Industrial Engineering in the Age of Business Intelligence: Selected Papers from the Virtual Global Joint Conference on Industrial Engineering and Its ... in Management and Industrial Engineering) 303108781X, 9783031087813

Citation preview

Lecture Notes in Management and Industrial Engineering

Fethi Calisir   Editor

Industrial Engineering in the Age of Business Intelligence Selected Papers from the Virtual Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2021, October 30–31, 2021

Lecture Notes in Management and Industrial Engineering Series Editor Adolfo López-Paredes, INSISOC, University of Valladolid, Valladolid, Spain

This book series provides a means for the dissemination of current theoretical and applied research in the areas of Industrial Engineering and Engineering Management. The latest methodological and computational advances that can be widely applied by both researchers and practitioners to solve new and classical problems in industries and organizations contribute to a growing source of publications written for and by our readership. The aim of this book series is to facilitate the dissemination of current research in the following topics: • • • • • • • • • • • • • •

Strategy and Entrepreneurship Operations Research, Modelling and Simulation Logistics, Production and Information Systems Quality Management Product Management Sustainability and Ecoefficiency Industrial Marketing and Consumer Behavior Knowledge and Project Management Risk Management Service Systems Healthcare Management Human Factors and Ergonomics Emergencies and Disaster Management Education

Fethi Calisir Editor

Industrial Engineering in the Age of Business Intelligence Selected Papers from the Virtual Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2021, October 30–31, 2021

Editor Fethi Calisir Department of Industrial Engineering Istanbul Technical University Istanbul, Turkey

ISSN 2198-0772 ISSN 2198-0780 (electronic) Lecture Notes in Management and Industrial Engineering ISBN 978-3-031-08781-3 ISBN 978-3-031-08782-0 (eBook) https://doi.org/10.1007/978-3-031-08782-0 © 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

This book compiles extended versions of a selection of the best papers presented at the Virtual Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE) 2021. They represent a good sample of the current state of the art in the field of industrial engineering and its application areas. The papers presented in this book address methods, techniques, studies, and applications of industrial engineering with the theme of Industrial Engineering in the Age of Business Intelligence. Business intelligence is a decision support system based on a set of information technologies, processes, and architectures that gather and analyze raw data and transform it into meaningful information or knowledge that drives effective and profitable business actions. Business intelligence has a significant and direct effect on the organizations’ operational, tactical, and strategic business decisions. The importance of business intelligence is widely recognized as its usage proliferates in organizations across all types of industries throughout the world. Hence, over the past few years, the growth of business intelligence has accounted for the largest share of global business investment in information technology. However, only some organizations have fully benefited, and there have been even cases of organizations with reduced competitiveness. The ever-increasing business intelligence in all industries has put unprecedented pressure on managers to articulate effective and applicable strategies to change organizational structures and eliminate obstacles keeping them from taking full advantage of the modern business intelligence systems. All these mean that industrial engineering skills will be the keystone for achieving maximum value from investments in business intelligence. This book will shed light on the role of industrial engineering in this endeavor. Contributions have been arranged into three parts: • Industrial Engineering • Engineering and Technology Management • Healthcare Systems Engineering and Management. I want to express our gratitude to all the contributors, reviewers, and international scientific committee members who have aided in the publication of this book. I would also like to express our gratitude to Springer for their full support during the v

vi

Preface

publishing process. Last but not least, we gratefully acknowledge the only sponsor of the GJCIE 2021, the Elginkan Foundation. Istanbul, Turkey November 2021

Fethi Calisir Conference Chair

Contents

Part I 1

Industrial Engineering

A Novel Interval-Valued Spherical Fuzzy EDAS: An Application to IT Auditor Selection . . . . . . . . . . . . . . . . . . . . . . . . . . Akin Menekse and Hatice Camgoz Akdag

2

Supply Forecasting for Lebanon After Beirut Blast . . . . . . . . . . . . . . . Nabil Nehme, Khaled Shaaban, and Roy Bou Nassif

3

Multi-criteria Fuzzy Decision-Making Techniques with Transaction Cost Economy Theorem Perspectives in Product Launching Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cagatay Ozdemir and Sezi Cevik Onar

4

5

6

7

8

3 17

27

Assessment of Risk Attitudes of Generations: A Prospect Theory Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Cagri Budak, Ayberk Soyer, and Sezi Cevik Onar

39

A Literature Review on Human-robot Collaborative Environments Considering Ergonomics . . . . . . . . . . . . . . . . . . . . . . . . . Busra Nur Yetkin and Berna Haktanirlar Ulutas

