiency and tire life by [10]. According to [11], the pressure inside tire less than 0.6 bar from the recommended pressure lead to 4% greater fuel consumption, 45% of shorter tire life and risk of permanent damage to the tire, more detail information is summarise and shown in Fig. 2.

Fig. 2. Risks of under inflation air pressure in a tire.

According to [12], the driving with a too low or to high inflation pressure in the tire (more than ~0.5 bar) has the similar level by dangerous on blowout effect. Therefore an adopt pressure monitoring system in the vehicles tire is implement in almost all new manufactures road vehicles [13]. Many countries are planning to made obligatory requiring for vehicles to have a tire pressure monitoring system (TPMS) sensor installed on every wheels [14]. From another side an airless tire can help to avoid a problem with a tire blowout and installation of complicate sensors systems like TPMS. The pneumatic tire is usually composed of a several structural components (see Fig. 3a), joined together and even slightly overlapped during the vulcanization process, which are made either of rubber or rubber-based composites [15]. The airless tire (see Fig. 3b) is produced by a lattice structure of composite materials (rubber and offend fiberglass) and have a main advantage that doesn’t required an inflation air pressure [16]. Therefore by construction advance the airless tire doesn’t subject to puncture or blowout problem comparing to pneumatic tire, what positive can influence on safety driving and avoiding some an accidents connected with tire problems. To develop a tires in a way of best efficiently using in different conditions (off-road, winter, city etc.), the factors that influencing on the friction and the contact mechanics of the tire with the road surface should be well studied as well like a dynamic behaviour of object under

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Fig. 3. Vehicles tires structure compositions: a) pneumatic tire, re-draw from [15]; b) airless tire, re-draw from [16].

a different loads. According to [17], a numerical modelling using the finite element method (FEM) or similar methods is an alternative to experimental testing, since, its time and economical values are more effective for a research connecting with a tire behavior. Already exist a wide range of theoretical and experimental researching for a pneumatic tire behaviour depends from a different load conditions [18]. From another side a proper mathematical modelling of airless tire and numerical testing in different conditions in current time just started to be an active direction for a researching [19]. The approach presented in this research of the numerical simulation sequence enables the engineer to determine efficiently dynamic properties and behaviour of vehicle airless tire at design stage. The present research is based on numerical FEM modelling and comparing analysis between pneumatic (with different inflation pressure) and airless tire behaviour characteristics and in future will be held in simulation of tires contacts with the road surfaces under different loads conditions.

2 Numerical Model and Boundary Conditions Comparison analysis between pneumatic and airless tires based on FEM computational modelling carried out with commercial software ANSYS®. For modelling was used standard composite road vehicle tire with a properties from [15 and 18] researches and it’s analogy airless tire with a properties from [20 and 21] researches. For both tires nonlinear materials models selected for comparison, which include ideally elastic materials for which the stress strain relationship is derived from a strain energy density function. The both tires, created 3D model, was divided into few components with different material properties. In pneumatic tire, in the middle rubber layers, ply cords extent to the beads and laid substantially at 90◦ to centreline of tread, carcass being stabilised by circumferential belt. The airless tire model is less complicated and just divided to the components with bounded contact. To model the pneumatic tire pressure (inflation pressure), in first case, to the inner surface volumes of tire was inflated value of 241.4 kPa (35 psi – recommended for tire which under research) and in another cases pressure was reduced to 221.6 kPa; 201.2 kPa; 181.3 kPa (by -0.2; -0.4; -0.6 bar). In the airless tire,

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between in inner sections was upload inflated value of 101,3 kPa, equal to atmospherically air pressure. The support surfaces of tires in the modelling was chose the surfaces in contact with a wheel rim. The investigation area is a 3D volume, and divided into hybrid elements mesh tetrahedrons with hexahedral. The mesh refined near different material connecting points and around restrictive objects. Near boundary layers, up to five Inflation Layers (IL) created with an expansion factor of 1.1–1.3. The grid convergence study performed by developing different meshes for both of the tires to determine how the mesh quality affects simulation results. The mesh independence study results is given in Table 1, were to summarise the main characteristics of the meshes, and it is very clear that simulation time is highly dependent on the number of mesh nodes (PT – pneumatic tire; AP – airless tire). At Fig. 4 presented boundary conditions for tires modelling. Table 1. Mesh independence study. Grid parameters

Tire type

Max elements size

№ of IL

Total № of elements

Tire deformation

100 steps solution

Mesh 1 (M1)

PT

-5; -6 (mm)

3

2,185,471

4.2

254 s

1,914,217

4.8

232 s

Mesh 2 (M2)

PT

-4; -4 (mm)

4

Mesh 3 (M3)

PT

-2; –3 (mm)

5

Mesh 4 (M4)

PT

-2; –2 (mm)

5

AT AT AT AT

3,845,314

4.9

607 s

3,627,882

5.3

548 s

6,101,827

5.1

967 s

5,872,203

7.3

908 s

7,378,855

5.2

1395 s

7,057,697

7.5

1204 s

It is important to note that the mesh resolution plays a pivotal role in the final simulation results. By the performed tests simulation with different mesh was obtained a first results of tires deformation, which show that by independent mesh study M3 and M4 account for nearly 3% difference in the estimated deformation, but the final simulation time required for convergence of the two meshes has a significant difference. From the final simulation results, it is clear that the simulation time is highly dependent on the number of mesh nodes. Due to the slight difference between M4 and M3 is best regarding computational costs and is further employed M3 for the numerical analysis carried out in the following researching for both type of tires. The numerical code was based on the FEM (Newmark method) and performed in the dynamic simulation. The equilibrium equation can be expressed, according to [22 and 23], as:     ext (1) [M ] X¨ t + [C] X˙ t + {F}int t = {F}t ,

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Fig. 4. View of the boundary conditions for numerical modelling: a) pneumatic tire; b) airless tire. int where {F}int t = [K]{X }t are vector of internal forces; {F}t are vector of internal forces; {X¨ }t , {X˙ }t , {X}t are the acceleration, velocity and displacement vector at solution time (t);. [M], [C] and [K] are matrices of masses, damping and stiffness, respectively. Since, the variable vectors is assumed to be linear within the time step, the equations for acceleration, velocity and displacement will have a form:

X¨ t = a1 (Xt − Xt−t ) + a2 X˙ t−t + a3 X¨ t−t ,

(2)

X¨ t = a4 (Xt − Xt−t ) + a5 X˙ t−t + a6 X¨ t−t ,

(3)

Xt = Xt−t + t X˙ t−t + (1/2 − β)t 2 X¨ t−t + βt 2 X¨ t .

(4)

The γ and β are dimensionless specify integration parameters which varied. Where a1 . . . a6 is an calculate integration constants in simplify view, extended view of that constants shown in Table 2. Table 2. Specify integration parameters of constants γ and β. Constant

a1

a2

a3

a4

a5

a6

Specify parameters

1/β · Δt 2

1/β · Δt

β – 1/2

γ · Δt · a1

1 + γ · Δt · a2

Δt · (1 + γ · a3 – γ)

The major advantage of the used Newmark method is the lack of time-consuming operations which involving stiffness matrix inversion and only mass diagonal matrix is inverted. However, the main disadvantage – method is conditionally stable, implying the following limitation on the time step according to the stability condition: γ ≥ 1/2; β ≤ 1/2; and t ≤

1 , √ ωmax (γ /2) − β

(5)

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where ωmax is the highest element frequency in the structural system mesh. Additionally, assumed that the road is not deformable, as a rigid body and contact with tire based on the penalty method with the friction coefficient set to zero. The loads influenced on tire was performed like forces from the road surface in sinusoidal waveform.

3 Results from the Numerical Modelling The example of numerical simulation results view on tires behaviour is shown in the example of uploading a 3 kN (middle range from upload external force during simulation), see Fig. 5. The wide range of simulation results was obtained from the numerical modelling of pneumatic tire (with different air inflation pressure) and airless tire, the main results is displayed for comparing analysis and include: external force vs. tire deformation (displacement) (Fig. 6a); radial stiffness vs. displacement (Fig. 6b); equivalent stress in rubber component vs. tire deformation (Fig. 6c); reaction forces on the support surface of tire vs. tire deformation (Fig. 6d). The functions of regression on Fig. 6 is the third degree polynomial with 95% coefficients confidence bounds for each line and goodness of fit.

Fig. 5. Numerical simulation results (with external force 3 kN): a) pneumatic tire (inflation air pressure 241.4 kPa); b) airless tire.

From the simulation results and according to obtained graphs of tires deformation depending from external force its seems that the pneumatic tire with a recommended inflation air pressure shows the lower value of deformation, than is airless tire (more deformed on ~8%, up to 5.6 kN external load) and more deformed pneumatic tires with reduced inflation pressures by 0.2; 0.4; 0.6 bar (more deformed on ~23%, ~26%, ~34%, respectively). Additionally, should be pointed that after uploading the external forces, which is more than allowed maximum weight what tires can hold according to manufacturing, the airless tire shows the worst results by deformation. For a wide range of displacements (tire deformations), the reaction force curves is quite the same shape

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Fig. 6. Graphs from numerical modeling results: a) external force vs. tire deformation; b) radial stiffness vs. displacement; c) reaction forces on the support surface of tire vs. tire deformation d) equivalent stress in rubber component vs. tire deformation.

have like external forces curves for both tire type with an only a lower values, since rubber material components of tires is ascorbate some energy during their deformation. For the largest deformation, the differences in each cases can be explained by the highly nonlinear behaviour of the material that constitutes the tires and does not allow to match the exact external forces. For this reason, concerns the radial stiffness vs. tire deformation diagrams are shown, which means that the radial stiffness of the tire decreases (stress increase) with an increasing deformation applied to from outer surface to the centre of the rim. This kind of obtained results can be explained that maximum allowed weight for carrying by both of tire it’s around 500 kg. Until the maximum load the airless tire shown the close results to pneumatic tire with recommendation inflation pressure, at the same time the reducing of inflation air pressure in tire provide a significant increasing of tire deformation. The more deformed pneumatic tire the more risk of permanent damage to the tire, which lead to road traffic accidents. After the maximum allowed external load, the airless tire shows extremely the worse characteristics by deformation

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and probably will have a failure, since by results its materials characteristics passed the Yield point and in plastic region of materials stress-strain characteristics. That’s why it’s not recommend to overload airless tire. The present research is held for future researches in area of composite tire (airless and pneumatic) investigation, with taking in account viscos-elastic properties of rubber material, for obtaining a realistic model and simulation dynamic processes of vehicle tire contacts with a road surfaces under loads.

4 Conclusions With reference to the theoretical research on the pneumatic and airless tires based on the numerical modelling using the Finite Element Method, like alternative to experimental testing, since its time and economical values are more effective for a research connecting with a tire behaviour. In the research was done numerical modelling and comparing analysis between pneumatic (with different inflation pressure) and airless tire behaviour characteristics (tire deformation, tire components material stiffness and stress results etc.), under investigation. From the obtained results was found that the pneumatic tire with a recommended inflation air pressure shows the lower value of deformation, than is airless tire (more deformed on ~8%) and more deformed pneumatic tires with reduced inflation pressures by 0.2; 0.4; 0.6 bar (more deformed on ~23%, ~26%, ~34%). After uploading the external forces, which is more than allowed maximum weight what tires can hold according to manufacturing information, the airless tire shows the worst results by deformation. Until the maximum external load the airless tire shown the close results to pneumatic tire with recommendation inflation pressure, at the same time the reducing of inflation air pressure in tire provide a significant increasing of tire deformation. The conducted research disclosed that airless tire is one of solution in the future to provide more safety using of a vehicle in order to avoid road traffic accidents by a tire puncture and blowout, flat tire problem and avoid complicate sensors installation, with a main remark – not overloading. Until the allowed maximum external load the airless tire shown the close results to pneumatic tire with recommendation inflation pressure, at the same time the reducing of inflation air pressure in tire provide a significant increasing of tire deformation, which lead to more risk of permanent damage to the tire, which lead to road traffic accidents. In the next step its will be worth to provide an experimental research with taking in account tire frequency analysis for validation of presented numerical modelling.

References 1. Abohassan, A., El-Basyouny, K., Kwon, T.: Exploring the associations between winter maintenance operations, weather variables, surface condition, and road safety: a path analysis approach. Accid. Anal. Prev. 163, 1–10 (2021). https://doi.org/10.1016/j.aap.2021.106448 2. United Nations Economic Commission: Statistics of road traffic accidents in Europe and North America. United Nations Publications (UNECE), vol. LVI, p. 185 (2021). https://unece.org/ transport/publications/2021-statistics-road-traffic-accidents-europe-and-north-america 3. World Health Organization (WHO): European regional status report on road safety 2019. WHO Regional Office for Europe CCBY-NC-SA3.0IGO, p. 133 (2020). https://apps.who. int/iris/handle/10665/336584

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A Single-Level Joint Formulation for Travel Demand Estimation Under Stochastic User Equilibrium Mohamed Eldafrawi(B)

and Guido Gentile

Sapienza Università di Roma, Via Eudossiana, 18, Rome, Italy {Mohamed.eldafrawi,Guido.gentile}@uniroma1.it

Abstract. The Origin-Destination (OD) travel demand is an essential component of transportation analysis, particularly for simulation models, such as static or dynamic traffic assignment that assess the impact of various strategic transportation plans. The conventional formulation of the OD estimation from traffic measurements is a bi-level optimization problem with equilibrium constraints. However, tackling bi-level problems for large-scale networks is computationally challenging, preventing the scalability of OD estimation. This paper presents a single-level join formulation of the travel demand estimation problem under Stochastic User Equilibrium (SUE) as a non-linear equation system. This single level formulation uses an extension of the SUE assignment fixed point formulation, making it transparent to the congestion and route choice model. A Jacobian free version of the Gauss-Newton algorithm is used to solve the model, which gave the freedom to incorporate multiple sources of traffic measurements in the estimation process. Numerical results on two networks illustrate the effectiveness and efficiency of the proposed methodology. In addition, estimation results indicate that the new formulation is robust with respect to count location coverage, measurement errors, and historical OD demand. Keywords: Stochastic traffic assignment · OD estimation · Trust region method · Gauss-Newton · Jacobian free GMRES

1 Introduction The Origin-destination (OD) matrix is essential for traffic management, control, and long-term transportation planning. This matrix contains information about the distribution of travelers’ activities between different urban traffic zones. Over the years, OD demand estimation has been one of the fundamental challenges in the transportation domain. However, estimating the OD demand using conventional survey-based approaches is notoriously difficult, expensive, and can take significant time. Therefore, the development of new methodologies which can estimate the travel demand using traffic measurements has been the topic of extensive research. In the early phase of OD estimation [3, 8, 16] assumed a linear mapping between OD demand flows, and link flows, considering the road networks are not congested. However, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 306–319, 2023. https://doi.org/10.1007/978-3-031-26655-3_29

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as the uncongested assumption is typically not satisfied in urban networks, later studies introduced a traffic assignment component inside the OD estimation framework to search for OD demand that produces the user equilibrium state [4, 6, 13, 20, 22] thus modeling the problem as a bilevel optimization problem. Nevertheless, finding a globally optimal solution to the bi-level optimization problem is difficult since it is a non-convex and nondifferentiable optimization problem. Moreover, iteratively solving the upper-level estimate problem and the lower-level equilibrium assignment problem is a common task of existing algorithms for tackling the bi-level optimization problem, which may reduce the approach’s practical value. Yang [20] provided two heuristic solution approaches for solving the general bi-level OD estimation problem, the iterative estimation-assignment algorithms, which iteratively solve the traffic equilibrium model and the OD estimation model, and sensitivity-analysis-based algorithms, which are based on the computation of the change in link flow with respect to change in travel demand. For large-scale networks with many OD pairs, simply using the bi-level formulation with an iterative solution is very expensive and computationally challenging. In recent years, some researchers [7, 11, 14, 15, 18] have developed one-level OD estimate formulations to address the difficulties presented by the bi-level formulation. Nie and Zhang [15] developed a new one-level formulation that integrates the Beckman integral term of (UE) formulation [1] from the lower-level objective function with the measurement deviations term from the upper-level objective function. Shen and Wynter [18] proved that the formulation proposed by [15] is inconsistent with the equilibrium conditions and proposed a new formulation, which can be seen as a special case of UE assignment with elastic demand. Those UE-based estimation models above assume that travelers have a complete awareness of the state of the network and do not have varying perceptions of the costs of their trips. In practice, however, travelers have taken alternative routes that are not equally priced [12, 17]. Incorporating such dispersal into behavioral modeling has led to the development of SUE models. These models permit perceived trip costs to vary between travelers and are more realistic than deterministic UE. Many researchers [9, 19, 21] proposed bi-level formulations for the OD estimation, and adopted an SUE in the objective function of the lower level. However, the bi-level SUE-based OD estimation formulation is not scalable to large networks due to its inefficiency. To address this issue, Ma and Qian [7] proposed an extension of the single-level path flow formulation proposed in [18] to the case of SUE and used two different models for the SUE; satisfaction-based and Logit-based functions presented in [17] using those SUE models which are an extension of Beckman integral in the formulations make the formulations not extensible with respect to the route choice model. Furthermore, the authors used a sensitivity analysis method to compute the gradients of the proposed formulations, which can limit the formulation’s ability to incorporate additional data sources in the estimating process. Compared with the previous studies, this paper contributes to the literature of the static OD demand estimation as follows: i) proposing a single-level formulation for jointly solving travel demand estimation and Stochastic User Equilibrium assignment as a non-linear equation system; ii) using the fixed point formulation of the SUE assignment to develop the model; iii) A Jacobian free version of the Gauss-Newton algorithm is used

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to solve the model, allowing us to incorporate multiple data sources in the estimation process. The rest of this paper is organized as follows: In Sect. 2, notation, definitions, and model formulation are presented. Section 3 describes our solution algorithm. Section 4 provides our numerical experiments on two test networks. Finally, Sect. 5 concludes the findings and discusses some future work.

2 Model Formulation 2.1 Demand and Supply The traffic network topology is represented using a directed graph (N , A), where N is the set of nodes and A ⊆ N × N is the set of links. Let Z ⊆ N be the sub-set of nodes, called zone centroids, where trips can start and end. The land is partitioned into traffic zones, each represented by a centroid, a single point where all activities are assumed to be concentrated, thus introducing a spatial approximation. The demand of users travelling from origin o ∈ Z to destination d ∈ Z is characterized by a non-negative flow dod ≥ 0 during the reference period. These are the entries of the so-called OD matrix. A trip is usually represented on the network by a path, i.e., a concatenated sequence of links from origin to destination. Let P ⊆ Z × Z be the set of OD pairs with positive demand. We assume that for each od ∈ P, there is a non-empty choice set Kod which is the set of all a-cyclic paths considered by users to make their trips connecting o ∈ Z to d ∈ Z on the network; K = Uod ∈ P. In static models, it is assumed that during the reference period, stationary conditions hold, i.e., all state variables describing traffic (flow and speed) are constant (steady state). The flow of users (volume) traverses the generic link a ∈ A during the reference period, denoted fa. The cost ca of using the generic link a ∈ A is: ca =

ma + ta , γa

(1)

where ta is the travel time of link a, while ma and γa are, respectively, the monetary cost and the value of time on link a. Costs are measured in terms of time, a physical unit. The value of time allows conversion to monetary units; it is assumed to be link specific because users have different perceptions of time related to physical and psychological comfort under different conditions (for example, on road networks: number of stops due to traffic lights, road surface quality, lane width and margins, visibility and lighting, and presence of side activity that requires driving attention; on transit networks: waiting and walking vs. riding, seating vs. standing and crowding, etc.). The travel time depends on the link flow through a (separable) cost function that reproduces congestion: ta = ta (fa ).

(2)

In this study, the Bureau of Public Roads (BPR) link travel time function is adopted:   βa  fa 0 , (3) ta (fa ) = ta . 1 + αa . ka

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where t0 a is the free flow time (which shall consider the possible intersection delay), ka is the capacity, while αa and βa are two calibration coefficients (by default, one can consider α = 1, β = 2). The generalized cost gk of each path k ∈ K is assumed to be additive, i.e., it is given by the sum of the costs of its links:   gk = ca = ca .δak , (4) a∈Ak

a∈A

where Ak ⊆ A is the set of links constituting path k and δak is one if link a ∈ A is included in path k and 0 otherwise. These Kronecker deltas can be organized into a link-path incidence matrix, which allows a compact representation of the above linear relation. Each user has a probability pk of choosing the generic path k ∈ Kod when traveling between origin o and destination d that depends on the cost of all alternatives considered for the pair od ∈ P: pk = pk (gh , ∀h ∈ Kod )

(5)

In this paper, without loss of generality, the Multinomial Logit route choice model is adopted:   min gk − god ek = Exp − θod   (6) min wod = god − θod · log eh h∈Kod

= Min(gk , ∀k ∈ Kod ),   ek gk − wod . = Exp − pk = θod h∈Kod eh min god

(7)

The minimum cost is added to the systematic utility of each alternative to improve numerical precision in case of low values of the Logit parameter θod (quasi-deterministic model). The so-called satisfaction wod represents the expected cost of the trip and can be used for economic evaluations. The flow qk of users travelling on path k ∈ Kod is given by multiplying the choice probability pk by the corresponding demand dod between origin o and destination d of the pair od ∈ P: qk = dod · pk .

(8)

The flow fa of users on link a ∈ A is then given by the sum of the flows on the paths that include it:   fa = qk · δak . (9) od ∈P

k∈Kod

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2.2 Stochastic User Equilibrium In this section, we present a simple formulation of path-based SUE as a fixed-point problem [2, 5]. By combining Eqs. (8), (5), (4), (1), (2) and (9), a circular dependency is defined among the variables of the model that can be solved as a fixed point problem to find q ∈ Q such that: ϕ(q) = q.

(10)

The k − th component of the vector function ϕ results from the combination of the above equations:       ma · δah , ∀h ∈ Kod = qk . + ta qr · δar dod · pk a∈A γa o d  ∈P r∈Ko d  (11) The feasible set Q, given by all non-negative path flows whose sum is equal to the demand flow, is non-empty, bounded, and convex. For simplicity, assume that the map f is defined over the entire Euclidean space, while its co-domain is contained in the feasible set: f (RK ) ⊆ Q, so that no fixed-point can exist outside it. The focus of the fixed-point problem is hence on the map, rather than on the feasible set. This is because the application of the function implicitly satisfies the constraints. Besides the linear relations between the path and link variables that represent the network topology, two non-linear models inform the above fixed-point equations; route choice model Eq. 5 and congestion model Eq. 2. When the demand flows load the paths, the costs of the links change due to congestion so that the route choices may become inconsistent with the performances on the network. A solution to the fixed-point problem represents an equilibrium condition where no user finds it convenient to change route. 2.3 Demand Estimation In this section, we present a novel single-level formulation of the OD matrix estimation problem under SUE conditions. In general, some traffic measurements on a subset of links are available for the model calibration: let ˆfa be the observed traffic volume on link a ∈ Afq ⊆ A and ˆta be the observed travel time on link a ∈ At ⊆ A Moreover, an initial estimate of each demand flow dˆ od is available. The estimation of the OD matrix based on the available traffic measurements can be cast as a system of nonlinear equations jointly with the equilibrium model:      ma

 dod (z).pk + ta qa . δah ∀ h ∈ Kod − qk = 0 ∀ od ∈ P, ∀ k ∈ Kod γa a∈A   β d . dod (z) − dˆ od = 0 ∀ od ∈ P   β f . fa − ˆfa = 0 ∀ a ∈ Af

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 β t . ta (fa ) − ˆta = 0 ∀ a ∈ At Subject to fa =



 od∈P

k∈kod

qk .δak ∀ a ∈ A.

(12)

where z are the parameters of the demand function d(z) that yields the od matrix. The first group of |K| equations represents the equilibrium model. The second group of |P| equations represents the need to take into account the initial estimate of the demand, f d with at predefined weight β , possibly equal to zero. The third and fourth groups of A + A equations represent the need to match the measurements with the corresponding variables, together with predefined weights β f and β t . The last group of equations should be used to substitute the link volumes in the other equations and are included in the formulation only to improve its readability. The number of variables is |K| path flows + n demand parameters listed in vector z. The system is, in general asymmetric. Ideally, the number of demand parameters should be smaller than the number of independent measurements to guarantee enough information for the calibration [10]. This conceptual statement will be proved using numerical examples on test networks in Sect. 4. However, estimating models with millions of demand parameters is common, usually one for each od couple. The result is a strongly unbalanced system with many more variables than equations. In this case, the solution of the system could diverge significantly from the actual demand to match the measurements better but produce a wrong estimate. This fact justifies the presence of the second group of equations that avoid significant diversions from the initial estimate. However, this is a contradicting objective since the calibration should correct the estimate to match the measurements. Thus β d > 0 implies that it will be impossible to have an exact solution to the system of equations. The above system of nonlinear equations in x = (q, z) can be formalized as a non-linear least square problem. Let y be the vector function of residuals: y(x) = 0.

(13)

The sum of squared residuals is a scalar function defined by: r(x) = y(x)2 = y(x)T .y(x),

(14)

which is null only at solutions and positive elsewhere. It can then be used to measure the distance of x to a solution. The nonlinear system of equations is therefore equivalent to the following unconstrained nonlinear optimization problem: Min(r(x)).

(15)

3 Solution Algorithm The above NNLS problem is solved using a Jacobian free version of the GaussNewton algorithm, where the trust region approach is implemented through the Levenberg-Marquardt method.

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Finding the solution direction involves solving a linearization of the model that is performed through an adaptation of the GMRES algorithm to rectangular systems of equations. An important feature of the proposed method (whose detailed presentation and justification is out of the scope of this paper) is that it only requires the product of the system Jacobian matrix J (computed at the current point x) by a generic vector v, which can be conveniently and accurately approximated by the following the difference quotient: J.v =

y(x + ε.v) − y(x − ε.v) , 2.ε

(16)

where ε is a small positive number. This product is the most expensive operation in each iteration of the GMRES algorithm and requires two model computations. Recall that the computation of model y at a particular point (in terms of path flows and demand parameters) requires the update of congestion and the route choices (that is like one equilibrium iteration for a classical MSA algorithm). In our experience, a few dozen GMRES iterations allow us to solve the linearized model and obtain a descent direction or a tentative solution (since we adopt a trust region approach). In essence, at the “price” of around one hundred model computations, we can perform one iteration of the Gauss-Newton algorithm for the solution of the joint formulation (equilibrium + estimation). For simplicity, the choice set kod for each pair od ∈ P is preliminary enumerated. Then, without loss of generality, a coloring method is coupled with the shortest path algorithm to identify a suitable set of convenient and independent routes by penalizing costs and overlapping.

4 Numerical Experiments Usually, it is difficult to evaluate the performance of an OD estimation method in realworld scenarios due to the extreme difficulty of acquiring accurate OD data. Our analysis employs a common validation framework from the OD estimating literature. Given a specific network, we generate a synthetic OD matrix and consider it the actual demand matrix. Then, we create the measurement data by executing the SUE model. Sequentially, the initial traffic state is generated by randomly perturbing the equilibrium traffic state. The effectiveness of the proposed methodology is then evaluated by comparing the estimated OD demand flow to the actual one. Different factors that may influence the efficiency of an OD estimate method include network topology, count location coverage, and data accuracy. To fully evaluate the performance of the proposed methodology, two numerical experiments were conducted to determine the impact of different factors; i) a small network that resembled the classic Braess’s network, and ii) a modified version of the grid network, first shown in [20]. The percentage root mean square error (PRMSE) metric is used to evaluate the performance of the proposed algorithm by measuring the difference between the estimated ODs and actual ODs:

 true 2 od ∈P dod − dod . (16) PRMSE = 100% × |P|

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Fig. 1. Braess network.

313

Fig. 2. Yang network.

4.1 Braess’s Paradox Network The network shown in Fig. 1 contains four nodes, five links, and three OD pairs. The parameters in the BPR function Eq. 3 are set as t10 = 850, t20 = 150, t30 = 700, t40 = 150, t50 = 850, and ka = 1000 for all links, actual demand flow from zone 1 to zone 4 equal 1200, from zone 2 to zone 4 equal 300 and from zone 3 to zone 4 equal 850. The equilibrium link flows are f1 = 515, f2 = 685, f3 = 599, f4 = 1415, f5 = 935. Basic Results: Initially, we use only link flows measurements in the estimation, assuming that links [1, 3, 4] are observed without measurement errors. As demonstrated in Figs. 3 and 4, the proposed algorithm correctly converged to the actual demand pattern and perfectly matched the flow levels on all network links. It takes only three iterations to make the estimated OD demand converge to the actual values. Each external iteration includes a few internal iterations allowed to solve the linearized model and obtain a descent direction.

Fig. 3. Estimated demand vs. actual demand vs. perturbated demand.

Fig. 4. Estimated link flow vs. actual link flow vs. perturbated link flow.

Impact of Count Location Coverage: The algorithm’s performance measured by the PRMSE of the OD demand flow over different count location coverage patterns is presented in Fig. 5. As demonstrated, the PRMSE for scenarios in which the number of count locations is higher than or equal to the number of OD pairs is zero, resulting in a traffic pattern similar

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Fig. 5. Impact of count location coverage on the OD estimation performance.

to what we observed in Fig. 5. Our method may show unfavorable performance for low sensor coverage, as indicated by relatively high PRMSE values. However, these values are reasonable since, in this case, the system is unbalanced with more variables than equations. Consequently, the system’s solution would diverge from the actual demand to better match the measurements, resulting in an inaccurate estimate of OD demand flow. For the same number of count locations, performance differs based on the position of the links and the information provided by the OD pairs in the network. For example, when just one count location is used, link number four or link number two, the estimation using link four is better than link two as shown in Fig. 5; this is because link four intercepts with all OD pairs, while link two only intercepts with OD pair (1, 4). However, incorporating additional information in the estimation, such as link travel time measurements, can likely improve the performance. We will explore these consequences briefly in the following case study. Impact of Measurement Errors: We have previously assumed that measurements are free of errors. Now we will assume that links (1, 3, 4) are observed with error and that the error percentage is proportional to the actual link flow. Experiments are therefore undertaken for situations including link flow count errors of 5%, 10%, 15%, 20%, 25%, and 30%, with no OD demand data. The results of the experiments are shown in Fig. 6. A strong correlation between the link measurement error and the PRMSE for the estimated OD demand flows can be seen. Therefore, when no OD demand is available, the PRSME for the OD demand increases roughly linear to the percentage of link measurement error.

4.2 Yang Network The second case study is performed using the grid network displayed in Fig. 2 which consists of 9 nodes, 9 OD pairs, and 14 links. Table 1 shows the actual and perturbated OD demand flow. The capacity and free flow travel time for each link are shown in Table 2 and suggested by [19]. The parameters of the BPR function are α = 0.15 and β = 4.

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Fig. 6. Change in estimated OD’s PRMSE with respect to link measurement error. Table 1. Actual and perturbated OD demand flow. OD pair

1–6

1–8

1–9

2–6

2–8

2–9

4–6

4–8

4–9

Actual

120

150

100

130

200

90

80

180

110

Perturbated

153

207

132

139

129

96

119

168

173

Table 2. Free flow travel time and capacity for each link. Link

1

2

3

4

5

6

7

8

9

10

11

12

13

14

t0

2.0

1.5

3.0

1.0

1.0

2.0

2.0

1.0

1.0

1.0

2.0

1.0

1.0

1.0

k

280

290

280

280

600

300

500

400

500

700

250

300

350

520

We obtained the actual link flows shown in Table 3 in the same way as in the previous case study by assigning the actual OD matrix to the traffic network using a path-based SUE. Furthermore, the Logit model’s dispersion parameter is set to 0.1. The initial link and path flows are obtained by incorporating random terms into the actual OD matrix and assigning them again to the network. Table 3. Actual link flows. Link

1

2

3

4

5

6

7

8

9

10

11

12

13

14

f

119

127

124

10

529

10

127

370

382

326

73

62

370

166

Basic Results: Figure 7 shows that when using nine count locations, which in this case are equal to the number of OD pairs, the estimated OD demand perfectly matches the

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actual demand. The results confirm the previous experiment’s finding that the estimation accuracy depends on the ratio between the number of count locations and the demand parameters.

Fig. 7. Comparison between estimated vs. actual vs. perturbated demand flow.

Impact of Count Locations Coverage: Similar to the previous case study, we test the algorithm performance using different numbers of count locations. For each unique configuration, fourteen random count location coverage cases were generated. Figures 8 and 9 show that the estimation error for the same number of count locations differs widely because each case is different in which links have measurements. Furthermore, Fig. 9 confirms the finding of the previous case study and shows that the average PRMSE of the OD demand flows improves as the ratio between the number of count locations and the number of OD pairs increases.

Fig. 8. PRMSE (OD).

Fig. 9. Average PRMSE (OD).

Impact of Historical OD Demand: The historical demand may contain useful information about the actual demand distribution, but it may also be outdated and have

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estimation errors. Unlike previous case studies, we test our methodology’s performance under different historical OD demand errors. The error is generated by perturbating the actual demand by 5%, 10%, 15%, 20%, and 30%. The demand weighting parameter is set to three values, [0, 0.01, 0.1]. Figure 10 shows that our proposed approach is robust and stable concerning the historical OD error. However, we should not set the demand weight to a large value since including the historical demand prevents the algorithm from diverting significantly from the initial demand. In this case, the estimation will not match the measurements but will stay close to the historical demand.

Fig. 10. Impact of historical OD demand data on the OD estimation accuracy.

Impact of Link Travel Time Observations: Since travel time or speed, measurements are more available and easy to obtain, for example, from FCD and mobile phone data. An additional test compares the results from the link flow measurements to those found when using both the link flow and the link travel time measurements. We assume that link travel time measurements are available for all network links while the link flow is limited to specific count locations. Observing these differences allows for analysis of the impact of link travel time measurements on the estimation. Figure 11 shows that the average PRMSE when using both link flow and travel time measurements together is always better than using only link flows or travel time, especially when there are few flow measurements. However, combining these two measurements makes solving the problem more challenging, as more variables are involved. Note, for example, that having only 1–2 links with flow observations is better than in the case of 6–7 links. Then, after nine links, there is a sharp increase in the performance. This is a somewhat expected outcome since we approach the cases where there is a balance between the number of variables and equations, making the system easier to solve.

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Fig. 11. Comparison of the OD estimation using link flow and travel time measurements.

5 Conclusion This paper presents a single-level join formulation of the travel demand estimation problem under (SUE) as a non-linear equation system. The new formulation addresses the computational complexity issue from the bi-level formulations. Different from other single-level formulations, ours is based on the fixed point formulation of the SUE assignment, which makes our formulation extensible to any congestion and route choice model. A Jacobian free version of the Gauss-Newton algorithm, where the trust region approach is implemented through the Levenberg-Marquardt method, is proposed to solve the new formulation. Numerical experiments are conducted on two test networks using two different traffic measurements. Results indicate that our formulations can perfectly estimate the OD demand with high precision when we balance the number of independent equations and the number of variables. Furthermore, including link travel time measurements improves the estimation in case we have a low number of link flow measurements. It also shows that the convergence of the solution algorithm is typically super-linear. In practice, around ten iterations suffice to solve the problem with high precision if our starting point is not too far from the solution, making it scalable for large networks. In addition, sensitivity analyses are undertaken on count location coverage, observation errors, and historical OD demand to demonstrate that our proposed formulation is stable and robust to these factors. Future work includes testing the proposed methodology in a large-scale network and comparing the performance of our proposed solution with bi-level formulation and the single-level formulation proposed in [7]. Furthermore, the calibration of the supply and desperation parameters of the Logit model in this mathematical framework will be studied, as well as an extension to the dynamic OD matrix estimation.

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Assessing the Mobility Impact on the Corporate Social Responsibility Mahnaz Babapourdijojin(B) and Guido Gentile Sapienza University of Rome, Via Eudossiana18, 00184 Rome, Italy [email protected]

Abstract. Companies have significant impacts on the lives of citizens worldwide in terms of working conditions, human rights, health, innovation, environment, and training. Companies need prevent, manage, and mitigate any negative impacts that they may cause on society and the environment. Mobility Management is the set of initiatives that each entity, both public and private, put in place to manage the mobility of its workers, with particular attention to systematic home-work-home travel. The home-to-work trip plan is a practical tool for the Mobility Manager of the company to investigate the employees’ travel habits and plan interventions to make mobility sustainable. In terms of Corporate Social Responsibility (CSR) which is the responsibility of enterprises for their impacts on society, mobility managers can analyze the effects deriving from the systematic home-to-work mobility of the employees and prepare the CSR report. They can use a software solution specially conceived for Mobility Management, and they can obtain the required data for analysis through the questionnaires via the web. The first questionnaire is completed by the mobility manager of the company also the second one by the employees. Also, since the mobility management policies can shift the modal split towards more sustainable behaviours, assessing the impact of systematic mobility of the employees of a company on different stakeholders is necessary. This paper evaluates two classes of stakeholders (employees and the company itself) by considering the choice made by employees among different modes of transport (private cars, motorcycles, bicycles, public transportation, and walking). Keywords: Sustainable mobility · CSR report · Mobility manager · Employees · Company

1 Introduction Due to rapid population growth and increasing urbanization, there has been a continued increase in the demand for travel in big cities worldwide, which imposes challenges on the urban transport system [1]. The 2030 Agenda for sustainability announced 17 sustainable development goals (SDG) and 169 targets [2], among them the 11th goal entitled “sustainable cities and communities”, which means making cities inclusive, safe, resilient, and sustainable. United Nations [2] has defined ten targets and 15 indicators for SDG11. One of these targets mentioned by the United Nations [2] is: By 2030, provide access to safe, accessible, and sustainable transport systems for all. Also, to improve © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 320–335, 2023. https://doi.org/10.1007/978-3-031-26655-3_30

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road safety with attention to the needs of those in vulnerable situations, children, women, persons with disabilities and older persons. 1.1 Mobility Management EPOMM is the European Platform on Mobility Management formed by committed Member States to support the development and spread of mobility management in Europe [3]. According to EPOMM [4], mobility management is a concept to promote sustainable transport and manage the demand for car use by changing travelers’ attitudes and behavior. Mobility Management measures do not necessarily require significant financial investments and may have a high benefit-cost ratio. The Decree of 27 March 1998” also called “Decree Ronchi” for sustainable mobility in urban areas, introduced [5] a home-to-work travel plan for companies and public entities with more than 300 employees in one office and companies with globally more than 800 employees. The aim was to reduce the use of individual private transport vehicles and better organize timetables to limit traffic congestion. According to the “European Directive 2014/95/EU: “The European Parliament acknowledged the importance of businesses divulging information on sustainability such as social and environmental factors, to identify sustainability risks and increase investor and consumer trust” [6]. This non-financial reporting is called Corporate Social Responsibility. According to European Commission [7]the definition of Corporate Social Responsibility (CSR) is as follows: "the responsibility of enterprises for their impacts on society". In addition, the role of the mobility manager was introduced in 1998 in Italian legislation [8]. The position of mobility manager was introduced for companies (both public and private) [3]with more than 300 employees in a single branch or with more than 800 employees in multiple offices [9]to engage businesses and workers in identifying alternatives to the use of the private vehicle. The mobility managers can use a software solution to produce the home-to-work trip plan and draft the annual CSR report. Also, they can obtain the required data for analysis through the questionnaires on the web. The questionnaires are completed by the mobility manager of the company and the employees. The Mobility Manager can obtain through the software a series of data in terms of employees’ habits, mobility indicators, graphs and so on. This paper evaluates two classes of stakeholders (employees and the company itself) by considering the choice made by employees among different modes of transport (private cars, motorcycles, bicycles, public transport, and walking). In this research, the user is the employee that commutes to work every day. Direct costs associated with the user are fuel cost, vehicle purchasing,engine oil, tire cost, tolls, pecuniary sanctions, vehicle insurance, accident cost, subscription to public transport tickets, and so on. This paper shows the monetary calculation of mobility impacts in euros per kilometer in the current state. The formulas used to calculate these costs are mostly the same for all modes of transport. We started with a private car and calculated all related costs. Then, we did the same for all other transport modes. Also, regarding the company itself, we calculated the lack of productivity due to the systematic mobility of the employees.

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2 Mode of Transport 2.1 Private Car We start with the calculation of the unitary cost of different fuels (Eq. 1) [10]. The average cost of the fuel, expressed in e/km, represents the main direct cost paid by the user, and it is obtained by doing the following considerations: fuel

fuel Cij

Pi

=

(1)

consumption

αj

i ∈ I I = {Gasoline, diesel, LPG, Methane}, j ∈ J J = {Small, Medium, Big}, CijFuel = Cost of fuel i for car j [e/km], PiFuel = Cost of fuel i [e/l], consumption αj = The fuel consumption of car j [km/l]. The prices of various fuels from 2005 to 2020 were analyzed. The average price of petrol, diesel, LPG, and Methane was 1.493 [e/l], 1.373 [e/l], 0.652 [e/l], and 0.924 [e/l], respectively. In addition, fuel consumption data of various cars with engine capacity from 900 cm3 to 2,300 cm3 were considered, so we made a linear regression and aggregated all the data in three classes of vehicles, depending on the engine capacity: Small cars (up to 1,300 cm3 ), Medium cars (between 1,400 and 1,900 cm3 ) and Big cars (greater than or equal to 2,000 cm3 ) (See Table 1). Table 1. The unitary cost of different fuel. Type of fuel

Vehicle consumption [km/l]

The unitary cost of fuel [e/km]

Small

Medium

Big

Small

Medium

Big

Petrol

23.71

18.76

14.26

0.0630

0.0796

0.1047

Diesel

37.65

37.22

36.84

0.0365

0.0369

0.0373

LPG

16.44

9.73

3.63

0.0397

0.0670

0.1797

Methane

39.44

39.10

38.79

0.0234

0.0236

0.0238

In addition, to calculate the unitary cost of a car purchase (Eq. 2) [10], we made a linear regression according to the price of various cars and the cars’ engine capacities. Then, like previous calculations, we aggregated all the data in three classes of vehicles, depending on the engine capacity. Cij$veh = i ∈ I I = {Petrol, diesel, LPG, Methane},

Pij$veh T veh × S veh

(2)

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j ∈ J J = {Small, Medium, Big}, Cij$veh = The unitary cost of purchasing car j with fuel i [e/km], Pij$veh = Purchasing price for car j with fuel i [e], T veh = The average life expectancy of a car[years], S veh = The average distance traveled by a car per year[km]. Then, considering 20 years of a car’s life expectancy and the average annual distance covered by a car equal to 11,125 km [11, 12], the unitary cost of buying a new car was calculated (See Table 2). Table 2. The unitary car purchase cost. Car price [e]

The unitary cost of a car purchase [e/km]

Small

Medium

Big

Small

Medium

Big

Petrol

19,844

26,778

33,082

0.089

0.120

0.149

Diesel

24,342

29,475

34,142

0.109

0.132

0.153

GPL

20,925

34,989

47,774

0.094

0.157

0.215

Methane

22,937

30,575

37,519

0.103

0.137

0.169

To calculate the engine oil cost (Eq. 3) [10], we started from the oil cost per liter, assumed by sectoral studies and market surveys equals an average of 10 euros per liter. So, according to the market survey, we considered changing the engine oil every 15,000 km [13]. Then, like before, we aggregated the data for three classes of cars (see Table 3). oilconsumption

CjOil = P oil × αj

,

(3)

j ∈ J J = {Small, Medium, Big}, CjOil = The unitary cost of engine oil [e/km], P oil = Price of the engine oil [e/l], oilconsumption = Engine oil consumption[l/km]. αj

Table 3. The unitary cost of engine oil. Engine capacity [cm3 ]

Small

Medium

Big

Engine oil consumption [l/km]

0.000271

0.000329

0.000383

The unitary cost of engine oil [e/km]

0.002706

0.003293

0.003826

To calculate the unitary cost of a set of tires (Eq. 4, Eq. 5) [10], the first input is the cost of the tire set. This cost, according to the market surveys, is linearly increasing

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with the engine capacity and power of the car. While there are no promises on a tire’s lifespan, generally, we can say that tires nowadays last for an average of 40,000 km [14] (see Table 4). CjTire =

PjTire α Tirechange

β Tire =

× β Tire

(4)

S veh

(5)

α Tirechanging

CjTire = The unitary cost of a set of tires for car j [e/km], PjTire = The cost of a set of tires for the car j [e], α Tirechanging = Kilometers for changing the tires[km].

Table 4. The unitary cost of a set of tires. Engine capacity [cm3 ]

Small

Medium

Big

Tires set price [e]

442

670

877

The unitary cost of a set of tires [e/km]

0.00308

0.0047

0.0061

To calculate the unitary cost of toll (Eq. 6) [10], we need to know that highway toll varies according to vehicle height and number of axles, based on the vehicle classes [15]. C toll =

β × P toll S veh

(6)

where β – the average km travelled by each user on toll roads. Here we consider the value of 1,580 km [10]. P toll – the average highway toll in Italy. We can perform the arithmetic mean between the toll in the flat sections and the one in the hill/mountain sections only for class A [15] to obtain an average highway toll in Italy (0.079 euro per kilometer). So, we calculated the value of 0.01122 euros per kilometer as a toll cost in Italy. The pecuniary sanctions cost indicator is the ratio between the average cost of fines for a driver in one-year, which equals 132 euros [10] and the average distance covered by a car. So, the unitary cost of a pecuniary sanction is equal to 0.0118 euros per kilometer. To calculate the unitary cost of vehicle insurance [10], expressed in e/km, we start with the average price of car insurance [16] for three classes of cars and divide it by the average annual distance travelled by car (see Table 5).

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Table 5. The unitary cost of car insurance. Engine capacity [cm3 ]

small

medium

big

Price of insurance [e]

507

648

776

The unitary cost of car insurance [e/km]

0.046

0.0582

0.0698

Another important indicator is to calculate the injury and mortality costs of accidents. From ISTAT and ACI Statistics [17], for a 10-year scenario (2010–2019), 166,868 accidents on average occurred in Italy. Therefore, 169,764 and 2,240 people got injured and died in car accidents, respectively [17, 18]. According to ISTAT Statistics [18], for the period 2005 to 2020, there were, on average, 37,729,767 cars in Italy (with an average distance covered of 11,125 km/year each). Also, the average social cost for the compensation of an injured person in accidents equals 42,219 euros, and for a killed person is about 1,503,990 euros [19]. The calculation is as follows (Eq. 7) [10]: C injury/mortality = P injury/mortality × γ injury/mortality ,

(7)

C injury/mortality = The cost of injury/mortality in a car accident [e/km], P injury/mortality = Indemnity for the injured/dead person [e], γ injury/mortality = Number of injured/dead people for km traveled by a car [#/km], 169764 # = 4.04 × 10−7 [ km ], γ injury = 11125×37729767

C injury = 42219 × 4.04 × 10−7 = 0.017 [

],

=

C mortality

= 1503990 × 5.34 × 10−9 = 0.008 [

2240 11125×37729767

=

5.34 × 10−9

γ mortality

# [ km ],

].

2.2 Motorcycle We have already calculated the average petrol price. So, like what we have done in the private cars section, we make a linear regression for the fuel consumption of motorcycles and their engine capacity (see Table 6). Table 6. The unitary cost of fuel. Engine capacity [cm3 ]

Small

Medium

Big

Fuel consumption[km/l]

38.19

35.11

29.5

Fuel price [e/l]

1.493

1.493

1.493

The unitary cost of fuel[e/km]

0.039

0.043

0.051

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To calculate the motorcycle purchase cost, we know that it is possible to obtain the prices of different vehicles disaggregated per engine capacity by analyzing the official lists of motorcycle manufacturers. In addition, the odometer of Italians marks on average 4,700 km travelled every 12 months [20], so we consider 4,700 km for small and medium motorcycles, and for big motorcycles, we assume the doubled value of 9,400 km. Also, life expectancy for small and medium motorcycles is considered five years, and for big motorcycles, 12 years [10] (see Table 7). Table 7. The unitary cost of purchasing a motorcycle. Engine capacity [cm3 ]

Small

Medium

Big

Average price e

4,520

5,459

7,173

The unitary cost of purchasing a motorcycle

0.1923

0.232

0.063

To calculate the unitary cost of engine oil, as we did for the private car, we start from the motorcycle engine oil cost per liter, assumed by sectoral studies and market surveys to be equal to the average of 12 e/l [21]. Then we multiplied this unitary value by the oil consumption expressed in l/km [10] (See Table 8). Table 8. The unitary cost of engine oil. Engine capacity [cm3 ]

Small

Medium

Big

l ] Engine oil consumption [ km

0.0002

0.00026

0.00032

The unitary cost of engine oil

0.002308

0.003001

0.003693

To calculate the unitary cost of a set of tires, like what we did for the calculation of fuel cost, we consider 4,700 km travelled by small and medium motorcycles [20]. In the case of big motorcycles, we assume the doubled value of 9,400 km. Also, we considered 6,000 km for changing tires [10] (see Table 9). Table 9. The unitary cost of a set of tires. Engine capacity [cm3 ]

Small

Medium

Big

Price of a tire set [e][10]

105

150

200

The unitary cost of a set of tires

0.0137

0.0196

0.0522

The calculation method of toll cost is like what we did for the car, but here we consider 6,300 km as an average total kilometer travelled by motorcycle. So, the unitary cost of toll equals 0.02 euros per kilometer.

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Also, in the case of pecuniary sanction, we assume that each motorbike user pays about 60 e/year for fines [10], and according to the previous section we consider an average value of 6,300 km for the annual distance covered by a medium motorcycle. So, the unitary cost of pecuniary sanction equals 0.00952 euros per kilometer. The calculation of the unitary cost of motorcycle insurance is like the calculation of car insurance cost (see Table 10). Table 10. The unitary cost of motorcycle insurance. Engine capacity [cm3 ]

Small

Medium

Big

Price of insurance [e] [10]

105

150

200

Kilometers per year travelled by motorcycle [km/year]

4,700

4,700

9,400

The unitary cost of motorcycle insurance [e/km]

0.0223

0.0319

0.0213

As mentioned earlier, another important indicator is a calculation of the number of injuries and mortality in accidents. According to ISTAT and ACI Statistics [17, 18], for a 10-year scenario (2010–2019), 47,100 and 622 people were injured and died in motorcycle accidents in Italy, respectively. According to ISTAT, from 2005 to 2020 [18], there were 6,572,084 on average registered motorcycles in Italy. Also, we assume an average distance of 6,300 km/year covered by each user. Other values are the same as in the previous section. γ injury =

47100 6300×6572084

# = 1.14 × 10−6 [ km ],

C injury = 42219 × 1.14 × 10−6 = 0.048 [

],

=

C mortality

= 1503990 × 1.502 × 10−8 = 0.0226 [

622 6300×6572084

= 1.502

× 10−8

γ mortality

# [ km ],

].

2.3 Public Transport The only direct cost (See Eq. 8) [10] regarding public transport is the cost of an annual ticket (see Table 11). CTIC = PTIC /SPD ,

(8)

where CTIC = cost per kilometer for the purchase of the annual public transport ticket [e/passenger-km]; PTIC = price of the annual public transport ticket; SPD = the average distance covered per year by a user [km].

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Table 11. The required data to calculate the average price of annual public transport tickets in Italy. Daily mobility index [12]

Working days per year [22]

Single trip average length [km/trip] [10]

The average price of annual public transport ticket in Italy [e [23]

2.8

253

8.5

310

SPD = 2.8 × 253 × 8.5 = 5978 [km/year], CTIC = 310/5978 = 0.052 [e/passenger-km].

Like previous sections, we calculate the cost of injury and mortality in the accident for public transport users. According to ISTAT and ACI Statistics [17, 18], in a 10year scenario (2010–2019), 2,100 and 27 public transport users got injured and died in Italy, respectively. In addition, we obtained a passenger-kilometer indicator by analyzing ANFIA statistics [24]. γ injury =

2100 34283333333

# = 6.125 × 10−8 [ pass−km ],

C injury = 42219 × 6.125 × 10−8 = 0.0026 [

],

=

C mortality

= 1503990 × 7.88 × 10−10 = 0.0012 [

27 34283333333

=

7.88 × 10−10

γ mortality

# [ pass−km ],

].

2.4 Bicycle To calculate the cost of purchasing a bicycle (See Eq. 9) [10], we multiplied the daily mobility index value, the average working days of a year, and the average length of a single trip done by bike to calculate the average annual distance covered by bicyclists (see Table 12). Also, we consider the five years of life expectancy for bicycles [10]. Table 12. The required data to calculate the cost of purchasing a bicycle. Daily mobility index The average working days of a year The average length of a single trip by bike [12] 2.8

253

10.7

C bike =

P bike × T bike

S bike

(9)

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CBicycle = The unitary cost of purchasing a bicycle [e/km], = Cost of purchasing a bicycle [e], TBicycle = The bicycle life expectancy [years], SBike = The average distance covered by a bicycle in one year [km], S bike = 2.8 × 253 × 10.7 = 7, 580 km. PBicycle

Also, it is necessary to calculate the cost of injury and mortality of bicyclists in accidents. In 2018, there were 16,224 injured people in bicycle accidents in Italy [25]. Also, according to the ISTAT and the Police of Lombardi [17, 26, 27], the average number of dead people in accidents involving bicycles from 2010 to 2020 was 254 (see Table 13). Table 13. The required data to calculate the cost of mortality and injury in accidents. Mobile population index (%) [12]

Modal split (bike) % [12]

Total population of Italy [28]

Total kilometer

84.3

4.3

59943333

7580

Number of bicyclists = 0.843 × 0.043 × 59943333 = 2172886.

16224 # γ injury = 2172886×7580 = 9.85 × 10−7 [ km ], −7 injury C = 42219 × 9.85 × 10 = 0.0415, 254 # = 1.542 × 10−8 [ km ], γ mortality = 2172886×7580

C mortality = 1503990 × 1.542 × 10−8 = 0.0232 [

].

2.5 Walking In the case of walking, the only cost to calculate is the cost of injury and mortality. According to the ISTAT [18], from 2005 to 2020, on average, 20505 and 608 pedestrians got injured and died, respectively (see Table 14).

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M. Babapourdijojin and G. Gentile Table 14. The required data to calculate the cost of injury and mortality.

Daily mobility index

Working days per year[24]

The average length of a single trip walking [km] [10]

Mobile population index (%) (Average from 2015–2019) [12]

Modal split % [12]

Total population of Italy (million)

2.8

253

1.6

84.3

22.2

59,943,333

Number of pedestrians = 0.843 × 0.222 × 59943333 = 11218156.

γ injury = C injury =

20505 11218156×2.8×253×1.6 = 1.612 42219 × 1.612 × 10−6 = 0.068[

γ mortality

=

608 11218156×2.8×253×1.6

=

# × 10−6 [ km ],

],

4.78 × 10−8

C mortality = 1503990 × 4.78 × 10−8 = 0.072 [

# [ km ],

].

3 The Lack of Productivity Systematic mobility in the home-work trip also has repercussions on work productivity. The following indicator, lack of productivity for the company, means that worker fatigue negatively affects their daily work. Danilo Guitto [10]considered the formula as follows (see Eq. 10): company

Ce

= (PLwork + CLwork ) × Tework × β, e e

(10)

e ∈ E E = {chief/manager, office worker/worker}, company Ce = The Cost for the company from the trip of employee” e” [e], work Te = Travel time from home to work for employee” e” [h], = Work productivity of employee” e” [e/h], PLwork e = Labor cost of employee” e” [e/h], CLwork e ß = Parameter [%]. The parameter ß is a percentage of the time that considers the “stress factor” of the employees connected to their trip and eventually the delay to reach the workplace due to the mode of transport chosen. In the case of private cars and motorcycles, we assume ß is equal to 20%, and for public transport 10%. With bicycles and walking, we don’t have a loss of productivity [10] (see Table 15).

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Table 15. The required data to calculate the lack of productivity [10]. PLwork e

CLwork e

Average work productivity

Average labor cost

1.3

15.2

57.6

9.5

48.0

68,296

4.2

7.7

29.1

Office worker

37,068

35.3

4.4

16.8

3.8

19.3

Worker

24,892

59.2

3.4

12.9

Type of employees

Average annual income [e]

Chief executive

105,390

Manager

(%)

4 Results Figure 1 shows all the unitary costs of different items related to transport modes. One of the essential costs to compare between transport modes are costs of injury and mortality in accidents. Figure 1 shows that the unitary cost of injury and mortality is worse for bicycles and motorcycles than for private cars and public transport but is better for bicycles and motorcycles than pedestrians. So, according to Eq. 7, it means that the number of injured or dead people per kilometer is higher for pedestrians than other modes of transport. Another research conducted in England [29] has expressed similar results in this regard. That study [29] calculated fatality rates for England by distance (fatalities/billion km) by age, travel mode, and gender. For most age groups, pedestrians had the highest fatality rates compared to cycling, considering the rate by distance. In addition, cyclists face higher fatality rates than drivers. According to this study, the number of fatalities per kilometer for pedestrians was 3.67 × 10–8 . In our study, according to Eq. 7, this value was 4.78 × 10–8 for pedestrians. Also, Fig. 1 shows that public transport has the lowest injury and mortality costs among all those transport modes. The unitary costs for various transport modes are presented in Fig. 2, which shows that the cost of fuel and purchasing is the highest among all other costs for car users and motorcyclists. To calculate the cost of systematic mobility of employees, we considered a company with 1,300 employees. By a software solution conceived for mobility management, we could obtain the required data for analysis. So, the surveys were completed by the mobility manager of a company and the employees on the web. The correspondence rate was 42%. Among them, 36.88% were female, and 63.12% were male. Also, if we analyze the modal split of employees, we can see that about 69% of the employees use a private car, 23.9% use public transport, 0.74% on foot, 0.36% use a bicycle, and 6% motorcycle. In addition, Fig. 3. Shows the productivity failure results for various modes of transport, and Fig. 4 shows all the costs resulting from the mobility of the employees.

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M. Babapourdijojin and G. Gentile

0.180 0.160 0.140 Euro/Km

0.120 0.100 0.080 0.060 0.040 0.020 0.000 Fuel

Purchase

Engine oil

a set of tires

Insurance

Toll

Pecuniary Sanction

Injury

Mortality

Annual ticket

Car

0.060

0.136

0.0033

0.005

0.058

0.011

0.012

0.017

0.008

0

Motorcycle

0.044

0.162

0.0030

0.029

0.025

0.020

0.010

0.048

0.023

0

Public Transport

0

0

0

0

0

0

0

0.003

0.001

0.052

Bicycle

0

0

0

0

0

0

0

0.042

0.023

0

Walking

0

0

0

0

0

0

0

0.068

0.072

0

Fig. 1. The unitary costs of different items related to transport modes [e/km].

Euro/Km

The unitary costs for various transport modes[Euro/Km] 0.180 0.160 0.140 0.120 0.100 0.080 0.060 0.040 0.020 0.000 Car

Motorcycle

Public Transport

Bicycle

Walking

Fuel

Purchase

Engine oil

a set of tires

Insurance

Toll

Pecuniary Sanction

Injury

Mortality

Annual ticket

Fig. 2. The unitary costs for various transport modes [e/km].

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Productivity failure [€]

-408689 0 0 -73123 -19679 -315886 -450000 -400000 -350000 -300000 -250000 -200000 -150000 -100000 -50000 Total

Walking

Bicycle

Public Transport

Motorcycle

0

Car

Fig. 3. Productivity failure results for various modes of transport.

The costs result from the systematic mobility of employees[€] -107747 -182560 -438107 -500000

-35249 -37434 -20119

-304528 -400000

-300000

-200000

-100000

Injury and mortality Pecuniary sanctions

Tolls

Insurance

Vehicle Purchasing

Engine oil and tires

0

Fuel Fig. 4. All the costs result from the systematic mobility of the employees.

5 Conclusion According to mentioned calculations and results, the mobility manager can propose multiple interventions to increase the sustainable mobility of the company in daily commuting trips. The monetary analysis of the mobility impacts, in its current state and resulting from intervention simulations, can be a core part of the annual Company CSR report (see Fig. 5).

Fig. 5. Simulation logic of interventions.

To compare the current state and those resulting from intervention simulations, the definition of key performance indicators (KPI) is fundamental. The KPIs are modal split;

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M. Babapourdijojin and G. Gentile

the Kilometers travelled per year; the average distance of a single trip; the number of trips per year; pollutant emissions, and fuel consumption. Concerning several interventions that we suggest as follows, it is possible to calculate the already introduced KPIs and compare them with the current state: Company parking spaces for cars; The daily cost of parking the car in the company; The number of company parking spaces reserved for carpooling crews; To consider the locker rooms for bicyclists; The incentives for acquiring the annual ticket for public transport; Providing the shared E-bike at the company; Providing a car-pooling software in the company; The incentives for petrol coupon given to car-pooling crew; Flexibility of working hours; Providing the shared E-cars at the company; The incentives for acquiring the electric bicycle; and the number of remote working days. In conclusion, we can say that each company has multiple objectives in the field of mobility management: to increase as much as possible the productivity of the employees, to guarantee the welfare of its employees and draw up the CSR Report (only a class of companies). To do so, the mobility manager can get the advantages of a software solution dedicated to mobility management to put some interventions and compare the simulation results with the current state. It is essential to understand which of the proposed interventions will be beneficial to decrease the cost of commuting, persuade employees to use more sustainable modes of transport rather than the private car, and increase their productivity.

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12. ISFORT. 17 Rapporto sulla mobilità degli italiani (2020). (In Italian) 13. Minotti, G.: Car engine oil change: where to do it and how much it costs (2019). https://www. today.it/motori/auto-moto/cambio-olio-motore.html 14. Pirelli & C. S.p.A. How to maximize your tyre life (2019) 15. Autostrade per l’Italia,. Toll classes (2022). https://www.autostrade.it/en/il-pedaggio/le-cla ssi-di-pedaggio. (In Italian) 16. IVASS. INDAGINE SUI PREZZI R.C.A. AL 1° LUGLIO 2014 (2014). (In Italian) 17. ISTAT & ACI. Road accidents (2020) 18. ISTAT. Road accidents (2005–2020). http://dati.istat.it/ 19. The Ministry of Infrastructures and Transport. Studio di valutazione dei Costi Sociali (2010). (In Italian) 20. Gentili, M.: L’identikit del motociclista (2019). https://www.dueruote.it/ 21. https://www.dueruote.it/news/attualita/2019/01/22/l-identikit-del-motociclista.html. (In Italian) 22. Pneumatici Homepage. Motorcycle engine oil (2022). https://www.pneumatici.it/olio/olio-elubrificanti/olio-motore-motocicletta 23. Giorni- lavorativi Homepage. Working days (2022). https://www.giorni-lavorativi.com/EN/ giorni-lavorativi_festivi_2021.htm#a20 24. UIL Servizio Lavoro, Coesione e Territorio. Tariffe Trasporto Pubblico Locale in Italia (2021). (In Italian) 25. ANFIA. dossier trasporto passeggeri e mobilita focus sul trasporto collettivo su gomma (2020). (In Italian) 26. Agenzia Italia. The numbers of the daily massacre of cyclists on our roads (2020). https:// www.agi.it/cronaca/news/2020-05-03/strage-ciclisti-investiti-8504727/ 27. Polis Lombardia. Incidenti stradali a ciclisti in Regione Lombardia (2020). (In Italian) 28. ISTAT & ACI. Road Accidents, year 2019 (2019) 29. Statista. Italy: Total population from 2016 to 2026 (2022). https://www.statista.com/statistics/ 263745/total-population-of-italy/ 30. Feleke, R., Scholes, S., Wardlaw, M., Mindell, J.S.: Comparative fatality risk for different travel modes by age, sex, and deprivation. J. Transp. Health 8, 307–320 (2018)

Bicycle Sharing Systems: A Comparative Analysis in Greece and Cyprus Georgia Savva, Giannis Adamos(B) , and Eftihia Nathanail Traffic, Transportation and Logistics Laboratory, University of Thessaly, Pedion Areos, 38334 Volos, Greece [email protected]

Abstract. In recent years, cities have been at the epicenter of the transition to sustainable mobility, with many of them exploring ways to integrate bike-sharing systems to public transportation. In the context of the development of bicyclesharing systems at an international level, the goal of this paper is to investigate the acceptance level of these systems by Greek and Cypriot users. Towards this direction, a structured literature review was initially conducted on the legislative and regulatory framework in Greece and Cyprus, as well as on existing sustainable mobility projects. Then, a survey was conducted in Volos and Nicosia to examine the current situation in these cities. From the data collected, descriptive and inferential statistics were performed, and the results demonstrated that the participants have positive beliefs regarding the use of shared mobility systems. The intention of using a bicycle-sharing system is determined by various factors, such as age, gender, income, etc. Keywords: E-bikes · Soft modes of transport · Docked and dockless bike systems · Custainability

1 Introduction One of the most important aspects in promoting the use of bicycles is the establishment and operation of “automated” bike sharing systems also referred to as “public bicycle systems”. A definition for the above-mentioned systems is: “The leasing bicycle system (bike sharing system) is a long-term public system that can serve transportation from one point to another, without the necessity of returning the bicycle to the starting point. This means that the users can enjoy the ease of use and all the advantages of using a bicycle without the purchase and maintenance costs, or one of the responsibilities of owning a bicycle” [1]. The first bike sharing system appeared in Amsterdam in 1965. Since then, the increase in bike sharing programs is astounding. Starting in March 2011, public use bicycle programs have been in operation in many countries across the world, with over 1,600 bicycle programs that provide to the public more than 18 million bicycles [2, 3]. A bike sharing system can be described in two categories [2]: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 336–346, 2023. https://doi.org/10.1007/978-3-031-26655-3_31

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• i) Docked bike sharing. This system allows people to pick up and return bicycles from a specific station of their choice using applications based on information technologies. • ii) Dockless bike sharing. These systems are designed to provide the public with flexibility. This is done using Global Positioning Systems (GPS) technology, which is embedded directly into the bicycles, meaning the bicycles do not have specific starting stations or destinations. The bicycles also come with their own lock system allowing the user to secure it in any one of the recommended public places within a defined area of service. The rest of the paper is structured as follows: Sect. 2 presents the main findings of the state-of-art review, Sect. 3 describes the methodological approach, followed by results in Sect. 4. Conclusions are summarized in Sect. 5.

2 State-Of-The-Art Review The municipality of Utrecht (Netherlands), after several attempts and meetings with the local government in 2010, concluded that developing a bike sharing system would have a big operational cost. Thus, the municipality decided to cooperate with a company called OV–fiets. The cooperation of OV–fiets with the municipality had a very desirable outcome. Now, since 2019 there are four bike sharing systems in operation. Another example of good implementation of bike sharing systems is Malaga (Spain). In 2013, Malaga designed bicycle paths (lanes) that added up to 30 km in length. However only a 0.4% of the population would use a bicycle as a means of transportation. The first four bike sharing stations were inaugurated in 2013. A year later, a total of 20 stations were completed with an availability of 400 bicycles. The acceptance and response of the public towards the system was very positive as within just the first year of operation a total of 17,000 users were recorded with every bicycle having an average use of 8 times per day. A study by Ma et al. [2] was carried out in the Netherlands, specifically in the city of Delft, where the three existing bike-sharing systems were tested. The systems are “Mobike”, “ov-fiets” and “Swapfiets”. The research showed that most respondents had used all three systems. However, a significant percentage of the respondents (77.5%) prefer the “OV-fiets” system, because this system is located next to the railway. Most users who used one of the three systems are between 18–34 years old and male. The results from the comparison of the systems showed that the “OV-fiets” system excels in several attributes compared to the other systems. Saving time compared to other transport systems (59.2%) and the ease of movement without having to walk (55.6%) are two of the biggest motivations to use this system. A significant percentage, however, is also observed in the “Swapfiets” system, in which the bikes are of better quality (52.7%). As for the “Mobike” system, it was observed that users prefer it because it is a dockless system, which enables users to park anywhere (59.2%). The survey conducted by Ji et al. [4] in the city of Nanjing in China, examined how much the two existing systems are acceptable by the public. One of the systems is “Docked bike-sharing”, which appeared in January 2013 with a volume of 60,000 bikes and the other system is “Dockless bike sharing” that was established in January 2017

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with a volume of 450,000 bikes. In total, 674,390 trips were generated by the docked system as opposed to the dockless system which served 2,559,176 trips. The survey showed that the docked system was preferred in the Hexi new town area at Area A, especially by frequent users during the morning hours. On the contrary, the dockless system was used more at Area C. This was probably due to the increased traffic, which possibly resulted to people being unable to search for docking stations. In addition to that, an important factor appears to be that users assess it difficult to find available bikes. In the research by Felix et al. [5], which was performed in Lisbon, Portugal in 2016, the city created a bike-sharing system, which recorded up to 7 trips per bike per day in its first year of operation. Similarly with the results from the Ma et al. (2020) study, the number of male cyclists was higher than female. Also, a percentage of 45% wore a helmet in the first year of the survey. However, this percentage decreased to 30% in the following two years. Finally, the research determined that after the creation of appropriate infrastructure, the bike-sharing users increased significantly. Focusing on the research by Benedini et al. [6], which took place in Brazil, in the city of Sao Paulo, the city’s bike sharing system was tested. The survey was implemented in June 2017 with 605 participants. Respondents ranged in age from 21 to 40 years old, and 76% of them were male. The highest percentage of respondents had low income (36%). It was observed that users were willing to travel a distance greater than 20 km for exercise and in their free time (38%). On the contrary, for shopping they would only travel less than 2 km (37%). As for the motivations to use this system, a percentage of 63% emphasized that they consider health benefits to be the most important factor. The study of Scott & Ciuro [7], which took place in Hamilton, Canada, examined various parameters. For instance, the weather conditions, the hours of daylight and the public holidays. This research used data extracted from 114 bicycle rental stations, collected from April 1, 2015, to March 31, 2016. A total of 203,427 trips were executed during this period. The results of the research showed that during the winter months, i.e., when the temperature decreases, the use of bicycles increased rapidly, specifically in the months of September to December. Additionally, the research of Si et al. [8] examined user behavior and which factors encourage users to use this system. The research was carried out in China, where bike sharing has a decisive role in the sustainability of cities. A total of 1,234 users participated in the questionnaire, conducted in December 2018. A percentage of 53.2% were women and most of them were between the age of 26 to 35 years old. Furthermore, 76.5% stated that they have a bachelor’s degree and use the bike sharing system twice to 3 times a week. The research of Leister et al. [9], examined the factors that tend to influence people to use the bike sharing system. The results showed that the most important motivation was the easy access to the rental stations. A percentage of 44.3% of users were women, and most trips were made during the weekend. Lastly, it was observed that the lack of infrastructure seemed to prevent users from using this system more frequently. Finally, the research of Cao & Shen [10] in Beijing, examined the largest bicycle rental system in the world called “Mobike”. A heavy usage of bicycles was observed during the morning hours (8:00–9:00) as well as the afternoon hours (18:00–19:00). The distance that users appear to travel ranged from one to five kms. Also, there was an

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increased demand of bicycles on weekdays. The greatest usage of bikes was recorded by the residents of the Hindi area (36%), which may have resulted from the great influx of people visiting the area and the high number of schools in the area.

3 Method and Data Collection To capture travelers’ perceptions about factors influencing their intention to use bike sharing systems, a questionnaire survey was organized in Greece by the Traffic, Transportation and Logistics Laboratory (TTLog) of University of Thessaly. The questionnaire included 27 questions in total, organized in four parts, referring to the characteristics of the trips, the bicycle sharing systems, the evaluation of existing systems in Nicosia and the demographic characteristics of the respondents. The core part of the questionnaire was responded using a 5-point scale, ranging from 1 (Strongly disagree/very unlikely/not at all/never) to 5 (Strongly agree/very likely/a lot/always), depending on the topic addressed. The questionnaire was available on SurveyMonkey from May 18, 2020, to July 10, 2020, for individuals throughout Greece and Cyprus. It was distributed through social media and emailing. It is noted that data collection was anonymous and in compliance with the European Union’s General Data Protection Regulation (GDPR). TTLog values the privacy of respondents, keeps the collected data safe and protected from unauthorized access and refrains from sharing any personal information with third parties. Descriptive and inferential statistical analyses were conducted with the use of Microsoft EXCEL and IBM SPPS Statistics programs. A confidence level of 95% and confidence interval of 5% were assumed in the analyses.

4 Results 4.1 Sample Profiles The final sample was 286 persons, consisting of 157 women and 129 men. Most participants are between 18–25 years old (53.1%), 31.5% of them are older than 25 years old and the remaining of them are up to 41 years old (15.3%). In addition, most of the respondents (62.9%) are highly educated, 36.4% of them have received a secondary level of education and the remaining 0.7% are primarily educated. As far as employment status is concerned, it was observed that 54.2% of the participants are employed, 39.9% students, 4.9% unemployed and the remaining 1.1% stated a different status. 47.2% of the respondents have a monthly net-income greater than 1,500e, 31.1% between 1,001– 1,500e, and the remaining 21.7% less than 1,000e. The questionnaire resonated more with people from Cyprus with a percentage of 57% and then Volos with a percentage of around 43%. Focusing on trip purpose at city level, it was observed that most of the participants (43.4%) travel for work, 32.2% for leisure, 20.6% for education and the remaining 3.8% for other purposes. Most of the participants (51%) use car for their trips at city level for short distances. A percentage of 12.5% of 18 to 25-year-old citizens tend to use the bicycle for their daily mobility.

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4.2 Descriptive Statistical Analysis The answers resulting from the questions on beliefs prove that using a bicycle is a pleasant and relaxing experience, as the percentages corresponding to “agree” and “I completely agree” are 38.8% and 35.5%, respectively. In addition, 40.2% agree and 54.9% strongly agree that it benefits fitness and health, while 38.8% and 56.6% that it is an economical commuting alternative, respectively. Also, 41.3% of the respondents agree that there is autonomy and flexibility and 53.8% completely agree that the use of bicycles contributes to achieving sustainable mobility. Regarding the belief that moving around the city is dangerous, 39.5% agree in the belief that it requires a lot of physical effort, 41.3% of people disagree. Finally, 55.9% and 45.1% agree that it depends significantly on the weather conditions and the morphology of the ground respectively, while 54.2% completely agree that appropriate infrastructure is required. In addition, focusing on bicycle sharing systems and in terms of knowledge of the existence of the system, 66% of respondents are aware of such systems. Most of the respondents had not used this system for their movement the period of the survey. It was also indicated that the participants are willing to use bike sharing systems in the future. Regarding the factors for future use, 29.7% of the respondents consider “saving money” as an important factor. In addition, 38.8% would use the bike sharing system to contribute to the reduction of air pollution The cost that the respondents would accept for using a bike sharing system would be 0.50e per hour. It seems that 66.1% of the sample would be interested in using both a conventional and an electric bicycle. Then, the respondents chose which they think would be an ideal location for the installation and operation of a bike sharing system with a percentage of 67.1% being public spaces and 64.3% being university facilities. Terminal and commercial centers were also rated highly (41% and 19%, respectively). 4.3 Inferential Statistical Analysis The results of the inferential statistics carried out with the use of non-parametric tests, are analyzed in order to address the main scope of this paper, i.e., compare and evaluate the operation of bicycle sharing systems in two European countries, Greece and Cyprus. Specifically, it was assessed whether there are statistically significant differences in the average score given by the respondents to the tested dependent variables, in relation to various (independent) variables, such as gender, age, city of residence, income and level of education. In general, it was observed that people have a positive attitude towards the use of bike sharing systems in Greece and Cyprus and this results from the potentiality of using the bike sharing system, the reasons for using it, the concerns about using the system, the initiatives of Municipalities and the beliefs about possible initiatives of the Municipalities for the bicycle sharing systems. For the analyses, a confidence level of 95% and a confidence interval of 5% were assumed, and the non-parametric Mann-Whitney two-sample U statistical model was applied. In the following paragraphs, the main findings are presented, focusing on two main parameters, which showed the most interesting results: gender and city of residence (Volos vs. Nicosia). It must be noted that several parameters and various tests were

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run. Regarding the possibility of using a bike sharing system, the highest score for commuting to the workplace was given by age group H1, which includes people ≤ 25 years old (Average rating-M = 3.0, Standard Deviation-SD = 1.31), followed by group H2 with people 26 to 40 years old (M = 2.7, SD = 1.35) and the H3 group with people older than 40 years (M = 2.5, SD = 1.56). The difference between categories H1 and H3 seems to be statistically significant (p-value < 5%). Motivated to use a bike sharing system to save money, people from the H1 group (M = 3.1, SD = 1.18) agree more than people from the other groups: H2 (MT = 2.6, SD = 1.19) and H3 (M = 2.7, SD = 1.33). This difference is statistically significant between groups H1 and H2 (p-value < 5%) and groups H1 and H3 (p-value < 5%). The inability to buy a personal bicycle is equally agreed by people of age H1 (M = 2.5, SD = 1.32) and H2 (M = 2.1, SD = 1.8) with people of age H3 agreeing relatively less (M = 2.0, SD = 1.28). Statistically significant differences are indicated between groups H1 and H2 (p-value < 5%) and between H1 and H3 (p-value < 5%). Regarding the concerns about the use of a bike sharing system, the place/station of the pick-up/drop-off of the bike seems to affect more the people of category H1 (M = 3.6, SD = 1.07) and H2 (M = 3.6, SD = 1.13) from H3 category individuals (MT = 3.1, SD = 1.34), with statistically significant differences observed between H1 and H3 categories (p-value < 5%). The concern to continue using a bike sharing system for the cost of renting is considered equally likely by people of the age group H1 (M = 3.7, SD = 1.18) and H2 (MT = 3.5, SD = 1.27) and less so by of group H3 (MT = 3.0, SD = 1.26). A statistically significant difference exists between groups H2 and H3 (p-value < 5%) and between H1 and H3 (p-value < 5%). Regarding the belief about the possible initiatives of the Municipality of Volos/Nicosia to collaborate with private entities for the establishment and operation of bike sharing systems, people of the H3 age group agree more (M = 4.0, SD = 0.82), followed by the H2 age groups (M = 3.8, SD = 0.94) and H1 (M = 3.6, SD = 1.02), with statistically significant differences between H1 and H3 (p-value < 5%) (Table 1). Statistically significant differences were indicated in most variables in relation to how people with residence in Volos and Nicosia rated them, as well as the average values between them. In general, residents of Volos rated higher most variables (Table 2). With reference to the possibilities of using a bike sharing system, specifically as regards commuting to the workplace (M = 3.3, SD = 1.26), to an educational institution (M = 3.4, SD = 1.31) and for shopping (M = 3.3, SD = 1.27) the people with the city of residence in Volos show statistically significant differences (p-value = 0). As for the reasons for using the system, the residents of Volos use them more to help limit the use of vehicles (M = 3.6, SD = 1.16), in the reduction of traffic congestion (M = 3.9, SD = 1.01) and in the reduction of gas emissions (M = 4.1, SD = 0.96) (p-value < 5%). Regarding the concerns about using a bicycle sharing system, the rental cost is statistically significant between the people who live in Volos and the people who live in Nicosia (p-value = 0). The people who live in Volos are more worried about the cost (M = 3.9, SD = 1.12) from people living in Nicosia (M = 3.3, SD = 1.28). The opinion that the Municipality could act as a good practice for other municipalities is considered equally important by the people who live in Volos (M = 4.4, SD = 0.62) and less so

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Table 1. Average rating and summary test results for the comparison of responses based on age. Variables

≤ 25 years old (H1)

26–40 years old (H2)

> 40 years old (H3)

Mann-Whitney U

p-value

M

M

M

H1 vs. H2

H1 vs H2

SD

SD

SD

H2 vs. H3

H1 vs. H3

H2 vs. H3

H1 vs H3

Intention to use a bike sharing system to move to: Work

3.0

1.31

2.7

1.35

2.5

1.56

6006 1800 2690 0.105

0.38

0.044*

Educational institution

3.0

1.37

2.3

1.32

2.5

1.46

5444 1920 2603 0.007* 0.77

0.022*

Shopping center

3.0

1.35

2.6

1.22

2.6

1.42

5705 1979 2865 0.027* 0.1

0.139

Leisure destination

3.6

1.23

3.3

1.36

3.0

1.49

5993 1790 2624 0.096

0.024*

0.36

Reasons to use a bike sharing system: Money savings

3.1

1.18

2.6

1.19

2.7

1.33

5218 1896 2706 0.002* 0.68

0.048*

Inability to buy a personal bicycle

2.5

1.32

2.1

1.08

2.0

1.28

5686 1778 2506 0.024* 0.31

0.009*

Comfort/flexibility 3.1

1.20

2.9

1.18

3.0

1.18

6373 1917 3231 0.360

0.76

0.725

Contribution to the 3.3 reduction of private vehicles’ use

1.23

3.4

1.18

3.5

1.42

6703 1915 3173 0.789

0.75

0.594

Contribution to the 3.6 reduction of congestion

1.18

3.6

1.12

3.6

1.32

6678 1884 3244 0.751

0.64

0.755

Contribution to the 4.0 reduction of air pollution

1.05

3.8

1.13

3.8

1.87

6311 1876 3298 0.292

0.61

0.884

3.1

1.34

6732 1614 2666 0.831

0.07

0.034*

Concerns about using a bike sharing system: Bicycle pick up/drop off location/station

3.6

1.1

3.6

1.13

Bicycle condition

3.8

1.0

3.6

1.16

3.6

1.18

6404 1941 3195 0.389

0.848 0.640

Bicycle availability at a specific time slot

3.9

1.01

3.7

1.07

3.8

1.20

6366 1929 3221 0.347

0.800 0.698

Rental cost

3.7

1.18

3.5

1.27

3.0

1.28

6171 1524 2232 0.188

0.03* 0.001*

Payment options

3.2

1.26

3.2

1.27

2.8

1.35

6807 1647 2797 0.949

0.106 0.09

Provision of protection equipment

3.0

1.30

3.2

1.21

3.3

1.35

6237 1817 2805 0.242

0.429 0.095

(continued)

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343

Table 1. (continued) Variables

Payment policy

≤ 25 years old (H1)

26–40 years old (H2)

> 40 years old (H3)

Mann-Whitney U

p-value

M

SD

M

SD

M

SD

H1 vs. H2

H1 vs H2

3.9

1.05

3.2

1.29

3.3

1.40

4963 1877 2668 0*

H2 vs. H3

H1 vs. H3

H2 vs. H3

H1 vs H3

0.619 0.033*

Initiatives of Volos and Nicosia Municipalities to organize a bike sharing system: The Municipality would become more modern

4.2

0.65

4.1

0.82

4.0

0.89

6348 1912 2987 0.302

0.726 0.232

The Municipality would become a “good practice”

4.3

0.65

4.2

0.80

4.1

0.82

6339 1930 3009 0.294

0.796 0.262

The Municipality would become a more popular destination for visitors/tourists

3.7

0.95

3.6

0.96

3.9

0.95

6503 1611 2880 0.499

0.066 0.140

Beliefs about possible initiatives of Volos and Nicosia Municipalities Set up and operation of a bicycle sharing system

4.1

0.81

4.0

0.77

3.9

1.02

6262 1880 3202 0.230

0.625 0.642

Cooperation with private actors

3.6

1.02

3.8

0.94

4.0

0.82

6027 1817 2656 0.098

0.395 0.027*

Design of promotion activities

4.3

0.69

4.1

0.71

4.3

0.72

6089 1793 3261 0.117

0.324 0.782

M: Average rating, SD: Standard Deviation, *p-value < 5% Table 2. Average rating and summary test results for the comparison of responses based on city of residence. Variables

Volos (V)

Nicosia (N)

M

M

SD

SD

MannWhitney U

p-value V vs. N

Intention to use a bike sharing system to move to: Work

3.3

1.26

2.4

1.34

6316

0*

Educational institution

3.4

1.31

2.3

1.28

5580

0*

Shopping center

3.3

1.27

2.4

1.24

6265

0*

Leisure destination

3.6

1.17

3.2

1.41

8597

0.03*

2.7

1.24

8514

0.03*

Reasons to use a bike sharing system: Money savings

3.0

1.16

(continued)

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G. Savva et al. Table 2. (continued)

Variables

Volos (V)

Nicosia (N)

M

SD

M

SD

MannWhitney U

p-value

Inability to buy a personal bicycle

2.5

1.3

2.2

1.21

8567

0.03*

Comfort/flexibility

3.3

1.1

2.8

1.20

7435

0*

Contribution to the reduction of private vehicles’ use

3.6

1.16

3.2

1.22

8486

0.02*

Contribution to the reduction of congestion

3.9

1.01

3.4

1.26

8002

0.003*

Contribution to the reduction of air pollution

4.1

0.99

3.7

1.20

8314

0.01*

V vs. N

Concerns about using a bike sharing system: Bicycle pick up/drop off location/station

3.7

1.07

3.4

1.18

8634

0.04*

Bicycle condition

3.9

0.94

3.6

1.16

8493

0.02*

Bicycle availability at a specific time slot

3.9

0.98

3.8

1.12

9715

0.641

Rental cost

3.9

1.12

3.3

1.28

7398

0*

Payment options

3.2

1.29

3.1

1.28

9542

0.474

Provision of protection equipment

2.7

1.27

3.4

1.23

7094

0*

Payment policy

3.9

1.03

3.3

1.29

7557

0*

Initiatives of Volos and Nicosia Municipalities to organize a bike sharing system: The Municipality would become more modern

4.3

0.64

4.0

0.81

8181

0.003*

The Municipality would become a “good practice”

4.4

0.62

4.1

0.78

8192

0.003*

The Municipality would become a more popular destination for visitors/tourists

3.8

0.87

3.6

1.01

8898

0.086

Beliefs about possible initiatives of Volos and Nicosia Municipalities Set up and operation of a bicycle sharing system

4.1

0.79

4.0

0.86

9176

0.180 (continued)

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Table 2. (continued) Variables

Volos (V)

Nicosia (N)

M

SD

M

SD

MannWhitney U

p-value

Cooperation with private actors

3.7

1.02

3.8

0.93

9392

0.327

Design of promotion activities

4.3

0.67

4.2

0.72

9338

0.274

V vs. N

M: Average rating, SD: Standard Deviation, *p-value < 5%

by the people who live in Nicosia (M = 4.1, SD = 0.78). This difference is statistically significant (p-value < 5%).

5 Conclusions This research focused on the comparative evaluation of the operation of bicycle sharing systems in Volos (Greece) and Nicosia (Cyprus). According to the survey findings, it seems that people in Nicosia rarely use the existing bicycle sharing systems in the city. In contrast to the respondents in Volos, who stated that they would be interested in a bicycle sharing system for their daily trips. Descriptive statistics showed that the respondents believe that using a bicycle is a pleasant and relaxing experience and showed positive beliefs and intentions to use bike sharing. Focusing on users’ characteristics, it was indicated that men are more willing to purchase a personal bike and are less concerned about the provision of protection equipment when using a bike sharing system compared to women. In terms of age, people between 26 and 40 years old, show a higher intention to commute to work on a bicycle sharing bike. In addition, the age group that includes people under 25 years old believe that they would use a bike sharing system to save money. Regarding employment, statistically significant differences were observed between students and workers, specifically in the possibility of using a bicycle sharing system for commuting to their work and saving money. In addition, regarding the city of residence, people who live in Volos would prefer the bicycle sharing system for commuting to the workplace, an educational institution, and a shopping center. The residents of Volos would use this system to reduce traffic congestion and reduce air pollution. People who live in Nicosia do not consider the cost of using the system important, as Nicosia is considered a city with a higher per capita income than Volos.

References 1. Handbuch, E.: Optimising Bike Sharing in European Cities. OBIS (2011) 2. Ma, X., Yuan, Y., Oort, N.V., Hoogendoorn, S.: Bike-sharing systems’ impact on modal shift: a case study in Delft, the Netherlands. J. Clean. Prod. 259, 120846 (2020) 3. Wang, Y., Lindsey, G.: Do new bike share stations increase member use: a quasi-experimental study. Transp. Res. Part A Policy Pract. 121, 1–11 (2019)

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4. Ji, Y., Ma, X., He, M., Jin, Y., Yuan, Y.: Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: a case study in Nanjing, China. J. Clean. Prod. 255, 120110 (2020) 5. Felix, R., Canbra, P., Moura, F.: Build it and give ‘em bikes, and they will come: the effects of cycling infrastructure and bike-sharing system in Lisbon. Case Stud. Transp. Policy 8(2), 672–682 (2020) 6. Benedini, D.J., Lavieri, P.S., Strambi, O.: Understanding the use of private and shared bicycles in large emerging cities: the case of Sao Paulo, Brazil. Case Stud. Transp. Policy 8(2), 564–575 (2020) 7. Scott, D.M., Ciuro, C.: What factors influence bike share ridership? An investigation of Hamilton, Ontario’s bike share hubs. Travel Behav. Soc. 16, 50–58 (2019) 8. Si, H., Shi, J., Tang, D., Wu, G., Lan, J.: Understanding intention and behavior toward sustainable usage of bike sharing by extending the theory of planned behavior. Resour. Conserv. Recycl. 152, 104513 (2020) 9. Leister, E.H., Vairo, N., Sims, D., Bopp, M.: Understanding bike share reach, use, access and function: an exploratory study. Sustain. Cities Soc. 43, 191–196 (2018) 10. Cao, Y., Shen, D.: Contribution of shared bikes to carbon dioxide emission reduction and the economy in Beijing. Sustain. Cities Soc. 51, 101749 (2019)

Detection of Edges in Transport Networks Which are Critical for Public Service Systems Peter Czimmermann(B) University of Zilina, Zilina, Slovakia [email protected]

Abstract. When we study the robustness of a public service system (for example the emergency health care system), it is important to identify the edges that are critical for this system. By critical edges we mean those for which the elongation of its traverse time has a high negative effect for the whole designed service system. In our contribution, we complete the ideas from our previous papers, and we suggest an algorithm for the detection of critical edges in transport networks based on the computation of the probability of changes of transportation performance. Keywords: Distance of vertices · p-Median problem · Transportation performance · Erlang distribution

1 Introduction The public service system can be represented by a weighted p-median in network G = (V , E, w, t), where V is the set of vertices, E is the set of edges, w(u) is the weight of the vertex u, and t(e) is the driving time through the edge e - measured in minutes. We suppose that t(e) is an integer for every edge e. The driving time from vertex u to vertex v can be denoted by d (u, v). The set of customers is denoted by U and the set of service centers by S. The driving time between vertex u and the nearest vertex from the set S is denoted by d (u, S). The robustness of public service systems can be determined by the detection of critical edges. Unlike the other authors who studied various definitions and detection of critical edges in networks (for example [1, 2] and [3]), in our paper, an edge is critical if the elongation of its traverse time has a high impact on the designed public service system. For the detection of the critical edges, we use the increase of transportation performance, which is caused by the elongation of traverse time on these edges. The concept of transportation performance was suggested in [4] and developed in [5]. The transportation performance is  w(u) · d (u, S). (1) W = u∈U

Let Y = {e1 , e2 , · · · , ek } ⊆ E, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 347–353, 2023. https://doi.org/10.1007/978-3-031-26655-3_32

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be the set of edges. Let t = (t1 t2 , · · · , tk ), be the vector of extensions of driving times on the edges from Y. The transportation performance with these extensions is denoted by Wt . The change of transportation performance is given by formula  VY t = Wt − W . (2)  VY t is the function of k variables t1 , t2 , · · · , tk with the domain 0 , ∞)k . The value of VY t for concrete numbers t1 , t2 , · · · , tk can be computed directly – it is enough to evaluate affected edges as follows: ei → t(ei ) + ti . Case  k = 1 was studied in [4, 5] and case k = 2 in [6], where it was shown that VY t is continuous, concave, and piecewise linear function with domains 0 , ∞) or 0 , ∞)2 . In our contribution, we complete the ideas developed in the above-mentioned papers. We suggest an evaluation of edges or pairs of edges, which allow us to choose the most critical edge or pair of edges. We also introduce an algorithm, which allows us to compute the evaluation of any pair of edges. This method can reveal which pairs of road sections cannot be subject to reconstruction simultaneously.

2 Evaluation of the Single Edge In this section, we consider the evaluation of every edge from network G. We start with a simple example. Example. We consider a simple network with one customer in vertex u (where w(u) = 1) and one service center, located in vertex v (Fig. 1). The change of transportation performance for the edge e is given by formulas  t, for t ∈ 0, 1, Ve (t) = 1, for t ∈ 1, ∞). Graph of the function Ve (t) is in Fig. 2. Occurrence of car accidents and similar situations in traffic is often modelled by Poisson distribution [7]. It is clear that the events with higher extension of traverse time on the edge occur with lower probability. We model this probability by Erlang distribution, which is usually used in queuing systems for waiting time prediction. According to theory [8], it is the probability distribution of the waiting time in the Poisson processes. Its probability density function (with parameters α = 2 and λ = 1/2) is f (t) =

t −t/2 ·e . 4

(3)

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Fig. 1. Network from example.

Fig. 2. Graph of V (t) for e.

Since Ve (t) is measurable function of random variable t, its expected value is ∞ E(Ve (t)) =

Ve (t)f (t)dt

(4)

0

We mentioned above that Ve (t) is piecewise linear function. Hence this integral is convergent, and we can assign to every edge e a non-negative real number e → Ee = E(Ve (t)). At this point, it would be useful to consider the exact computation of Ee in large real networks. The first step is the computation of linear segments of Ve (t). It means that we determine points T0 , T1 , . . . , Tm ∈ 0, ∞), where T0 = 0, and the function Ve (t) is linear on each of the intervals Ti , Ti+1  and Tm , ∞) for i = 1, 2, . . . , m − 1. • For every customer j compute weighted distances w(j) · dG (j, S) and w(j) · dG−e (j, S) to the closest service center in networks G and G − e. • Let xj = w(j) · dG−e (j, S) − w(j) · dG (j, S). • Order the numbers xj into a non-increasing sequence and omit repeated values. Remaining numbers form a sequence of points T0 , T1 , . . . , Tm . If the edge e is a bridge (bridge is an edge whose deletion increases the number of components of a network), then instead of network G − e, we use the network G and increase the traverse time on edge e by a large constant. Thus, we are able to compute formulas on every interval Ti , Ti+1 , and Tm , ∞).

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1. Compute values Ve (t) for t = T0 , T1 , . . . , Tm, Tm + 1.  2. From coordinates of points [Ti , Ve (Ti )] and Ti+1 , Ve (Ti+1 ) compute the formula Ve (t) = ai t + bi valid on interval Ti , Ti+1  for i = 0, 1, . . . , m − 1. 3. From coordinates of points [Tm , Ve (Tm )] and [Tm + 1, Ve (Tm + 1)] compute the formula Ve (t) = am t + bm valid on interval Tm , ∞). On each of the intervals Ti , Ti+1 , we have T i+1

T i+1 Ve (t) 4t e−t/2 dt = (ai t + bi ) 4t e−t/2 dt = Ti T i

 2 T b i Ti − 2i ai Ti =e 4 + ai Ti + 4 + 2ai + bi /2

2  T ai Ti+1 bi Ti+1 − i+1 2 + ai Ti+1 + 4 + 2ai + bi /2 −e 4

(5)

and for interval Tm , ∞), we obtain ∞ = e−

Tm 2



t

(am t + bm ) 4t e− 2 dt

Tm am Tm2 4 + am Tm

+

b m Tm 4

+ 2am + bm /2

(6)

Moreover, if the edge e is not bridge, then am = 0. We present this situation using real data from our country. We detected the most critical edges in the Bratislava region – Fig. 3. (Source of this picture is OpenStreetMap.) The results from Fig. 3 can be seen in the Table 1. We have five most critical edges defined by Ee . Computation was done by computer macOS with 16 GB RAM and 2.9 GHz IntelCore i7 processor. Network of this region contains 166 vertices, 229 edges and the emergency health care system contains 25 stations with ambulances. The average time of computation of Ee for the edge e is 3.0686 s.

Table 1. The most critical edges in Bratislava region. 1. Vertex

2. Vertex

Ee

1

Bratislava-Vrakuna

Crossroad 123

760.169

2

Crossroad 123

Crossroad 124

760.169

3

Stupava

Crossroad 93

704.877

4

Crossroad 98

Crossroad 124

629.500

5

Pezinok

Crossroad 112

556.701

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Fig. 3. Five most critical edges (black line segments) in Bratislava region.

3 Evaluation of Pairs of Edges In this section, we suppose that a pair of edges Y = (e1 , e2 ) is affected and their traverse times are extended by values t1 and t2 . It was shown in [6] that VY (t1 , t2 ) is concave, continuous, and piecewise linear function of two variables with the domain 0, ∞)2 . For the evaluation of any pair of edges, we use the expected value of hyper-Erlang distribution of two random variables. In [6], we suggested an approach for the expression of the VY (t1 , t2 ) formula. However, this method is cumbersome. Hence, we introduce a new method, which provide us an efficient algorithm for the evaluation of pairs of edges. We use the fact that the edge values are non-negative integers. Hence, it is enough to compute the values of VY (t1 , t2 ) in points of a bounded integer grid and in the middle point of any unit square of this grid. This grid is defined on points of Cartesian product {0, 1, . . . , l} × {0, 1, . . . , l}, where l is an appropriate constant. The upper bound for this constant is specified at the end of this section. Every unit square of the grid can provide at most four formulas. We suppose that the unit square is defined on points A = (i, j), B = (i + 1, j), C = (i, j + 1), D = (i + 1, j + 1). The middle of this square is in point E = (i + 1/2, j + 1/2). It was shown in [6] that the boundaries of any linear segment are given by line segments with equations ±t1 ± t2 = c, t1 = c or t2 = c for an appropriate integer c. If t1 + t2 = c is a diagonal of the square ABCD, then B, C, and E are points of this line. If t1 − t2 = c is a diagonal of the square ABCD, then A, D, and E are points of this line. Function VY (t1 , t2 ) has on this square at most four formulas, which can be defined on triangles ABE, ACE, BDE, and CDE – see Fig. 4.

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Fig. 4. Unit square ABCD.

It is easy to show that VY (t1 , t2 ) is linear on each of these triangles and it has form VY (t1 , t2 ) = at1 + bt2 + c We can compute values VY (A), VY (B), VY (C), VY (D), and VY (E). Together with points A, B, C, D, and E, we have enough information to compute coefficients a, b, and c for each of the triangles. For triangle ABE we have: • a = VY (B) − VY (A) • b = 2VY (E) − VY (B) − VY (A) • c = VY (A)(i + j + 1) + VY (B)(j − i) − 2VY (E)j For triangle ACE we obtain: • a = 2VY (E) − VY (A) − VY (C) • b = VY (C) − VY (A) • c = VY (A)(i + j + 1) + VY (C)(i − j) − 2VY (E)i For triangle BDE we have: • a = VY (B) + VY (D) − 2VY (E) • b = VY (D) − VY (B) • c = VY (B)(j − i) − VY (D)(i + j + 1) + 2VY (E)(i + 1) For triangle CDE we obtain: • a = VY (D) − VY (C) • b = VY (C) + VY (D) − 2VY (E) • c = VY (C)(i − j) − VY (D)(i + j + 1) + 2VY (E)(j + 1) For the evaluation of pair {e1 , e2 }, we can use appropriate probability distribution with density function f (t1 , t2 ). Exact form of f (t1 , t2 ) will be a part of our future research. (We plan to start with hyper-Erlang distribution [9].) Pair {e1 , e2 } can be assigned by the expected value ∞  E(VY (t1 , t2 )) =

VY (t1 , t2 )f (t1 , t2 )dt1 dt2 . 0

(7)

Detection of Edges in Transport Networks

This integral can be computed piece by piece on every triangle: ¨ E(VY (t1 , t2 )) = VY (t1 , t2 )f (t1 , t2 )dt1 dt2 .

353

(8)

∀ 

In real situations, the time of delay of an ambulance can be expressed in minutes and has rarely reached one hour (we speak only about delays caused by situations on edges of transport networks). Hence, the number of triangles on which we need to compute these integrals is less than 4 · 60 · 60. For the mentioned constant, we have l ≤ 60.

4 Conclusion We presented algorithms for the effective detection of edges and pairs of edges that are critical for service centres in transport networks. Our future research will focus on the processing of more data, from which we can obtain better models of behavior of service systems when some edges of the network are affected. We also plan to spread the method from the previous section for cases k ≥ 3. Hence, we need to describe the behavior of VY (t1 , . . . , tk ) on unit, k-dimensional hypercubes. Acknowledgement. The research of the author is supported by the Slovak Research and Development Agency under the Contract no. APVV-19-0441 and by the research grants VEGA 1/0216/21 “Designing of emergency systems with conflicting criteria using tools of artificial intelligence”, and VEGA 1/0077/22 “Innovative prediction methods for optimization of public service systems”.

References 1. Scott, D.M., Novak, D.C., Aultman-Hall, L., Guo, F.: Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. J. Transp. Geogr. 14(3), 215–227 (2006) 2. Sullivan, J.L., Novak, D.C., Aultman-Hall, L., Scott, D.M.: Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: a linkbased capacity reduction problem. Transp. Res. Part A 44, 323–336 (2010) 3. Yu, E.Y., Chen, D.B., Zhao, J.Y.: Identifying critical edges in complex networks. Sci. Rep. 8, 14469 (2018) 4. Janacek, J., Kvet, M.: Characteristic of a critical network arc in a service system. Transp. Probl. 12, 141–146 (2017) 5. Kvet, M., Janacek, J.: Identification of the maximal relevant distance in emergency system designing. In: Mathematical Methods in Economics, pp. 121–126, Ceske Budejovice (2019) 6. Czimmermann, P., Kohani, M.: Computation of transportation performance in public service systems. In: IEEE COMPENG (2018) 7. Cerny, J., Kluvanek, P.: Principles of mathematical theory of transport. VEDA, Bratislava (1991) 8. Karian, Z., Dudewicz, E.: Handbook of Fitting Statistical Distributions with R, p. 790. CRC press, Boca Raton (2011) 9. Bocharov, P.P., D’Apice, C., Pechinkin, A.V., Salerno, S.: Modern Probability and Statistics: Queueing Theory. VSP, Brill Academic Publisher, Utrecht, p. 75 (2004)

Reliability Model of Autonomous Transport with Life Support Systems Based on Closed Biotechnological Complexes Sergey Glukhikh(B) Center for Industrial Implementation of Applied Research Institute of the Russian Academy of Sciences BIOCENTER – SAS, Microdistrict B, House 34/83, Pushchino, Moscow Region 142290, Russia [email protected]

Abstract. Among autonomous transport systems operating in an isolated environment, systems with a crew constitute a special group. Such systems include space and underwater stations, arctic and Antarctic stations, and other objects. In these facilities, one of the central places is occupied by life support systems for crew members, the efficiency and reliability of which largely determine the duration of missions of autonomous vehicles. At present, all life support systems are considered as independent functional circuits. At the same time, the main emphasis in the process of their research is placed on the features of their technical feasibility. In the process of long-term operation, the reliability of such systems is critical for the life of the crew. Basic reliability models should be synthesized early in the development. The purpose of this article is to study the structure of an integrated life support system from the standpoint of the reliability of its functioning. The article describes an approach to creating an integral life support ecosystem for autonomous transport systems of long-term operation. The paper presents an integrated architecture of biotechnological life support systems for autonomous vehicles, broken down into five closed biotechnological cycles (loops): oxygen loop, methane loop, carbon dioxide loop, food loop and water loop. The reliability models for these closed biotechnological loops based on the Fault Tree Analysis are developed in the paper. Keywords: Autonomous transport systems · Life support systems · Biotechnology cycles

1 Introduction The modern level of development of human civilization causes the need for research and work on autonomous transport systems (ATSs) that can stay for a long time in an isolated environment. Such systems are orbital space stations, underwater stations for ocean exploration, drifting ice stations, etc. Currently, duration of the autonomous mode of such facilities with a crew working on board is determined by the food reserves and the ability to generate the conditions necessary for life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 354–366, 2023. https://doi.org/10.1007/978-3-031-26655-3_33

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In order to increase the duration of the autonomous mode, active works are performed to investigate the possibility of developing closed life support systems for ATSs, with regeneration of human waste, associated organisms, as well as waste products of physiochemical, first and foremost technological, processes in the system [1]. Here, the completely closed life support systems working in total isolation from the outside world are of special interest. At present and in the foreseeable future, such systems can function only with the use of living organisms, mainly higher plants, microalgae and microbes [2]. In the process of long-term operation, the reliability of such systems is critical for the life of the crew. The work within NASA [3] has shown that basic reliability models should be synthesized early in the development. The purpose of this article is to study the structure of an integrated life support system from the standpoint of reliability of its functioning.