49

The Use of Gamification in Sales: The Technology Acceptance Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cigdem Altin Gumussoy, Nilay Ay, Kubra Cetin Yildiz, and Aycan Pekpazar

61

Efficiency Evaluation of Turkish Sports Federations Using DEA-Based Malmquist Productivity Index . . . . . . . . . . . . . . . . . . . . . . Mirac Murat, Yildiz Kose, and Emre Cevikcan

77

On the Terrain Guarding Problems: New Results, Remarks, and Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haluk Eli¸s

93

vii

viii

9

Contents

Retention Prediction in the Gaming Industry: Fuzzy Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Ahmet Tezcan Tekin, Ferhan Cebi, and Tolga Kaya

10 Sentiment Analysis on Public Transportation During Covid-19: An Exploratory Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Busra Buran 11 Car Rental Prediction Using Segmented and Unsegmented Customer Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Basar Oztaysi, Aydeniz Isik, and Elmira Farrokhizadeh 12 A Goal Programming Model for Optimizing the Reverse Logistics Network of Glass Containers and an Application . . . . . . . . 147 Raci Berk ˙Islim, Sule ¸ Itır Sato˘glu, and Hakan Durbaba 13 Risks in Supply Chain 4.0: A Literature Review Study . . . . . . . . . . . . 163 Sevde Ceren Yildiz Ozenc, Merve Er, and Seniye Umit Firat 14 Supply Chain Risk Prioritization and Supplier Analysis for a Footwear Retailer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Esra Agca Aktunc, Simay Altintas, Bengisu Baytas, Nazli Dur, and Asli Zulal Ozokten 15 Autonomous Vehicle Travel Between Multiple Aisles by Intelligent Agent-Based Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Ecem Eroglu Turhanlar, Banu Yetkin Ekren, and Tone Lerher 16 Scientometric Analysis of a Social Network . . . . . . . . . . . . . . . . . . . . . . 209 Kadir Oymen Hancerliogullari, Emrah Koksalmis, and Gulsah Hancerliogullari Koksalmis 17 Assessing Multilevel Thinking Using Cognitive Maps to Measure Global Managers’ Cognitive Complexity in Addressing Management Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Elif Cicekli 18 Usability Evaluation of Ataturk University Web Site with Morae Program and the Effect of Pandemic During Testing Process: An Application for Undergraduate Students . . . . . . 229 Elif Kilic Delice, Tuba Adar, and Merve Ceren Taskent Part II

Engineering and Technology Management

19 Automated Anomaly Detection in Real-Time Data Streams: An Application at Token Financial Technologies Company . . . . . . . . 245 Dicle Aslan 20 The Digital Future of the Construction Project Management . . . . . . 255 Levent Sumer

Contents

ix

21 Why Do People Use Social Networks in Turkey? A Structural Equation Modeling Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Gulsah Hancerliogullari Koksalmis, Ilknur Cengiz, and Emrah Koksalmis 22 Modeling Customer Churn Behavior in E-commerce Using Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Beyza Tuba Ulas, Simay Imer, Tolga Ahmet Kalayci, and Umut Asan Part III Healthcare Systems Engineering and Management 23 Role of Occupational Burnout Among Health Care Professionals: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Shadi Bolouki Far and Orhan Korhan 24 Impact of Operational Constraints on the Implied Value of Life . . . 319 Onur Ozturk, Mehmet A. Begen, and Gregory S. Zaric 25 Appointment Scheduling in Healthcare Systems: A Scientometric Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Kadir Oymen Hancerliogullari 26 Determinants of the Community Mobility During the COVID-19 Pandemic in Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Fethi Calisir and Basak Cetinguc Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359

Part I

Industrial Engineering

Chapter 1

A Novel Interval-Valued Spherical Fuzzy EDAS: An Application to IT Auditor Selection Akin Menekse and Hatice Camgoz Akdag

Abstract Evaluation based on Distance from Average Solution (EDAS) is an efficient multi-criteria decision making (MCDM) method, which determines the desirability of alternatives based on the total distance of alternatives from their corresponding averages for each criterion. Spherical fuzzy sets, as the recent extensions of ordinary fuzzy sets, use the idea of Pythagorean and Neutrosophic sets and enable decision-makers to express their membership, non-membership, and hesitancy degrees independently and in a larger domain than most other fuzzy extensions. On the other hand, interval-valued spherical fuzzy sets provide an increased area of fuzziness modeling capacity than the first single-valued type. This paper proposes a new interval-valued spherical fuzzy EDAS method and provides extra space for catching the vagueness in the nature of decision-making problems. The feasibility and practicality of the proposed model are illustrated with an application for evaluating the information technology (IT) auditor selection problem. Sensitivity analyses for criterion and decision-maker weights and a comparative analysis are also presented in the study. Keywords Multi-criteria decision making · Interval-valued spherical fuzzy sets · EDAS · IT auditor selection