2 Related Works Currently, the problems of long-term life support of crews are most actively studied primarily in the context of space missions, in particular, Mars exploration programs [4]. Long-term space missions, for example, to explore other planets or the Moon, require special requirements for the reliability of life support systems, which are among the most critical in the conditions of such missions, both during the flight and when they are on the surface of the objects under study. One of the proposed life support systems for a flight to Mars is given in general form in [5], which formulates the most general requirements from the point of view of human needs. The implementation of a life support system based on a four-layer molecular sieve for removing carbon dioxide, controlling air impurities, generating oxygen and purifying water suitable for human consumption is described in [6]. A distinctive feature of a long-term manned orbital station is the assumption that it is possible to restore individual failed components of any systems by the crew. This requirement is laid down in the selection of crew members, who train in the elimination of single failures, and the system itself is considered as maintainable [7]. At present, the accumulation of data on failures and reliability analysis in NASA is carried out on the basis of the experience gained as a result of the implementation of various missions. Reliability is most often calculated with a correction according to the available experimental data. These data form a database of reliability, which allows managing risks during the implementation of missions [8] using a special application focused on the specifics of space flights [9]. The matrix of predicted potential failures and their consequences can be calculated on the basis of available data and focused on specific features of vehicles, for example, a probabilistic risk assessment taking into account the failure modes of the Space Shuttle [10]. This approach allows developers of all spacecraft systems to use one reliability model. A similar approach in the analysis of modes and consequences of failures is widely used in other areas, in particular, in aviation [11]. Examples of the use of this reliability risk analysis method can be found in many important projects such as design of the Airbus and Concorde [12], the lunar

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module [13] and other highly critical systems applications. Along with the described, other approaches are also used, among which one of the most common is fault tree analysis [14]. The existing life support systems that are used for relatively short-term space missions, typical, for example, for international space stations [15], unfortunately, do not have super-reliability in the conditions of long-term and remote missions, when it becomes impossible to replenish the supplies necessary for the life of the crew. This confirms the experience of working on space stations in conditions when detours were required to supply the crew with everything necessary for life in the conditions of failure of regular life support systems to prevent disasters in a real flight [16]. At present, all life support systems are considered as independent functional circuits. At the same time, the main emphasis in the process of their research is placed on the features of their technical feasibility. During long-term operation, the reliability of such systems is critical for the life of the crew. The purpose of this article is to study the structure of an integrated life support system from the standpoint of reliability of its functioning. The article describes an approach to creating an integral life support ecosystem for autonomous transport systems of long-term operation. The paper presents an integrated architecture of biotechnological life support systems for autonomous vehicles, broken down into five closed biotechnological cycles (loops): oxygen loop, methane loop, carbon dioxide loop, food loop and water loop. The reliability models for these closed biotechnological loops based on the Fault Tree Analysis are developed in the paper.

3 Biotechnological Cycles in ATS Life Support Systems Closed, though not yet fully closed, life support systems are already being used: these are spacecraft and some classes of underwater ATSs. In designing life support systems for space stations, the problem of reducing expendable stocks is already of practical importance to minimize extremely expensive, complex and unsafe shipments from the Earth. Atmospheric composition in a spacecraft cabin can change within a few days because of its limited size, whereas on the Earth it would take centuries. Therefore, life support systems and their ground testing prototype, the artificial biospheres, are a kind of “ecological time machines” for predicting the possible ecological future of the Earth [1]. Below, Fig. 1 shows a structural diagram of integration of the main closed biotechnological cycles into promising LSS which, due to this inclusion, can be called biotechnological life support systems (BLSS) [2]. ATS BLSS water cycle. The initial water supply of an ATS is spent by the crew for physiological and domestic needs. Wastes as the excrements of crew members, laundry and showering washouts enter the multifunctional integrated recycling circuit with obtaining water suitable for reuse. The main devices of the closed water cycle are: a digestion tank; culture liquid, microalgae and biogas separators; a fermentation unit; a photobioreactor; filters; and a water accumulation tank.

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ATS BLSS oxygen cycle. The closed oxygen cycle operates in close relationship with the onboard air conditioning system (ACS). Oxygen consumers on board of ATS are the crew and bacteria producing food protein concentrate in a fermenter. The oxygen cycle also includes a photobioreactor and a phototrophic element used to convert carbon dioxide into oxygen. Oxygen is purified from admixtures and inclusions before it is supplied to the ACS. ATS BLSS carbon dioxide cycle. Like the oxygen cycle, the carbon dioxide (CO2 ) cycle is closely associated with the ACS of ATS, which routinely releases excess CO2 from the air of the ATS, where it appears because of breathing of crew members. The main CO2 processing devices in BLSS are a photobioreactor and a phototrophic element, where microalgae and higher plants, respectively, absorb CO2 for the synthesis of their own biomass, which eventually becomes a food ingredient for the crew. ATS BLSS methane cycle. In the modern LSS of ATS with physicochemical conversion of CO2 into oxygen, methane released at the intermediate stage of the process must be pumped out of the ATS. In BLSS, methane is a very important raw material for biosynthesis of the most important food ingredient of the crew: animal protein concentrate. In biomass fermentation, methane is as a carbon and energy source for the growth of protein-producing bacteria. The methane cycle also includes a digestion tank, a fermenter, culture liquid and biogas separators. ATS BLSS nutrient cycle. The main units of the nutrient cycle are a digestion tank (as a converter of waste into microelements and biogas with separation of the latter into methane and CO2 ); a fermenter (as a source of animal protein concentrate, microelements, vitamins and amino acids); a phototrophic element as a source of plant food in the form of higher plants; a photobioreactor as a source of microalgal biomass enriching the crew’s ration with protein, microelements and amino acids. The auxiliary devices in the nutrient cycle are gas and liquid separators and centrifuges. The crew prepares food manually using the above ingredients. Integration of the aforementioned closed cycles forms the structure of the entire five-cycle prospective ATS BLSS. The number of cycles in BLSS may vary depending on the goals and objectives of ATS. The main principles and requirements for integration into ATS BLSS are high operational reliability, low weight and dimensions, low power consumption, high efficiency, ergonomics and serviceability. Figure 1 shows an integral functional block diagram of a 5-circuit BLSS, which includes the following main subsystems: • Solid phase converter. In the converter, the solid phase isolated with the separator from the culture liquid of the digester is partially fed as a ground to the phototrophic element. The remaining solid phase undergoes pyrolysis to obtain a trace element complex for the nutrient medium of the fermenter. • Subsystem for preparing nutrient medium. The programmed automatic preparation of nutrient medium is carried out in a device including mini silos for storing, replenishing and dosing nutrient medium components, a device for dry stirring, and a dissolution tank.

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• Fermenter. The fermenter is used to synthesize animal biomass based on bacterial methanotrophic microorganisms. For this purpose, the fermenter is fed with the nutrient medium, the inoculum, the air from the ACS, and the methane obtained from the digester biogas. • Photobioreactor. Microalgal biomass in BLSS is cultivated in a transparent photobioreactor fed with the fugate after CL separation, post-fermentation carbon dioxide from the fermenter, and the inoculum of Spirulina or Chlorella microalgae. At the outlet of the photobioreactor, oxygen is obtained in addition to microalgal biomass. During biosynthesis, the culture liquid in the reactor is lighted with daylight lamps in the optimal range. • Microalgae separator. The microalgal biomass is separated from the liquid phase in a separator. After dewatering, the biomass is fed to the food preparation complex. • Culture Liquid (CL) Separator. CL separation after the digestion tank is performed with a separator; then the separated fugate is used to prepare the nutrient medium for the fermenter. • Biogas separator. Biogas from the digestion tank enters the biogas separator, where it is separated into methane and carbon dioxide. • CL separation, CL separator. After the fermenter, CL is fed into a centrifuge for isolation of animal biomass. The released centrate is fed into the photobioreactor. • Centrate filter. The centrifuge separated from the CL obtained because of bioprocessing cycles is an aqueous solution of different elements. The centrate filter performs the function of purification to obtain potable water. The released elements are returned to biosynthetic processes to replenish their trace element composition. • The digestion tank. The digestion tank is an important element of recycling of all BLSS and crew wastes. It is involved almost in all closed biotechnological cycles of BLSS. Mixed inoculum for the digestion tank is selected depending on waste composition. The biogas obtained in the digestion tank is separated into methane and carbon dioxide in the biogas separator; CL is separated into the solid phase and the centrate with the separator. • ACS and the system for CO2 removal from the ATS atmosphere. ACS performs the function of air conditioning in ATS, as well as removes carbon dioxide from the air and maintains its concentration within acceptable limits. • CO2 storage tank. All carbon dioxide released during the biotechnological cycles of BLSS and because of breathing of the crew is collected in a centralized storage tank, from where it is distributed to devices to be used in different processes and cycles. • Phototrophic element. Higher plants are cultivated on board the ATS in the phototrophic unit for growing specially bred species of cereal crops, vegetables, and other plant biomass. In this unit, as well as in the photobioreactor, the main process takes place: the conversion of carbon dioxide into atmospheric oxygen. Plant waste of the phototrophic element is recycled in the digestion tank. Ground consumption is replenished by the solid phase from the digestion tank. • Food preparation. The following three types of biomasses obtained as a result of closed biotechnological cycles, i.e., the plant biomass from the phototrophic element, the microalgal biomass from the photobioreactor and the animal biomass from the

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fermenter for methanotrophic bacteria, become available for the crew to prepare various meals with the balanced composition of protein, trace elements, essential amino acids and vitamins according to special recipes.

Fig. 1. Integral functional block diagram of a 5-circuit BLSS (Abbreviations: CO2 - carbon dioxide; O2 - oxygen; ACS - air conditioning system; CL - culture fluid; PBM - plant biomass; ABM - animal biomass; MABM - microalgal biomass; CH4 - methane; PB - photobioreactor; PTE - phototrophic element; BG - biogas; EM - exometabolites of the crew; DW - domestic water; NM - nutrient medium; SP - solid phase).

4 Fault Tree Model of ATS BLSS Reliability Fault tree analysis (FTA) is a type of failure analysis, in which an undesired state of a system is examined. FTA analysis involves five steps.

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Step 1. Determination of undesirable events leading to disruption of the life support of the crew. These events are accepted as critical for life, for the study of which a model should be built. Step 2. After identifying unwanted events, the causes that may lead to their occurrence are identified. In this case, any events with a probability greater than zero are taken into account. Unfortunately, obtaining exact values of these probabilities is usually impossible due to the lack of practical data. Step 3. Building a fault tree according to standard methods [11]. Step 4. Examine the Fault Tree for Undesired Events for Possible Design Improvements in order to reduce possible risks of undesirable events. In this case, various application software environments can be used for the quantitative and qualitative assessment of these risks [17]. As a result of this step, the entire set of undesirable events is detected. Step 5. For all identified undesirable events, monitoring systems are developed to prevent their occurrence in order to increase the reliability of the life support system as a whole. These systems can be quite specific to different subsystems and are carried out by experts in the relevant technical fields. The most difficult and labor-consuming component of FTA is construction of a Fault Tree. Depending on the problem at hand, the FTA Fault Tree is determined by a complex of failures in each of its subsystems (Fig. 2).

Fig. 2. Fault tree of a 5-circuit BLSS.

Let us build Fault Trees for each of the BLSS subsystems based on its functional scheme described in Sect. 3 of the article. The Fault Trees for each of the five main closed circuits of the integral BLSS are shown in Fig. 3–7. Fault tree analysis is widely used in the analysis of the reliability of complex systems in order to build models for the development of undesirable events, which allows: • understand the interrelationship of factors influencing the occurrence of undesirable events; • analyze the compliance of the real reliability of the system under study with the specified requirements, as well as determine the risks of occurrence of undesirable events;

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Fig. 3. Fault tree for Oxygen circuit.

• determine the list of the least reliable elements of the system and the most probable ways of occurrence of pre-failure conditions; • to form recommendations for the development of systems for diagnosing critical failures, to monitor the risks of violation of the security of a complex system. FTA can be used in further studies as a design tool that helps create reliability requirements for all system components.

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Fig. 4. Fault tree for Methane circuit.

5 Conclusions Transportation systems for long-range and long-term autonomous missions must be highly reliable, stable and easy to maintain, especially with regard to life support and preservation systems. The present article attempts to show the ways to provide a longterm autonomous mission of a crew in an isolated environment without the possibility of replenishing vital supplies. The minimum required closed cycles of life support systems are presented. The aspects and relationships between BLSS and the standard onboard equipment are shown. The principles underlying the closed biotechnological cycles, as well as the circuits for their integration into BLSS, can also be used at the initial stage of planetary exploration. Much attention is paid to reliability of the entire BLSS and the components of its closed cycles. Reliability was analyzed by the demonstrative method of constructing a Fault Tree.

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The reliability and safety of BLSS for crews during long-range and long-term missions in an isolated environment is the decisive criterion for their introduction into ATSs. Closed biotechnological cycles are based on living biological agents, preservation of which deserves close attention. Their backups must be stored on board the ATS under optimal conditions. BLSS components, both individually and as part of closed biotechnological cycles, should be thoroughly studied under both ground and orbital conditions. Integrating fault trees Fig. 4–7 into the general fault tree (Fig. 3), we will get a general model for analyzing the reliability of the entire biotechnological complex as a whole.

Fig. 5. Fault tree for Carbon dioxide circuit.

The resulting reliability models based on fault trees can be studied by specialists of the relevant subsystems to develop measures to improve their reliability, as well as systems for monitoring the occurrence of all types of undesirable events.

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Fig. 6. Fault tree for Food circuit.

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Fig. 7. Fault tree for Water circuit.

References 1. Sinyak, Yu.E.: Life Support Systems of Manned Spacecraft (the Past, the Present and the Future). http://www.imbp.ru/webpages/win1251/Science/UchSov/Docl/2008/Sin jak_ speach.html – 11.11.2016). Accessed 07 July 2008 2. Glukhikh, S.: Closed Biotechnological Cycles in Life Support Systems of Autonomous Transport Systems. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) Reliability and Statistics in Transportation and Communication. RelStat 2021. Lecture Notes in Networks and Systems, vol. 410, pp. 389–398. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-961961_36

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3. Seedhouse, E., Shayler, D.J.: Handbook of Life Support Systems for Spacecraft and Extraterrestrial Habitats. Springer Cham, p. 1200 (2020). https://doi.org/10.1007/978-3-319-095 75-2 4. Jones, H., Ewert, M.: Ultra reliable closed loop life support for long space missions. In: AIAA-2010–6286, 40th International Conference on Environmental Systems 2010 (2010) 5. Connolly, J. F.: Mars design example. In: Larson, W.K., Pranke, L.K. eds. Human Spaceflight: Mission Analysis and Design, McGraw-Hill, New York (1999) 6. Doll, S., Eckart, P.: Environmental Control and Life Support Systems (ECLSS), in W. K. Larson, and L. K. Pranke, eds., Human Spaceflight: Mission Analysis and Design, McGrawHill, New York (1999) 7. Heydorn, R.P., Railsback, J.W.: Safety of Crewed Spaceflight, in W. K. Larson, and L. K. Pranke, eds., Human Spaceflight: Mission Analysis and Design, McGraw-Hill, New York (1999) 8. Perera, J. Field, S.: Integrated risk management application (IRMA) (2005) 9. Risk Management: Futron Integrated Risk Management Application (FIRMA). http:// www.futron.com/riskmanagement/tools/futronintegratedriskmanagementapplication.htm. Accessed July 2008 10. Stamatelatos, M.: Probabilistic Risk Assessment: What Is It And Why Is It Worth Performing It? Technical report, NASA Office of Safety and Mission Assurance (2000) 11. Stamatis, D.H.: Failure Mode and Effect Analysis: FMEA from Theory to Execution Revised. Asq Pr (2003) 12. Lievens, C.: System Security, Caepadues Editions, Toulouse (1976) 13. Bussolini, J.J.: High reliability design techniques applied to the lunar module, lecture series no. 47 on reliability on avionics systems, September 1971 (1971) 14. Blokdyk, G.: Fault tree analysis a complete guide. 5STARCooks (2021) 15. Likens, W.C.: A Preliminary Investigation of Life Support Processor Reliabilities. In: International Conference on Life Support and Biospherics, Huntsville, AL, Feburary 18–20, (1992) 16. Russell, J.F., Klaus, D.M.: Maintenance, reliability and policies for orbital space station life support systems. Reliab. Eng. Syst. Saf. 92(6), 808–820 (2007) 17. Ruijters, E., Stoelinga, Mariëlle, I.: A fault tree analysis: a survey of the state-of-the-art in modeling analysis and tools. Comput. Sci. Rev. 15, 29–62 (2005)

Economics and Business

Effective Change Management and Continues Improvements on Smart Governments Ioseb Gabelaia1(B)

and Abdelra Sherif2

1 RISEBA University of Applied Sciences, Riga, Latvia 2 Public Sector-Management Consultant, Riyadh, Kingdom of Saudi Arabia

Abstract. To establish a smart government, governments have attempted to modernize and embrace a digital transformation. Moreover, governments have increased attention to smart information technologies to support public administration. Smart governance technology facilitates better planning and decisionmaking. It expands processes and transforms the ways that public services are provided. Further, artificial intelligence takes centre stage during the journey of the smart government transition. Saudi Arabia’s swift progress in economic sectors meant investment in continuous modernization to safeguard efficacy and velocity. This paper explores and analyses effective change management and continuous improvements on smart governments focusing on Saudi Arabia. The data was obtained through in-depth interviews and survey from professionals in the digital change management sector. Descriptive qualitative methodology and thematic analysis technique was utilized to analyse and interpret findings. The results revealed that change management has a positive relationship with the smart government, supporting research H1. The findings also revealed that continuous improvement is a critical success factor for a smart government therefore positively impacting H2. Keywords: Change management · Continues improvement · Smart government · Artificial intelligence · Digitalization · Pandemic

1 Introduction The governments have faced increasingly sophisticated social-technical challenges such as the need to ensure social inclusion, sustainability, public participation, public health, and safety. Besides, the traditional methods have proven to fall short and innovative approaches are needed. Moreover, most governments have sought to improve their relationships with citizens, organizations, and businesses. In response to these needs and challenges, governments have adopted strategies primarily characterized by complex information and communication technologies designed in highly innovative and creative ways. Many initiatives and strategies are introducing contemporary approaches to different service delivery models that go hand in hand with the constant changes. Furthermore, the smart government can be explained as the government’s use of intelligently networked information and communication technologies to manage administrative roles © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 369–383, 2023. https://doi.org/10.1007/978-3-031-26655-3_34

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and government-associated business processes. Integrating physical, digital, public, and private sectors ensures active and passive engagement and collaboration with the citizens. The development of smart governments has been a significant focus of many countries, especially Saudi Arabia. Artificial Intelligence is more likely than any other technological advancement to uplift nations. It will transform change management, particularly the human component of change management. As a result, governments are concentrating on the advantages of AI, seeing it as a huge opportunity to capitalize on, to modernize processes and gain a competitive edge in service delivery and individual/business satisfaction. Artificial intelligence is changing how humans live, work, and interact. It encourages creating more effective change management. It gives change managers the potential to measure digital activities in real-time. In addition, AI eliminates bias, ensuring that all informed decisions further identical logical thinking. The development of smart governments calls for proper change management and continuous improvement. Change management in implementing smart government is a highly complex matter as it takes more than technology to implement, requiring human, organizational, social, and cultural factors. Change management is a systematic approach that transforms organization or government goals, objectives, core values, processes, and technologies. Moreover, continuous improvement is also critical in ensuring sustainable development and development of smart cities and governments. Following the outbreak of the Covid-19 pandemic, it has become crucial for governments to provide structures to the citizens to help increase engagement with the government agencies. Digital transition and transformation have thus become necessary for the governments to meet the current and future needs, which requires the government to transit significantly and rapidly by changing work processes and replacing organizational hierarchies. Adopting new technologies and the successful transitioning to smart government comes with many challenges. It is because the smart government does not only entail technologies but also other aspects such as human factor. Change management is thus vital and impactful in ensuring the successful implementation and adoption of smart technologies to enhance the productivity and profitability of the smart government. The paper aims to evaluate the impact of continuous improvement and change management on smart government with a case study of Saudi Arabia to understand their roles in ensuring sustainable smart government properly. Therefore, two hypotheses were developed. H1: There is a positive relationship between change management and smart government, and H2: Continuous improvement strategy significantly impacts smart government. Qualitative and quantitative research methods were used for primary data collection. Data was obtained through in-depth interviews and surveys with professionals in the digital change management sector. A thematic analysis technique was employed to analyze and report findings. The results revealed that change management has a positive relationship with the smart government, supporting research H1. The findings also revealed that continuous improvement is a critical success factor for a smart government, positively impacting H2.

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This paper is essential to researchers as it represents an added value to the knowledge gap on the impact of change management and the continuous improvement of smart governments. While the need for new technologies for cities and governments has increased, little attention is given to change management and continuous improvement. Therefore, creating awareness of the issue and further research is necessary.

2 Literature Review 2.1 Smart Governance According to [1], the smart government describes the initiatives involving investment in emerging technologies integrated with innovative strategies to develop a more agile and resilient government structure and a better governance infrastructure. Moreover, according to [2], smart government is the extensive use of ICTs by the government characterized by the movement to using open data and innovative technology to improve the understanding of societal needs and enhance the government interactions with citizens, private entities, NGOs and other governments. Furthermore, [3] explained various dimensions of smartness that help in understanding and creation of smart governments. [4] presents three components of a smart government accepted globally. First is a smart ecosystem where governments are increasing their efforts in building publicprivate ecosystems to solve societal problems such as healthcare, mobility, education, and workforce training and development. Second, smart connectivity and data through the increased advancement of sensor technology and the developing ubiquity of the Internet of Things (IoT) that obscures the distance between the cyber and physical world and lastly using smart platforms and engagement through increased information sharing between the government, citizens, and organizations, contributing to more scientific and precise decision-making outcomes. According to [5], smart governance utilizes innovative digital technologies and intelligent initiatives in information processing and decision making. Therefore, smart governance is a product of the adoption of smart government. [6] outline six defining aspects of smart governance: using ICTs, external collaboration and participation, internal coordination, decision-making process, e-administration, and outcomes. [1] discuss the two aspects constituting smart governance, including the data and evidence-based policymaking and collaborative, open, and citizen-centric form of governance. Innovative technologies are essential in helping ensure evidence-based policymaking through data processing, integrating large amounts of data, and increasing the ability to manage knowledge [7]. According to [8], the use of innovative technologies by the government to better their performance and decision-making has increased. This has been triggered by understanding that government information and data belong to the public and thus need government forms that effectively relay the data and information to the public. 2.2 Smart Cities The concept of smart cities has recently gained much attention both by governments and researchers, as smart governments are seeking to develop most of their cities to be

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smarter [8]. Most governments have incorporated the concept of smart cities in their growth and development initiatives [9]. In addition, smart cities have been aimed at improving the quality of life in the cities by solving city inhabitants’ problems. One of the prominent examples of a smart city is the Songdo smart city in South Korea [8]. Moreover, the already existing cities have also been involved in transitioning to computing systems, such as in Singapore, where a solid technology base was developed to maximize urban infrastructures and public services [10]. However, according to [8], the development of smart cities has also led to a rise in the concept criticizing the smart cities concept that does not consider the people’s voice and human capacities. Thus, he opines those smart cities must be created with public participation. 2.3 A Digital Response to Covid-19 Globally and in Saudi Arabia According to [11], in a Deloitte survey, digital technologies have significantly transformed the public sector. Notably, technologies are vital, but digital transformation calls for governments to look beyond the digital tools and into the culture, structures, and mindset. For example, the Covid-19 pandemic has changed the focus of digital government to the dire need to provide access to information and government services [12]. This has seen increased adoption of digital technologies in response to the significant needs of the people, such as testing and contact tracing. [13] suggest that digital technologies have been applied to respond to Covid-19 in four primary dimensions: contact tracing, social behavior monitoring, information gathering and communication concerning the pandemic, and diagnosis and treatment. Many technological applications were developed to help control the virus’s spread. According [14], digital contact tracing has been adopted globally by developing digital contact tracing apps. For example, in South Korea, linked location, surveillance, and transaction data were used for contact tracing [13]. 2.4 Saudi Arabia Vision 2030 Saudi Arabia’s vision 2030 is vital in understanding the functioning of the smart government and responding to the research subjects through this research work. The kingdom adopted vision 2030 in 2016 to diversify its economy and reduce its dependence on oil [15]. The vision’s main goals are to provide a better quality of life to its citizens and to meet their needs and in addition to its environmental conservation and natural resources [16]. According to [15], adopting a nationwide vision ensures sustainability by reconstructing the economy and the government. However, the government will face many challenges and obstacles requiring continuous improvement and change management to achieve these transformations. Saudi Arabia’s government has laid down a national strategy for digital transformation, a five-year strategy with three phases. The first action plan was between 2006–2010 to enable each person to access government services efficiently and effectively in an integrated, fast, and safe electronic way [17]. The second plan was between 2012 and 2016, enabling all people to obtain effective government services through multiple electronic ways. The current action and the third plan are 2020–2024, which focuses on establishing a smart government. According to [18], the Kingdom of Saudi Arabia (KSA) has set

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smart cities as one of the significant ways to accomplish smart government vision 2030. The government has focused on developing a knowledge-based society and adopting knowledge-based technological innovations [16]. KSA has already started implementing the idea of transforming cities into smart cities. Yanbu industrial city is the first smart city in Saudi Arabia. Through the collaboration of the companies, a unified telecom, and IT network was built to improve the digital infrastructure and enhance the efficiency of the IT environment to facilitate the projects in the city [16]. Another major smart city developed in line with vision 2030 is NEOM city. Other proposed cities include the Riyadh smart city, Jeddah smart city, Al-Madinah smart city, and Al-Ahsa smart city. Vision 2030 has paved the way for enhancing digital transformation, and Covid-19 has just allowed the promotion and testing of the transformation and transition process. 2.5 Change Management and Continuous Improvement According to [19], change management refers to a well-structured approach to transitioning or transforming organizations, groups, and individuals from their current level to a desired future state. It is an organizational process that focuses on motivating the employees and other stakeholders to embrace and implement changes in the organizational environment [19, 20] explains that change management is essential in ensuring the successful implementation of technological and software systems. Change management can either be reactive by responding to the changes in the external environment or proactive to help accomplish the desired goals, continuous improvement, and program-by-program basis. Change management consists of the challenging side that entails changes in processes, systems, technologies, and strategies. The soft side entails a change in attitude and behavior, such as communication. According to [21], smart government programs such as smart cities cause significant changes in government functions. Providing government services through innovative technologies and electronic tools would require a change in procedures and legal processes regarding decision-making processes. The central area of impact is the decision-making of the government. Notably, these changes require the acceptance of the government, employees, and other stakeholders such as labor unions [21]. According to [19], successfully effecting changes will require changing people’s mindsets and performing a re-engineering process. [22] stated that digital technology is just, but one tool needed to assist the development of smart government and cities; however, to obtain maximum benefits that come with the digital transformation, it will require a great effort to change people’s perceptions, and behavior demonstrate to them why the change is essential and usher them to the right direction. Change management plays a vital role in ensuring the successful implementation of a change program and e-government and smart governments. The poor management strategy has been the main reason behind the slow implementation of smart government and cities in most governments globally, especially in developing countries [19]. Furthermore, according to [21], there are three essential change management elements: stakeholder management, communication management, and training. All the stakeholders in an organization need to be identified, manage the relationships, and ensure they receive information regarding projects’ progress, needs, and benefits.

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[23] highlight the significant organizational capabilities needed to ensure the success of the smart government program as technological management, data management, cybersecurity, change management, and innovation management. The changes must be planned and implemented in different governmental departments to help adopt new innovative solutions. Proper change management is vital in ensuring the successful implementation of the smart government. The process is very complex, especially for smart government operations, and a poor change management system can lead to failure in its successful implementation. Therefore, the smart government program must not only be viewed as a technological aspect but also must consider the human aspects and other factors vital for its implementation, effectiveness, and sustainability. It should be noted that smart government is more of an organizational issue than a technological issue. Regarding continuous improvement, [24] opines that continuous improvement is a critical success factor for implementing smart government and successful and sustainable organizational change. Continuous improvement is the sustained effort to improve the operation of an organization, which requires management and employee commitment. [25] defines continuous improvement as a purposeful and clear set of principles, strategies, and initiatives adopted to create a continuous systemic and cumulative improvement in deliverables, processes, procedures, and operating systems. [26, 27] add that continuous improvement is a culture of developed and sustained improvement to promote efficiency and combines efforts to achieve a set of goals. [28] give a more appropriate definition for this study from the perspective of the public sector by defining continuous improvement as a systematic approach to managing the initiatives forecasted to ensure gradual improvement in organizations by achieving phased objectives. However, [29] opine that continuous improvement involves continuous change and the evaluation of the outcome to inform further and future actions for continuous improvement of the organizational performance. For this research, continuous improvement is a systematic and accelerated process of transition and transformation in an organization’s activities to improve the current state. The impacts of the changes obtained are reflected in the strategies, infrastructure, structures, policies, culture, work process, and services received by the citizens and other stakeholders within that particular government. According to [30], continuous improvement requires well-coordinated and implemented systems and processes incorporated with effective change management. Therefore, continuous improvement goes hand in hand with change management to form the most critical elements in the implementation and sustainability of smart government [24]. Notably, continuous improvement strategies primarily rely on the key players’ commitment to contribute to the improvement process [26, 27]. In ensuring continuous improvement, various continuous improvement methodologies have been developed, such as Lean, TQM, and Six Sigma, among others. The continuous improvement becomes essential with the increased innovation and adoption of digital transformation among governments and cities. According to [24], continuous innovation requires continuous improvement, incremental learning, and increasingly radical innovations and change. Organizations need continuous and discontinuous improvements to improve performance, productivity, and competitive technological

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advantage [31]. Additionally, continuous improvement improves processes and systems, re-engineers a firm’s efforts, and prevents wastage in the entire process. It promotes employees’ and citizens’ engagement and support towards achieving the set goals and objectives. To the literature review, change management and continuous improvement are vital in the successful implementation of strategies for organizations and our case, the government [19, 21–24, 28]. However, there is limited literature on the impact of the two on smart government. Moreover, scholars have not given considerable attention to the smart government strategy concerning change management and continuous improvement.

3 Research Methodology The research aimed to evaluate the impacts of continuous improvement and change management on smart governments with a case study on Saudi Arabia. The research methodology is then vital in accomplishing this objective. [32] explain that research methodology explains the data to be collected, the data sources, data collection techniques, and the analysis approach to be used. The chapter thus explains the methodology employed in primary research. Appropriate research design is essential in facilitating data collection, and a suitable method needs to be selected to help correctly respond to the research topic through accurate and appropriate data collection [33]. The study topic largely determines the king of research design that is most appropriate, which the researcher can adopt. The research adopted a descriptive qualitative research design for primary research and the literature review for secondary research. According to [34], a qualitative research design allows the researcher to gain in-depth information regarding the research topic. It allows the respondents to gain a comprehensive insight into their understanding of the research topic and gives a chance to encourage cooperation that enables capturing accurate and in-depth details. According to [35], since qualitative analysis does not need large sample sizes, it becomes possible to do a comprehensive and rigorous evaluation of the selected participants. However, it is disadvantaged regarding the small sample size, limiting its possibility of generalization and being time-consuming. The research design was thus chosen to help the researcher obtain deepened insights into the impacts that continuous improvement and change management have on smart government. According to [36], the target population is the set of persons or groups from which the researcher intends to do research and draw conclusions from the study. Therefore, identifying the appropriate population is essential in ensuring accurate and relevant data is obtained to help meet the research objectives. The study was based in Saudi Arabia. Therefore, the target population was conducted in digital experts who have taken part in smart government projects, other experts in charge of executing smart government strategies, and those involved in implementing smart cities strategy. Therefore, selecting experts involved in smart government and city projects will ensure obtaining accurate, highly relevant, reliable, and valid data. The sampling method is essential to avoid bias in the research and ensure that the data collected is valid and accurate [36]. The research employed a purposeful random sampling method where all the experts were listed and given equal chances to be included

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in the research. The use of random sampling gives every member of the target population an equal chance of being selected for the study, reducing the bias rate [37]. It also validates the generalization of the study findings on the total population. In addition, the researchers conducted in-depth interviews and surveys to collect data. The interview consisted of two respondents for in-depth interviews and thirty-six digital experts for the survey study. The in-depth interview was applied as the data collection instrument. The interviews were conducted via Zoom due to the Covid-19 pandemic. For the interviews, twelve questions were prepared to ask experts. Thus, for the survey study, fourteen questions. All questions were purposefully developed to obtain maximum information and help to investigate research questions better. Interviews were conducted via Zoom call due to Covid-19 circumstances. Interviewees received a content form in advance prior to the interview day. The most extended conversation was 90 min, and the shortest was 31 min. All the data was recorded and transcribed. After transcription, recordings were deleted per confidentiality. Transcribed data were analyzed, and final versions were sent to the interviewees to reread and give their consent to use this research. Furthermore, a survey study was conducted over the past year where participants voluntarily provided feedback on questions. A thematic analysis method was applied in identifying, analyzing, and reporting the qualitative data obtained. The thematic helps provide a detailed description of the research findings and a comprehensive response to the research question. [38] six steps of thematic analysis were followed in the analysis process comprising familiarizing with data, generating initial codes, searching for the themes, reviewing themes, defining, and naming the themes, and developing the report of the findings. However, the six steps we summarized into three significant steps: • Phase one: Coding - Coding entails organizing data into given categories. The categories may be formed either based on a theme or an idea. Nvivo software was utilized to help in data coding. • Phase two: Identifying and naming patterns, themes, and relationships - This involves identifying data patterns, naming and reviewing the themes, and drawing the relationship between the primary data and the secondary data to compare interview findings with the literature review findings and explain any possible differences. • Phase 3: Data summary and reporting - The phase involves summarizing the findings and relating the data obtained to the research question, aim and objectives, and the set hypothesis [39]. The research deals with the general ethical principles as engagement with human respondents are involved. The primary ethical considerations were informed consent, confidentiality, anonymity, and privacy. The researcher was required to assure the observance of informed consent in conducting the interviews. The participants were also informed that the participant in the interview is voluntary, and they can opt out any time they wish [35]. For anonymity, the respondents were not required to give their names and were assigned an alphabet, such as respondent B, which was also randomly given following no order. The participants were also informed that the data should not be used for any other purpose outside the research.

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4 Research Findings and Results This research aimed to reduce a knowledge gap in the existing scholarly work concerning the impact of continuous improvement and change management on smart government, specifically in the Kingdom of Saudi Arabia. The research was successfully conducted, and primary data was recorded for analysis. Hence, the chapter provides the results and the discussions of the study. In the research, all the participants have been involved in Saudi government digitalization projects. It showed that the research successfully involved the correct type of participants supporting the accuracy and reliability of the findings. In the survey, most of the participants were male, with 88.6%. More importantly, 85.7% of participants were from the private sector, 8.6% from the state institutions. Furthermore, 25.7% of respondents had more than ten years of professional experience, followed by 20% with 7–10, 3–5, and 1–3 years, and 14.3% with 5–7 years of professional experience. It indicated high reliability in answering the research aim. The researchers asked various questions regarding the implementation of the smart government in Saudi Arabia regarding the progress obtained, the successful projects, the challenges faced in the implementation, and the success factors. From the findings, all the respondents agreed that the country had been far ahead in implementing its smart government program, even as stipulated in vision 2030. The respondents pointed out that there has been an increase in the use of artificial intelligence, ICTs, networked government, and technologies to facilitate government administrations. In addition, some pointed to increased citizen involvement in the government, increased efficiency, and effectiveness in government administration. Furthermore, from Fig. 1, the researchers concluded that digital transformation, artificial intelligence, and change management for continuous improvements are critical for developing smart governments. Regarding artificial intelligence encouraging effective change management, respondents were somewhat torn about its effectiveness; overall, it showed a positive relationship. These findings are like those of [11], who opined that Saudi Arabia is among the top governments in implementing ICT and artificial intelligence. Moreover, according to [16], the government has initiated the smart government strategy through smart cities such as Istanbul, increasing efficiency and effectiveness in rendering government services and increasing citizens’ engagement. These research findings assert the findings of [40], which revealed that using ICT has enhanced accountability, openness, quality of services, and people’s engagement in public administrations. Furthermore, the respondents pointed out the various challenges facing the implementation of smart government projects in Saudi Arabia, such as mind-scrapping, investment, security and privacy, the integration of the human component, change management, and culture. Figure 2 illustrates some of the findings. These findings support the findings of [41]. Their study revealed Mindscaping as the most significant challenge facing Kuwait, investment facing India, and Security and Privacy as the greatest challenge facing the USA in implementing the intelligent government through the IoT. [23] also pointed out that people’s engagement in implementing smart government strategies is critical. In response to the challenges, the findings revealed that various aspects, such as

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Fig. 1. The importance of implementation of Smart Government in Saudi Arabia. Change management and continues improvements, Artificial Intelligence and Digital Transformation.

cybersecurity, resilience, public participation, awareness, and the business community, need to ensure the sustainability of the smart government services and changes. The findings also demonstrated that organizational capability includes change management, continuous improvement, cybersecurity, technology management, human resources, structure and processes, political commitment, transparent governance, digital awareness, and IT infrastructure as the main success factors for a competent government. It asserts the findings of [23] that reveal that institutional, organization, leadership, and strategy are the critical success factors in implementing smart government. Moreover, it supports [19], who points to change management as a critical success factor for any smart government. The participants were asked about Saudi Arabia’s vision 2030, how smart government is facilitated through smart cities, and the major digital transformation and changes that have taken place. The interview findings demonstrated that the Kingdom of Saudi Arabia had embraced the development of smart cities to help achieve vision 2030. The respondents highlighted that the city had become more innovative, characterized by six: smart governance, smart economy, smart mobility, smart environment, smart people, and smart living. Respondent G stated, “the initiative extends government closer to citizens, investors, and all community sectors at a national level & the city level. The smart government ensures that services are accessible in a smooth and transparent method to all beneficiaries, ensures equality in service rendering and quality of service & eliminates the impact of prioritizing certain cities or communities from the service delivery”. These findings align with the Saudi Arabia vision 2030 goals and objectives outlined in [17]. The respondents opined that sophisticated technology used in smart cities enables the government to offer services to the public more effectively and efficiently. Respondent P claimed that the “policy environment stands as the compass to drive the digital transformation & gear the smart government to deliver on its expectations to improve the

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Fig. 2. Challenges of Smart Governments.

quality of life & improve the overal Saudi context to be more appealing as an economy”. In contrast, respondent G underlined that “smart government is extending the government & all its services being rendered to various beneficiaries through digital channels, to enable the government to operate more efficiently and stand closer to its beneficiaries. People should care about it because all the government interactions impact the life & the quality of life of those beneficiaries”. Hypothesis 1: There exists a positive relationship between change management and smart government. The research explored the theme of the impacts of change management on smart government in terms of productivity by asking the participants related questions. The participants gave varying responses, but all stated that change management ensures smart government success. Some opined that smart government is a disruptive transition, and its changes may face resistance requiring the need to manage change in attitude, culture, processes, structures, and systems. The benefits of smart government through change management were named as increased support from the citizens and all stakeholders, increased usage of smart applications launched by the government, proper technology management, and faster adoption of new technology and structures. These research findings support the hypothesis that there is a positive relationship between change management and smart government by increasing productivity in servicing the citizens and carrying out administrative roles. [19] asserts that change management is essential in ensuring the successful implementation of technological and software systems. [19] adds that poor change management is the primary cause of the failure of many transformational strategies and the slow implementation of smart governments in most countries globally. [22] also explains that realizing maximum benefits from the smart government requires excellent efforts in changing the mindset of people and behavior and informing people why the transformation is essential. Inability to effectively perform this may lead to failure in implementation and low project productivity. Additionally, according to [23] smart governments need change management to

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facilitate far-reaching, rapid, and continuous changes in organizational structures, processes, and models. Thus, this research upholds the hypothesis that there is a positive relationship between change management and the productivity of smart government. Hypothesis 2. A continuous improvement strategy has a significant positive impact on smart government. The research aimed to obtain data on the impact of continuous improvement on smart government implementation. The interview asked questions such as how continuous beneficial change is in implementing smart government strategy and the extent to which continuous improvement is observed in the implementation process of smart government. The respondent gave varying responses concerning continuous improvement; however, they all suggested that it is vital in ensuring successful, faster, and sustainable changes in smart government strategy. Respondent G stated, “Continuous improvement allows for proper change management and successful implementation of the strategy step-by-step based on the objectives set.“ Respondent P stated, “Smart government cannot be achieved all at once but requires bit by bit implementation of the set objectives; this requires a well-sustained continuous change and improvement to facilitate the accomplishment of the whole strategy.“ The findings also revealed that the Saudi Arabia government had maintained continuous changes, specifically in the implementation of smart cities and change of its structures, which has contributed to its success. Moreover, the findings revealed that change management is vital in ensuring sustained improvement. The respondents also suggested that continuous improvement ensures incremental knowledge and learning, smooth change in structures, and improvement of processes. These findings support [24] opines that continuous improvement is a critical success factor for implementing smart government and successful and sustainable organizational change. [31] suggest that the impacts of the changes obtained through continuous improvement are reflected in the strategies, infrastructure, structures, policies, culture, work process, and services the citizens receive as stakeholders of a government. It has been mirrored in this study’s findings, where government administrative processes have been made accessible electronically, changing structure and process. Additionally, [24] supports this study finding by stating that continuous improvement is going together with change management to ensure the implementation and sustainability of smart government.

5 Conclusion The research evaluated the impact of change management and continuous improvement on smart government with a case study of Saudi Arabia. The research was effectively completed, and data was collected and analyzed. This chapter presents the study’s limitations, conclusions, and recommendations for further research. Governments worldwide have continued increasing their reliance on and adopting innovative technologies and ICTs to make their operations smarter. As a result, smart governments and smart cities have been a major global issue of focus in many nations.

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Numerous initiatives toward smart government have been developed in many countries, including Saudi Arabia. The research sought to evaluate the impacts of continuous improvement and change management on smart government. The research analysis revealed that change management is a critical success factor for smart government. Smart government is a disruptive innovation initiative and brings with it many changes. An appropriate change management strategy leads to a successful implementation and benefits realization from a smart government. Changes in mindset, behavior, and attitude are critical in ensuring that all the stakeholders, including the nation’s citizens, play, support the strategy, and can harness the expected benefits. Therefore, the study supports the hypothesis that continuous improvement and change management positively impact smart government. The study recommended that the governments should seek to integrate the human element in the implementation of the smart government to make it effective in meeting the needs of people. Moreover, a proper change management strategy and continuous improvement plan should continuously be employed in implementing a smart government to ensure maximum productivity. Furthermore, Future research should consider a larger sample size to validate generalization and allow various nations to be included. A mixed research methodology should be considered in further research to gain deeper insights into the concept of change management and continuous improvement of smart government. Future researchers should study continuous improvement and change management with smart government separately for more in-depth analysis.

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12. Hassounah, M., Raheel, H., Alhefzi, M.: Digital response during the COVID-19 pandemic in Saudi Arabia. J. Med. Internet Res. 22(9), e19338 (2020) 13. Phillips, J., Babcock, R.A., Orbinski, J.: The digital response to COVID-19: exploring the use of digital technology for information collection, dissemination and social control in a global pandemic. J. Bus. Contin. Emer. Plan. 14(4), 333–353 (2021) 14. Budd, J., et al.: Digital technologies in the public-health response to COVID-19. Nat. Med. 26(8), 1183–1192 (2020) 15. Kosárová, D.: Saudi Arabia’s vision 2030. In: Security Forum 2020, p. 124 (2020) 16. Doheim, R.M., Farag, A.A., Badawi, S.: Smart city vision and practices across the Kingdom of Saudi Arabia—a review. Smart cities: Issues Challenges, 309–332 (2019) 17. Saudi Arabia National Portal (2021). https://www.my.gov.sa/wps/portal/snp/content/news/ newsDetails/CONT-news-200720214 18. Saudi Vision. Saudi Vision 2030 (2017). https://vision2030.gov.sa 19. Nograšek, J.: Change management as a critical success factor in e-government implementation. Bus. Syst. Res. Int. J. Soc. Advancing Innov. Res. Econo. 2(2), 13–24 (2011) 20. Stojanovic, L., Stojanovic, N., Apostolou, D.: Change management in e-government: ontoGov case study. Electron. Gov. Int. J. 3(1), 74–92 (2006) 21. STeP. Under Construction (2013). https://www.yashada.org/yash/ttt/static_pgs/CMCB.pdf 22. Picconi, F.: Change management and digital technology: a lesson learned during the COVID19 pandemic in Saudi Arabia. LinkedIn (2020). https://www.linkedin.com/pulse/change-man agement-digital-technology-lesson-learned-picconi-msc-ba 23. Guenduez, A.A., Singler, S., Tomczak, T., Schedler, K., Oberli, M.: Smart government success factors. Yearbook Swiss Adm. Sci. 9(1), 96–110 (2018) 24. Abdullah, M.A.: Continous Improvement: critical success factors in Saudi public sector. Portsmouth Research Portal (2017). https://researchportal.port.ac.uk/portal/files/8417626/ Abdullah_Alhaqbani_PhD 25. Anand, G., Ward, P.T., Tatikonda, M.V., Schilling, D.A.: Dynamic capabilities through continuous improvement infrastructure. J. Oper. Manag. 27(6), 444–461 (2009) 26. Bhuiyan, N., Baghel, A.: An overview of continuous improvement: from the past to the present Management decision. (2005) 27. Bessant, J., Caffyn, S., Gilbert, J., Harding, R., Webb, S.: Rediscovering continuous improvement. Technovation 14(1), 17–29 (1994) 28. Audretsch, D.B., Martínez-Fuentes, C., Pardo-del-Val, M.: Incremental innovation in services through continuous improvement. Serv. Ind. J. 31(12), 1921–1930 (2011) 29. Bessant, J., Caffyn, S., Gallagher, M.: An evolutionary model of continuous improvement behaviour. Technovation 21(2), 67–77 (2001) 30. Bessant, J., Caffyn, S., Gilbert, J., Harding, R., Webb, S.: Rediscovering continuous improvement. Technovation 14(1), 17–29 (1994) 31. Parra-Domínguez, J., Herrera Santos, J., Márquez-Sánchez, S., González-Briones, A., De la Prieta, F.: Technological developments of mobility in smart cities. Econ. Approach. Smart Cities 4(3), 971–978 (2021) 32. Sekaran, U., Bougie, R.: Research Methods FOR Business: A Skill Building Approach. Wiley, Hoboken (2016) 33. Creswell, J.W., Creswell, J.: Research design, pp. 155–179. Sage publications Thousand Oaks (2013) 34. Creswell, J.W., Creswell, J. D.: Research Design: Qualitative, Quantitative, and Mixed Methods Approach. Sage publications, Thousand Oaks (2017) 35. Silverman, D.: Qualitative Research. Sage Publications Ltd, Thousand Oaks (2020) 36. Flick, U.: Introducing Research Methodology: A Beginner’s Guide to Doing a Research Project (2015)

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37. Rutberg, S., Bouikidis, C.D.: Focusing on the fundamentals: a simplistic differentiation between qualitative and quantitative research. Nephrol. Nurs. J. 45(2), 209–213 (2018) 38. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006) 39. Clarke, V., Braun, V.: Teaching thematic analysis: overcoming challenges and developing strategies for effective learning. Psychologist 26(2) (2013) 40. Alajmi, M., Mohammadian, M., Talukder, M.: Smart Government systems adoption: the case of Saudi Arabia. Int. Rev. Bus. Res. Pap. 16(1). (2020) 41. AlEnezi, A., AlMeraj, Z., Manuel, P.: Challenges of IoT based smart-government development. In: 2018 21st Saudi Computer Society National Computer Conference (NCC), pp. 1–6. IEEE (2018)

Accounting Outsourcing: Increasing the Possibility of Its Use in Latvia Natalia Konovalova1(B) and Ludmila Rozgina2 1 RISEBA University of Applied Sciences, Meža Str.3, Riga, Latvia

[email protected] 2 International Association of Researchers and Scientists, Gara Str.21, Riga, Latvia

Abstract. Nowadays, many enterprises have the need to use the services of outsourcing firms, which for a certain fee can professionally and efficiently carry out some important auxiliary functions. The most in-demand is such type of outsourcing service as outsourcing in the field of finance and accounting. In this area, outsourcers offer their services in the field of setting up, maintaining, restoring accounting, compiling reporting, conducting acts with tax authorities as well as providing other financial services. One of the main trends in the development of outsourcing is the growth of small and medium-sized businesses since it is small and medium-sized enterprises that are the main customers of outsourcing services. The relevance of the topic lies in the fact that nowadays outsourcing service acts as an integral part of the Latvian business environment. The issues of its correct and effective use are extremely relevant for modern companies since this affects not only cost indicators, but also the level of management of the enterprise as a whole. The authors conducted a survey of suppliers and recipients of accounting outsourcing, as a result of which factors affecting the transition of enterprises to outsourcing accounting services were identified, and the importance of these factors for making a decision on outsourcing was determined. Based on the study of the current methods of transferring enterprises to accounting outsourcing services and the results of the survey, an approach for evaluating the feasibility of transferring enterprises to accounting outsourcing was developed. Keywords: Accounting outsourcing · Business environment · Suppliers of accounting outsourcing · Recipients of accounting outsourcing · Survey

1 Introduction The current state and prospects for the development of the national economy largely depend on the effectiveness of the activities of commercial organizations. That is why an important aspect of the successful functioning of organizations is to improve the efficiency of activities, including based on the optimization of accounting. It should be noted that the role of accounting work in organizations is being transformed, modified [1]. The process of making management decisions is based on the processing and systematization of accounting data, which becomes one of the important tools for business management. The timeliness of reflecting the facts of business life © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 384–393, 2023. https://doi.org/10.1007/978-3-031-26655-3_35

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increases the efficiency of the company as a whole, and the efficiency of the activity is largely ensured by the use of reliable accounting information. Currently, outsourcing is one of the most popular ways to organize accounting in the business environment [2]. Outsourcing refers to the transfer of part of internal work to a third-party organization for a certain monetary reward [3]. The increase in the number of companies that recognize outsourcing as the best way to keep records contributes to the growing popularity of consulting companies providing such services. Effectively developing the accounting business, correctly prioritizing, concentrating efforts on the areas that will bring maximum profit is not an easy task. And accounting outsourcing can help to solve this problem in enterprises [4]. The purpose of the study is to provide an analytical review of the accounting outsourcing market in Latvia based on the information received by the authors on the state of this market, to identify trends and opportunities for the provision of such services, to assess the impact of restraining factors and threats on the development of accounting outsourcing, as well as to develop proposals to increase the possibility of using accounting outsourcing in Latvia. The authors put forward the following objectives of the research: – Define the essence of using outsourcing accounting services. – Identify risks associated with outsourcing activities. – Evaluate methods and approaches to assess the need for accounting outsourcing in the enterprise. – Analysis and assessment of the accounting outsourcing market and its development trends in Latvia. – Conduct a survey of outsourcing specialists and their customers. – Evaluate the feasibility of small businesses transition to accounting outsourcing. To achieve the goal and objectives of the research authors used such research methods as literature review, observation, survey, interview, comparative analysis, economic analysis, statistical method. The theoretical basis of the study was scientific articles, special literature, legislative and regulatory acts.

2 Theoretical Framework of Accounting Outsourcing The development of the modern economy is accompanied by a revision of business technologies and organizational management structures by switching to various forms of partnership, which allows you to adapt to dynamically developing production and sales conditions [5]. One of the effective forms of business organization has become outsourcing, which provides for solving the problems of the functioning of companies [6]. Outsourcing can also be defined as an organizational solution, a transfer to a thirdparty organization of some business functions or parts of the enterprise business process [7]. Its essence lies in the distribution of the functions of the business system in accordance with the principle “I keep for myself only what I can do better than others, I convey to the external performer what he does better than others.” The concept of “outsourcing” originated in the 1980s. Since then, the presented concept has been gaining its relevance and popularity among modern companies. Today,

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outsourcing worldwide is becoming a recognized way and standard of interaction in business, while being considered a highly effective tool for ensuring financial and economic activities in companies of various forms of ownership and areas of activity [8]. Most companies in various industries confirm the benefits of outsourcing, however, discussions do not subside regarding the sustainability of such a business model in the long term and about trends in its further development. Proponents of accounting outsourcing argue that the negative consequences of ignoring this process include a lack of quality control and accounting management capabilities and narrowing the scope for innovation. Opponents of outsourcing note that wage growth makes outsourcing less attractive and in the long term such cooperation may be unprofitable [9]. Next, the opinions of different authors for the definition of outsourcing are collected. Thus, Lux W., Schoen P. believe that outsourcing is a partial or complete transfer of production processes, including planning, management and control functions, an external organization [10]. Heywood Dz. Brain define outsourcing as the transfer to other (external) organizations of previously independently performed works (services) or production functions [11]. Bravar J.-L., Morgan R. believe that outsourcing is the contractual use of material, property, and knowledge of a third party with a guaranteed level of their quality, flexibility and value of value criteria and estimates for the provision of services previously provided by the internal forces of the company, with the possible transition of existing personnel to a service provider and/or transformation/update of processes and technologies supporting business [12]. Daft R. argues that outsourcing should be understood as the transfer of certain internal operations to suitable intermediaries, allowing for almost instant significant savings and improved product quality [13]. According to Sneidere R. outsourcing is the transfer of certain tasks, business functions or business processes to a third-party organization, which are usually not part of the company’s main activities, but, nevertheless, are necessary for the full functioning of the business [14]. Haywood Dz. Brain emphasizes that the transfer of the internal division of the enterprise and all related assets to the organization of a service provider offering to provide a certain service for a certain time at a specified price is outsourcing [11]. Kalenjyan S.O. defines outsourcing as a long-term transfer of management functions and, if necessary, appropriate resources to external performers who can perform these functions more efficiently [15]. The authors of this article also studied the theoretical approaches to outsourcing offered by Bloomberg Financial Glossary and The American Heritage Dictionary. So, Bloomberg Financial Glossary defines outsourcing as the acquisition of a significant number of intermediate components from external suppliers [16], and The American Heritage Dictionary states that outsourcing is the provision of services or the supply of products by external suppliers or manufacturers to reduce costs [17]. According to experts, it is economically feasible for small and medium-sized enterprises to use outsourcing services [18]. Accounting, payroll, reporting to regulatory authorities and many other functions are an integral part of the accountant’s work. However, the maintenance of a full-time accountant entails the corresponding costs of paying his labour, contributions to funds, the purchase of software, etc. An organization that has applied to the services of an outsourcer does not need to maintain an accounting staff, and, therefore, it becomes possible to save on costs. Thus, the transition to outsourcing of accounting services means the release of financial resources for the company [19].

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The most popular functions for outsourcing are accounting and tax, personnel accounting, legal services, reporting according to international standards (IFRS), budgeting, planning, etc. Thus, the main types of accounting outsourcing can be distinguished: 1) accounting consulting, 2) selective outsourcing, 3) full outsourcing, 4) accounting on behalf of the chief accountant. Accounting consulting - provision of consulting services on a paid basis. Selective outsourcing - transfer of individual accounting functions to a third-party organization. At the same time, the client company performs some of the functions independently, for example, working with primary documents. This method is most popular among managers who want to reserve the opportunity to control the actions of their employees [20]. Full outsourcing assumes that accounting is fully entrusted to a third-party organization. Thus, the company has the opportunity to abandon full-time accounting. Accounting on behalf of the chief accountant assumes that the accountant of the outsourcing company has the right to sign various accounting documents, thereby exempting the client from working with the documentation. However, this method, unlike selective outsourcing, deprives the client of the ability to control accounting processes [21]. The essence of the concept of “outsourcing” was studied by many authors. However, today there is no unity in the understanding of this term, so the authors proposed their own definition of the concept of “outsourcing,” most fully reflecting its essence. Outsourcing, according to the authors of this article, is the transfer on the basis of a contract of authority to perform certain functions (partially or completely) to a third-party organization that has professional experience in this area and relevant qualifications.

3 Trends of Accounting Outsourcing in Latvia Accounting outsourcing in Latvia is relatively young but tends to develop quickly enough. The demand for outsourcing services is growing that is explained by the desire of enterprises to optimize the ratio of basic and supplementary (servicing) functions. Thus, during the study, the authors found an interesting trend and dependence between the growth rate of gross income of all Latvian companies and the growth rate of gross income of Latvian enterprises engaged in accounting outsourcing (Fig. 1). It was revealed that during the analyzed period from 2015 to 2021, the gross income of accounting outsourcing enterprises increases, and the gross income of all Latvian enterprises decreases. The highest growth rate of gross income of accounting outsourcing enterprises was recorded in 2015 and amounted to 12.2%. In subsequent years, there was a slowdown and decline, but since 2019 there have been growth trends, which in 2021 amounted to 6.45%. At the same time, the gross income of all Latvian enterprises is decreasing and has negative dynamics (−6.38% in 2020). And only in 2021 the rate of decline in gross income of all Latvian enterprises slowed down, but the indicator remains negative. Obviously, the decline in total gross income of enterprises was influenced by the COVID pandemic, but at the same time it gave impetus to the development of accounting outsourcing.

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15

12.19

Gross income growth of accounng outsourcing enterprises and all Latvian enterprises

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0

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2021 -5.12

All Latvian enterprises

Fig. 1. Chain of gross income growth of all Latvian enterprises and accounting outsourcing enterprises (authors’ own elaboration based on [22–26].

Further, the authors analyzed the trend in the number of outsourcing enterprises in Latvia and their income (Fig. 2).

Number of accounng outsourcing enterprises and their revenues 300000 250000

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Fig. 2. Number of accounting outsourcing enterprises in Latvia and their revenues [22, 25].

The results of the study show that despite the trend of a decrease in the number of accounting outsourcing enterprises, their income is increasing. This means that in the context of competition in the accounting outsourcing market, stronger and professionally competent enterprises survive. And those who work in the market increase the volume of their activities and, accordingly, their incomes are growing.

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4 Analysis of Factors Affecting the Transition of Accounting Outsourcing: An Empirical Approach To identify factors and reasons for switching to accounting outsourcing, the authors used an empirical survey-based approach. This survey was attended by 398 respondents - accountants, including 193 accountants who provide outsourcing services and 180 clients who receive outsourcing services. As a result of the study, it was revealed that the main factors influencing the transition of outsourcing services are Cost savings, Service elasticity, Use of modern accounting programs, Reduction of operational risk, Professional qualification of outsourcing accountants, Save time on non-core enterprise functions. The authors determined the significance of each factor according to the opinion of the respondents. Table 1 demonstrates results of the survey obtained from respondent – accountants. By respondents’ opinion the most significant factor, which ensures the transition on accounting outsourcing is reduction of operational risk (72.2% of total respondents). 65% of respondents consider that the most significant factor is professional qualification of outsourcing accountants. High significance in cost savings (61%). Other factors are significant as well, but their significance less than 50%. Table 1. Significance of factors affecting the transition of accounting outsourcing /as a percentage of total respondents – all accountants/, (authors’ own elaboration based on the survey). Factors

Cost savings

Significance of factors (1 – most significant, 2 – medium-significant, 3 – insignificant) 1

2

3

61.1%

12.2%

23.3%

Service elasticity

31.1%

33.3%

33.3%

Use of modern accounting programs

32.1%

43.3%

13.3%

Reduction of operational risk

72.2%

11.1%

14.4%

Professional qualification of outsourcing accountants

65.4%

21.2%

11.1%

Save time on non-core enterprise functions

43.8%

32.5%

12.7%

These same questions were asked to customers who receive accounting outsourcing services. By customers’ opinion, the most significant factors are reduction of operational risk and using of modern accounting programs (accordingly 81.3% and 72.1%). Other factors such as cost savings, professional qualification, service elasticity are significant, and this fact is confirmed by more than 50% of respondents, who are customers of accounting outsourcing services (Table 2). Authors also revealed the reasons for switching to accounting outsourcing making more deeper survey among customers, who already receive an accounting outsourcing services. There are many reasons, called by customers such as engagement of specialists

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Table 2. Significance of factors affecting the transition of accounting outsourcing /as a percentage of total respondents – customers, using accounting outsourcing/, (authors’ own elaboration based on the survey). Factors

Significance of factors (1 – most significant, 2 – medium-significant, 3 – insignificant)

Cost savings

1

2

3

64.7%

13.2%

15.3%

Service elasticity

65.1%

12.3%

18.7%

Use of modern accounting programs

72.1%

15.3%

12.1%

Reduction of operational risk

81.3%

9.1%

7.4%

Professional qualification of outsourcing accountants

67.2%

19.2%

1.1%

Save time on non-core enterprise functions

63.8%

22.8%

12.5%

with specific knowledge, economic effect, risk shearing, lack of own resources, reallocating internal resources and others. Among them the main reason of transition on outsourcing is engagement of specialists with specific knowledge and skills (55% of respondents confirm that). 35% of respondents point the reason of economic effect, 20% of respondents switched on accounting outsourcing due to the lack of own resources (Fig. 3).

5%

Risk sharing

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Access to new technologies

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Improving competitiveness

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Fig. 3. Reasons for switching to accounting outsourcing (developed by the authors based on the survey, respondents - customers, using accounting outsourcing).

As for the barriers to the transition to accounting outsourcing, respondents cite many reasons such as high costs for service, tax and accounting law shortcomings, lack of information about service, Information leak fears, Insufficient accounting control.

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Conducting a survey authors used Likert scale with 5 points of values (from very strong barrier to very weak barrier). So, according to respondents’ opinions the most substantial barrier with moderate effect is lack of information about service. Very strong barrier is information leak fears. Survey results showed that insufficient accounting control is also substantial barrier with strong effect (Fig. 4).

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Fig. 4. Reasons hindering the transition to accounting outsourcing (developed by the authors based on the survey, respondents - customers, using accounting outsourcing).

Other barriers to switching on accounting outsourcing such as high costs for service, tax and accounting law shortcomings, reducing the numbers of accountants are less significant.

5 Conclusions Despite the fact that over the past 6 years, the number of outsourcing accounting providers in Latvia has been decreasing, the gross income of enterprises providing outsourcing services is increasing. So, for the period from 2015 to 2021, the gross income of Latvian outsourcing companies increased by 33%. Among the Baltic countries, Estonia is a leader in the provision of accounting outsourcing services. Latvia ranks second and Lithuania ranks third in the number of outsourcing companies among the Baltic countries. As a result of the study, the authors found out the significance of the important factors influencing the transition to accounting outsourcing. Thus, the most important factors of the transition to accounting outsourcing are cost savings, the elasticity of service provision, the use of modern accounting programs, reduction of operational risk, qualification of professional outsourcing accountants, saving time on non-core functions of the enterprise.

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The survey of clients receiving accounting outsourcing services revealed that the main consumers of these services are representatives of small and medium-sized businesses. Evaluating the accounting processes, the authors revealed that 80% of respondents – outsourcing receivers, transfer all range of accounting services to outsourcing. And only a small share (20% of respondents) outsources certain separate functions. As a result of the study, it was revealed that among the main reasons hindering the transition to outsourcing services, respondents - recipients of services cite a lack of information about accounting outsourcing services, unjustified hopes for the effectiveness of the service, a decrease in the number of accountants, insufficient accounting control and fear of information leakage. To increase the possibility of using accounting outsourcing authors developed recommendations for Association of Accountants of the Republic of Latvia (LRGA) and for outsourcing accountants. Thus, recommendations for the Association of Accountants of the Republic of Latvia are following: • Improve the skills of outsourcing accountants through a wider offer of training programs, advanced training courses, and seminars. • To prevent outsourcing services without the necessary experience and qualifications. • Do not allow the provision of low-quality accounting services at a price below cost. • Ensure closer communication between outsourcing accounting service providers and their clients through joint events: seminars, meetings, discussions, and presentations. • More clearly define the term “outsourcing accountant” with a list of functions and responsibilities, as well as distinguish the category “outsourcing accountant” into a separate profession. Recommendations for outsourcing accountants: • Post on their public sites more detailed information about what is included in the specific cost of accounting services so that a potential customer can access this information. Basically, the price depends on the taxation system, the number of transactions, and the number of employees. • More detail and clearly specify options of charging for services so that customers have the opportunity to choose the most acceptable option for them. • Provide a differentiated, individual approach to customer service depending on the size of the company and the volume of functions transferred to the outsourcing service. • Provide a differentiated approach to outsourcing accounting services separately for large enterprises, medium enterprises, and small enterprises.

References 1. Egiyi, M.A., Florence, A.: Outsourcing accounting functions: risks and benefits. Int. J. Acad. Manag. Sci. Res. (IJAMSR) 4(10), 3–7 (2020). ISSN: 2643-900X

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2. Tsygalo, Y.M., Dorozhkin, A.V., Dorozhkina, E.E.: Assessment of outsourcing risk management efficiency. ESPACIOS 39(47), 27 (2018) 3. Owusu-Sekyere Bismark Adu: Does outsourcing of accounting services increase profitability of SMEs, Conference: RESEARCH WORK (2015) 4. Krell, E.: Finance and Accounting Outsourcing Assessing and Planning for Success. Chartered Prof. Accountants Canada, 19 (2018) 5. Faitusa, I.: Advantages and disadvantages of outsourcing accounting worldwide and in Latvia. In: Proceedings of the International Conference Economic Science for Rural Development. Jelgava, LLU ESAF, 9-10 May 2019, pp. 256–263. no. 52 (2019). https://doi.org/10.22616/ ESRD.2019.130 6. Mikhaylova, A.A., Mikhaylov, A.S., Savchina, O.V.: Macroeconomic dataset for comparative studies on coastal and inland regions in innovation space of Russia. Data Brief 27, 104640 (2019) 7. Aubakirova, D., Konovalova, N.: Management of financial stability in airlines: problems and solutions. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2019. LNNS, vol. 117, pp. 573–582. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44610-9_56 8. Saksonova, S., Kant¯ane, I.: Mergers and acquisitions: examples of best practice in Europe and Latvia. Contemp. Issues Finan. Curr. Challenges Across Europe. 98, 95–110 (2016). https:// doi.org/10.1108/S1569-375920160000098007 9. Association of Business Service Leaders in Latvia (ABSL Latvia). Report (2021). https://inv estinlatvia.org/assets/upload/ABSL%202021.pdf. Accessed 08 Aug 2022 10. Lux, W., Schoen, P.: Implications of outsourcing in IT-management, seminar paper (2021) 11. Heywood, Dz. Brain.: Outsourcing: in search of competitive advantages (2004) 12. Bravar, J.-L., Morgan, R.: Efficient outsourcing. Balance Business Book (2007) 13. Draft, R.: Management., Business Book (2011) 14. Sneidere, R.: Accounting outsourcing services in Latvia: problems and possible solutions, Economics and management, 18(1), 26–38 (2013). https://www.researchgate.net/public ation/314404153_Accounting_Outsourcing_Services_In_Latvia_Problems_And_Possible_ Solutions. Accessed 07 Sept 2022 (2013) 15. Kalenjyan, S.O.: Outsourcing and delegation of authority in the activities of companies. Learning Book, Business, p.95 (2019) 16. Bloomberg Financial Glossary. http://blacksoil.vn/glossarysuite/bfgloso.html. Accessed 07 Sept 2022 17. The American Heritage Dictionary. https://www.ahdictionary.com/. Accessed 07 Sept 2022 18. Saksonova, S., Ko¸leda, O.: Evaluating the interrelationship between actions of Latvian commercial banks and Latvian economic growth. Procedia Eng. 178, 123–130 (2017) 19. Konovalova, N., Caplinska, A.: Impact analysis of factors influencing bank capital management. J. Entrepreneurship Sustain. Issues 8(1), 484–495 (2020) 20. Troac˘a, V.-A., Bodislav, D.-A.: Outsourcing. The Concept. Theor. Appl. Econ. XIX, 6(571), 51–58 (2012) 21. Savchina, O.V., Savchina, O.V., Bobkov, A.L., Sharashidze, A.Z.: On the state of the mortgage market in the Russian federation in the conditions of global economic crisis. J. Appl. Econ. Sci. 11(6), 1096–1103 (2016) 22. Central Statistical Bureau Republic of Latvia. https://www.csp.gov.lv/en. Accessed 08 Aug 2022 23. Accounting Law https://likumi.lv/ta/en/en/id/324249. Accessed 08 Aug 2022 24. Zvirbule, B.: Opportunities to improve accounting regulations in terms of improving Latvia’s business environment (2020) 25. Lursoft. European Business Register. https://www.ebr.lv/en/, https://www.lursoft.lv/?l=en. Accessed 08 Aug 2022 26. Small Business Outsourcing Statistics. https://clutch.co/bpo/virtual-assistants/resources/ small-business-outsourcing-statistics. Accessed 08 Aug 2022

Advances in the Research Domain of Crowdfunding: A Systematic Literature Review Oksana Adlere(B) and Svetlana Saksonova University of Latvia, 5 Aspazijas bulv., Riga, Latvia [email protected], [email protected]

Abstract. In the past half-decade, a considerable amount of literature has been published on the phenomenon of crowdfunding, considering its augmented role as an alternative form of financing for small and medium-sized enterprises. Crowdfunding is a method to obtain money from large audiences where each individual provides a small amount of money, instead of raising large sums from a small group of sophisticated investors [12]. Depending on the remuneration for the audience, crowdfunding has taken three major forms, namely, crowdlending, crowdinvesting, and reward crowdfunding. Regardless of the type of crowdfunding, it is considered to be two-sided process, where participants of the process are funders (the crowd), and funded entities (fundraisers). The process is enabled by an on-line tool – a crowdfunding platform. Considering this, the paper aims at revealing of underexplored participants of the crowdfunding process (funders, platforms, and fundraisers) in the research domain’s type (crowdlending, crowdfunding, and reward crowdfunding). To examine the state-of-the-art situation of the researched phenomenon, we use the systematic literature review (SLR) method, and synthesis. To conduct the analysis, we examined peer-reviewed articles of the research domain, published in 2021 journals indexed in data base Scopus. The results of the paper suggest that the research domain of crowdfunding in relation to the role of crowdinvesting and reward crowdfunding platforms is less examined in the literature, providing a fruitful area for further research. Keywords: Crowdfunding · Systematic literature review · Crowdlending · Crowdinvesting · Reward crowdfunding

1 Introduction In the past half-decade, crowdfunding provoked interest of myriad of researchers. A significant amount of literature has been published on the phenomenon of crowdfunding, considering its augmented role as an alternative form of financing for small and mediumsized enterprises. Crowdfunding is a method to obtain money from large audiences, where each individual provides a relatively small amount of fund, instead of raising large sums from a small group of sophisticated investors [12]. Depending on the remuneration for the audience, crowdfunding has taken three major forms, namely, crowdlending, crowdinvesting, and reward crowdfunding. These © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 394–402, 2023. https://doi.org/10.1007/978-3-031-26655-3_36

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forms are not homogenous. Crowdlending (lending-based crowdfunding) specializes in debt financing; crowdinvesting (investment-based crowdfunding or equity crowdfunding) allows unlisted companies to raise equity financing from investors; in reward crowdfunding (reward-based crowdfunding), funders promise backing in exchange for a non-monetary rewards [51]. The first two types of crowdfunding are financial return models (return-based crowdfunding). Beyond this broad classification, each type varies widely in the number of sub-types. Peer-to-peer lending (peer-2-peer, p2p) is a rather often denotation for a vast range of crowdlending platforms. Nonetheless, regardless of a type of crowdfunding, this is a two-sided process, where participants of the process are funders (the crowd), and funded entities (fundraisers). The process is enabled by an on-line tool – a crowdfunding platform. The growth of crowdfunding of all the types has been one of the fastest of any kind of financial innovation documented in recent history [51]. The expansion of the crowdfunding phenomenon has led to emerging of uncountable studies on it. Being a very mature research domain, crowdfunding has become a fruitful soil of reach data on investors’ behaviour and fundraising campaigns’ success factors. Moreover, a considerable amount of valuable literature reviews, bibliometric and meta analysis on the research domain of crowdfunding, covering general topics, have been published, revealing, inter alia: misbalances between the relevance ascribed to various aspects in the research and in practice [14], territories of the globe where the phenomenon of crowdfunding is most studied [37], the key features of research trends in crowdlending [54]; and analysing more specific themes of the research domain, such as: critical choice parameters for innovators who tap into crowdinvesting [62], crowdfunding success factors [60], facets of social capital effecting crowdfunding [20], investors’ decision-making factors [39], scientific development and landscape of contributions in crowdinvesting field [49]. Also, crowdfunding is a fruitful field for multiple intersecting discussions, such as FinTech definitions [56], balanced investment portfolio creating principles [59], and even migration factors [58]. So far, however, there has been little discussion about the role of crowdfunding platforms themselves. Some literature reviews, such as that conducted by Basha et al. [10] provides several avenues for future research for crowdlending in examining determinants of platforms’ performance. Considering this, we aim at rendering of more structured comprehension of the extent to which the participants of the crowdfunding process are examined in the scientific non-synthetic (non-literature-reviews based) literature. Therefore, the goal of the paper is revealing of underexplored participants (funders, platforms, and fundraisers) of the crowdfunding process in the research domain’s type (crowdlending, crowdfunding, and reward crowdfunding). In order to segregate the crowdfunding process by its participants, we use a definition suggested in the European Union Regulation on European crowdfunding service providers for business [32], according to which “the provision of crowdfunding service generally involves three types of actors: the project owner that proposes the project to be funded, investors who fund the proposed project, and an intermediating organisation in the form of a crowdfunding service provider that brings together project owners and investors through an online platform”. The Regulation applies to return-based crowdfunding (i.e., crowdlending and crowdinvesting). Nonetheless, we extrapolate the

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definition of crowdfunding process participants to the reward crowdfunding, since the participants are the same regardless of a crowdfunding type. Notwithstanding, we correct the titles of the participants to make them suitable for all considered crowdfunding types. Therefore, for the sake of usage of unified definitions, in the paper, we name the participants of the crowdfunding process as follows: (1) funders (possessors of funds, investors, contributors, backers); (2) platforms (crowdfunding service providers, intermediating organisations, crowdfunding platforms); (3) fundraisers (projects originators, projects owners).

2 Methodology To achieve the goal of the paper, we use a systematic literature review (SLR) as a main research method, in the paper. The SLR is a systematic, explicit, and reproducible method used to identify, evaluate, and synthesize the existing literature [50]. In research domains with a growing body of literature, the SLR can assist in consolidating the topic from the point of view of a “status quo of current research” [40], and increase awareness within the field, revealing current perspectives [34]. Furthermore, the SLR can show growing research trends and directions of the field [44], and can provide the evidence of the field’s maturity. Therefore, our research can be classified as a domain-based hybrid-narrative literature review [28] for revealing of “grey spots” in the research domain with a framework for setting future research agenda. The SLR planning stage consists of formulating the review and developing and validating the review protocol [64]. A sufficient amount of research literature in the research domain of crowdfunding delineated our selection criteria and SLR protocol. We included only papers in the English language published in journals, indexed in data base Scopus, published in 2021. In addition, we limited our search by subject area – “Business, Management and Accounting”, and “Economics, Econometrics and Finance”, as well as by document type – “article”. The search words were: crowdfunding, or crowdlending, or crowdinvesting, or peer-to-peer, or peer-2-peer, or p2p. The logic of the selection was as follows: articles on reward or reward-based crowdfunding should contain a word “crowdfunding” in all cases; in the cases where for crowdlending and crowdinvesting are used titles “lending-based crowdfunding” and “investment-based crowdfunding” or “equity crowdfunding”, the word “crowdfunding” would appear anyway. In addition, we used the word “peer-to-peer” and its variants (“peer-2-peer”, “p2p”), since it is the terminology which sometimes is applied to one of the crowdlending sub-types. The search was made in title, abstracts, and key words of the publications. The above mentioned selection resulted in the following search query: TITLE-ABSKEY (crowdfunding OR crowdlending OR crowdinvesting OR peer-to-peer OR peer-2peer OR p2p) AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “BUSI”) OR LIMIT-TO (SUBJAREA, “ECON”)) AND (LIMIT-TO (PUBYEAR, 2021)) AND (LIMIT-TO (LANGUAGE, “English”)).

3 Results and Discussion Following the SLR protocol, we conducted further analysis on a sample of 44 research papers. These papers were shortlisted from 613 papers initially chosen. To analyse the

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initial results of the search, we read abstracts and, if that was necessary, whole papers. We did not exclude papers which were not cited, targeting at rendering broader insight of the topic. Further exclusion criteria were as follows: – it was impossible to define to which type of crowdfunding and/or to which crowdfunding process’s participant the paper was related; – papers based on synthesis of what was researched previously (others literaturereviews-based studies), and where specific issues of crowdfunding were revised throughout the regulatory prism; – research topic of the paper was not related directly to crowdfuding, and where information from crowdfunding platforms was used to assess the topics, which were not related to crowdfunding itself; – research topic was related to donation-based crowdfunding, which was not in the scope of our research. The selected papers were grouped in a synthesis matrix (Table 1) by their relevance to 1) a type of crowdfunding (crowdlending, crowdinvesting, reward crowdfunding), and 2) a type of a crowdfunding process participant (funders, platforms, fundraisers). Some of the papers are mentioned twice, in the table, since their topic focuses on multiple participants and/or crowdfunding types. Based on the review of the articles initially chosen, it could be stated that the factors determining crowdfunding campaigns success is one of the core issues for crowdfunding researchers. Furthermore, frequently, it is difficult to segregate the crowdfunding process participants, since great amount of studies consider the interaction and influence of signals made by one participant of the process to another one, (fundraisers to funders, in most cases), targeting at providing implications for how a fundraising campaign should be positioned within crowdfunding solicitation marketplace. Generally, the focus of the reviewed literature is information asymmetries, signalling, social capital, communication channels, and rating-based models. In the table, we mention only those articles, which can be explicitly classified by participants of the crowdfunding process and by crowdfunding types. In addition, the papers with recurring topics, such as success factors of crowdfunding campaign (mostly for the reward crowdfunding), and investors’ motivation (mostly for the crowdinvesting), were not repeatedly included in the table. Nevertheless, the column of “funders” contains the most entries. This shows that the issue of being aware of the drivers of why and how funders invest or contribute attracts researchers’ greatest interest. However, in “platforms” column of the table, we mention all papers, where any activity, features, and/or roles of the platforms are researched and discussed. Even though, the number of entries were diminished in the column “funders” and “fundraisers”, the column “platforms” has the fewest number of entries, in “crowdinvesting” and “reward crowdfunding” cells. It is noteworthy that crowdlending platforms attracted slightly more of the researchers’ attention. This disproportional allocation of crowdfunding researchers’ interest might be explained by the proliferation of the crowdfunding types themselves, i.e., across both developed and emerging markets, the dominant form of crowdfunding is crowdlending [51]. Also, we can suppose that crowdlending platforms are more researched, since they play greater role in the crowdfunding process

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Table 1. Selected research articles’, published in 2021, topic relevance to crowdfunding types and crowdfunding process participants. Type of crowdfunding

Funders

Platforms

Fundraisers

Crowdinvesting

(Aggarwal et al.), (Bade (Aggarwal et al.), and Walther), (Battaglia (Cosma et al.) et al.), (Daskalakis and Karpouzis), (Feola et al.), (Goethner et al.), (Hornuf et al.), (Jo and Yang), (Johan and Zhang), (Meoli and Vismara)

(Andrieu et al.), (Battaglia et al.), (Blaseg et al.), (Cerpentier et al.), (Di Pietro), (Eldridge et al.)

Crowdlending

(Babaei and Bamdad), (Byanjankar et al.), (Caglayan et al.), (Daskalakis and Karpouzis), (Li et al.), (Martínez-Climent et al.), (Ribeiro-Navarrete et al.)

(Broccardo et al.), (Chen et al.), (Gallo), (Klein et al.), (Maskara et al.), (Ribeiro-Navarrete et al.)

(Abbasi et al.), (Anh et al.), (Chin et al.), (Xiang et al.)

Reward crowdfunding

(Adamska-Mieruszewska et al.), (Baber and Fanea-Ivanovici), (Bouaiss and Vigneron), (Bürger and Kleinert), (Chakraborty and Swinney), (Garel and Pendeven), (Rose et al.), (St John et al.)

(Chiesa and Dekker), (Ryu and Suh)

(Adamska-Mieruszewska et al.), (Bakri et al.), (Cappa et al.), (Chakraborty and Swinney)

Source: authors’ elaboration based on the SLR

(perform more functions over a longer period), as compared with crowdinvesting and reward crowdfunding. In this connection, crowdlending platforms’ decision-making and projects’ selection criteria are crucial facets of ensuring of investments’ safety, so they are studied more extensively. Having revealed the participants of the crowdfunding process by crowdfunding types, which were less studied in the selected period, i.e., crowdinvesting and reward crowdfunding platforms, we assume that the role performed by the platforms is a subject to in-depth analysis for a number of possible future studies.

4 Conclusion The goal of the current study was to determine underexplored participants of the crowdfunding process by crowdfunding types. The results suggest the evidence of most attention attracting fields of crowdfunding, in the research period. Although the analysis showed that the research field of crowdfunding is significantly mature, there is a paucity

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of research with regard to the crucial participant of crowdfunding process, i.e., crowdinvesting and reward crowdfunding platforms. Meanwhile, the results demonstrated that performance of crowdlending platforms is more investigated, in the literature. In addition, we discuss some possible causes for that, namely: the role of the platforms in crowdlending is more significant than in other crowdfunding types, also, crowdlending itself is more prevalent type of crowdfunding. The most important limitation lies in the fact that we analysed only articles in journals published in 2021, indexed in data base Scopus. Therefore, we suggest that a study similar to this one should be carried out on the example of broader periods and multiple data bases. In terms of directions for future research, further work could be conducted with regard to the role of crowdinvesting and reward crowdfunding platforms, in the crowdfunding processes.

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Formation of the Insurance Market of the Region Taking into Account the Impact of Specific Risks Evgenia Prokopjeva1,2(B) , Svetlana Saksonova3 and Natalya Chezybaeva4

, Tatyana Shibaeva4

,

1 Siberian Federal University, Svobodny Ave. 79, Krasnoyarsk, Russia

[email protected] 2 N.F. Katanov Khakass State University, Lenin Ave. 90, Abakan, Russia 3 University of Latvia, Aspazijas blvd. 5, Riga, Latvia

[email protected] 4 Khakass Technical Institute – Branch of Siberian Federal University, 27 Shchetinkina str.,

Abakan, Russia {f_tshibaeva,F_NChezybaeva}@sfu-kras.ru

Abstract. The functioning of the insurance market of large countries with regions is usually characterized by imbalances. These imbalances arise due to the presence of specific risks inherent in individual regions and territories. The purpose of the article is to propose an approach to systemic management of regional risks based on grouping risks specific to the regions of the country and identifying typical risks for a particular region or group of regions and analyzing their causes. Research methodology: dynamic analysis of insurance indicators, spatial analysis of the territorial development of the insurance market, the method of classification and grouping of risks, the method of rating are used. The presence of certain risks in a particular region is individual. The factors of the development of the regional insurance market, which have a restraining nature, will make it possible to compile a list of risks typical for a particular region. Priority types of insurance are proposed to be divided into two groups: stimulating the development of the region’s economy and socially significant types of insurance. As the analysis has shown, the list of factors of regional development is diverse, so the authors consider it appropriate to formalize them to give a quantitative assessment. To do this, a ranking method is used, according to which each region is assigned a certain rating by factors and an average rating is output. If the development of the region is balanced, the quantitative and qualitative indicators of the insurance market should correspond to its socio-economic development. Keywords: Depth of insurance market · Insurance density · Payout level · Socio-economic risk · Regional risk management · Rating assessment

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 403–413, 2023. https://doi.org/10.1007/978-3-031-26655-3_37

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1 Introduction The functioning of insurance markets in many countries is characterized by significant imbalances, which have only intensified in recent years. This leads to differentiation in the level of development of insurance markets in different regions, which ultimately weakens the country’s insurance market as a whole, since it ceases to function smoothly and systematically. In this regard, the study of the problems and factors of uneven development of regional insurance markets seems very relevant. The purpose of the article is to propose an approach to the system management of regional risks based on the grouping of risks specific to the regions of the country and the occurrence of typical risks for a particular region or group of regions and the analysis of their causes. Research methodology: dynamic analysis of insurance indicators, spatial analysis of the territorial development of the insurance market, the method of classification and grouping of risks, the method of rating is used. The issues of research of the insurance market and its causes and its uneven development, including at the level of individual regions, are considered by many authors. In particular, the study of the interaction of the insurance market and economic growth is important for the disclosure of the topic. It is emphasized that in some countries the insurance industry causes economic growth, while in other countries there is no such dependence. These relations are country-specific, so the question of whether the insurance industry contributes to economic growth will depend on a number of national characteristics [1, 20]. Other authors note that a significant causal relationship has been established between the development of insurance and economic growth. However, this relationship is different in different countries due to different levels of initial income and location. The impact of insurance development on the country’s economic growth is indirect, since it depends on the effectiveness of insurers’ investments [7, 12]. There is a positive and significant relationship between life insurance, risky types of insurance, trade openness, stock market development and economic growth in the long term [14, 15, 17]. Of interest are studies that include an analysis of factors affecting regional insurance markets in different countries. Convergence of the insurance market with other segments of the financial market is highlighted as a significant factor [2, 11]. Thus, in countries where bank insurance is the main channel for the distribution of life insurance, the profitability of financial transactions is higher, but in countries with specialized insurance institutions, the cost of insurance services is lower [6, 10]. Thus, the factors of effective development of the insurance market are various forms of interaction with other financial institutions [16, 23], as well as the processes of innovation [25] and digitalization, actively occurring in the financial sector [5, 18, 21, 22]. Also significant factors in the development of regional insurance markets are the development of the infrastructure of the insurance and investment markets [19, 26]. Global economic and financial crises are also a significant factor that negatively affects the stability of financial organizations [8, 9]. These consequences are particularly difficult to predict for emerging financial markets [13], which include the Russian insurance market. The Covid-19 pandemic, which has a non-economic nature, also had an impact on the development of the insurance market [3].

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And, of course, it should be noted that the insurance market has been influenced by modern world policy aimed at socially responsible investment [4]. Based on this, we can talk about a variety of factors that determine the level of development of the national and regional insurance market, in particular, the degree of territorial uniformity of its development.

2 Analysis of Regional Disparities in Insurance Development Let’s consider the manifestation of imbalances in the functioning of the insurance market on the example of Russia as one of the largest countries in the world. Quantitatively, the disproportions of the insurance market are manifested in the following: 1. the gap between insurance premiums and payments is increasing, both in the insurance market as a whole and in the main market segments (life insurance, property insurance, liability insurance of car owners, hazardous production facilities, etc.), as well as in the regional context; 2. the most important economic indicators of insurance differ significantly, also by market segments and regions: – the depth (penetration) of the insurance market, which is measured as the share of the insurance premium in gross domestic product (GDP) or gross regional product (GRP). This indicator varies by regions of the Russian Federation from 0.2 to 3% per year with an average value of 1.7%; – insurance density, which represents the insurance premium per capita. The premium per capita in the regions of Russia varies from 18 euro to 264 euro with an average value of about 120 euro. Significant differences in the indicators of regional insurance markets are indicators of inefficient insurance activity and, as a result, increased risk. The external manifestations of this are the following problems: 1. lack of a competitive market and objective pricing in voluntary insurance; 2. different quality of insurance services and lack of uniform standards; 3. unsatisfied demand for insurance services in some regions and excessive supply in others; 4. significant fluctuations in unprofitability, which increase the risk of loss of financial stability of insurers. Due to these features, the study of the causes of regional differences in the development of insurance, their quantitative and qualitative assessment, as well as the development of measures to enhance insurance activities in problem countries and regions is of particular relevance.

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Clearly, the differences in insurance between regions are presented in Figs. 1, 2 and 3 in the form of indicators of the depth and density of the insurance market, as well as the ratio of insurance payments and premiums. For a visual representation, all regions of Russia are grouped by federal districts, of which there are 8. 4 Depth of life insurance, %

3.5

0.84

Insurance depth, %

3 2.5 0.37

2 1.5

2.97

0.38 0.27

1

1.05

0.5

0.30

0.18

1.85

0.77

1.25

0.25

0.18 0.68

0.98

0.72

0 CFD

NWFD

SFD

NCFD

VFD

UFD

SibFD

FEFD

Fig. 1. The share of the insurance premium in the amount of GRP in the federal districts of the Russian Federation in 2021. Source: Calculated according to: Official website of the Bank of Russia. [electronic resource]. URL: http://www.cbr.ru. *CFD - Central Federal District, NWFD North-Western Federal District, SFD - Southern Federal District, NCFD - North Caucasus Federal District, VFD - Volga Federal District, UFD - Ural Federal District, SibFD - Siberian Federal District, FEFD - Far Eastern Federal District.

. 350 300

Density of life insurance, eur.

75

Insurance density, eur.

250 200 150

264

28

100 143

50 0 CFD

NWFD

19

12

22

45

4 18

62

84

SFD

NCFD

VFD

UFD

25 16 50

72

SibFD

FEFD

Fig. 2. Amounts of insurance premiums per capita by federal districts of the Russian Federation in 2021. Source: Calculated according to: Official website of the Bank of Russia. [electronic resource]. URL: http://www.cbr.ru.

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The high share of insurance in the gross regional product remains in the CFD, which is 4 times higher than the indicator of the NCFD (the latter is the most problematic region by this criterion). The USD, SFD, FEFD and SibFD also have low indicators of market depth. The current structure shows the concentration of insurance capital in the CFD of Russia. At the same time, the share of life insurance in the gross regional product is several times lower than the same indicator for the entire insurance market. This is due to the limited demand for long-term life insurance services in the regions and the development of the financial market in the CFD. 120.00

Level of payments in life insurance, % Payout level, %

100.00 34.89 80.00 60.00

35.58

39.08

40.00 20.00

43.25

36.77

30.77

35.05

43.09

46.50

43.04

46.67

VFD

UFD

SibFD

FEFD

30.28 71.94 37.87

45.99

52.05

NWFD

SFD

0.00 CFD

NCFD

Fig. 3. The ratio of insurance payments and premiums by federal districts of the Russian Federation in 2021. Source: Calculated according to: Official website of the Bank of Russia. [electronic resource]. URL: http://www.cbr.ru.

The following are the indicators of insurance premiums per capita in general for the insurance market and for the life insurance market in particular. The insurance density in the CFD is 15 times higher than in the NCFD. Low values of insurance density are also observed in the SFD and SibFD. This indicator is the most important for the analysis of the insurance market, as it really reflects the provision of insurance services to the population. The premium on life insurance per capita is also several times lower than the insurance premium as a whole, which characterizes a limited share of long-term insurance funds. The value of the level of payments, on the contrary, is the highest in the NCFD (72%). In other regions, it ranges from 40–50%. This is due to the fact that there is a low proportion of voluntary insurance in the regions of this district and cases of insurance fraud are widespread. Thus, the calculations carried out confirmed the presence of significant disparities in the cost indicators of insurance in the regions of Russia, which is an indication of the presence of various risks for insurance entities. The variation of relative insurance indicators indicates the absence of uniform approaches and price guidelines in the regional insurance policy.

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3 Methodology of Insurance Portfolio Formation Taking into Account Regional Risks As a result of the analysis, the most problematic regions that require a comprehensive approach to regional risk management have been identified. This approach includes: 1) grouping of risks specific to individual regions of Russia; 2) identification of typical risks for a particular region or group of regions, analysis of their causes; 3) development of priority types of insurance as a universal method of risk management. In order to manage regional risks, a methodology has been developed for the formation of a regional insurance portfolio based on the identification of typical regional risks. The existing imbalances in insurance are primarily due to the specifics of regional risks. Therefore, in problem regions, it is necessary to develop certain types of insurance that are important for managing these regional risks. Depending on the management object, risks at the regional level are grouped into 4 groups. 1. Natural and climatic risks should be differentiated based on the peculiarities of the geographical location and climatic conditions of a particular region. A significant burden on the financial support of this risk falls on the regional budget and, in part, on the risk carriers themselves. 2. Technogenic and environmental risks are characteristic of regions where large-scale extractive industries, metallurgical enterprises and other organizations whose activities are associated with increased risk are concentrated. As a rule, such enterprises are located in the regions of the UFD, SibFD, the FESD and in NFD latitudes and, above all, where there are mineral reserves or natural resources. 3. Socio-economic risks are a fairly broad category, including the main risks of citizens related to the standard of living and social protection. At the same time, this group of risks depends on the overall economic development of the country and a particular region. The minimum protection against such risks is provided within the framework of social insurance, citizens and enterprises must provide additional protection independently, but the possibilities of this are mainly determined by the economic situation of the region. 4. Transport risks constitute a special group, since this is a fairly large-scale category, including the protection of vehicles themselves, both citizens and enterprises, the responsibility of vehicle owners, carriers of passengers and cargo, as well as for damage caused to third parties. The presence of certain risks in a particular region is individual. Therefore, their presence, significance and types of insurance necessary to protect against them, it is advisable to consider in the context of regions and specific examples. In most regions there are economic, industrial, environmental, man-made and natural risks. In this regard, the most important types of insurance requiring incentives are: insurance of property of citizens and organizations; insurance of industrial risks; liability insurance of dangerous industrial facilities; health insurance of citizens.

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In addition, almost all regions of Russia are exposed to financial and investment risks. But within the framework of managing these risks, insurance is rather a method additional to market methods. Taking into account the influence of the insurance market on the socio-economic sphere of the region, priority types of insurance can be divided into two groups: 1. stimulating the development of the economy of the region: insurance of industrial risks; liability insurance of hazardous industrial facilities; insurance of property of enterprises from fire and natural disasters; agricultural insurance. 2. socially significant types of insurance: insurance of citizens’ property against fire and natural disasters; insurance of environmental risks; voluntary medical insurance; cumulative life insurance. Accounting and systematization of regional risks allows to identify their factors and, on the basis of indicators corresponding to these factors, to develop a rating model for assessing the level of regional risk.

4 The Enlarged Model of Rating Assessment of Regional Insurance Potential As the analysis has shown, the list of factors of regional development is diverse, therefore it is necessary to formalize them in order to give a quantitative assessment. The most convenient way to do this is by ranking. To do this, each federal district was assigned a certain rating from 1 to 8 according to the main indicators of regional risk. To compile the ratings, quantitative indicators were used according to the data of statistical agencies and other information databases, allowing for gradation by region. The results of the rating of regions by regional factors significant for all federal districts are presented in Table 1. Based on the results of the analysis, the average rating score was derived from 10 indicators corresponding to significant factors affecting the development of insurance markets. In the formed rating, the CFD took the first position, which is quite expected – it ranks first in economic indicators. The second place was shared by the NWSD and VFD. This is followed by the UFD, SibFD, SFD and FEFD. The NCFD closes the list, which is in last place in almost all indicators. If the development of the region is balanced, the quantitative and qualitative indicators of the insurance market should correspond to its socio-economic development. The generalized results of quantitative indicators of insurance in the regional section are presented in the form of a rating in Table 2. When comparing the rating of socio-economic development of federal districts with their rating on the level of insurance, it can be noted that the ranks of federal districts according to these criteria practically coincide. Significant deviations are observed only in three federal districts. In the SFD and SibFD, the ranks of socio-economic development are higher than the ranks of the insurance market development. In the first case, this is due to natural and climatic, geographical features, as well as the existing industry structure, which together does not create incentives for the activation of insurance activities. In

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Table 1. Rating assessment of the impact of regional risks on the socio-economic development of the region. Risk assessment indicators

Rating of the Federal District CFD NWSD SDF NCFD VFD UFD SibDF FEFD

Budgetary security

1

2

7

8

6

4

5

3

Financial investments

1

2

6

8

5

3

4

7

Investment attractiveness

1

5

6

8

3

2

4

7

Investments in fixed assets

1

4

6

7

3

2

4

5

Financial results of organizations

1

3

7

8

4

2

5

6

Foreign trade turnover

1

2

7

8

3

4

5

6

Economic situation of enterprises 1

5

6

8

2

3

4

7

The level of economic diversification

2

4

1

8

3

5

7

6

Mining

5

6

7

8

3

1

2

4

Manufacturing industries

1

3

6

8

2

4

5

7

Volume of agricultural production 1

7

3

5

2

6

4

8

Retail trade turnover

1

4

3

8

2

6

5

7

Income level and quality of life of 1 citizens

3

5

8

6

4

7

2

Environmental situation

1

3

5

2

4

8

6

7

Tourism potential

3

2

1

6

4

7

5

8

Average rank

1.5

3.7

5.1

7.2

3.5

4.1

4.8

6

Final rank

1

3

6

8

2

4

5

7

Source: compiled according to: Federal State Statistics Service. [electronic resource]. URL: https:// rosstat.gov.ru.

the FEFD, the opposite situation is observed – the rank of insurance development is significantly higher than the rank of socio-economic development. This is due, on the contrary, to the harsh climatic conditions and the corresponding structure of production, which require insurance protection. This leads to the conclusion that regional factors of socio-economic development have a direct impact on the development of the insurance market. The insurance system in Russia throughout the entire period of its existence will be distinguished by a significant degree of differentiation in the territorial context. The reasons for this are inherent in the very conditions of the functioning and development of the regions. The factors determining the nature of the socio-economic development of regions and influencing the insurance market (regional insurance factors) were investigated.

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Table 2. Rating assessment of insurance development in federal districts. Indicator

Rating of the Federal District CFD NWSD SDF NCFD VFD UFD SibDF FEFD

Number and composition of registered insurance business entities

1

3

7

8

2

5

4

6

Volume of insurance premiums 1

3

5

7

2

4

5

6

The share of insurance premiums in GRP

1

2

4

8

3

7

5

6

The share of life insurance 1 premiums in the gross regional product

3

5

7

2

6

5

4

Insurance premium per capita

1

2

7

8

5

3

6

4

Life insurance premium per capita

1

2

7

8

4

5

6

3

Average rank

1

2.5

5.8

7.7

3

5

5.2

4.8

Final rank

1

2

7

8

3

5

6

4

Source: compiled according to: official website of the Bank of Russia. [electronic resource]. URL: http://www.cbr.ru.

5 Conclusions The conducted research has shown that the uneven development of the country’s insurance market by region is manifested in the difference in the indicators of insurance premiums per capita, the share of insurance premiums in the gross regional product and the ratio of insurance payments and premiums. Significant differences were revealed in all the given indicators. Such unevenness limits its effective development and significance in the country’s economy. It is necessary to identify the factors and causes of this situation, the associated regional risks and develop methods for their regulation. Regional risks were grouped into four groups: natural and climatic; man-made and environmental; socio-economic and transport risks. Based on this, the risks typical for the federal districts under consideration were identified and priority types of insurance were identified, some of which are aimed at stimulating the region’s economy, the other at social development. As a result, an enlarged model of rating assessment of regional insurance potential based on factors and indicators of socio-economic development and development of insurance in federal districts has been compiled. The conclusion is formulated that regional factors of socio-economic development have a direct impact on the development of the insurance market. The reasons for this are inherent in the very conditions of the functioning and development of the regions. At the same time, the level of development of the insurance market has the opposite effect on the development of the region, which is shown in the form of a matrix.

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E. Prokopjeva et al.

Thus, in order to smooth out regional imbalances in the development of the insurance market, it is necessary to increase the overall level of socio-economic development of the region, as well as to stimulate the development of priority types of insurance that are important for the economy and social sphere of the region.

References 1. Bayar, Y., Dan Gavriletea, M., Danuletiu, D.K.: Does the insurance sector really matter for economic growth? Evidence from Central and Eastern European countries. J. Bus. Econ. Manag. 22(3), 695–713 (2021) 2. Belozyorov, S.A., Pisarenko, Z.: Empirical analyses for financial convergence of Russian insurance market. Econ. Reg. 39(3), 198–208 (2014) 3. Braslina, L., et al.: Factors and barriers of implementing early warning, support and second chance support systems for SMEs in the Baltic states. In: Goonetilleke, R.S., Xiong, S., Kalkis, H., Roja, Z., Karwowski, W., Murata, A. (eds.) AHFE 2021. LNNS, vol. 273, pp. 25–32. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80713-9_4 4. Cekuls, A.: Role of the leadership and the values in social entrepreneurship. In: 18th International Multidisciplinary Scientific Geo Conference Surveying Geology and Mining Ecology Management, SGEM, vol. 18, no. 5.3, pp. 951–958 (2018) 5. Comanac, A., Tanzi, P.M., Ancarani, F.: Insurance companies and E-marketing activities: an empirical analysis in the Italian market. In: Marano, P., Rokas, I., Kochenburger, P. (eds.) The “Dematerialized” Insurance, pp. 85–113. Springer, Cham (2016). https://doi.org/10.1007/ 978-3-319-28410-1_4 6. David Cummins, J., Rubio-Misas, M.: Country factor behavior for integration improvement of European life insurance markets. Econ. Anal. Policy 72, 186–202 (2021) 7. Han, L., Li, D., Moshirian, F., et al.: Insurance development and economic growth. Geneva Pap. Risk Insur. 35(1), 183–199 (2010) 8. Konovalova, N., Caplinska, A.: Approaches to evaluation of banks’ financial sustainability. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2020. LNNS, vol. 195, pp. 758–768. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68476-1_70 9. Konovalova, N., Caplinska, A.: Impact analysis of factors influencing bank capital management. Entrepreneurship Sustain. Issues 8(1), 484–495 (2020) 10. Kozarevic, S., Peressin, L., Valentinuz, G.: Efficiency of the transition of insurance markets in Southeastern European post-communist countries. South-East. Eur. J. Econ. 11(2), 139–158 (2013). Association of Economic Universities of South and Eastern Europe and the Black Sea Region 11. Kuznetsova, N.P., Pisarenko, Z.V.: Financial convergence analysis: implication for insurance and pension markets. Bus.: Theory Pract. 17(2), 89–100 (2016) 12. Lee, H.S., Yong, Z.J., Lim, Q.M.: Insurance development and economic growth. Finan. Stat. J. 1, 1–17 (2018) 13. Lvova, N.A., Abramishvili, N.R., Darushin, I.A., Voronova, N.S.: The challenges of public companies’ assessment and diagnostics on the emerging market. In: Proceedings of the 32nd International Business Information Management Association Conference, IBIMA 2018 – Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional expansion to Global Growth, pp. 7499–7510 (2018) 14. Mdanat, M., Kasasbeh, H., Abushaikha, I.: The effect of insurance activity on per capita income in the Southern Mediterranean: an empirical analysis using Jordan as a case study. Theoret. Econ. Lett. 9(4), 912–928 (2019)

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15. Mohy ul din, S., Regupathi, A., Abu-Bakar, A.: Insurance effect on economic growth – among economies in various phases of development. Rev. Int. Bus. Strat. 27(4), 501–519 (2017) 16. Oana, V.F., Daniela, M.L.: Investment strategies in the field of general insurance. In: Proceedings of the 27th International Business Information Management Association Conference Innovation Management and Education Excellence Vision 2020: From Regional Development Sustainability to Global Economic Growth, IBIMA, pp. 2916–2922 (2016) 17. Pavic Kramaric, T., Galetic, F.: The role of the overall economic development on the insurance market growth-evidence of the European Union. J. Appl. Finan. Bank. 3(3), 157–168 (2013) 18. Porrini. D.: Regulating big data effects in the European insurance market. Insur. Mark. Co. 8(1–16), 6–15 (2017) 19. Prokopjeva, E.L.: Factors for the effective functioning of the regional insurance markets in Russia. Vopr. Ekon. 2019(10), 146–155 (2019) 20. Prokopjeva, E., Kuznetsova, N., Kalayda, S.: Insurance market development and economic growth indicators: the study of relationship in the world. Econ. Ann.-XXI 185(9–10), 48–60 (2020) 21. Prosvetova, A.A.: Digital technologies and insurance market in Russia. In: Ashmarina, S.I., Mantulenko, V.V. (eds.) ISCDTE 2021. LNNS, vol. 304, pp. 313–319. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-83175-2_40 22. Rupeika-Apoga, R., Thalassinos, E.I.: Ideas for a regulatory definition of FinTech. Int. J. Econ. Bus. Adm. 8(2), 136–154 (2020) 23. Saksonova, S., Ko¸leda, O.: Evaluating the interrelationship between actions of Latvian commercial banks and Latvian economic growth. Proc. Eng. 178, 123–130 (2017) 24. Saksonova, S., Jansone, M.: Economic factors of labor migration analysis. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2020. LNNS, vol. 195, pp. 649–659. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68476-1_60 25. Salkovska, J., et al.: Four conceptual perspectives of innovation components. In: Kantola, J.I., Nazir, S. (eds.) AHFE 2019. AISC, vol. 961, pp. 72–82. Springer, Cham (2020). https://doi. org/10.1007/978-3-030-20154-8_7 26. Sholoiko, A.S.: Characteristic of American insurance market infrastructure. Sci. Eur. 23– 2(23), 16–18 (2018)

Possibilities and Barriers in Implementing of AI-Based Automation Techniques in Management Nicolas Dolle1(B)

and Irina Kuzmina-Merlino2

1 Aalen University of Applied Sciences - Technology and Economics, Beethovenstraße 1,

73430 Aalen, Germany [email protected] 2 Transport and Telecommunication Institute, Lomonosova 1, Riga LV-1019, Latvia [email protected]

Abstract. The goal of the study is to find out the factors that influence the implementation of AI–based automation techniques in management and, based on them, to determine possible criteria for designing a digital maturity assessment model for the further research. The study was conducted on the basis of a questionnaire developed by the authors, which had open access during the period May–July 2022. The content of the questionnaire is based on the analysis and generalization of existing approaches in the literature to assess the readiness of companies for digital transformation. The questionnaire includes a sequential decomposition of digital change assessment objects and grouping of their characteristics into the following core dimensions: organizational changes, strategy, technology, operations, middle management identity. Based on the opinion of 54 senior and middle management representatives of multinational European companies registered in Germany and Latvia, the factors that contribute to or hinder the process of implementing of AI-based automation techniques in management have been identified. In the course of comparing the results of the survey with other previously published studies in the direction of assessing of the companies’ digital transformation readiness, assessment criteria were determined that can be used in the authors’ further research to develop a company’s digital maturity assessment model. Keywords: Management · Automation · Artificial intelligence · Leadership

1 Introduction Progress in the field of artificial intelligence is one of the most significant technological phenomena in recent years; however, this progress is uneven. According to a study by the European Investment Bank, conducted from April to July 2021, the implementation of advanced digital technologies as a whole did not progress in 2020–2021, remaining unchanged at about 61% of European Union firms [1]. These firms have not progressed either digitally or on the way to becoming digital, even under the influence of the pandemic. This article clarifies the possibilities and barriers in automation of management © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 414–423, 2023. https://doi.org/10.1007/978-3-031-26655-3_38

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and leadership tasks based on artificial intelligence techniques and identifies the most important structural changes in companies in the process of digital transformation. The goal of the study is to find out the factors that influence the implementation of AI– based automation techniques in management and, based on them, to determine possible criteria for designing a digital maturity assessment model for the further research. Based on the opinion of the respondent, the factors that contribute to or hinder the process of implementing of AI-based automation techniques in management have been identified. The survey results revealed different opinions of respondents on the nature of digital processes, which can be explained not so much by the type of activity or the size of the businesses, but mainly, by what stage of the digitalization process a particular company is at. In the course of comparing the results of the survey with other previously published studies in the direction of assessing of the companies’ digital transformation readiness, assessment criteria were determined that can be used in the authors’ further research to develop a company’s digital maturity assessment model.

2 Literature Review and Research Questions Analysis of the EIB Publications “Digitalization in Europe 2020–2021” [2] and “Digitalization in Europe 2021–2022” [3] showed that many European companies “as a response to the COVID-19 crisis invested in digitalization. In the European Union, 46% of firms report that they took action to become more digital—for example, by providing services online—according to the results of the EIB Investment Survey (EIBIS) conducted from April to July 2021. However, significant differences exist across firm size classes, sectors and countries. Comparing the different EU regions, 48% of firms in Western and Northern Europe reported taking steps or investing to become more digital, compared with 43% in Southern Europe and 37% in Central and Eastern Europe. A substantial share, 26%, of EU firms are in the “neither” category (no digital investment whatsoever), while only 18% of US firms have failed to invest [3]. In contrast to the more general digital transformation, the adoption of new advanced digital technologies is stalling. Beyond the short-term response to COVID-19, another structural element for the digital transformation of the EU economy is the implementation of advanced digital technologies such as 3-D printing, advanced robotics, the internet of things, big data analytics and artificial intelligence, drones, augmented or virtual reality, or platforms. Platforms and advanced robotics remain the most widespread digital technologies. While digital uptake has increased overall, the adoption of new advanced digital technologies is stalling. The share of EU firms implementing advanced digital technologies – 3-D printing, advanced robotics, the internet of things, big data analytics and artificial intelligence, drones, augmented or virtual reality, or platforms – increased significantly from 2019 to 2020. However, the share stayed more or less constant from 2020 to 2021, reaching 61% in 2021, compared with 63% 2020 and 58% in 2019 [2, 3]. Additionally, the worlds IT spendings are growing continuously as well. In 2023, based on Gartner’s conducted research, the world will approximately reach an IT spending of 4,809 Bn$ of market share [4]. Table 1 illustrates the development of the market on a basis of 2021.

416

N. Dolle and I. Kuzmina-Merlino Table 1. Worldwide IT spending forecast (millions of U.S. Dollars) [4]. 2021 Spending

2022 Growth in %

2023

Spending

Growth in %

Spending

Growth in %

11.1

221,590

4.4

Data systems

191,001

6.4

212,218

Software

735,869

14.7

806,800

9.6

902,182

11.8

Devices

808,580

16.0

767,872

−5.0

790,888

3.0

IT services

1,207,966

12.8

1,283,192

6.2

1,389,169

8.3

Communications

1,458,527

3.8

1,464,551

0.4

1,505,733

2.8

Overall IT

4,401,944

10.2

4,534,632

3.0

4,809,561

6.1

Digital infrastructure plays a critical role in unlocking investment in the digital transformation. Among EU firms, 16% consider access to digital infrastructure to be a major obstacle to investment according to the latest EIBIS results. However, the assessment varies significantly across EU countries and regions within the same country. A significant factor hindering the progress of digital transformation is the lack of people with digital skills supporting digital transformation. The above information characterizes only the general trends of the ongoing process of digital transformation of companies. Based on the results of the analysis, it can be concluded that three components are needed to accelerate the growth of digital innovation: a favorable infrastructure, a strategic vision for the development of the country and its regions, as well as the providing of adequate state support for business financing and improving digital literacy of employees. Digital changes in any company, by the opinion of the authors, should be managed and understood by all the levels of the corporate governance. The authors of this article absolutely agree with the opinion prevailing in the scientific literature that before the transformation process begins, it is necessary to implement digital cultural changes in the company, that is, to create a flexible network structure and architecture to successfully overcome possible distress. Based on the results of the theoretical study, the following research questions are formulated, which are the main objectives of the survey. – RQ1: What are of the possibilities and barriers in automation of management and leadership tasks based on artificial intelligence techniques? – RQ2: What are the most important structural changes in companies in the process of digital transformation? – RQ3: What factors could be the basis for the assessment model of the company’s readiness for digital transformation?

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3 Research Methodology The logical structure of the study is aimed at consistently achieving the goal of the article. To study the factors that could influence the implementation of AI–based automation techniques in management, a questionnaire was developed; the content of which echoed the research conducted by the European Investment Bank and available scientific publications in the direction of studying the degree of readiness of European companies for the new digital age. Evidences from EIB [1–3] allowed the authors to determine the priority areas of digital investments and the factors that affect its size and intensity. The content of the questionnaire is based on the analysis and generalization of existing approaches in the literature to assess the readiness of companies for digital transformation. The content of the questionnaire includes a sequential decomposition of digital change assessment objects and grouping of their characteristics into the following core dimensions: organizational changes, strategy, technology, operations, middle management identity. In the context of the research topic of this article, the greatest attention was paid to the issues of technology. Within the framework of these directions, 9 questions and some demographics data about respondents are presented in the survey. The study was conducted on the basis of a questionnaire developed by the authors, which had open access (link: https://forms.gle/Z4VArzgvfBC8s31L7) in the period from May to July 2022. The authors managed to get answers from 54 senior and middle-level representatives of multinational European companies registered in Germany and Latvia. Two methods of data analysis have been used to get answers to research questions and achieve the goal of the study. The data analysis method that has been used first is commonly described as descriptive analytics. The authors were analyzing the outcomes of a study that has been raised with primary data over time. Answers of the respondents were qualitatively and quantitatively analyzed. Afterwards, the authors were applying diagnostic data analytics methodologies to find out “why” the answers of the respondents were provided in the given way. This has been done by interpreting the received data in comparison to the current situation in modern business environments [5]. Based on the opinion of the respondent, the factors that contribute to or hinder the process of implementing of AI-based automation techniques in management have been identified. The survey results revealed different opinions of respondents on the nature of digital processes, which can be explained not so much by the type of activity or the size of the businesses, but mainly, by what stage of the digitalization process a particular company is at. In the course of comparing the results of the survey with other previously published studies in the direction of assessing of the companies’ digital transformation readiness, assessment criteria were determined that can be used in the authors’ further research to develop a company’s digital maturity assessment model.

4 Research Results During the survey 58 respondents’ responses were received; 4 of them were not fully completed, and therefore were not taken into account in the analysis. Demographics characteristics of the respondents are given below. The median age group is 45 years’ old. The respondents’ gender distribution is male 88%, female 12%. Respondents work

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mostly in multinational corporations of over 500 employees. 65% work in the companies with centralized organizational structure; the remaining 35% work in companies with a decentralized management structure. The most represented industry was service sector (41%), production sector (35%), public sector (12%), and other sectors, e.g., education, sciences, culture (12%). Job areas presented (position in the company): owner (23%), top level (12%), director level (18%), manager (12%), and operative level manager (35%). The respondents’ answers were analyzed in accordance with the structure of the questionnaire; thus, the answers are presented below in the context of seven sections of the questionnaire. Middle Management Identity “Are the middle management is ready to adopt AI-based Management Automation?” The purpose of this section is to get more information about the skills needed by managers to implement automation methods based on artificial intelligence; how their roles and responsibilities change in organizational changes. Middle managers believe that digitization has a significant impact on their personality and the functions performed, as the traditional approach to management is being replaced by leadership. The most important skills which are necessary were defined leadership skills (39%), technical skills (33%), followed by relational skills and organizational skills (28%). Thus, when talking about managerial skills that will be needed in the future, most of the answers focus more on people-oriented skills than on technical and, even less, organizational skills. Half of the respondents believe that the first place among the most important issues is occupied by change management. About 61% of respondents said that educational problems are also important problems for middle managers. 44% see problems in talent management. 67% of the survey participants do not yet see managers using artificial intelligence tools and feeling comfortable with them. Strategy of the Organization The 2nd section describes points of “How does the company manage the business in relation to AI-based Management Automation?” About 67% of the respondents do not see any or no clear vision for AI techniques in management processes. 50% have no or no clear impression that their organization has a clear understanding of AI technologies and methodologies. Furthermore, still 39% of the participants have no or no clear IT strategy aligned with there systems and digital strategy. 61% have no or not a clear AI roadmap. The survey results confirmed our previous assumption that, being as a ‘driver’ of changes, middle managers will be more involved in solving strategic tasks than everyday ones [6]. Customer Relationship Within the next section, the questionnaire aimed to find out how AI-based management could improve the relationship with customers and “How to become closer to our customers”. For 67%, AI changed the relationship with their customers. 50% of the respondents said that AI technologies were opening new possibilities to have multichannel interactions with their clients, and 78% of all participants have the vision to better understand their customers’ needs. Customer orientation is an important element

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of any organization’s strategy; there are no radical changes here. Achieving customer engagement in the sense of sustainable partnership is the most valuable is one of the strategic goals [7]. Technology The 4th section of the survey was devoted to technological issues and their requirements in accordance with artificial intelligence-based management. “What digital technologies and social networks are important for automating management tasks and how comfortable do you feel with them?” The respondents’ responses were distributed as follows: 50% of the respondents see the integrated system strategy as the most important factors for defining technological requirements; 39% see innovation and digital business as the most important drivers of technological requirements. Also, lean principles were not very important, but important for 50% of the respondents. Considering the usage of IT in the organization, the image of the responses is very diverse. 67% of the respondents see that the business processes and IT services are orientated based on the digital channels and customer’s needs. 56% see problems within the integration of IT technologies into the existing system landscape and 50% have friction losses within their IT landscape and organization. According to technologies, there are just a few of the participants using AI algorithms in their daily business life: 33% use classification algorithms, and decision support algorithms. 22% have real time algorithms for machines or administrative processes in place and are using decision making algorithms which are taking decisions on their own, automatically. 50% do not use any decision algorithms that can decide on their own, and 39% do even not use any decision support algorithms as well as onboarding algorithms. Furthermore, 33% do not use any real time algorithms, algorithms for human recourses management, as well as for delegating tasks. The analysis of the respondents’ responses allows us to conclude that most of the respondents do not believe that the company’s business units and the organizational and operational functions performed by them are well connected with each other using digital platforms. A significant part of respondents believe that their organizations’ technology platforms are being used inefficiently; technology platforms are not easy to use and do not improve employee engagement. Governance Questions about how a company uses its corporate, business and functional strategies to implement management automation methods based on artificial intelligence. The majority of respondents (67%) noted that their organization does not have a clearly developed strategy, as well as there is no methodology or process for measuring the level of digitalization and readiness for AI in their organization. In addition, 17% did not give any comments on this issue. Organizational Changes This group of questions was asked in order to find out the manager’s opinion on whether their organization is ready for digital transformation? Ambiguous answers to this question were received. 44% of the respondents have a clear contact person in terms of AI applications and digital processes. 39% do think their top management acts as role model for digitalizing the company. On the other hand, 44% do not have any or no clear team organization when it comes to developing digital processes and AI in the company.

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Barriers and Possibilities In the last section of questionnaire, the biggest problems faced by companies on the path of digital transformation were considered, which, in essence, are factors influencing the AI-based automation techniques implementation in management.

Fig. 1. Factors influencing the AI-based automation techniques implementation in management.

The following factors were seen as the most important or at least important factors for the digital transformation journey (Fig. 1). Integrating new technologies (78%), ineffective gathering and leveraging of data (72%), lack of a clear leadership vision (61%), insufficient internal skills (61%), and information growth and plethora of AI tools (61%). Comparing the results of the survey with other previously published studies, the authors identified similarities in respondents’ responses regarding the possibilities and barriers in automation of management and leadership tasks based on artificial intelligence techniques within digitalization process. In the Table 2 the results of a study of 100 top-level managers conducted by the Center for Creative Leadership and Corporate Leaders [8], Harvard Business Analytical Review [9], 94 surveyed respondents taken from EarlyStrategies [10] and the authors’ research are summarized.

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Table 2. Main barriers for AI implementation in management (developed by the authors). Center for creative leadership [8]

HBR analytic review Brand, Emili [9]

EarlyStrategies [10]

The authors

Insufficient internal skills (55%)

Embracing DT across our entire organization (46%)

Digital divide between Insufficient internal levels of management skills (61%)

Lack of a clear vision for a digital customer journey (46%)

Aligning digital transformation with business objectives or KPIs (45%)

The executive management does not deliver clear guidelines

Lack of a clear leadership vision regarding automation (61%)

Integrating new technologies (36%)

Effectively allocating resources to the right transformational areas (43%)

Sub-organizations react more flexibly to changes, but the head org. not so

Integrating new AI technologies into current business model (78%)

Resistance to change (34%)

Finding top talent to support digital initiatives (43%)

The risk of losing interaction with people

Information growth and plethora of AI tools (61%)

Ineffective gathering and leveraging of data (32%)

Creating/supporting a culture of continuous learning (40%)

Increasing data-related Ineffective requests from the VIP gathering and management leveraging of data (72%)

Lack of budgets (24%) Legacy technology that Reluctance of top cannot meet current management accept needs (34%) cultural changes

Lack of budget (28%)

Lack of executive Identifying support and leadership technologies (11%) best-suited to each functional area (32%)

Decreasing employee motivation, since AI-tools

Lack of executive support and leadership (15%)

Security issues (10%)

Organizational culture Horizontal becomes less human, interaction for all ‘less personal’ employee categories

Staying ahead of digital transformation trends and opportunities (30%)

Analyzing the responses of respondents from different studies, despite the different formats of the questionnaire, we can define common problem areas that companies face in the process of digitalization. Firstly, there is a digital gap between middle-level management and top-level management; the second problem (group of problems) is related to the need for organizational changes within the companies themselves (finding and attracting talent to support new digital initiatives and creating a corporate culture of continuous improvement). In the Table 3 below different approaches interpreting the essence of the digital transformation of the company were summarized. In each case, the main dimensions that are most susceptible to digital changes are identified.

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N. Dolle and I. Kuzmina-Merlino Table 3. Core dimensions in Digital Transformation (developed by the authors).

PwC [11]

Capgemini Consulting [12]

elementai.com [13]

MIT Sloan and Deloitte [14]

Early strategies [10]

Integration of value chain

Customers

Insight (customers)

Customer centricity

Digital tools & social media

Processes and Toolkits

Technology

Technology

Technology

Technology adoption

Business model

Strategy

Governance

Strategy

Governance

Systems and Data; Agile IT architecture

Operations

Insights (business information)

Operations

Middle Management Identity

Organization, digital culture

Organization and Culture

Culture

Organization & Culture

Organizational changes

The results of this study and their comparative analysis with the results of other previously published studies in the direction of assessing the readiness of companies for digital transformation allowed us to determine the measurement criteria that can be used by the authors in further research to develop a model for assessing the digital maturity level of the company. Five organizational aspects that can be used as criteria for assessing the level of maturity of a company’s AI are Strategy, Data, Technology, Personnel and Governance. Each dimension is integral. According to these criteria, the level of readiness of the company to implement AI-based Automation techniques at each stage of the digital process (early, latest, highest) can be assessed.

5 Conclusion Based on the opinion of the respondent, the factors that contribute to or hinder the process of implementing of AI-based automation techniques in management have been identified. The most important factors that create barriers to digital transformation are: the integration of new technologies, inefficient data collection and use, lack of strategy, insufficient digital skills, as well as the growth of information and the abundance of artificial intelligence tools. There is also a similarity in the responses of respondents regarding the most important structural changes in companies that companies need to undertake in the process of digital transformation. The development of the company’s digitalization strategy, the improvement of corporate governance system and corporate culture create opportunities for the successful application of AI techniques in management. Therefore, in the process of digital transformation, the most important structural changes should occur in these directions. In the course of comparing the survey results with other previously published studies in the direction of assessing the readiness of companies for digital transformation, evaluation criteria were identified that can be used by the authors in further research to develop a model for assessing the digital maturity of the company. The authors will

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take into account the five identified criteria (strategy, data, governance, technology, and personnel) when developing a model for assessing the digital readiness of companies in the further research.

References 1. European Investment Bank: Investment Report 2021/2022, 12 January 2022. Key Findings, Recovery as a springboard for change (2022). https://www.eib.org/en/publications/inv estment-report-2021 2. European Investment Bank: Digitalization in Europe 2020–2021, Evidence from EIB survey, 21 July 2021 (2021). https://www.eib.org/en/publications/digitalisation-in-europe-2020-2021 3. European Investment Bank: Digitalization in Europe 2021–2022, Evidence from EIB Survey, 05 May 2022 (2022). https://www.eib.org/en/publications/digitalisation-in-europe-20212022 4. Gartner.com: Gartner Forecasts Worldwide IT Spending, Stamford, Conn., 14 July 2022. https://www.gartner.com/en/newsroom/press-releases/2022-06-14-gartner-forecastsworldwide-it-spending-to-grow-3-percent-in-2022 5. Fleckenstein, M., Fellows, L.: Modern Data Strategy||Data Analytics, E-book, Chap. 13, pp.133–142. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68993-7_13 6. Kuzmina-Merlino, I., Dolle, N.: Artificial intelligence techniques for automating management and leadership tasks: literature review. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2021. LNNS, vol. 410, pp. 482–492. Springer, Cham (2022). https://doi.org/10.1007/ 978-3-030-96196-1_44 7. Bonnet, D., Westerman, G.: The new elements of digital transformation. MITSloan Management Review, Harvard Business School, 19 November (2020) 8. ccl.org: Digital Transformation Readiness Survey Summary. Center for Creative Leadership, p. 7 (2018). https://www.ccl.org/wp-content/uploads/2018/04/Digital-TransformationSurvey-Report.pdf 9. Brand, E.: Digital Transformation Refocused: New Goals Require New Strategies. Harvard Business Review Analytic Services – Red Hat, Harvard Business School Publishing (2022). hbr.org/hbr-analytic-services 10. Demailly, C.: How Middle Management Copes with Digital Transformation. A Study by Early Strategies, 22 September 2016 (2016). https://www.slideshare.net/EarlyStrategies/mid dle-management-vs-digital-transformation-stada-webinar-16sep16 11. pwc: PwC Chair in Digital Economy. Towards a value-centric maturity model. Australian Government, Department of Industry, Innovation and Science (2017). https://research.qut. edu.au/cde/wp-content/uploads/sites/279/2021/03/Digital-Business-Part-B.pdf 12. Capgemini.com: Digital Transformation Review, Taking Digital Transformation to the Next Level. Lessons from the Leaders, 12th edn (2017). https://www.capgemini.com/wp-content/ uploads/2019/02/Report-Digital-%E2%80%93-DTR12.pdf 13. Element AI: The AI Maturity Framework. A strategic guide to operationalize and scale enterprise AI solutions. The Five Dimensions of Enterprise AI, White paper. elementai.com (2021). https://s3.amazonaws.com/element-ai-website-bucket/AI-Maturity-Framew ork_White-Paper_EN.pdf 14. Kane, G.C., et al.: Achieving digital maturity to drive growth. Findings from the 2017 Digital Business Global Executive study and research project. MIT Sloan Management Review in collaboration with Deloitte University Press (2017)

Assessment of the Dependence of Insurance Volumes on Various Socio-Economic Factors of Regional Development in Countries with a Transitive Economy Svetlana Saksonova1(B)

, Evgenia Prokopjeva2,3

, and Oksana Adlere1

1 University of Latvia, Aspazijas blvd. 5, Riga, Latvia

[email protected], [email protected] 2 Siberian Federal University, Svobodny Ave. 79, Krasnoyarsk, Russia 3 N.F. Katanov Khakass State University, Lenin Ave. 90, Abakan, Russia

Abstract. The insurance market is one of the driving forces in the socio-economic development of the country. Therefore, the study of socio-economic factors of regional development, the assessment of the dependence of insurance volumes on various socio-economic factors is becoming an urgent problem both for countries with a stagnating economy and a rapidly growing one. The purpose of the proposed paper is to develop a correlation analysis methodology for assessing the dependence of insurance volumes on various socio–economic factors of regional development and evaluating its results. The methodology proposed by the authors includes: – determination of the list of factors for the development of regional insurance markets based on the study of the specifics of the development of countries and regions. – selection of quantitative indicators that can be used to assess the significance of socio-economic factors of the insurance market and their characteristics. – assessment of the correlation between the corresponding socio-economic indicators of the regions and the volume of insurance premiums (according to statistics). – identification of significant factors by comparing correlation pairs for purposeful management of them. According to the proposed methodology, the authors conducted a correlation analysis, which made it possible to assess the significance of factors and assess the relationship between socio-economic development indicators and insurance indicators. The authors suggest using the proposed methodology in various countries and regions, since the results of the analysis allow us to quantify the significance of a particular factor that has a positive or negative impact on the indicators of insurance markets, primarily the volume of insurance premiums and payments. Keywords: Insurance market · Dependency assessment · Socio-economic factors of insurance · Economic growth

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 424–436, 2023. https://doi.org/10.1007/978-3-031-26655-3_39

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1 Introduction The insurance market is one of the driving forces in the socio-economic development of the country. Therefore, the study of growth factors, the assessment of the dependence of insurance volumes on various socio-economic factors of regional development is becoming an urgent problem both for countries with a stagnating economy and with a rapidly growing one. It is especially important to identify and assess these factors for countries with a transitive economy, as unstable and subject to increased risks. In turn, the purposeful development of the insurance market in these countries will increase financial stability and ensure the growth of their economy, thereby reducing the risks of the global economy. In addition, in the modern economy, on the one hand, the processes of globalization are intensifying, on the other hand, the tendencies of regionalization are increasing. This is what makes it necessary to study regional problems of sustainable economic growth and the insurance market both on a global scale and at the national level. In the context of regional studies, it is necessary to clarify the understanding of the region that is used in this study. A region is defined as a complex of territories determined on the basis of geographical location (not always whole countries, but sometimes parts of them), characterized by similarity of geographical, natural, historical, economic, social and other conditions and processes. The insurance market is currently the most important driving force of social and economic development of countries and regions, as well as a protective mechanism in the face of global risks. Therefore, the study of the factors of its development, taking into account regional peculiarities, as well as the quantitative and qualitative assessment of the influence of these factors, seems extremely relevant. The purpose of the paper is to develop a methodology for correlation analysis and evaluation of its results, allowing to assess the dependence of insurance volumes on various socio-economic factors of regional development in order to develop the insurance market. Various aspects of the problem under study are presented in the scientific literature, but mostly fragmentary, or indirectly related to this topic. Thus, it should be noted the works describing the problems of sustainable development of economic systems, in particular, the relationship of indicators of insurance and other financial markets, and indicators of economic growth. Such authors and groups of authors conducted their research on these topics as Chang [5], Lee [11], Saksonova [20], Zaidi [23], Outreville [15], Sawadogo [22], Mohy ul din [14], Zheng [24]. The lack of economic growth leads to negative consequences, including migration, which negatively affects the insurance market, as shown in the research of Saksonova [21]. Important for the disclosure of the topic are studies by various authors and groups of authors devoted to specific issues of regional insurance and Europe and America, conducted on the examples of different countries and regions of the world, for example, Kjosevski [10], Sholoiko [25], Zheng [26], Braslina [3], Mdanat [13]. The authors of the article also studied works on various aspects of the legislative regulation of the insurance industry, for example, articles by Porrini [16], Grace [6], Russell Toth [18].

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Of particular interest to the subject of this article are the studies of authors and teams of authors devoted to the study of the factors of development of insurance markets - Prokopjeva [17], as well as general issues and trends of insurance, including environmental aspects: Batool [2], Baltensperger [1], Hong [7], Cekuls [4]. Innovative aspects of development were studied by teams of authors led by Salkovska [19], positive experience of development and ensuring financial stability of companies was studied by a team of authors led by Lvova [12], including in the regulated area of the financial market - the banking sector Konovalova [8, 9]. Studying the mentioned studies, the authors came to the conclusion that research on regional insurance issues both on a global scale and in individual countries is not fully represented. Summarizing the review of world and regional studies, it can be noted that research on regional insurance issues both on a global scale and in individual countries is not fully presented in world practice. The study of insurance factors is carried out using separate methods, for example, correlation analysis. At the same time, there is usually no systematic representation of the factors of insurance market development themselves, as well as their assessment, and the patterns of manifestation of certain factors in specific conditions have not been sufficiently studied. In the proposed paper, the authors have made an attempt to fill this gap. It is advisable to analyze regional factors, as well as the directions of their impact on the structure of the insurance market, using the example of insurance markets of countries with a transitive economy. This is due to the fact that this group of countries is quite numerous – this includes the states of Central and Eastern Europe, as well as the CIS countries – about 28 countries in total. But most importantly, the insurance markets of these countries are unstable, as are most economic processes. This instability, in turn, affects the stability of the global financial system and economy. Therefore, it is very important, based on the positive experience of various countries, to identify significant factors of a regional nature, systematize them and conduct a comprehensive analysis in order to further develop a regulatory system at the state and interstate levels.

2 Correlation Analysis Methodology for Assessing the Significance of Regional Factors in the Development of the Insurance Market and Its Application (Step-by-Step Analysis) One of the most common methods of studying relationships in the economy is correlation analysis. In this article, the authors use it to assess the significance of regional factors in the development of the insurance market. The factors of development of regional insurance markets are diverse and are determined by socio-economic, natural-geographical and other conditions of functioning of the country or territorial entity. The method of correlation analysis of the impact of socio-economic development factors on the insurance market consists in the following 4 steps: • determination of the list of factors for the development of regional insurance markets based on the study of the specifics of the development of countries and regions;

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• selection of quantitative indicators that can be used to assess the significance of socioeconomic factors of the insurance market and their characteristics; • determination (evaluation) of the correlation between the corresponding socioeconomic indicators of the regions and the volume of insurance premiums (according to statistics); • identification of significant factors for purposeful management of them. The correlation analysis of the factors of the functioning of the insurance market is carried out on the example of Russia as a vivid example of a state with a transitive economy, having a federal structure and different economic conditions in the regions. Step-by-step analysis • determination of the list of factors for the development of regional insurance markets (step 1) and selection of quantitative indicators (step 2). The study of factors was studied both for the insurance market as a whole and for its most significant segments – life insurance, property insurance, risky personal insurance, liability insurance of car owners. In world practice, life insurance is always studied separately as the most significant element of the capital market. The level of development of life insurance is an indicator of the socio-economic situation of the country, the region and the standard of living of citizens. For the analysis, it is most convenient to use socio-economic indicators that are most related to regional factors affecting the structure of the insurance market in the region. The choice of certain socio-economic indicators is also conditioned by the possibility of obtaining comparable data for all regions. The correspondence of regional insurance factors, as well as indicators of their quantitative measurement, is presented in Table 1. All of these indicators directly or indirectly reflect the socio-economic situation of the region, so one way or another should affect the amount of insurance premiums, including for certain types of insurance. • Assessment of the correlation between the corresponding socio-economic indicators of the regions and the volume of insurance premiums (step 3). To calculate the paired correlation coefficients as factor features, the authors used the most important quantitative socio-economic indicators for all regions of the country (subjects of the Russian Federation) according to Table 1. Insurance premiums by region, including the largest market segments, are taken as effective indicators. The calculation results are shown in Table 2. • Identification of significant factors for targeted management (step 4). The authors propose an algorithm for determining the significance of factors in the development of the insurance market based on spatial and temporal correlation analysis (Fig. 1).

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Table 1. Correspondence of regional insurance factors and indicators of socio-economic development of Russian regions. Regional insurance factors

Quantitative indicator for these factors

Characteristics of the indicator and its impact on insurance parameters

– the level of economic Gross regional product diversification and its sectoral structure – the state of small and medium-sized businesses

The basic indicator of the economic development of the region, therefore, its value directly affects the activity of financial markets, including the insurance market. GRP reflects the influence of factors such as the financial capabilities of the region, the economic situation of enterprises, investment attractiveness

– migration processes – urban and rural settlements and population density

Demographic indicator, however, it should have a direct impact on the amount of insurance premiums

The population of the region

– human potential (level of The number of citizens education, healthcare, etc.) employed in the economy – investment attractiveness and innovative activity – industry structure of the economy

The indicator should significantly affect the amount of insurance premiums, since only working citizens are able to make deductions for life insurance. The indicators of the number of citizens reflect the influence of the factor of regional development – the quality of life of citizens

– income level of citizens – the sectoral structure of the region’s economy

An indicator that indirectly affects the indicators of the insurance market. It can be used to judge the income of the population, as well as the ratio of accumulation and consumption of citizens

Per capita monetary income per capita

Deposits of individuals and The growth of deposits is noted legal entities with an increase in the incomes of enterprises and citizens and stimulates the demand for insurance, primarily life insurance (continued)

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Table 1. (continued) Regional insurance factors

Quantitative indicator for these factors

Characteristics of the indicator and its impact on insurance parameters

Consumer spending per capita

The growth of consumer spending is an indicator of income growth (a positive factor), while insurance costs are also increasing. In some cases, it may reflect inflationary expectations (negative factor)

– investment attractiveness and Investments in fixed assets innovative activity – the state of manufacturing industries (mechanical engineering, oil production, gas production and their processing, chemical, metallurgical)

An indirect indicator for the insurance market, as well as the most important indicator of economic activity in the region, which reflects the influence of such factors of regional development as the investment attractiveness of the region and the economic situation of enterprises

– investment attractiveness of the economy – agglomeration development and population density

Commissioning of residential buildings

The indicator of investment activity in the region, which should have the strongest impact on life insurance, since some of the relevant contracts are due to mortgage lending. Indirectly, this indicator is determined by the level of income of citizens

– profitability of enterprises – availability of extractive industries (ferrous and non-ferrous metallurgy, forestry, oil production)

Financial result of enterprises

The final indicator of the economic situation of enterprises, which reflects the financial capabilities of policyholders to purchase insurance coverage

– financial potential of the region and the level of activity in financial markets – industry structure of the economy

Debt on loans of individuals

The growth of credit activity occurs at the stage of economic growth and contributes to the growth of insurance of credit transactions (continued)

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Regional insurance factors

Quantitative indicator for these factors

Characteristics of the indicator and its impact on insurance parameters

Income of consolidated budgets

Budget revenues increase with an increase in the economic potential of enterprises and the region as a whole, which is an incentive for the development of all types of insurance, including with support from the regional budget

– investment attractiveness and Cost of fixed assets in the innovative activity economy

An indicator reflecting the level of technical equipment and investment activity, therefore indirectly it reflects the financial capabilities of enterprises. At the same time, there is a direct link between the cost of fixed assets and insurance premiums, which lies in the need for insurance protection of this property

– the volume of mineral Volume of mining reserves and natural resources – the level of development of foreign economic relations and openness of the economy

An indicator reflecting the volume of production and foreign economic activity. But the corresponding factor has different significance in each region due to natural and geographical differences

– structure of manufacturing industries (mechanical engineering, oil production, gas production and their processing, chemical, metallurgical, food, etc.)

It is a much more significant indicator for the development of the insurance market than the extractive industry. This is due to the fact that the developed manufacturing industry is largely diversified, brings good incomes to the enterprise itself and employees, and includes a variety of risks requiring insurance

Volume of manufacturing industries

(continued)

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Table 1. (continued) Regional insurance factors

Quantitative indicator for these factors

Characteristics of the indicator and its impact on insurance parameters

– availability of natural resources – the level of development of agriculture (animal husbandry, crop production) – seasonal nature of the economy

Agricultural production volume

It can potentially affect the volume of insurance activity, but under the condition of an actively functioning agricultural insurance market. This type of insurance is very relevant for agricultural producers, but has not yet been developed

– activity in financial markets Retail trade turnover – the sectoral structure of the economy (the development of the consumer market)

An indicator directly related to consumer spending and indirectly reflecting the relationship with the commercial insurance system

Source: Compiled by the authors according to: Federal State Statistics Service. [Electronic resource]. URL: https://rosstat.gov.ru

A very strong correlation (more than 0.9) is noted between the factorial feature “deposits of individuals and legal entities” and the effective feature “sum of insurance premiums” for all types of insurance except for compulsory insurance of car owners. A high correlation (0.7–0.9) is observed between the factor signs: “financial result of enterprises”, “consolidated budget revenues”, “value of fixed assets of enterprises”, “gross regional product”, “retail trade turnover”, “population”, “volume of manufacturing industry” and the effective sign “volume of insurance awards”. It is also worth noting that a high correlation is noted between the listed factors and such effective signs as life insurance and compulsory insurance of car owners. The average correlation (0.5–0.7) is observed between the factor characteristics: “the number of people employed in the economy”, “investments in fixed assets”, “debt on loans of individuals”, “population”, “consumer spending per capita” and all the productive characteristics. Other factors have a weak impact on the scale of insurance activity (correlation coefficient is less than 0.5). In general, it can be noted that the socio-economic factors of the region’s development have the strongest impact on the segments of personal insurance and compulsory insurance of car owners, since the demand for appropriate insurance services is determined by the financial capabilities of economic entities. Property insurance and risky types of personal insurance depend less on the factors considered. It can be concluded that the volume of regional insurance depends primarily on the scale of business, the financial stability of enterprises, and the volume of production in the region. This is due to the fact that the main major insurers are enterprises. At the

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Table 2. Paired correlation coefficients calculated by regions of Russia (Spatial analysis), 2020. Factorial feature

Effective sign, EUR Insurance premiums, total

Life insurance premiums

Personal Property insurance insurance premiums other premiums than life

Premiums for car owners’ liability insurance

Deposits of individuals and legal entities, EUR

0.9730

0.9665

0.9580

0.9756

0.7481

Financial result of enterprises, EUR

0.8331

0.8284

0.7916

0.8179

0.7831

Consolidated budget revenues, EUR

0.8237

0.8361

0.7606

0.8027

0.8112

Fixed assets of enterprises, EUR

0.7926

0.8150

0.7110

0.7400

0.9230

GRP, EUR

0.7443

0.7712

0.6736

0.7023

0.8099

Retail trade turnover, EUR

0.7338

0.7549

0.6458

0.6952

0.8539

Manufacturing industries, EUR

0.7144

0.7354

0.6161

0.6728

0.8923

The average 0.6566 annual number of employed, people

0.6847

0.5583

0.6093

0.8425

Investments in 0.6541 fixed assets, EUR

0.6829

0.5550

0.5930

0.8903

Debt on loans of 0.6337 individuals, EUR

0.6642

0.5258

0.5799

0.8707

The average population for the year, thousand people

0.5408

0.5739

0.4338

0.4872

0.7913

Consumer spending per capita, EUR/month

0.5369

0.5273

0.5301

0.5470

0.3979

(continued)

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Table 2. (continued) Factorial feature

Effective sign, EUR Insurance premiums, total

Life insurance premiums

Personal Property insurance insurance premiums other premiums than life

Premiums for car owners’ liability insurance

Average per capita monetary income, EUR/month

0.4287

0.4191

0.4314

0.4400

0.2909

The volume of mining, EUR

0.4163

0.4545

0.3352

0.3365

0.7038

Commissioning 0.4079 of housing, sq. m

0.4333

0.2911

0.3617

0.7201

Agricultural products, EUR

0.2569

0.1111

0.1515

0.5459

0.2144

Sources: Calculated by the authors according to: Federal State Statistics Service. [Electronic resource]. URL: https://rosstat.gov.ru; official website of the Bank of Russia. [Electronic resource]. URL: http://www.cbr.ru

Fig. 1. Algorithm for determining the significance of factors in the development of the regional insurance market. Source: compiled by the authors.

same time, the indicators of income and expenses of citizens are not significant factors in the formation of the structure of the insurance market of the regions of the Russian

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Federation, although in a particular region they can have a strong impact on the total insurance premiums.

3 Discussion and Conclusion The obtained results of the analysis confirm the hypothesis of the authors that regional factors of the insurance market development have a different impact depending on the conditions of the region’s functioning. The method of correlation analysis of the regional insurance market proposed by the authors makes it possible to identify the factors that have the strongest impact on the volume of insurance premiums in general and in individual segments of the insurance market. The authors propose to use the considered methodology of correlation analysis in various countries and regions, as it is focused on the assessment of regional insurance markets in order to identify and monitor problematic (constraining) factors of their development. Of particular importance are regional factors that are significant for the insurance market. On this basis, it is possible to predict changes in insurance premiums depending on changes in individual factors from among the significant ones or their totality. At the same time, problematic factors should be considered those that give a forecast of a decrease in insurance premiums or a lack of growth. According to the authors, the assessment of the dependence of insurance volumes on various socio-economic factors of regional development is important for forecasting trends in the insurance market, as well as in the development of regional financial policy directions. Correlation analysis can be considered as a method of assessing the significance of factors of the regional insurance market as a whole, as well as individual types of insurance that are most significant for the economy of the region. On this basis, it is possible to develop and implement measures that can stimulate the development of voluntary types of insurance and priority ones to strengthen the economic potential of a particular region.

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The Impact of User-Generated Content on Customer Purchase Intentions of Online Shoppers Ioseb Gabelaia(B) RISEBA University of Applied Science, Riga, Latvia [email protected]

Abstract. Today customer-oriented approaches are a priority in any marketing strategy. Online users have become aware of authentic and transparent companies, causing businesses to listen to diverse audiences. Besides, content marketing is essential means to generate key customer attention. Nevertheless, it should be noted that marketing campaigns require proper execution to obtain the expected return on investment. Engaging the audience and prioritizing the content is a terrific strategy for developing relationships and authority for customer retention. These days cyber-culture sets expectations for businesses, leading businesses to utilize artificial intelligence in strategic planning. Likewise, keeping up with the changing trends enables marketers to emphasize new opportunities to reach more customers. Hence, user-generated content is a contemporary communication method to build any brand’s trustworthiness. Besides, easy to maintain and relatively inexpensive. The research aimed to explore the relevance of user-generated content on the purchase intentions of online shoppers and identify the main factors influencing online purchase decisions. The quantitative research method collected data while online shop owners and shoppers were identified as two distinct target audiences. The analyzed results indicated several impact components. First, the attitude between target groups towards user-generated content was highly divided. While results showed that UGC positively supports brand awareness, its effectiveness was unclear. Hence, the author concluded that the impact of UGC content on the purchase intentions of online users has a positive impact. However, more research on the target audience is required to clarify the relationship between UGC and customer purchase intentions. Keywords: User-generated content · Content marketing · Purchase intentions · Online shoppers · Digital marketing

1 Introduction The internet launch opened the gate to many proposals in marketing communication, such as daily, business-related, and professional conversations. Today marketing professionals have more options to engage with the target audiences, thus, recognizing customers’ purchase intentions. With an exceptional quantity of marketing channels to collaborate © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 437–449, 2023. https://doi.org/10.1007/978-3-031-26655-3_40

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with, it is exhilarating to identify which approach will permit businesses to engage with online shoppers successfully. Moreover, nurture strong relations and eventually drive business sales. Amidst the massive range of marketing communication alternatives, one that stands powerful is content marketing, where user-generated content (UGC) leads conversations and impacts customer purchase intentions. Besides, brands compete to be visible online, which leads to ferocious competition for online shoppers’ attention. Thus, user-generated content is employed across various phases of buyers’ journeys, impacting customers’ purchase intentions through various engagement campaigns. Shoppers have become careful about the brands they purchase from. Authenticity and quality are real components of reliable content. Hence, no other content type is more authentic than UGC for online business shoppers. Besides, it is significant not to fake user-generated campaigns. Online shoppers will instantly identify the false campaigns or posts, harming the brand’s reputation. Lastly, the rule of thumb is to use UGC from your customers, brand advocates, and employees. People generally trust others, so it is necessary to think of UGC as modern-day word of mouth. Customers, especially online shoppers have higher capability to recognize user-generated content as reliable than content produced by brands; therefore, investing in an authenticity-driven content strategy is right now. In today’s cyber-culture, the most crucial aspect is that all content should incorporate a call to action. It encourages online shoppers to take the next step with purchase intentions. Moreover, it will maximize the chances of being noticed in a competition, which results in consistent engagement and keeps the business at the top of consumers’ minds. As customer purchase intentions are growing every second, content marketing planning has played an essential role in business growth to make one product, or brand stand out from the alternatives. Moreover, content marketing has been vital means of communication for a business to customers, where UGC remains the primary choice of marketing communication techniques. Due to its low cost, and user-friendliness, it helps business owners to reach online shoppers and challenge their purchase intentions. Content marketing applied effectively can benefit a business significantly. However, not all businesses know how to manage marketing campaigns right. Marketing professionals must educate themselves with relevant UGC marketing strategies to produce a massive profit from a small investment and engage online shoppers. The knowledge will allow them to manipulate the purchase intentions of online shoppers. Due to this, the research aimed to explore the relevance of user-generated content on the purchase intentions of online shoppers and identify the main factors influencing online purchase decisions. The quantitative research method collected data while online shop owners and shoppers were identified as two distinct target audiences. The analyzed results indicated that target group attitudes towards user-generated content were highly divided. While results showed that UGC positively supports brand awareness, its effectiveness was unclear. The impact of UGC content on the purchase intentions of online users has a positive impact. However, more research on the target audience, such as Millennials, Gen-Z, and Gen Alpha, is required to clarify the relationship between UGC and customer purchase intentions.

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2 Literature Review User-generated content is a considerable basis for identifying customer needs. However, established methods are not effective due to their noninformative or repetitive content [1]. Notwithstanding a growing emphasis on content marketing as a modern marketing tool, research in the field is limited [2]. Furthermore, [3] emphasized that user-generated content affects customer actions more than marketing-generated-content. [4] indicated that user-generated content has surpassed other marketing developments. In practice, current customers using UGC showcase their real-life experiences and motivate potential customers to try these products and services. Moreover, these diverse types of media content are publicly accessible [5]. Besides, [6] verified that UGC is created by the consumers or public instead of marketing professionals. Therefore, UGC’s decisions are building trust, customer engagement, and loyalty [4]. Moreover, [6] highlighted that UGC significantly impacts companies’ performance. With the growth of the internet and social media platforms, many UGC is advertised in textual or other formats [6]. Besides, with the growth of social media, many companies have been utilizing UGC to connect with customers by encouraging to engage with user-generated content [7]. However, it should be noted that not much research on the characteristics of UGC has been done or how it impacts customer engagement [8]. Social media offers consumers suitable ways of sharing information [5]. Besides, the roles of consumers and businesses are being distorted as online shoppers take on creative tasks that were previously controlled exclusively by marketers [9]. Furthermore, [10, 11] emphasized that since user-generated content and online brand communities are growing with attractiveness, consumers look for straightforward reviews. However, findings show that online customers do not guide their purchases based on UGC. In contrast, [12, 13] argued that user-generated content generated by these consumers represents a considerate shift of power from businesses to shoppers. Moreover, according to [4] consumer’s purchase intention is affected by a consumer’s approach towards UGC. Hence, it was discovered that the consumer’s attitude towards UGC positively affected online purchase intentions. According to [2, 14], sponsored and user-generated content is of high significance for the marketing strategy. Moreover, results indicated that sponsored content leads to a more negative brand attitude than user-generated content. These results suggest that content marketing is perceived similarly to user-generated content, although coming directly from a company. In contrast, [1] emphasized that companies usually rely on interviews and focus groups to characterize customer needs for marketing strategy. According to [15, 16], brands should adapt their public strategies based on the personality of the traits and expectations of the community. Hence, user-generated content steered to higher purchase intention than advertisements and brand posts [17]. Furthermore, [18] emphasized in their research that the effectiveness of the customer decisions is still unclear with user-generated content. To conclude, the impact of a particular brand positively affects shoppers’ buying intentions [7]. Content marketing is a crucial element in a company’s marketing mix. It is also a requirement for companies seeking to modernize their marketing practices through digitalization to enhance online branding [19]. Moreover, [20, 21] emphasized that content marketing becomes a sustainable approach to developing brands and connecting

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with consumers. Furthermore, according to [22], these categories individually do not persuade their shoppers to buy a given product. Nevertheless, they are designed to lead their shoppers to a purchase decision collectively. [23, 24] stated that professional communication specialists are increasingly engaged in content marketing. While [25, 26]) attested that given the increasing investment in branding within social media, having numerous communication platforms remain a major challenge. Nowadays, purchase intentions are essential to long-term organizational success [27]. Besides, purchase intentions are essential in competing with others on the market [28]. Furthermore, customers have confidence in product from other shoppers than from advertisings by product marketing companies [29]. Moreover, existing generations impact the purchase intentions [30]. According to [31], decision systems remain behind the estimation of prices, risks, and intentions on social markets. On the other hand, [32, 33] argued that shoppers have several ways to obtain information and develop purchase attitudes. Moreover, to gain more consumers, understand their behaviors and needs, and sustain effective relationships, businesses should recognize how shoppers behave on social media and how they differ in their purchase intentions [34]. Hence, trust has a definite outcome on purchase intentions. Consumers review products and services promptly in the framework of usergenerated content; however, the extent to which such information impacts shoppers are unknown [35]. Moreover, in research by [36, 37], information communication to shoppers to make purchase decisions is unpredictable and affects trustworthiness. Furthermore, [38] suggested that a lack of consumer trust negatively impact purchase intentions. [39, 40] in their research suggested that consumer behavior in digital settings is more influenced by brand persona than online brand experience. If a brand can create and sustain an appealing brand persona, then shoppers tend to have a positive attitude [41]. Moreover, lack of consumer trust is influenced positively by apparent risk and customer resistance to innovativeness and negatively by past experience [42, 43]. Lastly, the growth of the UGC might develop some disorders connected with buying [44].

3 Research Methodology To address the research question and to explore the impact of user-generated content on customer purchase intentions of online shoppers, the author used a two-step approach. First, available literature was examined and second by applying a survey based on a literature review. According to [46], a systematic literature review was designed to recognize the knowledge gap and establish a research ground. Furthermore, [45] argued that a systematic literature review was a process of reviewing and clarifying all available research appropriate to the research problem. The objective of the systematic literature review was to review evidence for research development. According to [46], scientific reviews should be valid, reliable, and repeatable. Table 1 underscores the methodology in the systematic literature review—the

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review comprises three stages. Stage one involved setting research objectives moreover identifying key research themes. [46] stated that in the planning stage, researchers frequently lack accurate systematic reviews as they do not have adequate assistance conducting the review. Table 1. Systematic literature review (developed by the author).

Stage two involved establishing inclusion and exclusion criteria. At first, inclusion criteria were chosen, such as searching boundaries, keywords, and publication dates. Databases utilized were Scopus, WoS, and Google scholar. Moreover, keywords such as UGC, content, etc., were used. Scientific literature between 2017 and 2022 was examined. Second, all potential sequences filtered articles from three chosen databases. The author carefully chose only English-language scientific articles. First, 256 relevant papers were discovered without eliminating duplicates. Further, the author used exclusion criteria to filter existing literature. Table 2 demonstrates the method of using exclusion criteria with four primary stages identification, screening, eligibility, and inclusion. The identification stage epitomizes resources from where literature materials were collected. During the screening state, 57 duplicates were filtered and excluded.

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Further, in the eligibility stage, three main exclusion conditions mentioned in Table 1 were applied, which resulted in excluding 81 sources. Besides, the eligibility stage needed to examine the remaining articles, excluding 37 articles. Lastly, in the inclusion stage, the remaining 81 papers were explored more in-depth, eliminating more than 37 articles. Consequently, for the paper to explore the impact of user-generated content on customer purchase intentions of online shoppers, the author selected 44 articles. Furthermore, stage three in Table 1 involved validating systematic literature review results. The author revisited selected articles and ensured reliability. Besides main themes, ten various factors were detected based on the coding process. The whole process was carried out by the researcher independently.

4 Research Findings and Results The author examined available literature through a systematic review addressing the research question. Therefore, the analysis identified ten main factors impacting the purchase intentions of online shoppers through user-generated content. A quantitative research method was used; hence data was collected using Questionpro. Within one year, 175 respondents completed the questionnaire with thirteen Likert-scale and drop-down type questions within one year. 21.6% of respondents represented online shop owners, with 52.5% being online shoppers. Based on age distribution, the majority of respondents were Gen-Z and Millennials.

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Moreover, geographically seven different countries were identified. Furthermore, the majority of respondents were employed in the marketing field. Lastly, an important discovery was that most online business owners had the highest degree in Business and Marketing with 37.7%. The first question asked was to explore the frequency respondents ordered online. Consequently, 16% of respondent’s indicated that they order online “more than once a week”, 26.9% “once per week”, 25.1% “once per month”, 22.3% “3–5 times per month” and 9.7% “once in a year”. On the other hand, none of the respondents indicated that they do not shop online. For the trustworthiness of the research, the sample was aware of online shopping practices, providing added value to continuing research. Next, respondents were asked four different questions to study the relevance of usergenerated content on customer purchase intentions. Respondents had to rank statements on a scale of 1 to 5, with 1-being the lowest score and referring to “not at all” and 5-being the highest score, referring to “extremely.” Fig. 1 illustrates the outcomes. First, respondents elaborated on how authentic they feel is user-generated content. Results indicate that respondents were divided in their responses; however, there was a slight advantage towards the authenticity of UGC with more than 42.9%.

Fig. 1. The relevance of user-generated content (UGC).

The second question followed was how much they trust user-generated content. Most respondents trust UGC with 52%; however, around 29.1% not at all, while 18.9% indicated “somewhat.” These results show the division amongst the respondents; however, it opens the gaps to continue the research. Furthermore, the next question was how satisfied they with user-generated content were. In this case, 27.4% chose to be neutral and indicated “somewhat,” while 25.7% indicated “slightly” and “not at all.” Lastly, the vast majority indicated that they trust UGC. In practice, trustworthiness with authenticity is the top brand attribute that customers expect. However, it needs to be noted that public trust in brands is declining, and advertising specialists suffer when analyzing rating data. Consequently, the last question in this set was whether UGC positively supports brand awareness. Nevertheless, again, a slight positive incline toward the “extremely” relevance with 48.6%; however, respondents have been reluctant to make a solid choice, with 21.1%

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indicating “somewhat.” Therefore, before planning a UGC strategy, marketers must study the essential components of a brand, such as personalization, content, headlines, social sharing links, etc. To conclude, target group attitudes towards user-generated content were highly divided. While results showed that UGC positively supports brand awareness, its effectiveness was unclear. Hence, the author concluded that the impact of user-generated content on the purchase intentions of online users has a positive impact. However, more research on the target audience is required to clarify the relationship between UGC and customer purchase intentions. Next, during the coding process, ten main factors such as customer opinions, product reviews, product price, product characteristics, the usability of the online shops, online and offline word of mouth, trust in online stores, and product popularity, were detected that influencing purchase intentions of the online shopper. The analyzed results are highlighted in Fig. 2. Respondents had to rank factors to which extent they believe they influenced online purchase decisions on a scale of 1 to 5, with 1-being the lowest score and referring to “practically never,” and 5-being the highest score, referring to “Practically always.” First factor was customer opinions from open sources. UGC created from open sources is tricky, as marketers do not always know the reliability of the sources. 32.6% of respondents indicated that it practically always influences online purchase decisions; in contrast, 19.4% of respondents underlined that it once in a while. Similarly, 22.9% of respondents indicated that it sometimes does. Compared to customer opinions generated from trusted sources, 34.9% of respondents indicated it influences online purchase decisions once in a while, versus 24% indicating frequently. Lastly, comparing open and trusted sources, it can be concluded that trusted sources have a slight advantage. The third factor was product reviews. 30.3% of respondents indicated that product review frequently impacts online purchase decisions. Additionally, 20% underlined that it practically always influenced. In contrast, 18.9% indicated that it practically never influences. The results show that respondents are divided into several decision-making groups. Compared with the fourth factor, product price, 21.1% indicated that it practically never impacts online purchase decisions, while 29.7% indicated it practically always influences. Lastly, we see a slight advantage toward a positive incline in both factors. The fifth factor was product characteristics. In many cases, businesses might assume that product characteristics might not be as critical as it sounds; however, in the digital world, with so many alternative products, every character makes a difference. 28.6% of respondents indicated that it practically always influences online purchase decisions. On the other hand, 25.1% of respondents underlined that it influences once in a while. Lastly, 22.3% also highlighted that it frequently influences online shoppers’ purchase decisions. The sixth factor was the usability of the online shop. Customers will be more likely to engage with a campaign if it contains user-generated content, pushing traffic towards the online shops. Many customers have difficulty making decisions; they will hold off until they do more research; therefore, the user-friendliness of the shops is critical. 26.3% of respondents indicated that the usability of a shop frequently influences purchase decisions compared to 16.6% underlining practically never. 18.3% stayed neutral, arguing that it sometimes influences.

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The seventh and eighth factors were an online and offline word of mouth. As mentioned earlier, it is needed to think of user-generated content as modern-day word of mouth. Whether online or offline, online businesses still leave track visible to online shoppers. Therefore, online shoppers more or less like to buy or not; for some, it does not matter. In addition, customers tend to trust product recommendations from friends, family, or colleagues. Moreover, online recommendations are highly influential on purchase decisions. Lastly, an online shopper will pay an extra dollar if it is UGC rather than brand-generated content. 26.3% of respondents indicated that online WOM influences purchasing decisions, 13.1% indicated once in a while, and 9.1% indicated practically never. On the other hand, 32.6% indicated that offline WOM influences online purchase decisions, while 16% indicated practically never. To conclude, online and offline WOM is critical, and marketing practitioners must take them seriously.

Fig. 2. Factors influencing online purchase decision.

Customers believe and trust organic content compared to any other form of content. Therefore, businesses create high credibility to generate authenticity and integrity from and to online customers. The ninth factor identified was trust in online stores. 33.1% of respondents indicated that it practically always influences customers’ online purchase decisions. However, 14.9% believed that it practically never does. Moreover, 18.9% chose to be neutral. This factor is directly related to online and offline WOM factors and leads into factor ten, product popularity. Respondents were torn when deciding whether product popularity influences customers’ purchase decisions. To conclude, yet again, while results showed that identified ten factors positively influence online purchase decisions, its effectiveness was unclear. Hence, the author concluded that the impact of UGC content on the purchase intentions of online users has a positive impact. However, more research on the target audience is required to clarify the relationship between UGC and customer purchase intentions.

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5 Conclusion This paper aimed to explore the relevance of user-generated content on the purchase intentions of online shoppers and identify the main factors influencing online purchase decisions. A systematic literature review and survey clarified and explained the research problem. A systematic literature review identified ten main factors critical to building the paper and developing research on the relevance of user-generated impact. Online shopping is widely used and becoming an increasingly popular means of individuals’ everyday life. Moreover, online purchase decisions are a complex process influenced by many factors. Today, online shoppers have many ways to compare products, and various user-generated contents influence their features and authenticity. This paper showed that people shop online and have various means to make final purchase decisions. Moreover, it showed that user-generated content is preferred over brand-generated as it builds more trust and authenticity. Furthermore, it was found that user-generated content supports brand awareness; however, it was not clear how effective it was. Furthermore, online, and offline word of mouth and product prices were the most important factors influencing the purchase decisions. However, it must be noted that other factors were not far behind. The least important factor was product popularity. Therefore, target group attitudes towards user-generated content and identified factors were split. While results showed that UGC positively supports brand awareness, its effectiveness was unclear. Moreover, identified factors showed positive relationships toward online purchase decisions but still challenging to identify the best solution. Hence, the author concluded that the impact of UGC content on the purchase intentions of online users has a positive impact. However, more research on the target audience must clarify the relationship between user-generated content and customer purchase intentions. In addition, it is essential to continue studying how buyers’ behaviors are influenced after a post-covid environment. Limitations. The author acknowledged that one of the main limitations of this paper is the time and method when the survey was conducted. It would have been beneficial to conduct semi-structured interviews with the online shop owner to receive more in-depth data and support survey insights. Therefore, future research is suggested to explore more usage and benefits of user-generated content on online purchase decisions.

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Internet Retailers’ Valuation: Why Intangible Assets Matters and How to Assess Them Neli Abramishvili1(B) , Anthony Herman2 , Nadezhda Lvova3 , Nino Pailodze4 , and Ekaterina Yanshina2 1 National Research University Higher School of Economics, HSE University – St. Petersburg,

16 Soyuza Pechatnikov St., St. Petersburg, Russia [email protected] 2 National Research University Higher School of Economics, 20 Myasnitskaya St., Moscow, Russia {esgerman,eayanshina_1}@edu.hse.ru 3 Saint Petersburg State University, 7/9 Universitetskaya Emb., St. Petersburg, Russia [email protected] 4 Georgian Technical University, 77 Kostava Str., Tbilisi, Georgia [email protected]

Abstract. The paper considers the role of intangible assets in Internet retail companies’ evaluation. The research was aimed to reveal the relevance and determine the methods of intangible assets assessment in the context of Internet retailers’ valuation. The research methodology involved comparative analysis of approaches to valuation of intangible asset; assessment of the relevance of intangible assets from the perspective of strategic business advantages; testing a series of hypotheses including the following: 1) the share of intangible assets in the total asset structure of Internet retail companies significantly and positively affects the return on equity (ROE); 2) different approaches to accounting for intangible assets of Internet retail companies create a significant difference in assessing their value; 3) the share of intangible assets in the total asset structure of Internet retail companies has a significant and positive effect on capitalization. The hypotheses were tested using an econometric model based on panel data as well as using event analysis. The results of testing the hypotheses confirmed the assumptions about the positive impact of intangible assets on the ROE and capitalization of online retailers while rejecting the hypothesis regarding the difference in approaches to accounting for intangible assets. The proven impact on ROE and capitalization suggests that the presence of intangible assets on a company’s balance sheet contributes to improving its efficiency and directly affects the increase in its value. Keywords: Digital economy · Intangible assets · Internet retails companies · Business valuation · Capitalization

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 450–461, 2023. https://doi.org/10.1007/978-3-031-26655-3_41

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1 Introduction In the context of the Fourth Industrial Revolution, which continues seizing new fields of influence such as software, digital technologies and technological information is significantly increasing [1]. However, the contribution of the digital economy to GDP remains debated and underestimated. For example, in the United States, according to the Bureau of Economic Analysis, it has contributed from 4.5 to 5% of the GDP since the inception of the internet 40 years ago [2]. China has positioned itself as the leader in this regard, being the jurisdiction for such digital giants as Alibaba and Tencent. According to 2019 data provided by the China Internet Network Information Center, the share of the digital economy in China’s GDP was estimated at 34.8% [3]. However, transparent measurement of the digital economy impact remains a challenge. Notably, the contribution of the digital economy to the economic development is associated with at least three groups of issues: • From a legal perspective, it is necessary to analyze how intellectual property rights are protected [3, 4]. This in turn addresses a wide range of regulatory issues in the context of globalization, digital and financial access, stability, risk management, etc. [5]. • From an economic perspective, it should be determined what sectors and institutional units constitutes the digital economy. The starting point for its measurement is usually considered to be the information and communication technologies sector (ICTs), which is analyzed from the perspective of the main suppliers and consumers of digital services as well as the supporting infrastructure [6]. • As for the financial perspective, the major issues related to the digital economy concerns valuation of intangible assets, including their contribution to the company’s value. Nowadays, intangible assets have become one of the key elements of corporate intellectual capital. This type of intellectual property is a significant factor in ensuring competitiveness and continues to enable business development (for literature review see: [7, 8]). For knowledge-intensive and high-tech companies, intangible assets are of systemforming importance, which primarily concerns ICTs. This determined the choice of the research object – Internet retailers. Therefore, the research was aimed at determining methods for assessing intangible assets in terms of Internet retailers’ valuation. To this aim, the channels of intangible assets’ influence on the company’s value were suggested, and the relationship between intangible assets and performance indicators of high-tech Internet retailers was determined.

2 Literature Review The positive impact of intangible assets on corporate financial characteristics and business value is confirmed in a large body of literature [7, 9, 10]. However, not all intangible assets are equal in creating value. Let us compare the results of some empirical studies:

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• Glova and Mrazkova [8] studied the data of 304 European public companies from 2011 to 2015 and demonstrated that only R&D expenses are a significant factor in increasing the value, while the accumulation of total intangible assets does not lead to the expected positive effect. Thus, the authors conclude that the market rewards active strategies for managing intangible assets, ignoring the passive purchase of someone else’s intellectual property. • Ionita and Dinu [11] came to another conclusions. Having examined data of 42 public companies in Romania from 2016 to 2019, they claim that some of the intangible assets characterizing the company’s innovative competencies have a significant negative impact on their value. This applies in particular to R&D and patents. However, software has a positive impact on the company’s value as well as intangible assets reflecting economic competencies (for example, brands). • The conclusions of Ionita and Dinu [11] are implicitly confirmed in Cheglov et al. [12], in which the authors found that Russian retailers mainly rely on software and databases in increasing the volume of intangible assets. This leads us to the assumption that research in the impact of intangible assets on the financial profile of a company should not be limited to markets of the same development level. What it should be based upon is data of developed and emerging markets, which can render different results. This determines the methodology of the study which the data of American, Russian and Chinese companies were analyzed. This assumption is confirmed by the results of a study by Cecilia et al. [13], which states that identifiable intangible assets, human capital and structural capital have a significant impact on the value of the company. However, the authors emphasize that the role of intangible assets in the formation of the value of companies remains underestimated in emerging markets. At the same time, it is necessary to consider the industry specifics of the analyzed companies. In particular, Cosmulese et al. [14] analyzed the data of 180 companies listed on the NASDAQ and NYSE exchanges from 2007 to 2016. The results obtained proved a different level of influence in the value of registered and unaccounted intangible assets (strategies, product innovation, customer loyalty, future profits, goodwill, etc.) on the market value of manufacturing and service companies. Thus, the choice of the research target (Internet retailers) for testing the proposed hypotheses seems justified. In other words, in assessing the impact of intangible assets on the financial profile of a business, it is important to consider the economic sector of the company.

3 Methodology of the Research E-commerce is one of the main drivers of trade growth due to both external factors in the form of a pandemic, and internal factors related to the current trend of saving people time on offline purchases. And since the intangible assets are one of the main assets, this means that they should directly influence their performance indicators. Based on the studied materials, as well as a detailed analysis of the above scientific publications, the following hypotheses about the role of intangible assets in evaluating online retail companies is formulated:

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Hypothesis 1: The share of intangible assets in the total asset of Internet retail companies significantly and positively affects the return on equity (ROE). Hypothesis 2: Different approaches to intangible assets accounting in Russian and foreign Internet retail companies create a significant difference in their valuation. Hypothesis 3: The share of intangible assets in the total asset of Internet retail companies has a significant and positive effect on capitalization. To test the first hypothesis an econometric model was built. The data base consists of 66 of the largest public companies by capitalization from the Internet retail segment, as well as 90 of the largest public companies from the Technology segment. Financial reports for these companies were reviewed from 2018 to 2021. In the cases when the data were incomplete, the observation was excluded. In total, 311 observations were collected. Based on this data the following variables were calculated and then used in econometric model (Table 1). Table 1. Variables used in econometric model. Variable

Abbreviation

Comment

Constant

const

Column of units for calculating the free regression coefficient

Share of intangible assets in the total assets

Intangible_s

Logarithm of the book value of intangible assets to the total value of assets ratio

Company’s size

Size

Logarithm of book value of assets

Asset’s turnover

Turnover

The revenue to the book value of assets ratio

Share of intangible assets for Internet retail companies

Intangible_s_ecom

The variable is equal to Intangible_s, in the case when the company belongs to Internet-retail, 0 otherwise

Net income

Net_income

Net income (Profit and losses statement)

Share of intangible assets for Russian companies

Intangible_s_Russia

The variable is equal to Intangible_s, if the company is Russian, 0 otherwise

Q Ratio or Tobin’s Q

Tobin_q

The market value of a company divided by its assets’ replacement cost

Gross profit

Gross profit

Revenue minus cost of goods sold

Source: created by the author using [15]

Thus, the econometric model has the following form. yit = xit ∗ β + αi + it ,

(1)

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where yit – ROE logarithm for company i in the year t; xit – independent variables for each company i in the year t, affecting ROE; αi – individual effect of each company, unchanged over time; it – random component, that changes over time. 3 specifications of this model can be considered: 1. Pooled OLS. 2. Fixed-Effects Model (FE). 3. Random-Effects Model (RE). The choice between models (1) and (2) or (3) depends upon the assumptions of the Gauss-Markov theorem. In the case where the assumptions of homoscedasticity and the absence of autocorrelation are violated in model (1), models (2) or (3) are used. Furthermore, in case of violation of the premise of endogeneity, model (3) should be chosen, otherwise (2). To test the second hypothesis, consider the coefficient for the variable Intangible_s_Russia. It was assumed that due to the difference in accounting for intangible assets, it would negatively affect ROE in the case when the under-study company is Russian. To verify the validity of the third hypothesis about the positive impact of the share of intangible assets on the capitalization of the company, another research method (event analysis) was applied. The purpose of event analysis is to assess the deviation a security return during a particular event [16]. As part of this study, event analysis will determine the impact of various news on the capitalization of companies in the Internet retail industry. To identify whether there is an asymmetry of information between market players, the author selected the 3 largest online retailers from different countries: the American company Amazon, the Chinese giant Alibaba, and the Russian e-commerce company Ozon. The year 2021 was chosen due to the period of a relatively stable market that has recovered from the pandemic. Next, daily stock quotes were collected. Daily return (logarithmic) is determined by the formula [17]:   pt , (2) Rt = ln pt−1 where pt – price (close) for share on day t. After that, it is necessary to determine the abnormal return. The abnormal return on day t is calculated by the following formula [15]: ARt = Rt − Rt ,

(3)

_

where Rt – share’s normal return on day t. _

Thus, the excess of real return over the statistical estimate of normal return will be called abnormal return. Normal return can be obtained using the CAPM model, which assumes a linear relationship between the market return and an expected return of a particular stock [17].

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4 Research Results 4.1 The Impact of Intangible Assets on ROE According to the proposed methodology, a pooled OLS model was built to reveal the impact of intangible assets on ROE. The results are in Fig. 1.

Fig. 1. Results for Pooled OLS model.

The premise of homoscedasticity can be demonstrated using the Breusch-Pagan test. The main hypothesis of this test is that the variance of the residuals is not constant. Having carried out this static test and having received a critical value close to zero, it can be argued that there is heteroscedasticity in the model. Therefore, it is necessary to move to the FE or RE model. The endogeneity of factors was also compared using the Hausman test, which allows you to compare the two models and evaluate their endogeneity differences. As a result, a critical value close to zero was obtained. Thus, the covariance of x_it and α_i is close to zero and a fixed effects model should be considered. The results of the model are shown in Fig. 2 and 3.

Fig. 2. Results for FE OLS model.

Moving on to the assessment of the model’s quality. After checking the normality of the residuals of the model using the Kolmogorov-Smirnov test, a high critical value of 0.951 was found. Therefore, it can be argued that the distribution of the residuals in

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Fig. 3. Results for RE OLS model.

the model is close to normal. Multicollinearity was also tested using the VIF (Variance inflation factor). For each variable tested, a value below 10 was returned, which means no multicollinearity. The critical value of the F-statistic is close to zero, which allows us to conclude that the model is consistent (not all coefficients are equal to zero). The Model’s determination coefficient is 0.61, which explains more than half of the variance in the ROE. Finally, the direct verification of the proposed hypotheses was considered. The coefficient for the variable associated with the share of intangible assets in the total assets (Intangible_S) was 10. This means that the higher the share of intangible assets in the asset structure leads to a higher ROE. It is possible to test the hypothesis to check that the estimate is consistent and greater than zero (the dependence is indeed positive). If applied to a one-tailed Student’s t-test (assuming a positive sign of the coefficient) to the estimate of the corresponding Intangible variable, the resulting calculated statistic equal to 2.0857 is greater than the critical statistic (calculated for a significance level of 0.05) equal to 1.64. Therefore, a significance level of 0.05 intangible assets in the asset structure have a positive effect on profitability indicators. This means that hypothesis 1 is not rejected, and its reflection can be seen in the scatterplot (Fig. 4).

Fig. 4. Dependence of ROE on the intangible assets to the total assets ratio.

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4.2 The Relevance of Intangible Assets Accounting Different approaches to intangible assets accounting in Russian and foreign Internet retail companies create a significant difference in their valuation. It was assumed that due to the difference in accounting for intangible assets, it would negatively affect ROE in the case when the company under study is Russian. For a significance level of 0.05, the statistical confidence interval falls between (–55) and 49. Due to the large dispersion of the factor, the coefficient is statistically insignificant. This means that it is impossible not to reject this hypothesis for Russian issuers. On the one hand, shares of selected Russian issuers are traded on various international stock exchanges, and it is in the interests of managers of high-tech companies to disclose reliable reporting information. On the other hand, this conclusion remains vague as only 12 cases of Russian companies were included in the final sample. 4.3 The Impact of Intangible Assets on Market Capitalization A detailed presentation of the event analysis will be carried out only for Ozon. However, this algorithm has also been applied to Amazon and Alibaba. The Moscow Exchange of Innovations Index (MOEXINN) was chosen as the market return for Ozon, assuming that the average return for Ozon shares could be compared to the riskier segments of the market. The evaluation results are shown in Fig. 5.

Fig. 5. The dependence of the average return of Ozon shares on the return of the MSE.

Using this model, Ozon’s normal return was calculated, as well as abnormal return. The interval [t − 2, t + 7] was taken as the event window for this study, up to two days before the announcement of the news and 7 days after. It should be noted that in this case days are meant trading days. Furthermore, it is necessary to test the hypothesis which returns an average abnormal that slightly deviates from zero in a given event window. To test such a hypothesis, Student’s t-test with a significance level of 0.05 can be used. Below are the simulation results (Table 2). The event analysis partially confirms the hypothesis that investments in intangible assets have a positive effect on the company’s value. If the market reacts positively to news involving intangible assets for Ozon or Amazon, the market typically reacts negatively towards the same for Alibaba Group. This can be explained as follows:

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Company

Ozon

Amazon

Alibaba

Market return

MOEXINN

NASDAQ

HANG SENG

Coefficient of determination for CAPM

0.29

0.56

0.24

News

“Ozon will launch an online cinema. Experts estimated the project at $100 million.”

“According to Bloomberg, Amazon acquired Metro-Goldwyn-Mayer for $8.5 billion”

“Alibaba has invested $2 billion in online retailer Lazada”

Date

24.09.2021

18.03.2022

20.03.2018

Cumulative return

4.2%

7.4%

-1.5%

Result

Hypothesis is rejected

Hypothesis is rejected

Hypothesis is accepted

P-value for t-test

0.13

0.19

0.022

Source: https://www.forbes.ru/biznes/440969-ozon-rassmotrit-varianty-zapuska-onlajn-kinote atra, https://www.forbes.ru/forbeslife/459419-kompania-amazon-kupila-kinostudiu-mgm-za-845-mlrd, https://retailer.ru/alibaba-vlozhila-2-mlrd-v-onlajn-ritejlera-lazada

1. The market is not perfect, there is information asymmetry or some other inconsistencies, such as an incorrectly selected index. 2. Naturally, the investor evaluates not only investing in intangible assets, but the project in general. 3. The effect of news publication may be crowded out by some other negative news that has a stronger impact on investors.

5 Discussion The results contribute to the discussion of the intangible assets’ impact on the financial characteristics and value of a business. Conclusions about the positive impact of intangible assets on market capitalization confirm many previously obtained results including Nagaraja and Vinay [7] as well as Mohanlingam et al. [9]. The significance of intangible assets as a factor in the market capitalization reflects the trends of digital expansion [1, 5, 6] and simultaneously relates to the financialization processes [18]. With regards to other financial characteristics, the impact of intangible assets is for so definite. In contrast to the conclusions of Gamayuni [10], which relied on the ROE indicator, this research demonstrated that the impact of intangible assets on profitability measured by ROE is not significantly positive. This conclusion should be taken into account when creating abnormal profitability of a company in the context of intangible asset management [19]. The hypothesis of the significance of these accounting aspects has not been fully confirmed. However, the revealed differences correlate with ambiguous results of research

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on intangible assets, which were based on data from different markets [8, 11, 12, 20]. This fact confirms the conclusions of Cecilia et al. [13] and Lvova et al. [21] on the significant specifics of emerging markets, which should be considered when assessing the business value.

6 Conclusion With the help of the constructed econometric model, the proposed hypotheses were tested. Two were confirmed while one was rejected. The following details each of the findings. Hypothesis 1: The share of intangible assets in the total asset of Internet retail companies significantly and positively affects the return on equity (ROE). This hypothesis was confirmed by the model. Therefore, Internet retail companies that have a large share of intangible assets and continue to increase investing in various types of research and development whilst increasing ROE, thereby ensuring greater efficiency of the investor’s capital invested in the company. This is especially important for IT companies that are actively raising money and becoming more attractive for potential investors. By receiving more investments, the company can afford to hire additional qualified personnel, create new technologies and buy advanced equipment, which means that in the future it will be able to further increase intangible assets and, consequently, increase ROE. Thus, increasing the IP of the company will allow it to expand faster. Hypothesis 2: Different approaches to intangible assets accounting in Russian and foreign Internet retail companies create a significant difference in their valuation. The second hypothesis was rejected, but with some caveats. Since in Russian and other foreign markets there are different approaches to attributing intangible assets to the balance sheet, it was assumed that this difference would lead to significant discrepancies in their comparison. However, the test showed the opposite result. An important note is that the sample included only 12 Russian companies whose shares are listed on the stock exchange, and therefore it may not be entirely relevant. But there is another explanation that confirms the refutation of the hypothesis. Despite the fact that intangible assets do not yet have a unified system for their evaluation, however, work in this direction is being actively carried out, which means that medium and large companies follow the recommended standards that allow obtaining similar results. Hypothesis 3: The share of intangible assets in the total asset of Internet retail companies has a significant and positive effect on capitalization. The last of the considered hypotheses was also confirmed. Indeed, the presence on the company’s balance sheet of an Internet retailer intangible assets has a direct relationship with its capitalization. This was verified through event analysis. Capitalization is one of the main indicators of investment attractiveness for a company and increases its competitiveness in the market.

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Acknowledgements. This research [grant number SP-2-21-162] has been supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG).

References 1. Schwab, K.: The fourth industrial revolution: what it means, how to respond. https://www. weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-torespond/ (2016) 2. Brynjolfsson, E., Collis, A.: How should we measure the digital economy? Harv. Bus. Rev. 97(6), 140–148 (2019) 3. Chen, W., Wu, Y.: Does intellectual property protection stimulate digital economy development? J. Appl. Econ. 25(1), 723–730 (2022). https://doi.org/10.1080/15140326.2022.204 5846 4. Alimov, A.: Intellectual property rights reform and the cost of corporate debt. J. Int. Money Finance 91, 195–211 (2019) 5. Kirton, J.J., Warren, B.: G20 governance of digitalization. Int. Organ. Res. J. 13(2), 16–41 (2018). https://doi.org/10.17323/1996-7845-2018-02-02 6. Mueller, S.C., Bakhirev, A., Böhm, M., Schröer, M., Krcmar, H., Welpe, I.M.: Measuring and mapping the emergence of the digital economy: a comparison of the market capitalization in selected countries. Digit. Policy Regul. Gov. 19(5), 367–382 (2017) 7. Nagaraja, N., Vinay, N.: The effect of intangible assets on the firm value. Int. J. Eng. Manag. Res. 6(1), 307–315 (2016) 8. Glova, J., Mrazkova, S.: Impact of intangibles on firm value: empirical evidence from European public companies. Ekonomický c˘ asopis 66(7), 665–680 (2018) 9. Mohanlingam, S., Nguyen, L., Mom, R.: The effects of intangible assets on financial performance and financial policies of listed technology firms in Thailand. Apheit Int. J. 10(1), 1–17 (2021) 10. Gamayuni, R.R.: The effect of intangible asset, financial performance and financial policies on the firm value. Int. J. Sci. Technol. Res. 4(1), 202–212 (2015) 11. Ionita, C., Dinu, E.: The effect of intangible assets on sustainable growth and firm value – evidence on intellectual capital investment in companies listed on Bucharest Stock Exchange. Kybernetes 50(10), 2823–2849 (2021). https://doi.org/10.1108/K-05-2020-0325 12. Cheglov, V.P., Panasenko, S.V., Shishkin, A.V., Krasil’nikova, E.A., Maslova, A.E.: Intangible assets of a trade organization in the context of digital transformation. Webology 18, 1170–1186 (2021). https://doi.org/10.14704/WEB/V18SI04/WEB18190 13. Cecilia, F., Werbin, E., Díaz, M., María, P.-M.: Relevancia de los intangibles para la valoración de las acciones de las empresas en el mercado: evidencias desde el contexto argentino. Contaduría y Administración 66(3), 1–26 (2021). https://doi.org/10.22201/fca.24488410e. 2021.2558 14. Cosmulese, C.G., Socoliuc, M., Ciubotariu, M.-S., Grosu, V., Mate¸s, D.: Empirical study on the impact of evaluation of intangible assets on the market value of the listed companies. Econ. Manag. 24(1), 84–101 (2020). https://doi.org/10.15240/tul/001/2021-1-006 15. Brealey, R., Myers, S., Allen, F.: Principles of Corporate Finance, 13th edn. McGraw-Hill Education, 992 p. (2019) 16. Frunza, M.C.: Event Study Solving Modern Crime in Financial Markets Analytics and Case Studies. Academic Press, 329 p. (2015) 17. Binder, J.: The event study methodology since 1969. Rev. Quant. Finance Account. 11, 111– 137 (1998). https://doi.org/10.1023/A:1008295500105

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18. Saksonova, S., Kuzmina-Merlino, I.: The Principles of Creating a Balanced Investment Portfolio for Cryptocurrencies. In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds.) RelStat 2018. LNNS, vol. 68, pp. 714–724. Springer, Cham (2019). https://doi.org/10.1007/ 978-3-030-12450-2_68 19. Lvova, N., Pokrovskaia, N., Abramishvili, N., Ivanov, V.: Developing methodology of monitoring companies’ financial stability: abnormal profitability evaluation. In: Proceedings of the 28th International Business Information Management Association Conference – Vision 2020: Innovation Management, Development Sustainability, and Competitive Economic Growth, pp. 681–688 (2016) 20. Katalkina, O., Saksonova, S.: Crowdfunding cross-border business financing practice: the evidence from the baltic states. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) Reliability and Statistics in Transportation and Communication (RelStat 2021). LNNS, pp. 472–481. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96196-1_43 21. Lvova, N.A., Abramishvili, N.R., Darushin, I.A., Voronova, N.S.: The challenges of public companies’ assessment and diagnostics on the emerging market. In: Proceedings of the 32nd International Business Information Management Association Conference, IBIMA 2018 – Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional expansion to Global Growth, pp. 7499–7510 (2018)

Sustaining Well-Being of Teachers in Higher Education Ioseb Gabelaia1(B)

and Ramune Bagociunaite2

1 Graceland University, Lamoni, IA 50140, USA

[email protected] 2 Kaunas University of Applied Sciences, Kaunas, Lithuania

[email protected]

Abstract. Given the growing burnout of teachers during the pandemic, it is necessary to recommend that higher education institutions apply careful planning in developing teacher well-being programs. Therefore, the main objective of this paper is to investigate the well-being of teachers in higher education to reveal the well-being tendencies. Sustaining and developing teacher well-being programs represents a high priority for higher education institutions. This paper first conducted a systematic literature review to identify and critically appraise available secondary data. Second, a short survey was conducted to explain teachers’ responses to identified psychological factors from the systematic literature review. Moreover, the authors interviewed several teachers to collect more evidence. Lastly, the authors synthesized all information to present conclusions and recommendations. The findings of this paper illustrated significant factors in relation to the well-being of teachers and revealed the well-being tendencies. Moreover, it outlined a foundation for further research to investigate sustaining teachers’ well-being. This paper is the introductory paper of the research, which has been in progress for the last two years. The current paper offers much-needed evidence to the researchers and not only to enhance or propose or even create one universal model for teachers’ well-being. Keywords: Higher education · Well-being · Sustainability · Teachers · Covid-19

1 Introduction Today, there are several crucial concerns associated with the well-being of teachers. First, as a result of Covid-19 and technological advances, higher education stands on the edge of increased burnout of teachers. Teachers’ psychological well-being is a sole deed in shielding teachers’ mental health and offering an environment to flourish on professional and personal levels. The development of pioneering models within higher education aims to enhance more flexible working conditions for teachers by the goals and objectives set in a higher education institution’s strategic plan. Considering this, this paper explores the relationship between teachers’ devotion and psychological well-being in the workplace © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 462–472, 2023. https://doi.org/10.1007/978-3-031-26655-3_42

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while identifying the sense of proficiency, perceived acknowledgment, and aspiration for engagement and job satisfaction for teachers’ well-being. The following objectives were formulated: Firstly, to explore the psychological wellbeing of teachers in the workplace, second, to identify whether teacher well-being must be a priority for higher education, and lastly, to present an agenda for implementing teacher well-being programs. Given the increasing burnout of teachers, it is reasonable to suggest that in developing teacher well-being programs, careful planning should be applied. Hence, the authors used a three-step process to organize this paper structurally. Accordingly, primary data analyses were done on existing literature; therefore, the systematic analysis discovered several patterns. Moreover, the authors were able to identify several critical physiological factors for teachers’ well-being in the workplace. Further, the authors conducted a separate survey where teachers reflected on physiological factors related to job satisfaction. And lastly, the authors gained some more valuable information from semi-structured interviews. In this paper, the research results suggest how the higher education institutions may use the empirical findings to design an effective and sustainable program for teachers’ well-being. Positive psychology in the workplace and the need for professional support enhances the focus, sustaining the psychological capital for well-being.

2 Literature Review Modern higher education systems are faced with a framework of growing teacher shortages, frequent turnover, and a low attractiveness of the profession [1]. Therefore, it leads to the urgent need to acknowledge the well-being of teachers better and sustain quality frameworks for mental and cognitive well-being. Additionally, higher education institutions’ quality rests on their teachers’ quality, yet if the well-being of teachers is deteriorating, what does this mean about the quality of the higher education institution? According to [2, 3], a high level of well-being is associated with high teachers’ productivity, engagement, and presenteeism. Globally, teachers have the highest work-related stress and burnout levels compared to other professions [2]. However, understanding well-being and improving efforts is not straightforward [4]. Thus, interpretations of well-being vary not only intellectually but also cross-culturally. [5, 6] highlighted that faculty members constitute the core asset of every academic institution, taking on primary tasks of teaching, research, and service activities that contribute to university business. Besides, for faculty members as central players in the sustainability and growth of institutions, job satisfaction, and job performance are directly linked with teachers’ well-being. Moreover, [7] attested that job satisfaction is one key index of well-being in the work domain. On the contrary, [8] stated that the psychological needs of the teachers need to be addressed in any developed models. Lastly, [9] highlighted that sustainable well-being is addressed in seven principles of sustainable leadership and improvement that bring value to the current uncertain ecosystem in higher education. According to [10], teacher well-being is a critical factor affecting job performance and is still essential for supplementing quality teaching, [11, 12] argued that teachers’

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self-monitoring and self-efficacy on the relationship between emotional job demands of teaching and trust in colleagues impacts overall job performance. For example, [13] claimed that in Europe, well-being in the workplace had increased prominence in the policy and research agenda, and education is a critical context in which the challenge of occupational stress has been reported. Job satisfaction comprises psychological, physiological, and environmental conditions and factors guaranteeing positive feelings toward the work [14]. Satisfied teachers show an increased rate of productivity and a sense of well-being. Additionally, [15] recognized that emotional intelligence had become a critical factor in educational environments that facilitate teachers’ mental well-being. According to [16], teachers’ well-being is also influenced by direct and continuous relationships with students, which were backed by [17, 18], elaborating that the teacher well-being affects students’ well-being accordingly. Further, [19] argued that constant changes in social structure and political systems also affect the well-being of teachers. Additionally, not to forget that due to the Covid-19 pandemic, teachers globally have faced numerous and continuous changes in teaching structures. This fact has also contributed to teachers’ personal and professional well-being. Further, [20] stated that shifting all instructions online and the emergence of remote teaching negatively impacted learning, engagement, and mental well-being. The spread of Covid-19 has caused fear, anxiety, and other concerns in various parts of the world. Therefore, in determining what makes teachers most effective, teacher well-being has emerged as an essential component [21]. According to [22], it is essential to think of strategies that support teachers with long careers, transforming the profession into healthy and free burnout activity. Furthermore, [3, 23, 24] suggested that creating a supportive and cooperative environment for university teaching is a precondition for improving teachers’ well-being, hence facilitating the development of sustainable well-being programs.

3 Materials and Methods To address the research question and to explore the well-being of teachers in higher education by considering the relationship between teachers’ devotion and psychological well-being in the workplace, the authors applied a three-step approach, first, by analyzing available literature, second by utilizing a survey based on a literature review, and lastly, conducting semi-structured interviews. 3.1 Systematic Literature Review A systematic literature review, as defined by [25], was structured to identify the knowledge gap and create a research ground. Moreover, [26] argued that a systematic review is a means of evaluating and interpreting all available research relevant to the research question or theme of interest. The purpose of the systematic literature review was to summarize evidence and trends for the further development of this research topic. According to [25], scientific inquiries and literature reviews should be valid, reliable, and repeatable.

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Table 1 emphasizes the approach in the systematic literature review. The review consisted of three stages. The first stage involved setting research objectives moreover identifying key research themes, factors, and challenges for future research. It is a crucial part, as [25] stated, in the planning stage, researchers usually lack rigorous systematic reviews because they do not have sufficient guidance on how to conduct an adequate review. Table 1. Systematic literature review (developed by the authors).

The second stage involved setting inclusion and exclusion criteria in the literature review. This section is essential as it allows the authors to convert available data into insights, therefore, further developing the research. First, inclusion criteria were decided, such as searching boundaries, keywords, and publication dates. Databases utilized were Scopus, WoS, and Google. Well-being, teacher, Covid-19, and sustainable education were the keywords used. Scientific journals, articles, and reports after 2019, including 2022, were examined. Second, all possible combinations between identified keywords were used to filter articles in three selected databases.

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The authors selected only English-language articles with an open start date of 2019. First, using all the combinations of search items, a total of 567 relevant papers we found without removing duplicates. Table 2. Application of exclusion criteria (developed by the authors).

Further, the authors applied exclusion criteria to filter available literature. Table 2 illustrates the process of using exclusion criteria. There are four main stages: identification, screening, eligibility, and inclusion. The identification stage exemplifies sources from where literature materials were collected. The screening stage filtered duplicates, therefore, excluded 132 sources. Further, in the eligibility stage, three main exclusion conditions, such as articles that mentioned teacher well-being, but did not investigate it, articles that primarily focused on sustainable education, and reports that examined only work satisfaction and burnout, were applied, which resulted in excluding 175 sources. Furthermore, the eligibility stage required exploring the in-depth value of the remaining articles. Consequently, the authors conducted full-text reviews resulting in excluding 197 articles. Lastly, in the inclusion stage, the authors further explored the remaining 63 papers. To investigate the well-being of teachers and reveal their well-being tendencies in higher education, the authors selected 26 articles for this paper.

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Furthermore, going back to Table 1, a third stage involved validating systematic literature review results. Again, both authors were involved in the analysis and coding process, therefore cross-checked coding results, revisited selected articles, and ensured inter-rater reliability. The coding process involved the identification of main themes, physiological factors, barriers, and so on. Then, based on coding results, the four main themes were discovered, and 10 psychological factors were determined. The whole procedure of paper compilation was carried out by two researchers independently; consequently, in the matter of dispute, they discussed the issue until they reached an agreement. 3.2 Survey Method The accumulation of evidence through secondary investigations can be very beneficial in recommending new insights or distinguishing where additional primary findings might simplify an issue. Therefore, after rigorous systematic literature analysis, the authors identified ten psychological factors such as being engaged, excited, exhausted, warned, stressed out, safe, frustrated, overwhelmed, happy, and hopeful that impact the wellbeing of teachers in higher education institutions by considering the relationship between teachers’ devotion and psychological well-being in the workplace. Consequently, the authors developed a survey where respondents had to answer one straightforward but primary Likert Scale question. The question was as follows “During the past week, how often did you feel (space) at work?”. Respondents were given all ten psychological factors to choose from. Moreover, they had to rank physiological factors from “Practically Never” to “Practically Always.” In this proportion, “Practically Never” was “1” and “Practically Always” was “5”. The respondents were solely higher education teachers. Within two months, the authors sent approximately five hundred invites through emails. Available vast networks with higher education institutions made it practical to communicate with many such teachers. Consequently, 236 responses were recorded. For goodness and trustworthiness, it is essential to note that every respondent fully completed the survey; therefore, the authors did not have to eliminate any cases from the dataset. 3.3 Semi-structured Interview To develop the research further, in this paper, the authors randomly selected several teachers to conduct a semi-structured interview. The goal was to obtain more evidence on identified themes and physiological factors. Twenty-seven teachers were randomly selected, from which eight volunteered to have a discussion. Interviews were conducted via the Zoom software platform. On average, interviews lasted between 14–17 min. Consequently, obtained data was transcribed and crossed-checked with interviewees to avoid inconsistencies.

4 Results and Discussions The authors examined available literature through a systematic literature review to address the research question and explore the well-being of teachers in higher education by considering the relationship between teachers’ devotion and psychological

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well-being in the workplace. Consequently, the analysis identified four main themes: the sense of proficiency, perceived acknowledgment, and aspiration for engagement, and job satisfaction for teachers’ well-being. Therefore, this paper concentrated on the 4th theme, teachers’ devotion and psychological well-being in the workplace. Besides, for this purpose, the authors detected ten psychological factors. The authors employed a qualitative and quantitative research method. The research evidence was based on data from 236 teachers from 7 countries and six higher education institutions. Moreover, 15.7% of teachers had bachelor’s degrees, 64% with master’s degrees, and 20.3% with doctor’s degrees. Therefore, analyzing information from different countries and institutions was essential to obtain more evidence of teachers’ well-being in other geographical locations.

Fig. 1. Teachers’ reflections towards the current jobs.

The first pair of questions asked in the survey was about teachers’ relation to the current job they have, therefore, how positively they reflect on the current working conditions. Figure 1 illustrates three main questions: how effective do you feel at your job right now, how meaningful is your work, and how satisfied are you with your job right now? These questions are designed to explore effectiveness, meaningfulness, and satisfaction with the job teachers do. As we can see, teachers are optimistic about meaningfulness and satisfaction; however, they are somewhat confident about effectiveness. In contrast, there is negative reflection too, which is high with effectiveness. The feedback in Fig. 1 is even more effective if we look at teachers’ experience in teaching, as illustrated in Table 3. Most teachers (41.5%) have between 2 to 5 years of experience; therefore, their feedback on reflections is significant as it is experience-based. Besides, it underlines the level of psychological impact illustrated in further discussions. On the other hand, Table 2 underlines that most teachers (34.3%) have been in the same teaching positions for about two years. In contrast to more than four years, it shows a positive indication that teachers appreciate being in the teaching field. However, if we pay attention to the willingness to continue teaching, we can assume that teachers are not sure

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Table 3. Teaching experience and willingness to continue to teach.

if they are willing to continue teaching long-term. Therefore, teachers should evaluate their psychological well-being and job satisfaction yearly and accordingly decide. Furthermore, the authors explored ten psychological factors with a straightforward Likert Scale question. The question was as follows “During the past week, how often did you feel (space) at work?”. Respondents were given all ten psychological factors to choose from. Besides, they had to rank physiological factors from “Practically Never” to “Practically Always.” “Practically Never” is the lowest score, and “Practically Always” is the highest score. One critical factor about this question was that the survey was conducted in the third part of the academic semester; therefore, the authors acknowledge that some teachers might have overreacted to the questions due to the end of the academic semester. However, on the other hand, it accurately illustrates well-being and job satisfaction. Table 4 highlights teachers’ responses. Moreover, the table is broken down into two blocks: positive factors such as engaged, excited, safe, happy, and hopeful, and negative aspects such as exhausted, worried, stressed out, frustrated, and overwhelmed. From Table 4, the authors argue a positive relationship between teachers’ wellbeing and job satisfaction and positive psychological factors. Between 30.1–36.9% of respondents indicated they frequently felt engaged, excited, safe, happy, and hopeful at work. In contrast to negative psychological factors, the authors argue that well-being has a high-level impact. For example, teachers illustrated that 40.7% and 41.9% feel worried and stressed out at work. Table 4 somewhat echoes Fig. 1 as we see the correlation between effectiveness, meaningfulness, and satisfaction with psychological factors. Moreover, eight interviewees provided almost the same evidence. During the interview, teachers were asked the same three questions as in Fig. 1. One of the teachers stated that “to be effective at a teaching job and be on guard; teachers need psychological support not only from leadership but colleagues too.” Besides, another teacher stated that “being overwhelmed

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is a part of being a teacher. Psychologically, we should be strong; however, to have additional support will not hurt”. During the interview, the teacher made one very significant statement arguing that “teacher well-being is not a new dilemma. This issue has been there for many years, but nobody cared. However, somehow, during the pandemic, brought the issue up”. The question asked was, “do higher education institutions care about teachers, or is this a trend? The same interviewee continued stating that institutions talk about the well-being of teachers; however, the curriculum, course schedules, etc., are still hectic. Even looking at Table 4, it can be argued that the level of negative feedback is there; however, not reflected as it should be. Though, the interviews did provide some emotional feelings during the conversations. Indeed, the Zoom interview takes away human factors but still enables one to feel the emotions during the conversation.

5 Conclusions This paper aimed to explore the well-being of teachers in higher education, while identifying the well-being tendencies of teachers in higher education. A systematic literature review, survey, and interviews clearly explained and described the research problem. The systematic literature review provided four main themes and ten psychological factors, which were critically important to building the paper and developing the research on teachers’ well-being. These findings established a cornerstone for future research. From the study, it was argued that two blocks of psychological factors impact teachers’ well-being and job satisfaction. First, the survey results and interviews confirm direct relationships. The authors acknowledge that one of the main limitations of this paper is the time when the survey and interviews were conducted. Third quarter of the academic year might not be the best time to collect data. As a future reference, collecting such data at the beginning and the end of the academic semester would be recommended. This type of approach will enable authors to create contrast and comparisons.

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The current paper is the introductory paper of the two-year research on teachers’ well-being in higher education institutions. The present paper explored the well-being of teachers in higher education. It gives much-needed information to the researchers and not only to enhance or propose or even create one universal model for teachers’ well-being. Moreover, this paper acts as a cornerstone for the second part of the paper. Currently, a questionnaire with 31 questions is in progress, collecting data from three central geographic regions. The goal is to create contrast in the different areas and see how teachers’ well-being is treated.

References 1. Viac, C., Fraseri, P.: Teachers’ well-being: a framework for data collection and analysis. OECD Educ. Working Pap. 213 (2020) 2. Falecki, D., Mann, E.: Practical applications for building teacher wellbeing in education. Cultivating Teach. Resilience 175, 177–191 (2021) 3. Pace, F., D’Urso, G., Zappulla, C., Pace, U.: The relation between workload and personal well-being among university professors. Curr. Psychol. 40(7), 3417–3424 (2019). https://doi. org/10.1007/s12144-019-00294-x 4. Shirley, D., Hargreaves, A., Washington-Wangia, S.: The sustainability and unsustainability of teachers’ and leaders’ wellbeing. Teach. Teach. Educ. 92(2), 1–12 (2020) 5. Larson, L., et al.: The academic environment and faculty well-being: the role of psychological needs. J. Career Assess. 27(1), 167–182 (2019) 6. Lam, B.-H.: Social Support, Well-being, and Teacher Development. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3577-8 7. Gillet, N., Fourquereau, E., Tiphaine, H., Colombat, P.: The effects of job demands and organizational resources through psychological need satisfaction and thwarting. Span. J. Psychol. 18 (2015) 8. Seipel, M.T., Larson, L.M.: Supporting non-tenure-track faculty well-being. J. Career Assess. 26(1), 154–171 (2016) 9. Adler, A., Seligman, M.: Using wellbeing for public policy: theory, measurement, and recommendations. Int. J. Wellbeing 6(1), 1–35 (2016) 10. Huang, S., Yin, H., Lv, L.: Job characteristics and teacher well-being: the mediation of teacher self-monitoring and teacher self-efficacy. Educ. Psychol. 39(3), 313–331 (2019) 11. Solhaug, I., et al.: Long-term Mental Health Effects of Mindfulness Training: a 4-Year Followup Study. Mindfulness 10(8), 1661–1672 (2019). https://doi.org/10.1007/s12671-019-011 00-2 12. Buric, I., Sliskovic, A., Penezic, Z.: Understanding teacher well-being: a cross-lagged analysis of burnout, negative student-related emotions, psychopathological symptoms, and resilience. Educ. Psychol. 39(9), 1136–1155 (2019) 13. Skinner, B., Leavey, G., Rothi, D.: Managerialism and teacher professional identity: impact on well-being among teachers in the UK. Educ. Rev. 73(1), 1–16 (2019) 14. Alves, R., Lopes, T., Precioso, J.: Teachers’ well-being in times of Covid-19 pandemic: factors that explain professional well-being. Int. J. Educ. Res. Innov. 15, 203–217 (2021) 15. Molero, P., Ortega, F., Jimenez, J., Valero, G.: Influence of emotional intelligence and burnout syndrome on teachers well-being: a systematic review. Soc. Sci. 8(6), 185 (2019) 16. Jennings, P., et al.: Long-term impacts of the CARE program on teachers’ self-reported social and emotional competence and well-being. J. Sch. Psychol. 76, 186–202 (2019) 17. Kurt, N., Ayse, D.: Investigation of the relationship between psychological capital perception, psychological well-being and job satisfaction of teachers. J. Educ. Learn. 8(1), 87–99 (2019)

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18. Sahito, Z., Vaisanen, P.: A literature review on teachers’ job satisfaction in developing countries: recommendations and solutions for the enhancement of the job. Rev. Educ. 8(1), 3–34 (2020) 19. Garrick, A., et al.: Non-work time activities predicting teachers’ work-related fatigue and engagement: an effort-recovery approach. Aust. Psychol. 53(3), 243–252 (2020) 20. Petillion, R., McNeil, S.: Student experiences of emergency remote teaching: impacts of instructor practice on student learning, engagement, and well-being. J. Chem. Educ. 97(9), 2486–2493 (2020) 21. Roberts, A., et al.: Workforce well-being: personal and workplace contributions to early educators’ depression across settings. J. Appl. Dev. Psychol. 61, 4–12 (2019) 22. Virtanen, A., De Bloom, J., Kinnunen, U.: Relationships between recovery experiences and well-being among younger and older teachers. Int. Arch. Occup. Environ. Health 93(2), 213–227 (2019). https://doi.org/10.1007/s00420-019-01475-8 23. Yukhymenko-Lescroart, M.A., Sharma, G.: The Relationship Between Faculty Members’ Passion for Work and Well-Being. J. Happiness Stud. 20(3), 863–881 (2018). https://doi.org/ 10.1007/s10902-018-9977-z 24. Yin, H., Wang, J., Bai, Y.: Challenge job demands and job resources to university teacher well-being: the mediation of teacher efficacy. Stud. High. Educ. 45(8), 1171–1185 (2020) 25. Xiao, Y., Watson, M.: Guidance on conducting a systematic literature review. J. Plan. Educ. Res. 39(1), 93–112 (2017) 26. Brereton, P., Kitchenham, B.A., Budgen, D., Turnera, M., Khalilc, M.: Lessons from applying the systematic literature review process within the software engineering domain. J. Syst. Softw. 80(4), 571–583 (2007)

Problems of Banking Stability and Efficiency: Comparative Analysis of Latvia and Turkey Natalia Konovalova1(B) , Mustafa Akan2 , and Luís Moreira Pinto3,4 1 RISEBA University of Applied Sciences, Meža str.3, Riga, Latvia

[email protected] 2 Halic University, Istanbul, Turkey 3 CITAD Research Center, Lusiada University, Lisbon, Portugal 4 Faculty of Architecture, University of Beira Interior, Covilhã, Portugal

Abstract. The problems of banking stability and efficiency are a pressing issue in the economy of any state. During periods of turmoil and crises, when GDP growth is slowing, ensuring the stability of the financial and banking systems becomes particularly acute and requires constant regulation and control by supervisory authorities. The purpose of the study is to determine the factors affecting the stability of the banking system and to compare the degree of their influence in Latvia and Turkey. The main results of the study are to determine the positive and negative aspects of the regulation of banking activities in the two countries, identify their consequences and predict banking stability. Keywords: Banking stability · Efficiency · Capital adequacy · Capital safety margin · Comparative analysis

1 Introduction The issues of stability of the banking system are inextricably linked with the stability of the economy, since without a stable banking sector, the successful development of the state is impossible. The stability of the banking system consists in achieving an equilibrium state of self-regulatory activities of banks and their adaptation to external operating conditions. If this equilibrium and balance are achieved, the result will be an effective operation of the banking system aimed at achieving a positive effect for the economy of the entire state [1]. Currently, the most relevant functions of the banking system are such as the transformation of domestic savings into investments, the effective allocation of resources, the promotion of the implementation of the basic social functions of the state and the strengthening of the financial sovereignty of the state [2]. At the same time, in order to function stably, the banking system must provide a sufficient level of equity to cover the risks taken and maintain an effective level of activity [3]. The purpose of the study is to conduct a comparative analysis of the stability of the banking systems of Latvia and Turkey based on the determination of the safety margin of bank capital, to identify the level of efficiency of the banking industry, as well as © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 473–483, 2023. https://doi.org/10.1007/978-3-031-26655-3_43

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to predict the stability of the banking sector of the two analyzed countries using the extrapolation method. The objectives of the research are: – study the theory of financial stability of banks; – conduct a comparative analysis of the stability of the banking systems of Latvia and Turkey; – identify the capital safety margin in the banking industry of the analyzed states; – conduct a comparative analysis of banking performance through indicators of return on assets and return on equity; – predict the level of stability of the banking system of Latvia and Turkey based on the extrapolation method. Methodological, analytical materials and publications of the Bank of Latvia, the Central Bank of the Republic of Turkey, Banking Regulation and Supervision Agency of Turkey, the Financial and Capital Market Commission of Latvia, and Basel Committee on Banking Supervision regulatory documents on commercial banks’ capital adequacy evaluation were used in the research process. Publications in periodicals, statistical information of the Finance Latvia Association, the Banks Association of Turkey, the legal acts of Latvia and Turkey were taken into consideration. The comparative analysis of capital of Latvian and Turkish commercial banks was performed mainly based on financial information of banks: annual financial reports, balance sheet and notes to it, bank’s capital flow and capital adequacy reports, capital adequacy and risk management methods, official audit reports on financial standing of commercial banks available with annual reports of banks. Different Internet information sources - analytical and business publications, opinions of specialised agencies (Moody’s Analytics, Fitch Ratings) and financial publications in mass media - were used as well.

2 Banking Stability Theory: Literature Review As known, one of the most important parameters of the stable functioning of the banking system is the expansion of active operations of banks. But the amount of assets of the banking sector is directly dependent on the amount of its liabilities [4]. Consequently, the main dependence is that the expansion of active income-generating transactions affects the increase in the profits of the banking system, which in turn increases its passive part (liabilities). Based on this, the increase in active operations should be ensured by stabilizing macroeconomic conditions (predictability of economic conditions, growth in production and incomes of the population, reduction in inflation, etc.) [5]. Macroeconomic instability and uncertainty increase credit risks and reduce investment demand for credit resources. In addition, it points out the significant impact of inflation on the intermediary function of the banking and financial systems: rising prices divert customer funds from savings due to the fact that real interest rates on deposits (taking into account inflation) are reduced. Also, general economic instability reduces the investment attractiveness of the economy, including the attractiveness of investments in the banking sector [6].

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Ensuring the financial stability of the banking system is an important issue for macroprudential policies of central banks around the world. Macroprudential regulation is a policy aimed at reducing the impact of systemic risks. The unstable position of the banking system leads to a significant spread of financial problems in the economy as a whole [7]. The scientists Anna A. Mikhaylova et al., Savchina, O.V. et al. note, the state of the banking system is an important indicator of the economic situation of any country [8, 9]. Consider the points of view of different authors on the financial stability of credit institutions. The authors Saksonova S., Kant¯ane I., Ko¸leda O. note that the financial stability of a credit institution is a state of financial resources in which a commercial bank, freely maneuvering money, is able, through their effective use, to ensure an uninterrupted process of carrying out its economic activities [10, 11]. Factors that determine the financial stability of a credit institution can be divided into external and internal. External factors are divided into two independent groups: macroeconomic factors and factors characteristic of banking activities [7]. Macroeconomic factors of the external environment include general economic ones; political; legal; socio-psychological; force majeure and financial market factors [12]. As for the factors characteristic of banking activities, they include the state of the banking business, which depends on competition in the market, monetary policy pursued by the state, demand for banking services, as well as the level of regulation by the central bank and supervisory authorities [13]. Internal factors of the bank’s stability can be divided into three groups: organizational, technological, economic. Organizational factors include the bank’s strategy, management level; personnel qualification; relations with the founders, internal policy of the bank. Technological factors include the bank’s focus on the development of banking technologies, the market needs for new banking products and services [14]. The generally recognized economic factors of the bank’s financial stability are capital adequacy, asset quality assessment, the level of profitability; liquidity assessment; property base; Risk management system (procedures that ensure the identification, measurement, monitoring and control of risks, as well as the establishment of risk restrictions). The most important characteristic of the stability of any bank is its structural complexity, due to such types as capital stability, commercial (market) stability and functional stability. Capital stability is based on the amount of equity of a credit institution, which is one of the sources and factors of the bank’s constant competitive strength, determines the scale of the bank’s activities, the ability to pay off losses, and the adequacy of the amount of money supply to meet the requirements for issuing deposits [15]. Commercial (market) stability is characterized by a measure of the bank’s integration into the infrastructure of market relations, the degree and strength of relations with the state, the participation of banks in interbank relations. Commercial sustainability is also characterized by systemic importance for the banking sector and the economy as a whole, control of a certain share of financial flows in the country, the duration and quality of relations with creditors, clients and depositors, and the close relationship between money capital and the real sector of the economy [16, 17].

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So, according to many authors functional stability - may have 2 possible options: 1) specialization of a commercial bank on a limited range of services, which allows a specialized bank to more effectively manage a selected range of banking products; 2) the universalization of a commercial bank, the premise of which is the idea of its sustainability based on the fact that most customers prefer to meet the entire set of their needs for banking products in one credit institution [4, 7, 12].

3 Comparative Analysis of the Level of Stability and Efficiency of Banks in Latvia and Turkey The authors consider it appropriate to assess the level of sustainability of banks through their capital stability using capital adequacy indicators and capital safety margin. The results of the analysis of statistics from the banking sector of Latvia and Turkey showed that the banks of these countries have a well-capitalized base. The Tier 1 capital ratio and the Total capital ratio currently exceed the established minimum Basel requirements (Fig. 1 and 2).

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It should be noted that the global financial crisis of 2008 negatively affected the capital base of banks and during this period the banking sector of Turkey and Latvia turned out to be unprofitable and faced serious problems. In 2008–2010, the total capital adequacy indicator was barely kept at the minimum level of 8% and did not fulfil the requirements for the capital, taking into account the additional capital buffer (Fig. 2). However, with the introduction of mandatory requirements for an additional capital buffer, the situation has improved markedly since 2011 and continues the trend towards strengthening bank capital adequacy. Thus, for the period from 2011 to 2021, the total capital in the banking sector of Latvia increased 1.5 times and in 2021 amounted to 21.5%. And in the Turkish banking sector for the same period, the total capital increased

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1.4 times and in 2021 amounted to 18.3%. Significant growth is also observed in the Tier 1 capital ratio. During the analyzed period from 2006 to 2021, the Tier 1capital ratio in the Turkish banking industry increased 2.2 times and in 2021 reached almost 16%. There was also a positive trend in the Latvian banking sector. Bank capital of the first level (Tier 1 capital ratio) in Latvia increased 2.7 times during period from 2006 to 2021 and at the end of 2021 amounted to 21.3% (Fig. 1).

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It should be noted that a larger gap between total and Tier 1 capital is observed in the Turkish banking sector. This means that Turkish banks carry out a large diversification of equity elements, as a result of which the share of capital of the second level increases and the share of the first level capital decreases. Thus, during the analyzed period, the gap between total capital and first-level capital (Tier 1 capital) in the Turkish banking sector averages 3% (Fig. 3). At the same time, Latvian banks prefer to carry out their activities mainly at the expense of the first level capital, so the gap between the total capital and the capital of the first level in Latvian banks is insignificant. It should be noted that the COVID 19 pandemic did not have a significant negative impact on the banking system stability in the analyzed countries. Banks continued to work and serve customers in a pandemic, including remotely service, which allowed them not to lose their stability. What is the effectiveness of the activities of Turkish and Latvian banks? What is the link between the sustainability and profitability of credit institutions in Turkey and in

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Latvia? The authors investigated ROA and ROE indicators as a measure of efficiency (profitability).

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The level of ROA and ROE indicators is shown in Fig. 4. So, over the past 10 years from 2012 to 2021, the ROA and ROE indicators have been positive. This means that over the past 10 years, the banking industry in Latvia and Turkey has been profitable. As for the ROA indicator, during the analyzed period it was low (from 0.63% to 1.51% in the banking industry of Latvia, and from 1.15% to 2.04% in the banking industry of Turkey). The return on equity (ROE) during the period under analysis ranged from 7.51% to 14.3% in the Latvian banking sector and from 10.07% to 14% in the Turkish banking sector. The lowest value of these indicators in both countries was in 2020, which is explained by the COVID 19 pandemic. In general, the efficiency of capital (equity) and assets is higher in the Turkish banking sector. In Latvia, banks operate with less return, that is, each unit of invested capital or assets of commercial banks in Latvia generates less profit than each unit of invested capital or assets of commercial banks in Turkey. Turkish banks are more profitable and efficient due to a high share of net interest margin (NIM). At the same time, Turkey’s banking sector is well capitalized and has a low level of distressed assets. By comparing changes in ROA and ROE performance with changes in capital adequacy and capital safety margin, we can identify the inverse relationship: the higher the safety margin, the lower the efficiency, and vice versa. This fact suggests that banks do not use profits to increase their capital and diversify their equity elements too little.

4 Bank Stability Prediction Using Capital Safety Factor To predict the financial stability of the banking system of Turkey and Latvia, a capital safety factor was chosen. It is defined as the difference between the actual value of the total capital adequacy of banks and its minimum requirements established by the Basel Committee on Banking Supervision. The forecast is based on an extrapolation method that shows what results can be expected in the future if you move towards it at the same speed as in the past. As actual data for the calculation base, the dynamic range of the capital safety factor for the previous 16 years is taken. The results of the actual and forecast data are presented in the Fig. 5. The forecast shows that the dynamics of the capital safety margin and, accordingly, the banking stability of the banks of Turkey and Latvia will strengthen. However, the scenario for the Turkish banking system may be more smoothed out and at a low growth rate (on average annually by 0.5–0.6%) and over the 6-year forecast period, the capital safety margin may increase 1.4 times and reach 11.1% by 2027. The scenario for the Latvian banking system can develop more rapidly. By 2027, the average capital safety margin may increase to 20.5% (or 1.8 times). And the average annual growth rate starting in 2023 may be 1%. This scenario of strengthening banking stability can be both optimistic (upper confidence limit) and pessimistic (lower confidence limit). Consider the optimistic and pessimistic development forecast in the banking system of Turkey and Latvia (Fig. 6). In Latvia’s banking system, the range of the optimistic and pessimistic outlook from the realistic is uniform, dynamic, and moves with the same upward acceleration. The range of deviations from the average value to one side and the other in the forecast period is 4.3–4.5%. This is due to the relatively stable and predictable economic situation in the

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country, the preservation for many years of Latvia’s external credit rating at the level of “A−” with a stable assessment of the future (“Fitch Ratings”) [26].

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Banks' capital safety margin (actual and forecasted)/Latvia Banks' capital safety margin (actual and forecasted)/Turkey Fig. 5. Actual and forecasted banks’ capital safety margin in Latvian and Turkish banking sector (calculated by authors based on an extrapolation method).

The scenarios and dynamics of the forecast of banking stability in Turkey may be different. The range of fluctuations in the optimistic and pessimistic forecast from the realistic in Turkey is high, and its movement is directed opposite. Thus, the optimistic scenario may move upward in the coming forecast 5–6 years with a deviation from the realistic forecast in the range of 2.3–6.8% and reach the level of capital safety margin of the realistic forecast of Latvia 2024 by 2027. At the same time, the pessimistic forecast shows that in Turkey there may be scenarios of a decrease in the safety margin of bank capital from 7.8% (2021) to 4.3% in 2027. This contradiction between the optimistic and pessimistic forecast of the stability of the banking system in Turkey is due to the instability of the economy in Turkey in recent years, as well as the downgrade of Turkey’s credit rating by international rating agencies [26, 27]. Despite this, the study shows that Turkey’s banking sector has shown resilience to challenging macroeconomic conditions. Currently, the Turkish banking sector is well capitalized and has a low level of distressed assets. Turkish bank revenues are high due to the high share of net interest margin (NIM), as evidenced by the ROA and ROE indicators analyzed in this study.

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Forecast of banks' capital safety margin/Latvia Upper confidence bound of forecast /Latvia Lower confidence bound of forecast /Latvia Forecast of banks' capital safety margin/Turkey Upper confidence bound of forecast /Turkey Lower confidence bound of forecast /Turkey

Fig. 6. Forecast scenarios of banks’ capital safety margin in banking sector of Latvia and Turkey (calculated by authors based on an extrapolation method).

5 Conclusions The results of the study showed that the banking system of Latvia and Turkey is stable and able to withstand difficult economic conditions. The capital safety margin in Turkish and Latvian commercial banks is sufficient. At the same time, capital adequacy in the Latvian banking sector is higher than in the Turkish banking sector. The stringent requirements of the Basel Committee on Banking Supervision strengthened the banking system but reduced the possibility of expanding active operations that generate income. The effectiveness of the banking activities of the analyzed countries, expressed in terms of ROA and ROE, is positive, but not high. The efficiency of Turkish banks is higher than that of Latvian banks. Turkish and Latvian banks have reserves to improve their efficiency. Based on the study the authors made proposals for the further development and strengthening of the banking industry in Turkey and Latvia:

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1. Ensure the expansion of active operations of commercial banks to increase the level of profitability. 2. With the increase in profit of commercial banks, use the opportunity to increase capital (equity) from internal sources by capitalizing profits. 3. Since the capital (equity) function of banks covers risks, commercial banks are advised to conduct regular (monthly) compliance control between the level of risks taken and the level of capital adequacy. 4. It is necessary to conduct stress testing and modeling of problem situations, as well as check the adequacy and safety margin of capital (equity) in various stressful situations. 5. Predict the capital safety margin and performance indicators considering changes in macroeconomic conditions.

References 1. Shair, F., Shaorong, S., Kamran, H.W., Hussain, M.S., Nawaz, M.A., Nguyen, V.C.: Assessing the efficiency and total factor productivity growth of the banking industry: do environmental concerns matters? Environ. Sci. Pollut. Res. 28(16), 20822–20838 (2021). https://doi.org/10. 1007/s11356-020-11938-y 2. Pham, T.T., Dao, L.K.O., Nguyen, V.C.: The determinants of bank’s stability: a system GMM panel analysis. Cogent Bus. Manag. 8(1), 1963390 (2021) 3. Yin, H.: Bank globalization and financial stability: international evidence. Res. Int. Bus. Finance 49, 207–224 (2019) 4. Siddika, A., Haron, R.: Capital regulation and ownership structure on bank risk. J. Financial Regul. Compliance 28(1), 39–56 (2019) 5. Konovalova, N., Trubnikova, N.: Adjustment of banking activity according to Basel III requirements: experience and problems of Eastern Europe countries. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2017. LNNS, vol. 36, pp. 617–626. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74454-4_59 6. Shaddady, A., Moore, T.: Investigation of the effects of financial regulation and supervision on bank stability: the application of CAMELS-DEA to quantile regressions. J. Int. Financ. Mark. Inst. Money 58, 96–116 (2019) 7. Ali, M., Puah, C.H.: Does bank size and funding risk effect banks’ stability? A lesson from Pakistan. Glob. Bus. Rev. 19(5), 1166–1186 (2018) 8. Mikhaylova, A.A., Mikhaylov, A.S., Savchina, O.V.: Macroeconomic dataset for comparative studies on coastal and inland regions in innovation space of Russia. Data Br. 27, 104640 (2019) 9. Savchina, O.V., Savchina, O.V., Bobkov, A.L., Sharashidze, A.Z.: On the state of the mortgage market in the Russian Federation in the conditions of global economic crisis. J. Appl. Econ. Sci. 11(6), 1096–1103 (2016) 10. Saksonova, S., Kant¯ane, I.: Mergers and acquisitions: examples of best practice in Europe and Latvia. In: Contemporary Issues in Finance: Current Challenges from Across Europe, pp. 95–110. (2016). https://doi.org/10.1108/S1569-375920160000098007 11. Saksonova, S., Ko¸leda, O.: Evaluating the Interrelationship between actions of Latvian commercial banks and Latvian economic growth. Procedia Eng. 178, 123–130 (2017) 12. Adusei, M.: The impact of bank size and funding on bank stability. Cogent Econ. Finance 3(1), 1111489 (2015)

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13. Savchina, O.V., Pavlinov, D.A., Savchina, O.V.: Financial stability of electricity companies in the context of the macroeconomic instability and the COVID-19 pandemic. Int. J. Energy Econ. Policy 11(5), 85–98 (2021) 14. Mikhaylova, A.A., Mikhaylov, A.S., Savchina, O.V., Plotnikova, A.P.: Innovation landscape of the Baltic region. Adm. Public Manag. Rev. 33, 165–180 (2019). https://doi.org/10.24818/ amp/2019.33-10 15. Konovalova, N., Caplinska, A.: Approaches to evaluation of banks’ financial sustainability. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) RelStat 2020. LNNS, vol. 195, pp. 758–768. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68476-1_70 16. Akan, M., Konovalova, N.: Optimal incentives for economic growth in central European countries: a micro approach. Copernican J. Finan. Account. 10(3), 9–31 (2021) 17. Konovalova, N., Caplinska, A.: Impact analysis of factors influencing bank capital management. Entrepreneurship Sustain. Issues 8(1), 484–495 (2020) 18. Bank of Latvia: https://www.bank.lv/en/. Accessed 25 Aug 2022 19. Latvian Financial and Capital Market Commission: http://www.fktk.lv/en/. Accessed 28 July 2022 20. Basel Committee on Banking Supervision: http://www.bis.org/. Accessed 15 June 2022 21. Finance Latvia Association: https://www.financelatvia.eu/en/industry-data/. Accessed 01 Sep 2022 22. Central Bank of the Republic of Turkey: https://www.tcmb.gov.tr/wps/wcm/connect/en/tcm b+en/. Accessed 21 Sep 2022 23. Banking Regulation and Supervision Agency of Turkey: https://globaledge.msu.edu/globalresources/resource/10829/. Accessed 21 Sep 2022 24. Banks Association of Turkey: https://www.tbb.org.tr/en/home/. Statistical report: https:// www.tbb.org.tr/en/banks-and-banking-sector-information/statistical-reports/20/. Accessed 21 Sep 2022 25. HelgiLibrary, Banking, Statistical data: http://www.helgilibrary.com/pages/data/. Accessed 15 June 2022 26. Fitch Ratings: https://www.fitchratings.com/. Accessed 15 June 2022 27. Moody’s Analytics: https://www.moodysanalytics.com/solutions-overview/data/. Accessed 15 June 2022

Education and Training in Engineering

Blended Learning as One of the Factors for Attractiveness of Studies: Case Study of KTK Lina Girdauskien˙e(B) , Giedr˙e Adomaviˇcien˙e, and Judita Štreimikien˙e Kaunas University of Applied Engineering Sciences, Tvirtov˙es av.35, Kaunas, Lithuania [email protected]

Abstract. The aim of the article is to reveal the theoretical and empirical assumptions of blended learning, as one of the factors that increase the attractiveness of studies, using the case analysis of Kauno Technikos Kolegija (KTK). The first part presents the theoretical insights of blended learning and analyse the forms of this type of learning. During the theoretical analysis, the main constructs, criteria and indicators are defined. The second part of the article is devoted to the presentation of research results and scientific discussion. The aim of the research is to reveal students’ opinion about the implementation of blended learning at KTK. Survey (in written) and Focus group discussion methods are used in the research. It is received that blended learning increases the attractiveness of studies, because students positively evaluate the organisation of blended learning at KTK, notice the advantages of this form of learning, which allows the learner to partially control the time, place and duration of learning. Keywords: Blended learning · Attractiveness of studies · Face-to-face learning · Distance learning

1 Introduction Research and academic establishments are constantly looking for new forms of study programme implementation to make them more attractive to learners, flexibly adapted to students of various social and economic groups, and encourage students to participate in the study process actively. Various factors determine the attractiveness of studies. Shanka, Quintal, Taylor’s [6] research revealed that factors such as the distance from the student’s home to the city, security, living costs, tuition fees, the academic reputation of the institution, the quality of education, the flexibility of the organization of the study process make great influence on students’ choice of higher education institution. Widiputera et al. [8] identified the most important factors determining study attractiveness and choice: the competitiveness of study programmes, academic reputation, employment opportunities and study quality. The standards and guidelines for quality assurance of the European higher education area [7] emphasize that the attractiveness of studies includes the following: physical resources, such as library, spaces for free time activities and independent work, learning resources, IT infrastructure, human resources - academic and non-academic staff support, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 487–497, 2023. https://doi.org/10.1007/978-3-031-26655-3_44

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and organizational resources, such as study process organization, study modes, methods, etc. Modern life requires flexible forms of learning, applying blended learning methods, which would ensure both the availability of studies and a flexible work-life balance. Scientists and practitioners are constantly discussing how to create an effective compound that focuses on high-quality realisation of studies, and is attractive and accessible to all learners. One of the opportunities for increasing the attractiveness of studies is the application of blended learning in the study process. The term “blended learning” has been used frequently in academic and practical contexts for several decades. The term “blended learning” implies the idea that certain learning processes or technologies should be integrated (blended). Hrastinki [7] explored different models of blended learning. The analysis carried out allows us to state that blended learning, according to the nature of its organisation and implementation, can take several different forms, such as the combination of online technologies, pedagogical methods, learning technologies and face-to-face work tasks. In this article, the “attractiveness of studies” will be analysed in the terms of the application of blended learning in the study process. The aim is to reveal the theoretical and empirical assumptions of blended learning, as one of the factors that increases the attractiveness of studies, with the help of KTK case analysis. The first part presents the theoretical insights of blended learning and analyse the forms of this type of learning. Based on the theoretical analysis, the theoretical indicators (for quantitative research), categories and subcategories (for qualitative research) of the empirical research are distinguished. The second part of the article is devoted to empirical research, the purpose of which is to reveal students’ opinion about the implementation of blended learning at KTK. The results of quantitative and qualitative research are presented. Quantitative (written survey) and qualitative (Focus group discussion) methods are applied in the research.

2 Literature Review Constant changes in society’s life, globalization processes pose new challenges to society, and thus to studies in higher education. The main purpose of higher education institutions is to prepare a graduate who, being proactive in his professional activities and using the professional and general competencies acquired during the studies, would become a competitive specialist, would be able to adapt quickly in a new environment and would be ready for new challenges. Over the past decade, blended learning has grown in demand and popularity in higher education and has become a widespread learning phenomenon. It is increasingly evident that blended learning can overcome various limitations, be more responsive to the needs of the learner, and thus be more attractive and focused on the modern student. A meta-analysis of various empirical studies concluded that blended learning is more effective than online or face-to-face learning [1], Diep et al. [2] claim that blended learning will be a new traditional model of higher education, as it is more flexible, focused on the application of innovative study methods and procedures, and greater student involvement.

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Driscoll [3] identified four different concepts denoted by blended learning: • The combination or mixture of web-based technology modes (e.g., live virtual classroom, self-paced instruction, collaborative learning, streaming video, audio, and text). • The combination of various pedagogical approaches (e.g., constructivism, behaviorism, cognitivism). • The combination of any form of instructional technology (e.g., videotape, CD-ROM, web-based training, film) with face-to-face instructor-led training. • The mixture or combination of instructional technology with actual job tasks in order to create a harmonious effect of learning and working. Drawing on the Hrastinski [4] work, Kacetla, Semradova [5] proposed three different definitions of blended learning: • The combination of media and tools employed in an e-learning environment. • The combination of a number of pedagogic approaches, irrespective of the learning technology used. • The integrated combination of traditional learning with web-based online approaches. Thus, it can be concluded that there is a consensus that the main components of blended learning are face-to-face and distance learning (Fig. 1). In this article, blended learning will be understood as an integrated combination of traditional learning and online methods and tools, where part of the studied subject content is performed by the learners in a group/classroom, and part of the content is mastered by distance learning.

FACE-TOFACE LEARNING

BLENDED LEARNING

DISTANCE LEARNING

Practical, laboratory works, work in groups, defense of independent works, theoretical lectures using remote work tools, consultations Fig. 1. Model of blended learning.

The theoretical content of the subject is provided remotely, individual online tasks are performed, the practical and laboratory works necessary for the content of the subject are performed face-to-face, independent student works are presented, and discussions are held. Thus, this form creates optimal conditions for increasing the attractiveness of studies, because students can participate in the study process in a way that is acceptable to them, combine studies with other activities, and manage time effectively.

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3 Research Methodology The rapidly changing environment, the challenges of the pandemic and other factors made it necessary to search for new forms of study organisation, attractive ways and methods. KTK revised the organisation of the study process in such a way that it meets the needs of each student as much as possible and therefore proposed to organise studies in a blended way. This method has been used for the past two years, so it is important to find out students’ opinion about the organisation of blended learning at KTK. The aim of the research is to analyse the impact of the organisation of blended learning on the attractiveness of studies. Research Organisation. The research was conducted in 2021 and 2022, in two stages: • In the 1st stage a quantitative research (2021) was conducted – a written survey. • In the 2nd stage a qualitative research (2022) – Focus group discussion. A quantitative research was chosen because the aim was to measure the prevalence of the phenomenon of interest in a certain population, to measure the characteristics of the research object and to statistically substantiate the essential parameters, assumptions and determining factors of the object. This methodology was based on the deductive logic of the research process, using empirical data to verify the chosen theory. Thus, the researchers’ attention was directed to the examination and measurement of predetermined (known or expected) attitudes of people. After analysing the results of the quantitative research, a qualitative research methodology was applied for a deeper understanding of the phenomenon. It is based on the interpretive logic of the research, because the aim is to construct new or improve the existing theoretical assumptions by collecting and interpreting the obtained research data. Research Participants and Their Selection. Two methods of selecting research groups were used: for quantitative research (written survey) - a probabilistic sampling method, when the probability of each member of the studied population to be included in the sample is known. Thus, it was planned that all KTK students (1,088 in total) will participate in the research. In this way, it was aimed to test the theoretical assumptions with a statistically reliable sample. 925 KTK students participated in the quantitative research - a written survey. This is 85% of all students studying at KTK. 605 students who participated in the research study full-time (this is 88% of all full-time students), 320 study part-time (this is 79% of all part-time students). Applying 5% margin of error, it can be stated that the research sample is representative. The distribution of respondents according to study programmes is presented in Fig. 2. Students of all study programmes participated in the research. There is an uneven distribution of the number of respondents according to the study programmes because different number of students study in different study programmes. The distribution of respondents according to the year of study is presented in Fig. 3.

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Fig. 2. Distribution of respondents according to study programmes.

300 250 200 150 100 50 0

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Fig. 3. Distribution of respondents according to the year of study.

II and III year students constituted the majority of the research participants. Thus, it can be assumed that the respondents’ experience in evaluating the organisation of studies applying the blended learning method is sufficient. In the qualitative research (Focus group discussion), a targeted method of group formation was used, when the formed group includes individuals who are the most typical in terms of the investigated characteristic. Two Focus groups were formed. The groups were formed taking into account the following criteria: mode of studies (fulltime, part-time), student activity, social activities (group representatives). 8–10 students participated in the focus groups selected according to the specific research tasks. Research methods. A written survey method was used for the realisation of the quantitative research. This method was chosen because the aim was to find out the opinion of each member of the population. This method is convenient for research participants, the researcher’s role in it is secondary, impartial. The Focus group discussion method was used for the realisation of the qualitative research. The method was chosen because the research participants in the group could express their subjective views on certain issues of the implementation of blended learning in KTK orally and that information was recorded in the notes of the discussion. The group discussion was led by a moderator according to

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a pre-prepared interview plan. The structure of the interview plan: 1. Attitude about the necessity of blended learning in KTK. 2. Experience in blended learning. 3. Suggestions, comments, etc. on improving the organisation of blended learning in KTK. Research instrument. The research instrument was constructed according to the theoretical insights of blended learning. A structured questionnaire was used in the written survey. The questionnaire reflected two blocks of questions: general (name of the study programme, year of study, mode of study) and diagnostic (naming the concept of blended learning, opinion on the organisation of blended learning in KTK, didactic aspects of blended learning, advantages and disadvantages of blended learning). During the Focus group discussion, a set of questions was prepared about the above-mentioned structure of the interview plan for the organisation of blended learning.

4 Research Results The respondents’ responses show that mostly blended education is organised in such a way that laboratory and practical work takes place face-to-face, and theoretical lectures are conducted remotely. Presentations of independent work are often done remotely too. The answers are presented in Fig. 4.

2 4 2

Disagree

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65 69 60

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Practical, laboratory works areorganised in face-to-face mode, while theoretical lectures and thesis defense are organised remotely. Consultations are organised in face-to-face mode, and theoretical lectures and defense of independent works are organised remotely Usually practical, laboratory works are conducted in face-to-face mode, and theoretical lectures are conducted remotely

Fig. 4. Students’ opinion about the organisation of blended learning.

The students’ answers about the tools most often used in blended learning (more than one tool could be marked) are presented in Fig. 5. It is noticeable that part-time studies mostly take place in the Microsoft Teams environment, but Zoom is also used. It can be stated that the Moodle environment is intensively used because it contains the theoretical material of modules/subjects, independent work, and other information important to students.

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Fig. 5. The most commonly used distance learning tools.

An important didactic aspect in the study process is the provision of consultations. Therefore, it was important to find out the students’ opinion about the intensity of consultation in blended learning. The students expressed their opinion about the provision of consultation during blended learning. The answers are presented in Fig. 6. 100 80

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60 40 20

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0 Consultation are provided face-to-face Consultations are provided remotely Always Sometimes Never Fig. 6. Provision of consultations in different modes.

The opinion expressed by the respondents allows us to assume that most of the consultations take place face-to-face, when the teacher consults students and provides feedback. Remote consultations are provided sometimes and they are more often provided to students of full-time studies. When asked about advantages and disadvantages of blended learning, the respondents made a list of advantages and disadvantages (the number of answers was not limited). The answers are presented according to the number of the most frequently repeated answers in the research sample (Table 1). In conclusion, it can be stated that students notice the advantages of blended learning. They emphasize that this way of learning creates conditions for combining studies and professional activities, and better time planning. The respondents also note positive didactic aspects, such as a greater variety of methods and techniques and their combination, a deeper arrangement of the subject content applying diverse examples, and the possibility of consultation.

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Advantages

Number of responses Number of responses Disadvantages from respondents (N) from respondents (N) in the research sample in the research sample

Possibility to combine 563 work and studies

263

Less contact with teachers

Time is planned better 495

195

Less communication with classmates

Possibility to have more individual consultations

315

181

Sometimes it’s hard to force yourself to learn individually

A structure of subject 312 teaching is clearer when face-to-face and distance learning are combined

125

Internet connection interference

More various study 211 methods and techniques are applied

95

Lack of information

More online examples 112 related to the future specialty are provided Face-to-face classes 110 become more efficient Skills to work independently are developed

95

As one of the disadvantages of blended learning, the respondents pointed out that a closer connection with teachers and other students is lost, so it can be assumed that the students’ involvement in studies and the KTK community decreases. It should be noted that 66 respondents did not name the disadvantages of blended learning. The respondents were asked to evaluate the organisation of blended learning at KTK, where 1 - very bad, 5 - very good. Students’ assessment is presented in Fig. 7. In summary, it can be stated that the majority of respondents positively evaluate the organisation of the study process applying the method of blended learning. So it can be assumed that this form of learning is attractive to students. Summarizing the data of the quantitative study it can be stated that blended learning, when part of the study process is organized face-to-face and part is organized by distance, is positively evaluated by students and increases the attractiveness and accessibility of studies. When the general trends of the KTK students’ evaluation about the blended learning organisation were obtained, it was important to take a deeper look at this educational

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1 - very bad

2- bad

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Fig. 7. Assessment of blended learning organisation from the students’ point of view.

process, therefore, a qualitative research was organized in the II stage - Focus group discussion. The summarized thoughts of the Focus group participants are presented according to the structure of the interview plan. First, the participants of the Focus groups expressed their opinion about the necessity of applying blended learning at KTK. Focus group participants of both full-time and parttime studies unanimously expressed the idea that today’s abundance of information and various learning resources require a different approach to the organisation of the study process: “I liked that the studies are organized in a modern way”; “It is not necessary to sit in the classroom all day, when you can listen to the theoretical parts remotely or do some work independently”; “It is attractive, because the consultations were held individually and in a remote way that is convenient for students”; “Online lectures were attractive, teachers use various online examples”. The informants said that the application of blended learning helps them to manage their time better, focus more on finding new information and professional examples. Most of the informants from the Focus group of part-time studies noted that this way of learning is very attractive to them, as it creates optimal conditions for the harmony of work and study activities: “There were no more problems with the employer regarding going to studies”; “According to the organisation of blended learning, I was able to create an individual work schedule”. Since blended learning is taking place in KTK for the second year, the participants of the Focus groups were asked to share their experience of blended learning that they gained during the study process. The participants of the Focus group of part-time studies noted that this experience was new for them, maybe a little unusual at the beginning, because they had to make decisions independently, learn to manage time and prioritize activities:

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“In the beginning, the question arose: should I sleep or get up for remote lectures”; “There were questions about how to plan time, which activities require more time, etc.”. It was especially difficult for the first-year full-time students who were used to a strict teaching process at school: “At school, everything was planned for us, we just had to carry it out, and in KTK, especially in blended learning, we had to make our own decisions in order to complete the work on time, to be interested, to consult with teachers, etc.”. The informants of both groups pointed out that there was a lack of face-to-face consultations in the first year: “At first it seemed that remote consultations are not as effective as face-to-face consultations”; “It was more difficult because it was necessary to clearly formulate the questions, to delve into the problem yourself”. The students of the Focus group of part-time studies noted that in the first year, both students and teachers lacked the skills of applying information technology and managing tools, the ability to find information independently, and there were internference in the Internet connection: “In the beginning, both students and teachers felt uncomfortable, it was not always possible to connect or share the screen the first time”; “Later new skills were developed, the feeling of fear disappeared”; “Now we ask the teachers to allow the presentation of independent work remotely, because it is much more attractive, saves time, etc.”. However, all the informants said that any blended learning experience was useful and attractive to them. The participants of the Focus groups were asked to submit suggestions and recommendations about the further organisation of blended learning at KTK. All informants unanimously expressed the opinion that studies should continue to be organised in a blended form: “I would not like to return only to face-to-face learning, it is an attractive way of organising studies suitable for today’s student”; “It is necessary to continue, because we have developed new time planning, independent work skills, which are very useful in practice and work activities, etc.”; “My group colleagues are all in favor of developing blended learning at KTK”; Several Focus group participants of full-time studies drew attention to improving the delivery of subject content, stating that some teachers do not always make productive use of face-to-face study time:

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“Some teachers in face-to-face classes still allocate time for theoretical lectures, which could be done in remote classes”; “We would like to receive the most recent examples in remote classes”. The participants of the Focus group of part-time studies suggested improving the study schedule and preparing it as early as possible before the session. Summing up the thoughts expressed by the participants of the Focus groups, it can be assumed that blended learning is attractive for both full-time and part-time students, during which personal, professional skills and competences are developed.

5 Conclusions The analysis of the scientific literature allows us to say that blended learning is treated very differently, therefore it is very important that the educational institution has its own concept of blended learning and communicates it to both teachers and students. In this context, blended learning is described as an integrated combination of traditional learning and online methods and tools, where part of the subject content is performed by the learners in a group/classroom, and part of the content is mastered through distance learning. The analysis of the situation at KTK revealed that blended learning increases the attractiveness of studies, because students positively evaluate the organisation of blended learning at KTK, notice the advantages of this form of learning, which allows the learner to partially control the time, place and duration of learning.

References 1. Bower, M., Lee, M.J., Dalgarno, B.: Collaborative learning across physical and virtual worlds: factors supporting and constraining learners in a blended reality environment. Br. J. Educ. Technol. 48(2), 407–430 (2017) 2. Diep, A.N., Zhu, C., Struyven, K., Blieck, Y.: Who or what contributes to student satisfaction in different blended learning modalities? Br. J. Educ. Technol. 48(2), 473–489 (2017) 3. Driscoll, M.: Blended learning: let’s get beyond the hype. e-Learning 1(4), 1–4 (2002) 4. Hrastinski, S.: What do we mean by blended learning? TechTrends 63, 564–569 (2019) 5. Kacetla, J., Semradova, I.: Reflection on blended learning and e-learning–case study. Procedia Comput. Sci. 176, 1322–1327 (2020) 6. Shanka, T., Quintal, V., Taylor, R.: Factors influencing international students’ choice of an education destination–a correspondence analysis. J. Mark. High. Educ. 15(2), 31–46 (2006) 7. Standards and guidelines for quality assurance in the European Higher Education AREA (ESG) Approved by the Ministerial Conference in Yerevan, 14–15 May 2015 8. Widiputera, F., De Witte, K., Groot, W., Maassen van den Brink, H.: The attractiveness of programmes in higher education: an empirical approach. Eur. J. High. Educ. 7(2), 153–172 (2017)

Online Collaborative Learning: Use and Efficiency Evaluation Ryhor Miniankou(B) and Aliaksandr Puptsau European Humanities University, Saviˇciaus, 17, Vilnius, Lithuania {ryhor.miniankou,alexander.puptsev}@ehu.lt

Abstract. Collaborative learning, especially online, occupies the increasingly important place in the contemporary higher education. The article undertakes an analysis of key both theoretical and practical dimensions of collaborative learning. Approaches to the definition of collaborative learning are indicated, its key elements and principles of organization and implementation are highlighted. The authors consider collaborative learning not just as a set of new teaching and learning methods but primarily as a formation of life skills in the complex interconnected society. Collaborative thinking is inextricably linked with the creative and critical thinking process within the framework of community of inquiry. It is this framework that ensures theoretical coherence of understanding and use of collaborative thinking and learning in a socially situated and technologically interconnected learning environment. The authors describe collaborative learning as the special didactic system and consider some aspects of its implementation into any form of the university educational process – high-residence, low-residence, blended, or distance learning. As a conclusion, some criteria for evaluating the effectiveness of collaborative learning are formulated. Keywords: Digitalization · Collaborative learning · Didactic methods · Pedagogical technologies · Communication

1 Introduction The need for diverse forms of collaboration has probably get the key importance at the present stage of social development. It bases on developing the universal interconnectedness of social practices in the globalizing world, wide and diverse mobility, cosmopolitanization of everydayness, multitasking of people’s activities, and universal digitalization of social life, that requires a new type of thinking aimed at considering diversity and asserting the skills of fruitful interaction with flows of other people. University graduates are required to be able to creatively work on a variety of tasks and jointly solve complex problems of the dynamically changing social and professional environment. The intensive digitalization of education impacts significantly on the transformation of traditional learning models in the contemporary higher education. Universities seek to radically transform the organization of the educational process to improve the quality of education [4]. One of the promising areas of the transformations is active involving the paradigm and methods of collaborative learning. However, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 498–509, 2023. https://doi.org/10.1007/978-3-031-26655-3_45

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these issues are not yet in the spotlight at the universities of the region. Possible ways to change this situation are the subject of our paper. Looking at collaborative learning through the prism of the regional universities needs and developing relevant practical recommendations constitute the innovative dimension of our research.

2 The Concept and Features of Collaborative Learning Collaborative thinking is the necessary condition for successful life in the contemporary interconnected world with its distinctive social uncertainty. Such thinking presupposes a readiness for broad interaction with ideas of other people, reliance on them, as well as the ability of individuals to filter and evaluate these ideas in the context of their own values and beliefs. At the same time, it is closely integrated with creative and critical thinking: it is about developing communication and problem-solving skills, about creating an environment for synergistic thinking and innovation. In other words, a fruitful exchange of ideas and approaches, hypotheses, and proposals is the essence of collaborative thinking. Such an exchange is possible both in direct communication with participants of collaboration, moreover, thanks to the development of the Internet and the newest technologies, the communication is possible today, so to speak, with the whole world, and in dialogue with experiences of the past, the legacy of previous eras. At the same time, it is important to emphasize that collaborative thinking is opposed to collective, group thinking in the sense that it is aimed at individual research and understanding of complex and contradictory ideas and/or situations using various discussion formats, at constructing personal meanings and taking responsibility for their implementation [1]. Research reveals the key role of collaborative approaches in the contemporary higher education. The Organization for Economic Cooperation and Development sees collaboration and communication as the key skills for the 21st century for both teachers and students [6]. It is assumed that individuals should have the competencies of collaborative problem solving and the ability to cooperate using appropriate technologies after completing education and acquiring professional skills. It is important to keep in mind that collaborative learning is an umbrella term for the variety of approaches to educational activities, including combined efforts of students and/or joint work of students and teachers. It should be emphasized that collaboration is often mixed with other concepts, such as joint/group learning, consultations, communities of practice, self-study groups, team/group work, etc. Apparently, this is due to that collaboration is included in the most diverse forms of educational activity but does not replace them solving its own and, probably, more general educational tasks. In this context, the following characteristic of collaborative learning seems to be quite working and fruitful: “Collaboration can be viewed from the perspective of relationships and interactions, social and cognitive processes and capacities, organizational process, and learning. It is a complex concept and involves many related phenomena or variables, such as the intersection of attitudes and dispositions; interpersonal communication skills; individual and team cognition; team, task, knowledge and participant awareness; individual internalized and group externalized knowledge building; and shared mental models” [5]. Collaborative learning is today the excellent example of the skillful combination of diverse approaches

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in learning, which makes the respective learning practices truly unique. And most importantly, collaborative learning is not just a set of new teaching methods, but above all the social practice of building life skills in the complex interconnected society. Collaborative learning is sometimes confused with cooperative learning to the point of being identical. Early researchers of cooperative learning defined it as the use of small groups in the learning process working together on carefully structured problems or tasks [3]. Based on this characteristic Roberts [11] sees the difference between collaborative learning and cooperative group work in that collaboration focuses on creative and open interaction between students. Collaborative learning, of course, requires cooperation between group members but it goes beyond simply working together on a common project at its core. As J. Salmons emphasizes, the term “cooperative learning” should be used when students are required to work in small groups formed and managed by the teacher, while the term “collaborative learning” should be used when students are responsible for determining their own approaches to roles and alignment of processes for organizing, coordinating, and completing learning tasks [13]. Researchers and practicing educators offer various definitions of the concept of “collaborative learning”. To summarize, the following key elements are often identified in the studies: two or more agents; autonomy and voluntariness of participants; engaging in agreed interaction processes; share understanding of the problem area by group members; share decision-making by them; group movement towards a common goal or mutual benefit. Accordingly, the following characteristics of the collaborative learning process can be identified: the degree of interdependence and joint contribution or shared work to achieve a common result; interdependence and joint contribution take place at every stage of the interaction or process, no matter the type of collaboration; conscious engagement in such activities using appropriate structures and methods to support the multidimensional nature of collaboration [5]. Cognitive diversity, critical dialogue, discussion of meanings and synthesis of perspectives allow collaborative groups to achieve integrated solutions, which exceed capabilities of their individual members. It is quite naturally to turn to the constructivist methodology in the perspective of interpreting the collaborative learning features indicated above. The meaning of the principle of constructivism, regardless of its disciplinary comprehension, is that since modern social actor is autonomous by definition and able to creatively interpret cultural values and norms, the world is never completely closed and determined; on the contrary, it is always open to new interpretations and transformations. In other words, it is constituted and structured according to our needs, abilities, and possibilities, and therefore it is accessible to our understanding through interpretative schemes we create within the framework of social interactions. An interpretation-independent approach to the world does not exist, i.e., its schematic interpretation is universal. Thus, constructivism opposes positivism, or objectivism, according to which any of our explanations must be subject to empirical verification, and knowledge exists independently of our perception of it. This is what makes constructivism an indispensable condition for collaborative learning as a process of joint constructing knowledge that is especially clearly confirmed today by the practices of digitalization of education. Almost all researchers agree that the contemporary information and communication technologies create especially wide opportunities and prospects for collaborative

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learning. That’s why we can observe since the 1990s the development of the new interdisciplinary field in the theory and practice of education aimed at integrating learning outcomes, social processes, and technologies, called “computer-supported collaborative learning” (CSCL). The term first occurred as a topic of research at a NATO-sponsored workshop in Maratea, Italy, in 1989. Six years later, in 1995, the first CSCL conference was held in Bloomington, Indiana (US). Since then, such conferences have been held every two years. This led to the establishment of an international peer-reviewed journal (International Journal of Computer-Supported Collaborative Learning, ijCSCL) in 2006. The journal publishes more than 240 articles annually. Since 2003 the book series Computer-Supported Collaborative Learning has been established. About 20 volumes of the series have already been published, which summarize the experience of theorists and practitioners of collaborative learning from many countries of the world. Obviously, the Internet as a space for social interaction corresponds to the very nature of collaborative learning. Trying to understand the ideas of others and harmonize them with their own ideas and meanings students are involved in the process of meanings negotiation, which can lead them to the joint constructing new knowledge. The idea of CSCL is that students, working in small groups, can productively include collaborative learning at the core of the educational process and intellectual development, using advantages of appropriate forms of computer support. Students master collaborative group practices, individual cognitive skills, and technologically enhanced capabilities, which allow them to cope with the challenges of the contemporary social problems. At the same time, the special attention is paid here to collaborative online learning, since, thanks to the contemporary ICT, online collaboration can be considered as the cornerstone of the educational experience. Almost all online learning activities, from participating in discussions to working in small groups, can be seen as a collaboration, which is often defined as the “heart and soul” of online courses. As well-known American theorists and practitioners of collaborative learning R. Palloff and K. Pratt show in their studies [7, 8], collaboration forms the basis of the online learning community - it brings students together to support the learning of each member of the group, promoting creativity and critical thinking. Of course, as experience shows, online collaborative learning is fraught with many challenges and difficulties, both for students and teachers. Studies point [12] that there are possible student apathy and hostility to group work, difficulty in group selection, lack of necessary group work skills, inequality in student abilities, etc. Therefore, many teachers tend to avoid online collaborative learning. However, the path must be another one here. It is necessary to carefully develop the design of interaction between agents of the educational process to support online collaboration, which would consider both the usability and usefulness of learning environment, set clear goals and guidelines for achieving success, carefully structure classes to facilitate the collaborative process and help students to productively move towards a successful outcome. Technology is useful if it supports student needs at any point in collaborative activity. Importantly, by strengthening trust, shared values and goals, and interdependence, online collaborative learning can reduce the sense of isolation so often experienced by online students and foster a sense of community. Asynchronous discussions, the main element of online

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class, promote democracy and equality among students and lead to more meaningful and empathic communication [9]. Thus, it is obvious that the organization of online collaborative learning is worth the efforts. The main thing here is to understand how to implement it most effectively. Working in this direction, it is important for each teacher to find answers to a series of questions: What separates an effective collaborative learning activity from an ineffective one? How can technology support socialization and communication of students, and not hinder them? How does design of the learning activity impact the success of online collaboration? What is the best way to group students? What makes to form a cohesive, well-functioning team? These are just some of the questions, which are important for us to deal with in practice. To answer number of them we should turn to the field associated with so-called communities of inquire. The concept of the community of inquire, coming from the American philosophers Ch. S. Pierce and J. Dewey, is one of the basic concepts in constructivist methodology and widely used in the analysis and implementation of collaborative learning. In principle, community of inquire is any group of people focused on joint examining and solving problem situations. Moreover, the emphasis on the social context of the cognitive process is of particular importance here that requires intersubjective agreement between group members involved in a study. The concept, originally focused on the field of scientific knowledge, received subsequently wide application in various fields of human activity. As to the learning process, the idea of community of inquire means the following: education is the result of participation in community of inquire under the guidance of a teacher; students are encouraged to perceive knowledge about the world in its inconsistency and ambivalence; disciplines and fields of knowledge intersect and, therefore, relationships between them are always problematic; teachers are not indisputable authorities and can make mistakes; students are expected to be reflexive and develop rationality and reasonableness; there is rather understanding the relationship between studied objects then obtaining information at the core of educational process. These ideas were expanded and applied in the context of online learning within the framework of the so-called Canadian project launched in 1996 at the University of Alberta under the leadership of R. Garrison. The aim of the project was to develop conceptual foundations for the use of computer communication in educational practices. The research results were summarized in the series of publications by Garrison and his colleagues and students. Establishing the practices of the community of inquire, Garrison believes, is crucial for the development of education in the terms of its successful adaptation to the needs of knowledge society, in which communication technologies play the increasingly important role. According to Garrison, the community of inquire is a group of people involved in collaborative thinking through a purposeful and recursive process of reflection and discourse in order to form personal meanings and mutual understanding, which provide a comprehensive and consistent research perspective, in which the quality of learning depends on the dynamics of interaction within communities [1, 2]. In other words, collaboration is the basis of community of inquire, and collaborative constructivism is at the heart of its dynamics. It is important to emphasize that such learning activities involve constant monitoring of the study progress to effectively manage it.

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It means that metacognitive activity is needed to encourage participants of educational process to reflect on their thinking. Such reflection makes possible the constant transfer between functions of teacher and student, the adoption of appropriate roles and forms of responsibility. As already noted, new dimensions and opportunities for collaborative learning are opening with the development of the contemporary information and communication technologies, primarily in the aspect of online and blended learning. The possibilities to maintain the constant and sustainable discourse outside classroom will radically transform an educational experience. Former models of full-time and part-time (distance) education with all their achievements and the highest quality were dotted, asynchronous, as soon as students left their classroom. Today, we can create cohesive learning communities, in which an open, sustainable discourse gains priority and continuity since high-quality communication between teachers and students is possible without time and space restrictions. Thus, the paradigm of community of inquire has acquired qualitatively new opportunities and a new breath. We can say that this transfers students to a deeper level, which is the process of involving the work of thinking in collaborative transactional practices. CSCL offers for the learning process a multifaceted socio-technical environment that promotes collaborative interaction and learning in a variety of ways. Learning communities acquire common spaces and “scaffolding” for creation, construction, visualization, exchange, organization, and promotion of knowledge artifacts, or technological expansion of the mind. The learning process acquires the truly exploratory character aimed at creating such cognitive objects, which are determined by their openness, incompleteness and the ability to unfold endlessly through successive incarnations loaded with thoughts and affects in the form of texts or other artifacts. Students can use various combinations of synchronous and asynchronous technologies for communication, sharing knowledge and resources, and/or developing joint projects. The main problem in this case is establishing effective practices of intersubjectivity and mutual understanding. Such collaborative learning turns out to be an unfinished epistemological project that constantly raises new questions and becomes more complex as technologies, practices, and methods develop unpredictably.

3 Digital Collaborative Learning as a Pedagogical Didactic System The key aspects of collaborative learning identified in the previous section affect significantly all its components as a complex pedagogical didactic system, being to a certain extent the main exponents of its basic characteristics. Taking into account the described elements and characteristics of collaboration we can attribute as the main principles and features of digital collaborative learning the following [10]: – – – – –

maximum approximation of learning to social environment; student-centered learning; learning as research in theory and practice; development of critical, creative, constructive, and synergetic thinking; voluntary grouping of students;

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– – – –

student responsibility for their own learning; democracy in learning in groups and pairs; mutual trust and mutual assistance between students; maximum decrease of teacher instructive role and his/her control over the process of student learning; – student self-assessment of their group work; – active use of communicative computer technologies and services in learning. The main goal of learning is forming students’ competencies in a certain field of knowledge and practical activities. All actions of teachers are usually aimed precisely at this and with such an approach digital collaborative learning acts as an effective didactic component to achieve the main learning goal. At the same time, digital collaborative learning has its own internal general subject goals and a special taxonomy inherent only to it. According to Salmons [13], the taxonomy of collaboration has three main components: collaborative processes, levels of collaboration, and the trust continuum. Based on B. Bloom’s improved taxonomy, Salmons proposed to use and adopt the collaborative learning taxonomy, which takes into account participants’ experience with ICTs, level of relationship and trust among members, leadership experience, decision-making, and project coordination skills online (see Table 1). Table 1. Taxonomy of collaboration. Reflection

Individuals align their own knowledge, attitudes, and skills with group efforts. Individuals make sense of and prepare their roles in collaborative efforts

Dialogue

Participants in the collaborative process agree on and work with the group’s communication expectations, timelines, processes, and tools. They exchange ideas to find shared purpose and coherence with the plans and/or tactics needed to coordinate their efforts

Review

Participants exchange work for constructive mutual critique and to incorporate others’ perspectives. Participants evaluate which elements of each partner’s work should be included in the deliverables, and how they will be integrated into the whole

Parallel collaboration

Participants work to each complete a component of the project. Elements are combined into a collective final product, or the process moves to another level of collaboration

Sequential collaboration

Participants complete stages of the work, building on each other’s contributions through a series of progressive steps. All are combined into a collective final product, or the process moves to another level of collaboration

Synergistic collaboration Participants synthesize their ideas to plan, organize, and complete the creation of a product that melds all contributions into a collective final product

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It is also important to pay attention to certain external and internal factors that have a significant impact on the systematic application and quality of designing and functioning collaborative learning. These include: – legal framework and maintenance management of the educational process in region, country, or educational institution; – forms of providing education; – technical, technological, and software support of learning; – degree of qualification of teachers and staff expressed through system of pedagogical competencies, etc. Keeping in mind the above said, we further define and describe collaborative learning as a holistic digital pedagogical didactic system based on the general systems theory, according to which when designing, operating, and researching any system it is necessary to explore its characteristic features and properties dividing them into three sets. The first set combines the properties of the system that determine its structure. Based on this we single out the following components in the didactic system of collaborative learning: – participants of the educational process: teachers (instructors), students, support staff; – components of the didactic system: forms of classes; didactic goals; content of educational materials; didactic methods and techniques; educational technologies; forms of learning activities; control and assessment. All components of the didactic system are interconnected. Describing components of collaborative learning for their active use we will designate the most favorable of them to organize such learning. Let us present the didactic goals of collaborative learning through the prism of general and interdisciplinary learning goals, the development of types of thinking and communicative information culture. Being implemented in the framework of collaborative learning the general and interdisciplinary goals do not affect concrete subject learning goals giving this privilege to teachers. With regard to students, these goals involve forming the general communication skills among members of community of inquire, including: exchange of knowledge and practical skills and creation of new knowledge; negotiation of problems and forming hypotheses; discussion and analysis of research projects; formulation of conclusions and results of the discussion; conducting dialogue, discussions, debates, brainstorming, etc.; joint review of projects and artifacts; forming organizational skills and responsibility for group work; reviewing and conducting an expert evaluation. In addition, it is important to develop verbal and language skills: speaking, listening, and writing in process of group working, including such activities in foreign languages. Forms of conducting classes are very diverse in higher education. We do not set ourselves the goal of considering all these possible forms, of which there are several dozen, especially when it comes to non-standard forms. Note only that the most favorable forms of classes in collaborative learning are seminars, webinars, symposiums, conferences, group trainings, performances, etc.

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Based on the principles and features of collaborative learning discussed in the second section of the article, it can be distinguished several cardinal requirements for didactic teaching methods: research nature of learning; democratic group or pair student work; responsibility and mutual assistance when working in a group; the ability to self-assess the group work. It is especially important to emphasize the importance of using creative (non-standard) teaching methods in collaborative learning, for example, debates, discussions, brainstorming, empathy, synectics, inversion, etc. The second set of properties concerns the organization of collaborative learning as a didactic system within other didactic systems and technologies. Digital collaborative learning functions as a subsystem of a larger didactic systems, for example, the lesson as an integral system. The frequency of using collaborative learning is usually limited to one or more classes, but there may be other options. So, since digital collaborative learning can be carried out for a long time in some thematic and interdisciplinary areas (especially when communities of inquire work), for example, several semesters, teachers may need to plan such learning at once for the entire academic course and even for an educational program within the framework of large interdisciplinary blocks (modules). Answering the question of how the didactic components of digital collaborative learning depend on the didactic components of larger systems it should be said that this dependence is of a subordinate nature mainly. For example, the goals and content of the collaboratively studied educational material can be subordinate to the goals and content of course or educational program being studied, as well as the competencies formed by students. The third set of properties of a didactic system presents its interaction with systems of different nature: technologies and services, such as the Internet or cybernetic systems. In our case, this is the interaction and connection of collaborative learning with computer technical, technological and software provision. The implementation of a system of general education courses according to the model of liberal education at the European Humanities University (EHU) is an example of realizing collaborative learning project based on involving students in the research-like educational process to solve real social problems. This system bases on a number of principles, among which the understanding that the quality of knowledge in humanities and social sciences is determined by its appeal to the integrity of human experience, to the ontology of human existence is of particular importance. The goal of this knowledge is not simple declarations of the abstract freedom of the individual as a starting point, but an analysis of ways to be included in the tradition in frameworks of specific social, cultural, and political circumstances. One of the brightest examples of such an orientation of educational process is EHU course “Language and Thinking”. The purpose of the course is developing student skills and values to creatively and independently express their thoughts and ideas, formulating one’s own position and understanding views of other people, forming skills of participating in discussion and collaboratively searching for ways to solve certain social problems. The course is an introduction to the system of general education subjects. It is intensively conducted during the first 2–3 weeks of freshman classes and aimed to teach students how to actually read and write texts and, on this basis, think creatively, search for and formulate independent answers to problems posed. Unique intensive writing

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and discussing practices used in the course are aimed at deep mastering by students the relationship between thought processes and ways of expressing its results, which is ultimately focused on various identity construction practices. The course allows students to get acquainted with different types of thinking: creative, critical, constructive, logical, etc. Most of the tasks are performed by students in small groups working in the format of communities of inquire. At the same time, the strict teacher control is gradually reduced during the course, students get more and more opportunities for independent work, preparing various kinds of projects and their presentations and defending them in student community. Of course, the organization and implementation of collaborative learning face a number of difficulties and limitations. Among them, we could note the following: lack of teachers and students experience in organizing and conducting independent group work at a distance; the nature and quality of special computer software for collaborative distance and mobile learning in groups; peculiarities of methods specific to the study of concrete academic disciplines. To overcome these difficulties, it is recommended to introduce collaborative learning practices gradually: from solutions of simple problematic tasks to transition to the technology of cooperative learning with tighter teacher control to collaborative learning in its full format (communities of inquiry). At the same time, it is important to form in advance teachers and students practical skills in working with network software for collaborative activities, for example, holding video conferences in Zoom space, using a virtual whiteboard for writing Padlet, using various Google applications - presentations, drawing, co-writing texts, etc. It is also necessary to understand that collaborative learning contributes primarily to the search for solutions to educational problems in various alternative ways, which often involves obtaining different and sometimes ambiguous results. If in the social sciences and humanities the proposal of various solutions to the same problem is in the order of things, then in the field of science and math only one correct answer is often a rule. In other words, each concrete discipline requires a clear explication of peculiarities of its possible use of collaborative learning methods. This increases the load on teachers, which does not contribute to the enthusiasm for implementing collaborative learning.

4 Conclusion In conclusion, let us turn to the issues of criteria for the successfulness of collaborative learning that is actively discussed in research and methodic literature. Based on theoretical research and practical experience of collaborative learning, it seems possible to formulate some such criteria [1, 2, 5–7]: – high-quality organization of collaborative activities, which implies a clear definition of an environment of such activities, its planning and modeling both offline and online, management of the process of its implementation and a volumetric evaluation of its results; effective planning is the key to solve problems of collaboration; – critical introspection by teacher of his/her readiness to organize collaborative learning; teacher must make sure that he or she is comfortable with this form of activity;

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– selection and use of effective contemporary tools of collaborative pedagogical activities, especially in online format, assimilation of basic practices of collaboration by group members; – development and use of measures to counteract possible resistance to collaboration on the part of students, developing infrastructure that provides opportunities for monitoring of collaborative activity; – readiness of students to use the contemporary technologies and appropriate relevant practices; – effective distribution of resources, roles and powers in student group, the establishment of sustainable and effective leadership in the group; – timely teacher intervention in the case of ripening frustration or conflicts, emphasizing the fruitfulness of group diversity, maintaining trust, and developing collaborative ethic. Thus, to build, say, the “scaffolding” of collaboration and ensure its effective facilitation teacher needs to: clearly explain to students the essence, tasks, and tools of implementing collaborative learning; provide groups with a structured set of prompts and questions as to collaborative problem solving; control processes and intervene only when necessary to help students move to deeper levels of collaboration; provide effective feedback. It is obvious that these are far from simple tasks in terms of their implementation by teacher. Of course, students need to have fairly clear instructions aimed at organizing effective collaborative learning. However, each time these instructions will have to be concretized and individualized, taking into account specificity of the subject taught, objectives of classes, the nature of tasks, a degree of students preparedness and motivation etc., as well as the need for an adequate individual assessment of the results of each student’s educational activities, moreover assessment based on both his/her self-assessment and assessment by his/her peers. Thus, the learning practices of communities of inquiry will be implemented, which is the basic criterion for effectiveness of collaborative learning model and corresponding organizational efforts of teachers.

References 1. Garrison, D.R.: Thinking Collaboratively: Learning in a Community of Inquiry, p. 190. Routledge/Taylor and Francis, London (2016) 2. Garrison, D.R.: E-Learning in the 21st Century: A Framework for Research and Practice, 2nd edn., p. 202. Routledge/Taylor and Francis, London (2017) 3. Johnson, D.W., Johnson, R.T.: Cooperative learning and achievement. In: Sharan, S. (ed.) Cooperative Learning: Theory and Research, pp. 23–37. Praeger, New York (1990) 4. Miniankou, R.: Digitalization of social life and the transformation of higher education. In: Ostenda, A., Nestorenko, T. (eds.) Information Technology and Innovation for Society Development, pp. 6–21. Publishing House of University of Technology, Katowice (2021) 5. Newell, C., Bain, A.: Team-Based Collaboration in Higher Education Learning and Teaching: A Review of the Literature, p. 69. Springer, New York (2018). https://doi.org/10.1007/978981-13-1855-9 6. PISA 2015 collaborative problem-solving framework. In: PISA 2015 Assessment and Analytical Framework: Science, Reading, Mathematic, Financial Literacy and Collaborative References, pp. 131–188. OECD Publishing, Paris (2017)

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7. Palloff, R.M., Pratt, K.: Collaborating Online: Learning Together in Community, p. 112. Jossey-Bass, San Francisco (2005) 8. Palloff, R.M., Pratt, K.: Building Online Learning Communities: Effective Strategies for the Virtual Classroom, p. 292. Jossey-Bass, San Francisco (2007) 9. Posey, L., Lyons, L.: The instructional design of online collaborative learning. In: Luzzatto, E., DiMarco, G. (eds.) Collaborative Learning: Methodology, Types of Interactions and Techniques, pp. 363–382. Nova Science Publishers Inc., New York (2010) 10. Puptsau, A., Kazinski, A.: Modern Distance Communication Course: Design, Development and Teaching, p. 212. Ciklonas, Vilnius and Riga (2020). (in Russian) 11. Roberts, T.S.: Computer-supported collaborative learning in higher education. In: Roberts, T.S. (ed.) Computer-Supported Collaborative Learning in Higher Education, pp. 1–18. Idea Group Inc., London (2005) 12. Roberts, T.S., McInnerney, J.M.: Seven problems of online group learning (and their solutions). Educ. Technol. Soc. 10(4), 257–268 (2007) 13. Salmons, J.: Learning to Collaborate, Collaborating to Learn: Engaging Students in the Classroom and Online, p. 189. Stylus Publishing, Sterling (2019)

Human Resources Management and Training in Aviation Federico de Andreis(B) , Ubaldo Comite, Federico Maria Sottoriva, and Ilaria Cova Università Giustino Fortunato, Benevento, Italy {f.deandreis,u.comite,i.cova1}@unifortunato.eu

Abstract. Examining investigations of aviation accidents and incidents, it can be seen that technological progress has significantly reduced possible malfunctions. Statistics show that the majority of events that aviation faces today are caused by human error or rather by human interactions with technology. Technology, in order to express its full potential, must be supported by an appropriate organization. In fact, both rely on intangibles, i.e., nonmaterial assets, such as human capital and intellectual capital, that are not as readily available on the market as technology, but they must be prepared, trained, and shaped according to corporate objectives. If in the past accidents were more related to technical problems, nowadays the human aspects create the main problems, however they are an irreplaceable resource since they can be a tool for promoting safety in air transport, through a better interaction with technology. This analysis, developed as part of a research project of the Giustino Fortunato University, aims to investigate the relationship between human resources training and technological instruments, contributing to research in the field of safety and training. Keywords: Human resources · Aviation · Evidence based training · Human resources management

1 Introduction Aviation has always considered accident prevention as one of the fundamental elements of organizations, in order to ensure a high level of safety for passengers and operators. In fact, over the years, air transport organizations have made continuous efforts to improve safety and to adopt standards that would lead to an optimal management system, whose purpose is to minimize risks, both considering the probability of occurrence of an event and its consequences. The improvement in airline safety is down to a combination of several factors, although the introduction of the jet engine in the 1950s stands out as a major development. Jet engines provide, in fact, a level of safety and reliability unmatched by the earlier piston engines. Today, we can state that engine manufacturers have almost eliminated the chance of engine failure [4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 510–522, 2023. https://doi.org/10.1007/978-3-031-26655-3_46

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Later, the introduction of electronics, most notably the introduction of digital instruments in the 1970s, the advent of fly-by-wire technology in the 1980s, improvements in sensors, navigation equipment, air traffic control technology and anti-collision control systems are also significant achievements, driving safety improvements in aviation [17, 26]. It therefore follows that, for a long time, the main cause of accidents and incidents on a large scale has involved humans and their interaction with technical aspects. Indeed, disaster studies carried out particularly during the 1980s have allowed us to look at these events from a larger and more general perspective, coming at the conclusion that there is a complex combination of organizational, group and individual factors, even in the presence of technical incidents, that causes serious events [30]. Therefore, achieving better results in terms of safety through enhancement and innovation in personnel training, becomes the goal of aviation, or rather the continuing trend in training in aeronautical contests, to which organizations are tending [11]. The purpose of the research is to investigate the relationship between human resources training and technological instruments and to provides a strong warning to the aviation sector, pushing it to seriously be reckoned with. Understanding the right context and applying a better training of different disciplines, we can recognize potential catastrophic human factors and address them before these develop into a bigger problem or create a chain of problems that results in an accident or incident. Summarizing, the research aims to answer the following issues: – RQ1. How has the intervention of technology helped the human factor in the prevention of adverse events in aviation? – RQ2. How the innovative approach of EBT - Evidence Based Training goes beyond the mere simulation of scenarios in pilots’ flight simulators and can provide improved human performance? – RQ3. Why does the development and evaluation of behavioral skills lead to improved training results, enabling human resources to handle potentially dangerous situations never seen before? The research is structured as follows. In the following Sect. 2, the methodology of the research review is set out. Next, the findings of the literature analysis on safety and on the models used are shown in Sect. 3. In Sect. 4, three case studies are described, from which the contextual analysis, developed in Sect. 5, is drawn, focusing on the Evidence Based Training. Furthermore, conclusions and implications for future research and are presented in Sect. 6. This paper contributes to expand the literature on human resources management and training, emphasizing the Evidence Based Training (EBT) as a method offered by improvements in aviation safety technology and as an instruments to identify in air transport, operating safer, threats and major errors based on evidence gathered in operations and training.

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2 Methodology The present study is the result of a qualitative research aimed at providing a contextual analysis and it benefits of three case studies. The paper is based on the literature review in psychology, sociology, risk management and human factors to the air transport industry, with the aim of identifying the strengths and weaknesses of current systems and procedures to implement safety. Through a literature review, the research investigates the evolution of the aeronautical safety over the years providing a contextual analysis. Using three case studies it will be highlighted how these theoretical aspects emerged in real operational scenarios, leading front line operators to make decisive errors to which the accidents triggers were grafted. The Evidence Based Training projects will then be presented as global safety improvement initiative in the training environment, supported by the International Civil Aviation Organization (ICAO), to better face the aforementioned issues. The origin of EBT arose from an industry-wide consensus that a strategic review of recurrent and type-rating training for airline pilots was necessary, in order to reduce the airline accident rate.

3 State-of-the-Art 3.1 The SHELL Model In the past decade, the aviation industry has continued to become ever-safer, showing a slow but nonetheless significant reduction of accident rate for both jet and turboprop aircraft. Much of this improvement can be attributed to the increasing performance and reliability of aircraft technologies, which turned into a reduction in the number of accidents caused by purely technical failures. As a consequence, during the recent years there has been a renewed focus on the role of human error in air accidents, since the majority of events involve, in one way or another, human errors [7]. Despite these premises, a cause-and-effect view of critical events is still deeply present, since for a long time it was a very effective interpretation of the world, which enabled man to make scientific discoveries of enormous importance. However, for almost a century, this approach has been outdated. Humans has now achieved such a scientific progress that technique and technology influence almost every aspect of human life; people are living in an almost paradoxical situation in which technology has evolved faster than their ability to represent and manage it on a theoretical level. If we continue to use simple models, characterized by the spatio-temporal proximity between the cause of an event and the effect it produces, to control complex systems, we would risk to find what we are looking for, i.e., the error, and only that, believing that we can explain what happened and solve the problem once and for all. Moreover, error carries with it the concept of blame: the person who made the mistake is wrong and must be eliminated from the system to keep it healthy [23]. In the research for the causes that have generated an accident it is therefore easy to use a person-oriented approach, identifying in the notion of human error the explanation of the reasons why an event had an undesirable result. However, this accusatory approach, allows us to have only a partial view of accidents in complex systems. In fact, if in the

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investigation carried out on an accident, we can positively answer the question whether another person, being in the same situation, could have made the same mistake, then we can easily realize that the event could happen again since it is not caused by the individual, but from the context and the scenario in which it occurred [6]. For this reason, the study of why people make mistakes must also consider the environmental, physical, social, organizational and technological conditions that can cause people to make mistakes. The organizational error theory states that a situation of error propensity can increase the probability that a human error triggers an accident. Particularly, within an organization, when the number of organizational critical issues, lack of control, deficiency of training and design defects is large, a human action or decision is more likely to cause an accident [24]. Analyzing the actions and the decision-making process that led to the aviation accidents constitutes the main area of assessment in the risk management sector, therefore the training methods and evaluation approaches should be carefully reconsidered. In fact, through the analysis process it is necessary to study and prevent any incident (or most of them) making aviation an even safer industry. As stated before, human factor can be attributed to constitute the main cause of incidents and accidents. In 1990, James Reason analyzed the human factor as main element in organizations, and transferring it to aviation organizations it is easy to understand that operational errors are not mostly made by front liners themselves, but they were just the final receptors finalizing the latent errors that were present throughout the whole chain in the form of silent leaks [25]. In aviation there is a reference model for identifying the intervention areas in order to better integrate the human being in the work environment. This model, named as SHELL model, constitutes a solid starting point that leads to understand how the tools can be useful in improving safety. The model, designed by Edwards and integrated by Hawkins, identifies and analyzes the interactions between the various components that can generate interruptions or failures in the functioning of the organization [10, 16]. Hawkins illustrated the revised Shell Model into a building block diagram in 1984 (Fig. 1). The concept of the model concerns a graphic representation into a simple layout of human beings in the center, depicted as L-Liveware surrounded by four human factors interfaces: H-Hardware, E-Environment, S-Software, L-Liveware. Over the last 30 years, computers, related to automation and information data, have slowly start to crowd the interfaces represented by the model [15]. Surprisingly, it is not just the Liveware-Hardware interface where computers are used in the form of flight controls to disrupt the direct flow in the ergonomic design of man-machine interfaces. Nowadays multiple computers are involved in all Liveware interfaces. For this reason, the Hawkins variant of the SHELL model, acronym for Software, Hardware, Environment, Liveware, Liveware, presents the human component at the center of the system, surrounded by the four dimensions which the front line operator will have to interact with [20]. The SHELL model aims to improve the ergonomic design of work environments, so that people can operate with effectiveness, efficiency, safety and satisfaction. In order to highlight and solve the critical conditions that can threaten the safety of a work process, the four areas on which we must focus the study to reduce human errors are described as follows, highlighting the support of technology to them.

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Fig. 1. SHELL model (Source: model elements compiled by the authors).

L-S interface (Liveware-Software): includes all the procedures, rules and information that, presented on special screens or algorithms, regulate the operation of the system and define human performance. This interface must be designed through procedures and rules that do not conflict with human characteristics, it must not be impossible to apply or difficult to understand. These procedures should also be covered by the organization’s internal regulations and manuals. More to the point, the Liveware-Software (L-S) interface unexpectedly is facing a computer-change. A wide range of material, non-physically used in flight, belongs to this category. Procedures, maps, publications, documents, checklists, approach plates in previous years were made mostly in paper form. The establishment of the Electronic Flight Bag (EFB) created a new interaction with computers in this interface to be managed, again, by humans. Even if some documents belonging to this category were uploaded previously in LCD screen or iPad, nowadays we could observe a supremacy of computer technologies in this interface. L-H interface (Liveware-Hardware): it concerns the relationship between humans with technology. It must be designed taking into account the operator’s abilities, his characteristics and both physical and cognitive limits, to support and increase the human operators’ abilities. The L-H interface represents all the physical elements and systems of the aircraft such as wings, control surfaces along the entire hydraulic system, flight control. Despite the physical part of the aircraft falling into this interface, the one that best represents the direct connection between man-machine ergonomics is the cockpit. Here, the crew continually assesses data to manipulate those controls. The computer has been integrated in the form of automation in flight controls or fuel control system, for example. Clearly computers are now handling most of the flying thus eliminating a large amount of direct human interaction, with the aim of reducing as much as possible human errors and inefficiency, giving on the other hand to the pilot a visual reference of flight controls to manage. The ergonomic design of the human-computer interaction has changed using optical channels and calls to manage the flight [21]. L-E (Liveware-Environment) interface: represents the interaction between humans and the external environment. It includes both physical elements (such as noise, temperature, cabin pressure, day-time and night-time, weather, etc.) and social, organizational and economic climate. The L-E interface concerns the different human’s relationships with the inside and outside environment and the industry has introduced safety computer technologies and institutional elements, that represent constraints for the system, in order to recover from most of the accidents and fatalities caused by human errors in different phases of flight, such as

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controlled flying into terrain, the maintenance of inflight separations between aircraft, flying into bad weather or loss of control. Therefore, this interface has been crowded with multiple computer-based systems that need to be managed by humans, such as the introduction of Ground Proximity Warning System (GPWS), which helps to prevent ground collisions, the Traffic Collision Avoidance System (TCAS), useful to avoid midair collisions, the Stall Warning, that was implemented with stick shakers, the Airborne Weather Radar, adopted to increase safety from the common weather hazards or the Inertial Navigation System (INS), which allows the aircraft to navigate from point A to point B; L-L interface (Liveware-Liveware): concerns all the human components of the system. Describes how other people interact with the operator, analyzing the communication, coordination, leadership and interpersonal relationships problems. More specifically, this last interface L-L (Liveware-Liveware) accounts for the human interactions that occur during the flight. These interactions can be between CC (Cockpit Crew) and ATC (Air Traffic Control) or CC and FA (Flight Attendant). To minimize human error in this interaction, since the 1980’s, the aviation industry moved to what is called ACRM (Advanced Crew Resource Management) focusing on teamwork and highlighting concepts as communication, assertiveness, management, task delegation, leadership/fellowship, decision making. This approach, a non-computer human factors safety program, had a strong impact on safety and efficiency in the L-L interface, addressing many of the potential errors through an efficient teamwork management method inside the cockpit. About human interaction with technology, we can state that the future of aviation is now focusing on “Datalink” technologies, that forced to introduce updates of analysis models to cope with these Next Gen aviation integrated systems as well as the training methods that need to be re-designed. The unlikely events proposed in the old training simulator scenarios require to be remodulated, involving the management of widely automated flight and the analysis of the large digital information connected with workload for flight crew. A correctlydesigned system presents a state of constant balance between its components, with the related highly interactive connections. Any change in the conditions of an internal component of the system could threaten the overall reliability and therefore requires a realignment of the other components [1]. 3.2 Reason’s Model and the Latent Factors Theory Therefore, trying to broaden our gaze to all the contextual factors, internal and external, which the system interacts with, we introduce the “Latent factors theory” by J. Reason (1997), who proposed a model for analyzing the accidents dynamics in complex systems, which best represents the shift of focus from the person to the system [19]. According to this theory, accidents are hardly ever caused by a single factor, but they occur when there is a concatenation of different nature events. Each factor, human, technological, social or organizational, taken individually would not be strong enough to give rise to an accident, but when it concatenates simultaneously with other factors and conditions then it can generate an adverse event.

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Every accident has at its origin a strong interaction between active errors and latent factors. Active errors, generally committed by front line operators, can be identified as errors in the execution of activities or deliberate violations of various types and have immediate effect. Latent factors, on the other hand, are conditions that remain hidden and silent even for a long time and become visible only when they are combined with other local factors, overcoming the system’s defenses and generating the accident. These latent failures are believed to originate in fallible management decisions in decision making processes and are embedded at different levels in various organizational processes (Fig. 2).

Fig. 2. Latent factors model (Source: model elements compiled by the authors).

Moving forward, in the research on the promotion of safety in organizational contexts, Reason’s contribution is important to highlight. Reason’s model (Fig. 3), named Swiss cheese model, also called epidemiological from the analogy of latent factors with the pathogenic elements of a disease, is based on the research for the potentially dangerous and hardly visible factors, in order to eliminate or correct the preconditions that give rise to the potential for safety hazards to occur. The slices of cheese therefore represent the various levels of the system, each containing flaws or specific weaknesses. The interaction between the latent factors present at each level with the active errors triggered by the front line operators give rise to a trajectory of opportunities which, by penetrating the various defensive barriers through the individual systems flaws, generates the accident. The scheme therefore defines a structure for both reactive and proactive analysis, identifying the various latent factors relating to incorrect management decisions to anticipate unwanted consequences [9]. This leads to the concept of organizational accident, in which the causes are not to be found in human behavior but in the pathogenic elements residing in the system.

4 Case Studies After the analysis of safety promotion models, the following review of the case studies will present some examples of organizational accidents, highlighting planning errors, organizational and inter-organizational criticalities as well as pre-existing non-safetyoriented cultures, on which the actions or omissions of the front line operators, that triggered the accident, were grafted.

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Fig. 3. Reason’s Swiss cheese model (Source: SKYbrary aviation safety).

Case Study 1 - Asiana 214 The aim of analyzing this case study is to bring a factual example. This case study addresses how humans interact with automation and provides insights on the need of an improved crew resource management (CRM), aviation training and cultural issues. On July 6th , 2013, the Asiana flight 214 from Incheon crashed on its final approach to San Francisco International Airport. The plane hit the seawall at the edge of the runway. The Asiana Flight 214, carrying 307 passengers, suffered three fatalities. The real problem has to be attributed to the low speed of 103 knots instead of 137 as the target speed. The pilot in command was questioned and immediately told to the investigators that the auto throttle was set at 137 knots and assumed that it would have maintained that speed [22]. Therefore, the board members believed that the crash was caused by the pilots’ over reliance on the automated system. It is clear how the most common types of pilots’ errors are made. Modern awareness is being conscious of the current and future state of automation. Mode error occurs when there is a misunderstanding of how the automation should act or a lack of knowledge and training. Case Study 2 - Excalibur Airways On August 26th , 1993, an Excalibur Airways Airbus 320 took off from London-Gatwick Airport and exhibited an undemented roll to the right during take-off, a condition which persisted until the aircraft landed back at London-Gatwick Airport 37 min later. To control the aircraft a significant and constant pressure on the sidestick to the left was required due to the loss of spoiler control which affected the flight control system proper operation. Technicians, familiar with the Boeing 757 flap change procedures, lacked the required knowledge to correctly lock out the spoilers on the Airbus during the flap change operation that was done the day before the flight. Turnover technicians, during the next shift, compounded the problem. No mention of an incorrect spoiler lockout procedure was given since it was assumed that 320 was like the 757 [3]. The flap change was operationally checked, but the spoiler remained locked out incorrectly and was not detected by the flight crew during standard functional checks [27]. The lack of knowledge about Airbus procedures was considered a primary cause of this incident.

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Case Study 3 - Überlingen Accident On July 1st , 2002, two aircrafts were on a collision course. The air traffic controller was working alone while managing two stations at the same time. Supposedly due to the added workload, and the latency of radar systems, the ATC did not immediately realize the collision route that the two aircraft had. Only one minute before the ATC understood the danger and contacted one of the aircraft giving instructions that were contrasting what the TCAS instructed to do, the other aircraft crew did not receive any information from the ATC, so decided to descend (following the TCAS instructions), doing the same maneuver of the first aircraft instructed by the ATC. A few seconds later the two aircraft collided since they were both instructed to descend, one by the ATC and the other one by the TCAS. If both aircraft had followed the automated instructions, or if the ATC was more rapid in recognizing the danger and communicating instructions, the collision would not have occurred [18]. Throughout the investigation of the mentioned case studies it appears clear that human factor was a crucial aspect of the accidents, rather in the specific situations, rather in the specific situations, human factors in interfacing with technology. If at the beginning of aviation, human factor covered a very little percentage of all the accidents, due to machinery unreliability, nowadays human factor is the main reason we still have accidents [5]. Paradoxically, the high reliability of the equipment and cockpit automation have simplified pilot tasks so dramatically that may have led this generation of pilots being too reliant on technological assistance. We have analyzed three different case studies where accidents have occurred in three different conditions. In the first case (Asiana 214) pilots were too reliant on the aircraft features and this made them underestimate the danger. In the second case (Excalibur Airways) the maintenance played a key role in the incident. Finally, in the third case (Überlingen Accident), another component of the aviation industry played an important role, the ATC was unable to comply with the high attention that the role demanded. All these three situations point out the importance of single players as well as a team and these accidents were all system accidents, where the system is intended as the organization and training of people. All the players have made grave errors in underestimating their contribution and failing to monitor. In the previous analysis various factors can heavily contribute to accidents, errors are inevitable as part of human nature, but determining these errors, and helping people to avoid them can be a life savior movie [29]. Therefore, it is crucial to create an environment of mistakes prevention with proactivity and dynamicity reducing the consequences of single errors. An alternative training system could be one of the solutions proposed.

5 Discussion - The Application of Evidence Base Training What we have seen above, leads us easily to understand how technological innovation has not only reduced the number of accidents/incidents in the past, but it can also offer a possible way of improving training such as Evidence Based Training [28]. The concept was developed around 2006 on the growing consensus that FCT – Flight Crew Training was based on events that had become improbable in modern aircrafts. At the inaugural meeting to develop constituent elements of the International Air Transport Association

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(IATA) Training and Qualification Initiative (ITQI) in 2007, representations were made by stakeholders to include a strategic review of airline pilot training as part of the sponsored activity [14]. The way in which modern aircrafts have progressed in terms of design and reliability and the manner in which the operating environment has changed, and the realization that the human factors aspect has not been adequately addressed, have necessitated a strategic review of airline pilot training. In addition, the extensive amount of accidents and incidents reports and vast provision of flight data analysis gives the opportunity to identify risks encountered in real life operations and to create ad hoc training programs aimed at mitigating those risks that flight crew members face in operations. The working group that was created from this initial meeting came to establish a new methodology for the development and conduct of a recurrent training and assessment program, named EBT – Evidence Based Training [8]. The scope of the program was to identify, develop and evaluate the competencies required to operate safely, effectively and efficiently in a commercial air transport environment whilst addressing the most relevant threats according to evidence collected in accidents, incidents, flight operations and training [15]. The airline pilots training that had been the standard up until the advent of EBT was largely based on reports and investigations of hull losses - a term most often used to describe the status of an aircraft which has been destroyed or has otherwise been determined to have been damaged beyond economic repair - from early generations jets, but, more importantly, the common mitigation practice had been the repetition of an event in a training program up until considered satisfactory. In parallel to this situation the reliability and design of the aircrafts improved extensively, bringing to a situation where many of the accidents happened in aircrafts that were perfectly operational and without any malfunctions. One of the best examples could be CFIT (Controlled Flight into Terrain), when a flight with a perfectly functioning aircraft results in a hull loss where one of the leading factors to the accident is most often an inadequate situational awareness [12]. Evidence Based Training has then been developed as a new approach to flight crew training where the concept of Scenario Based Training, which is where a maneuver or a scenario is repeated until the pilot reaches proficiency with limited focus on whether she or he has realized or has been made aware of the reason why the expected level of proficiency could not be reached, evolves into the identification of a defined number of pilot competencies which are then assessed and subsequently developed with the idea that mastering a finite number of competencies should allow a pilot to manage an infinite number of unforeseen circumstances. The EBT program has subsequently been designed to develop and evaluate all areas of flight crew competency relevant to the recurrent training performed during a pilot operational “life” and has its foundation in the definition and creation of a framework of competencies, competency descriptions and related behavioral indicators, which comprehensively encompass the technical and non-technical knowledge, skills and attitudes to operate safely, effectively and efficiently in a commercial transport environment [13]. The core competencies (8 key competencies) that have emerged from this development are the following:

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Application of procedures; Communication; Aircraft Flight Path Management, automation; Aircraft Flight Path Management, manual control; Leadership and Teamwork; Problem Solving and Decision Making; Situation Awareness; Workload Management.

Each of these competencies can be broken down into multiple performance indicators which enable the trainer to identify the parts of the competence performed satisfactorily or not, leading to an overall idea for the trainer of the level of proficiency of the pilot. This process will ultimately lead the trainer to focus her or his guiding role towards the competencies the trainee needs to improve or develop or in which has reached a proficiency level. This method makes the trainer able to grade the performance in order to collect data for the progress monitoring of the pilot and also for a wider statistical trend analysis for the entire airline that can be filtered by rank, by single competence, by fleet etc. in order to highlight the area where improvements are needed. The idea behind the application of the grading technique is threefold: 1. The need to fulfil a regulatory requirement for the recurrent validation of the flight licenses has been maintained by the authorities, hence the presence of a grading framework in order to assess the proficiency of the pilot and the successful achievement of the required regulatory standards. 2. The possibility for the pilot to have her/his strengths and weaknesses highlighted and to have the guidance from the instructor to improve these areas. 3. As stated previously, the opportunity for the operator to collect an invaluable amount of specific data for the pilot and hers/his progression through the time operating for the airline and also a companywide collection of data across all ranks and positions, offering a more generalized status of the performances for the entire pilot workforce. These last two points are interesting to give the airlines some tools to approach training with a flexible and tailored approach, enabling the creation of very specific training packages for individual pilots or wider programs tackling for example rank or base specific deviations from the average results collected companywide [31]. All of these characteristics of the EBT program have the ultimate objective to improve the overall level of safety of the airline industry, creating a layer of safety barriers that can be visualized with the Reason’s Swiss cheese model, where each notorious slice assumes the name of one of the previously analyzed competencies and where the scope of the EBT program is to seal as many holes as possible, in order to make the barriers as error-proof as possible and enhance to the maximum the safety of the airline public transport.

6 Conclusions and Future Research Directions Safety has always been the primary and essential relevance in aviation. As seen, the implementation of technologies has reduced accidents to minimal [2], but still human

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error and the man-machine interaction remain not completely abatable, as demonstrated by the empirical part of the research, through the three case studies. Research conclusion is to make use of the technologies, in this case the EBT system technology, to create a tailored training for pilots focused on the 8 key-competencies and creating scenarios that reduce accident events continuously improving safety even more. These core competencies (Application of procedures, Communication, Aircraft Flight Path Management – automation, Aircraft Flight Path Management - manual control, Leadership and Teamwork, Problem Solving and Decision Making, Situation Awareness and Workload Management) valorized by the EBT can be implemented with pilot training and can be useful to manage any possible scenario that may pose a risk. As investigated in the discussion, the natural evolution for EBT is the tailored training of the pilot in order to make her/him progress in mastering the aforementioned competencies, but the source of inputs for EBT can be expanded and empowered by a proactive, rather that reactive, approach since it could be fully integrated into the Safety Management System of an airline. The sources that can feed the definition of the various tailored EBT objectives rather than coming from industry wide trends or events, which are not exactly readily available to the airline, could instead be made more proactive if the EBT program could draw its inputs from the Flight Data Monitoring system of the operator, together with inputs from its Safety department and integrated with the feedback from its Training Department, in order to allow a much quicker adaption and benefit the pilot workforce with up to date and almost live tailored training.

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Services Delivery Model for Education-as-a-Service Based Framework Boriss Misnevs(B)

and Igor Kabashkin

Transport and Telecommunication Institute, Lomonosova Str. 1, Riga, Latvia {bfm,kiv}@tsi.lv

Abstract. The digital revolution is bringing new competition to education compared to classical universities, especially in the form of more accessible online education. At the same time, interest is growing in the mobility of educational services, which is designed to bridge the gap between businesses that require new competencies, the inertia of universities in providing them within the framework of classical programs, and the student’s desire to instantly receive services to master new competencies. All this is accompanied by the development of a competency-based approach to education, which allows structuring the needs of society and clearly formulating the demand of society for the training of professionals. This need has already been formulated in the form of European standards and professional frameworks that create a regulatory framework for the implementation of new approaches to education. The paper describes the initial results of the study, the aim of which was to create a fundamental feasibility model for a cloud-based service-oriented education platform for the next generation of an educational institution. This article presents the Service Delivery Model, which provides a description of the main functions of the proposed digital educational platform. Keywords: Education as a service · Competence-based education · Education ecosystem · Education digital platform · Services delivery model

1 Introduction In recent years, the learning paradigm has increasingly shifted towards a competencybased approach. In general, this trend is quite understandable. The issues of competence in the field of digitalization have been worked out most actively today [1]. But even in this area, the issues of practical implementation of competency-based learning remain problematic. In addition to this, there is a need to change the classical model of the university, which in modern conditions does not have time to dynamically adapt to the needs of society, especially in the field of information technology and communications. This brings to life new educational models such as education as a service (EaaS). Even though the EaaS concept is actively discussed in the academic environment, approaches to its practical implementation remain insufficiently developed. The developers face a difficult task - to create a comprehensive model of a digital platform for a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 523–538, 2023. https://doi.org/10.1007/978-3-031-26655-3_47

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single space of higher and professional education, which will bring together all the main stakeholders - consumers of educational services (students and employees), providers of educational services (educational institutions and individual trainers-teachers) and sponsors of education (business, state, public organizations). Confidence in the possibility of solving this problem is inspired by the presence of most of the elements and technologies of such a platform that exist independently, as local educational services that solve partial problems [2].

Fig. 1. General view of competence-based digital framework for education as a service (created by authors).

Taking into account the above, we can state that the platform being developed (and, accordingly, the framework itself) as a complex system should have a multi-level structure with distributed functionality and many horizontal links on demand within the framework. At the same time, for the end user, most of the intermediate services should be transparent (invisible). Obviously, when solving this problem, mainly as a problem of integrating a variety of existing educational services, it is necessary to solve the problem of creating a fairly universal interface within a certain architecture. At the same time, this interface should be based on some entity that connects the interests of all stakeholders in education. As such an entity, the competences are used in the article (Fig. 1). All communications with users of the described framework should be related to certain competencies (requested or supplied). The user will be provided (if available) with the required educational service (external to the framework or internal) for the formation of the required competence. In the absence of the required service, the framework will be able to organize the search or creation of the required service for a specificcompetency request. Thus, another important requirement for the framework appears - it must be open to expanding the set of services. This article proposes a model of the EaaS conceptual framework, which was first identified by the authors in the paper [3]. The structure of the article contains 5 sections: the present introduction, a review of publications on the topic (Sect. 2), a description of the conceptual framework for

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the practical implementation of the EaaS (Sect. 3), a description of the service delivery model of the EaaS framework (Sect. 4) and conclusions (Sect. 5).

2 Related Works The model of a classical university does not correspond today to the high dynamics of the development of the needs of society, especially in the sphere of high technologies. This led to the development of the EaaS concept as a complementary, and in the future, as a replacement for the existing classical models of education. Complementing the ability of universities to adapt their curricula to market requirements using the EaaS model offers new services to all stakeholders in the educational services market. The trend to treat learning as a service, co-creating it with other stakeholders, is already being exploited by some universities [4]. The student-oriented approach, which is assumed in this case, can be implemented if students themselves are involved in this process, for example, using various marketing-oriented approaches [4]. The implementation of the EaaS concept assumes that students should know the competencies for successful entry into the labor market, and universities should know these competencies and effectively update their programs and courses to ensure their implementation [5]. The competence-based approach is developing most intensively in relation to the field of information technology. It is logical that the development of the first practical steps in the field of application of the EaaS approach is carried out during training in computer science [5]. Pilot projects for the implementation of the EaaS are built based on classical digital technologies [6]. At the same time, developers focus on the technical aspects of implementing the EaaS concept, relying on cloud technical solutions traditional for information technology based on the Infrastructure-as-a-Service model (IaaS) [7]. Individual universities are considering the whole range of service add-ons, introducing other services such as software as a service (SaaS) and platform as a service (PaaS) [8]. Several factors gave additional interest to the development of the EaaS concept: • the need to provide adequate support for the development of the 4th industrial revolution by specialists with the necessary digital competencies [9]; • educational mobility as an important component of the internalization of education, which allows acquiring the necessary competencies outside the programs of native universities [8]; • remote mobility trends for both students and teachers, especially in the context of the COVID-19 pandemic, which removed many of the psychological problems of distance and blended learning that previously existed in the academic environment [10]. A certain constraint on the development of the EaaS model of education is associated with the susceptibility of traditional forms of higher and professional education to formal restrictions on certification, licensing and accreditation. However, the requirements of a competency-based approach can change the current situation in this area, just as the pandemic changed attitudes towards the possibility of implementing all university programs remotely.

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Another factor raising interest in the EaaS concept is the expansion of the services of the Gig economy. The Gig economy and the platforms that implement it are changing the relationship between the employer and the employee, creating new economic and marketing models [11]. At the same time, the models and platforms of the Gig economy themselves can be considered as prototypes of the EaaS concept [12]. Companies within the frame of Gig economy generate revenues via cloud digital intermediation between actors of Gig economy by transferring some business operation costs to platform users [12, 13]. There is the same approach in education cluster of Gig economy. The main directions of activities in this sector are: • diversification of offered services and increasing the market share for the offered services [13]; • small tuition fees imposed on certification and registration of courses; • paid additional organisation services (examination charges) [14]; • paid outside main course academic services [15] and others. Case study of edX give an example of such kind approach in education [14, 15]. An analysis of research in the field of the EaaS paradigm shows that the main attention of researchers is focused either on the issues of educational services or on the technical implementation of various approaches to service-oriented education. At the same time, there is no holistic description of the EaaS model, considering all the factors necessary for its practical functioning. The purpose of this article is to describe the holistic ecosystem of the EaaS framework that ensures the interaction of all users on the EaaS platform and it service delivery model.

Fig. 2. Framework of EaaS ecosystem (adapted from [3]).

3 Conceptual Competence-Based Framework for EaaS The framework of the EaaS ecosystem at the macro level includes the basic principles of operation, users and the information environment (cooperation platform) that implements the main functionality of the system (Fig. 2).

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There are several key principles, the implementation of which should be incorporated into modern education ecosystems. The main ones are the following. 1. Principle of competency-based learning. This is the defining principle, which requires a change in the traditional model of education. In conditions of high dynamics of changes in the technological environment, education always turns out to be catching up in comparison with the needs of practical business. However, it is the presence of the necessary competencies that makes business and individual professionals in demand in the market.

Fig. 3. Main stakeholders of competence-based education (created by authors).

2. Principle of service-oriented education. Today, there are various parties in the education market that directly or indirectly need competency-based education (Fig. 3). At the same time, for each of the categories of users, there is a gap in the possibility of full access to competence-oriented education: • individual students who would like to work in certain companies or in specific narrow professional specialties do not know the specific competencies that they need to do this in addition to the general knowledge obtained at universities (Gap 1), and do not have information about where these competencies are located. Can purchase (Gap 2);

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• students who have the possibility of mobility within the Erasmus program do not have information about all the opportunities that can be provided to them to acquire the necessary competencies (Gap 3); • individual academic teachers with a free time resource do not have information about in which educational institutions their skills could be applied (Gap 4), and also do not have reliable information about market-demanded and emerging new competencies that they could teach after retraining (Gap 5); • individual professionals do not have information about the narrow competencies required by a particular employer (Gap 6), and about educational institutions where these competencies can be acquired (Gap 7); • Universities experience difficulties in finding teachers for vacant positions (Gap 8) and do not always know the competencies that are in demand on the labor market (Gap 9); • professional training centers, like universities, have the same difficulties, but in narrower segments of professional competencies (Gap 10 and Gap 11); • business enterprises do not know who can train specialists with the competencies they need (Gap 12), with great difficulty they are looking for specialists for the competencies they need (Gap 13), while they themselves are not ready to formulate the required competencies for training organizations (Gap 14); • public professional organizations have the opportunity to participate in the accreditation of educational organizations and the development of professional training standards, but do not use these opportunities to move towards competency-based learning (Gap 15 and Gap 16). 3. Principle of open recourses. The effective functioning of the education ecosystem is possible if all the main sources of information are open. 4. Principle of student-centered education. The existing paradigm of education assumes the priority of curricula, according to which all students who choose it should study. Meanwhile, each student requires an individual approach, taking into account his practical experience and academic background. 5. Principle of academia-business partnerships. This principle is declared by all. However, in practice, its implementation encounters various obstacles, both on the part of the academy and on the part of business.

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4 Services Delivery Model The service delivery model provides the framework within which users receive services. The arrangement or configuration of time, resources, location of services, and collaboration among all actors makes up the service delivery model selected that will best meet individual user needs. The general structure of the platform services for all users is represented by taxonomy in Fig. 4. This figure shows a basic set of services that can be refined and detailed during platform development and testing of its capabilities.

Fig. 4. The general structure of EaaS framework services (created by authors).

Description of services is given in Table 1.

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B. Misnevs and I. Kabashkin Table 1. Services delivery model. Description of services (created by authors).

Users

Services

Brief description of services

Individual students

List of potential mobility places based on student requests

Universities have dozens of partners in numerous student mobility programs. At the same time, for each student, if there is an individual request, it is quite difficult to determine the place of potential foreign study under the mobility program, taking into account his personal request for competencies. The portal allows you to solve this problem with any level of detail

Formation of groups of students with the same requests for mobility

Individual students on mobility programs may have access restrictions for certain courses. This happens, for example, when the course is elective, and there are not enough people who want to study it. If there are requests for the same courses/competencies from several foreign students from different countries, they can be combined into a group, which makes the organization of the corresponding course possible and expedient

Online processing of documents required for mobility

As the pandemic has shown, there are situations in which the personal participation of students in the preparation of documents necessary for mobility is limited or impossible. In addition, there may be some features of the passage of mobility programs in individual countries or universities, in contrast to typical ones. The portal allows you to process all documents online, taking into account all institutional and national features (continued)

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Table 1. (continued) Users

Services

Brief description of services

Formation of a digital trace of a student’s mobile activity

When implementing mobility programs, a student has the opportunity to visit several universities in one country or several countries, for example, within the framework of a cross-border partnership. A similar situation arises when a student is studying abroad at the same time at a university and a vocational training center. In any case, there is a problem with the certification and reliability of the acquired learning outcomes. This service allows you to automate and legitimize the individual trajectory of student learning

List of the required competencies If a student wants to purposefully to work in organizations of get a job in a particular interest to the student organization, he needs to know the list of competencies that the specified organization requires when applying for a job. The organization itself is also interested in this. The service implements the specified functionality List of courses that form the required competencies

For the formation of some competencies, it may be necessary to study not one course, but some set of them. The service facilitates the solution of this task

Formation of requests to employers to obtain a list of competencies required to work in the relevant organization

If a student wants to better prepare for a potential future job, the service helps him formulate a request for the competencies required for this (continued)

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B. Misnevs and I. Kabashkin Table 1. (continued)

Users

Individual academic staff

Services

Brief description of services

List of training centers and universities offering courses of interest

Professional competencies can be formed not only by universities but also by specialized training centers. The formation of some competencies can be carried out exclusively outside the framework of academic programs. This service creates an opportunity to expand the set of courses and competencies that go beyond those offered by universities

Course ratings

If it is possible to obtain the same competencies or take the same courses at different universities, the choice of a particular course in a particular educational institution can be made based on the rating assessment of these courses by previous generations of students. At the same time, this is an indirect assessment of the quality of the respective courses

Formation of proposals about their pedagogical opportunities

Currently, due to the increasing specialization of courses, not all university teachers have a full load in their courses. They have the opportunity to implement their professional activities in other universities and training centers. The service allows you to launch a search to meet this possibility

The offer of courses and competencies that can be provided by the teacher in the guest university

When implementing mobility programs for teachers, the problem arises of finding universities that have appropriate programs and courses (continued)

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Table 1. (continued) Users

Services

Brief description of services

Search for places to improve own As part of the mobility programs skills of teachers in educational for teachers, they have the task of organization improving their professional level. The service provides an offer to universities and professional centers that implement this opportunity Search for places to improve own As part of the mobility programs skills of teachers at enterprises for teachers, they have the task of and organizations improving their professional level. In some innovative or highly specialized areas, such advanced training is possible not only in universities but also in leading organizations that are market leaders in their respective fields. The service provides an opportunity to implement such a task Universities

Proposal of programs and courses Universities offering mobility for mobility for foreign students programs do not always provide detailed information about all the opportunities they provide. The service allows you to exclude personal questions and clarifications for all mobility programs Proposals for special courses for the development of specific competences

Universities offering mobility programs do not always provide detailed information about all the opportunities they provide. The service allows you to exclude personal questions and clarifications for all mobility programs (continued)

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Users

Services

Brief description of services

Vocational training courses for enterprises and organizations

Not only students but also employees of organizations, as well as the universities themselves, need to acquire new competencies, especially in narrow professional areas. The service is focused on providing these features

Invitation of teachers to vacant positions

Almost all universities face difficulties in attracting academic staff to individual courses, especially in new and highly specialized fields. The service facilitates the solution of this problem by searching for available specialists in the field of interest of universities

Partnership with other universities in the field of required competencies

Currently, universities, especially in small countries, need to train specialists with competencies that a single university is not able to provide. The service helps to find partner universities for the joint implementation of programs and courses that cannot be implemented by the university on its own

Business organizations Formation of a list of competencies required from employees

In order to obtain workers with the competencies required by a particular enterprise, it can form a list of such competencies to orient students to work at their place in the early stages of their studies at the university (continued)

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Table 1. (continued) Users

Services

Brief description of services

Providing internship places for students as potential future employees

The presence of competencies according to the formal data of certificates and diplomas is not always sufficient for their inclusion in work teams. A good way to solve the problem is to train students directly in enterprises. The service allows employers and students as potential employees to receive mutually important information during the internship process

Provision of internship places for The universities themselves, need academic staff to acquire new competencies for academic staff, especially in narrow professional areas. The service is focused on providing these features Formation of a list of competencies and courses where they would like to send their employees to improve their skills

Professional training centres

When recruiting new employees and developing areas of activity, organizations are interested in improving the skills and retraining of their employees both in local educational organizations and abroad. The service makes this possible

Proposal of programs and courses Small, highly specialized training for the development of specific centers face the problem of competences informing potential clients about the opportunities they have to develop fairly narrow competencies. The service expands opportunities for them in this area (continued)

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B. Misnevs and I. Kabashkin Table 1. (continued)

Users

Public professional organizations

Services

Brief description of services

Partnership with other training centers and universities in the field of required competencies

Currently, training centers in especially small countries experience a need to train specialists with competencies that a single education establishment is not able to provide. The service helps to find partner organizations for the joint implementation of programs and courses that cannot be implemented by a single training center

Development of professional standards

At the national level, professional associations and associations are entrusted with the functions of developers of national professional standards. The involvement of professionals from the academic sphere and industry for this purpose solves this problem more successfully

Organization of events to identify In the modern world, the intensive new competency needs development of new technologies in all industries brings to life the need for intensive information exchange between the academic community and business. Creating platforms for such information exchange both face-to-face and virtually is the task of this service

5 Conclusion The article describes the basic idea and proposes a digital framework for creating platform for higher and professional education based on Education as a Service (EaaS) model. The complexity of the problem being solved are noted. It is shown that due to the digital integration of existing educational services, it is possible to create a single education ecosystem for obtaining competencies on an individual request. The paper identified the existing design problems of such a framework and outlined possible solutions. The basic principles for the development of a digital platform that implements the ecosystem of the EaaS framework using a competency-based approach are formulated, and the services delivery model of the framework are described.

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The next step in the research will be to detail the approaches to creating the organizational, competency-based and pedagogical components of the proposed model. This will allow us to formulate the requirements and the main functionality for the practical implementation of the portal, which creates an environment for using the EaaS approach in practice. Acknowledgments. This paper has been financially and conceptually supported by the EU grant of ERASMUS+ project Ecosystem for European Education Mobility as a Service: Model with Portal Demo (eMEDIATOR), Agreement No 2021-1-LV01-KA220-HED-000027571.

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Author Index

A Abramishvili, Neli 450 Adamos, Giannis 336 Adlere, Oksana 394, 424 Adomaviˇcien˙e, Giedr˙e 487 Aizi, Mouna 232 Akan, Mustafa 473 Alomar, Iyad 61 Amani, Hizia 232 Anikin, Kirill 259, 269

G Gabelaia, Ioseb 369, 437, 462 Gazizova, Ekaterina 123 Gentile, Guido 306, 320 Giel, Robert 3, 213 Girdauskien˙e, Lina 487 Glukhikh, Sergey 354 Gorodnicka, Viktorija 61 Gorzelanczyk, Piotr 141 Grakovski, Alexander 101, 223

B Babapourdijojin, Mahnaz 320 Bagociunaite, Ramune 462 Bazhina, Darya 123 Bernadskii, Igor 123 Bodrova, Irina 37 Bouyaya, Linda 232

H Herman, Anthony

C Chaib, Rachid 232 Chezybaeva, Natalya 403 Comite, Ubaldo 510 Cova, Ilaria 510 Czimmermann, Peter 347 D D˛abrowska, Alicja 213 de Andreis, Federico 510 Djekrif, Fatma Zohra 232 Dolle, Nicolas 259, 269, 414 Doronkin, Pavel 201 Duin, Heiko 277 E Eldafrawi, Mohamed 306 Eschment, Lukas 277

450

J Juodvalkis, Darius 191 Jurkovic, Martin 141 K Kabashkin, Igor 523 Kalina, Tomas 141 Karpenko, Mykola 295 Kazinski, Andrei 15 Kitzmann, Harald 131 Konovalova, Natalia 384, 473 Krivchenkov, Aleksandr 223 Kuzmickis, Vitalijs 201 Kuzmina-Merlino, Irina 414 L Lukinskiy, Valery 123 Lukinskiy, Vladislav 123 Lvova, Nadezhda 450 M Malewicz, Jakub 3 Mažeika, Marius 191 Mazurkiewicz, Jacek 285

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Kabashkin et al. (Eds.): RelStat 2022, LNNS 640, pp. 539–540, 2023. https://doi.org/10.1007/978-3-031-26655-3

540

Author Index

Miniankou, Ryhor 498 Mironov, Aleksey 201 Misnevs, Boriss 223, 523 N Napier, James 277 Nathanail, Eftihia 336 Neu, Walter 277 Nobel, Thomas 277 O Ovezmyradov, Berdymyrat

113

P Pailodze, Nino 450 Pavlyuk, Dmitry 26, 75 Perevozcikova, Jelena 26 Pinto, Luís Moreira 473 Prause, Gunnar 131, 151 Prentkovskis, Olegas 295 Prokopjeva, Evgenia 403, 424 Puptsau, Aliaksandr 15, 498 R Richter, Klaus 177, 252 Rozgina, Ludmila 384 S Safonovs, Aleksejs 201 Saksonova, Svetlana 394, 403, 424 Savva, Georgia 336 Schroeder, Meike 151 Schüning, Thomas 277

Sherif, Abdelra 369 Shibaeva, Tatyana 403 Shoshin, Leonid 89 Sinko, Galina 162 Skaˇckauskas, Paulius 295 ´ Sliwi´ nski, Przemysław 285 Sopauschke, Daniel 177 Sottoriva, Federico Maria 510 Stopkova, Maria 141 Štreimikien˙e, Judita 487 Strimovskaya, Anna 162 Stukalina, Yulia 50, 75 Sugier, Jarosław 242, 285 Susanin, Vitalii 89 T Trepnau, Dietrich 252 Trostmann, Erik 177 Tsyplakova, Elena 162 W Walkowiak, Tomasz 285 Werbi´nska-Wojciechowska, Sylwia Wilhelm, Christian 259, 269 Wurst, Stephan 277 Y Yanshina, Ekaterina

450

Z Zervina, Olga 50, 75 Zhdanov, Vladislav 101

3