1.1 Introduction Multi-criteria decision making (MCDM) is a mathematical method for selecting the best feasible predetermined alternative from some potential candidates with respect to a set of conflicting criteria [8]. There are several MCDM models in the literature, and they continue to be developed by researchers in many aspects to be used to support A. Menekse Graduate School, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] H. Camgoz Akdag (B) Department of Management Engineering, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_1

3

4

A. Menekse and H. Camgoz Akdag

the solution of problems from many sectors, such as law [12]; transportation [27], economics [5], finance [23], banking industry [18]; health [17], energy planning [7], manufacturing [20], product development [33], construction [9]; water treatment [26], sustainability [11]; engineering management [4], military [30], supplier selection [6] and recently COVID-19 pandemic related problems [1]. EDAS [14] is an MCDM methodology that ranks the alternatives by evaluating their positive and negative distances to the average solution. EDAS, with its simple structure that requires less calculation than many other MCDM techniques, is used effectively in decision-making problems faced by many sectors. Spherical fuzzy sets [15] are the recent extensions of ordinary fuzzy sets having three-dimensional spherical geometry. In spherical fuzzy sets, the sphere is considered a volume rather than a solid, allowing us to independently assign membership, non-membership, and hesitancy parameters. Interval-valued spherical fuzzy sets [16] were later developed and provided and an increased fuzziness modeling capacity. Single and interval-valued spherical fuzzy sets have been increasingly used with various MCDM methods such as Analytical Hierarchy Process (AHP) [24]; Weighted Aggregated Sum Product Assessment (WASPAS) [3], Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [22] VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) [25]; COmbinative Distance-based ASsessment (CODAS) [13], Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form (MULTIMOORA) [21], Multi-Attributive Border Approximation area Comparison (MABAC) [19], Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) [31], Decision Making Trial and Evaluation Laboratory (DEMATEL) [10], COmplex Proportional ASsessment (COPRAS) [2] and Step-wise Weight Assessment Ratio Analysis (SWARA) [32]. To the best of our knowledge, there is no study about extension and application of the EDAS method in interval-valued spherical fuzzy environment, and the aim of this paper is to develop a novel MCDM based decision support tool by making use of the advantages of both approaches with an illustration presented for IT auditor selection. The rest of the paper is organized as follows: First, prior to constructing the proposed model, the preliminaries of interval-valued spherical fuzzy sets are given. Later, the proposed interval-valued spherical fuzzy EDAS is presented in a stepby-step manner. For the application, an IT auditor selection problem is handled, and the results are discussed through sensitivity analysis for decision-maker and criterion weights and a comparative analysis with six other fuzzy MCDM models. Limitations and future study avenues are outlined towards the end of the paper.

1.2 Preliminaries Definition and basic operations [16] of interval-valued spherical fuzzy sets are given below: A spherical fuzzy set ( A˜ S ) must satisfy the following condition:

1 A Novel Interval-Valued Spherical Fuzzy …

5

 2  2  2 0 ≤ μ A˜ S (u) + v A˜ S (u) + π A˜ S (u) ≤ 1, ∀u ∈ U

(1.1)

where μ, v and π are the membership, non-membership, and hesitancy degrees, respectively. Interval-valued fuzzy sets were later developed to increase fuzziness modeling capacity with an interval-type structure rather than a single point. Prior to the development of the proposed interval-valued spherical fuzzy EDAS, the definition and basic operations are presented below. Definition 1 An interval-valued spherical fuzzy set A˜ S of the universe of discourse U is defined as         (1.2) A˜ S = u, μ LA˜ (u), μUA˜ (u) , v AL˜ (u), vUA˜ (u) , π AL˜ (u), π AU˜ (u) |u ∈ U S

S

S

S

S

where 0 ≤ μ LA˜ (u) ≤ μUA˜ (u) ≤ 1; 0 ≤ v AL˜ S

S

S

S

≤ vUA˜ (u) ≤ 1 and 0 ≤ S

μ LA˜ (u) ≤ μUA˜ (u) ≤ 1 and 0 ≤ (μUA˜ (u))2 + (vUA˜ (u))2 + (μUA˜ (u))2 ≤ 1. S

S

S

S

S

For each u ∈ U, μUA˜ (u), vUA˜ (u) and μUA˜ (u) are the upper degrees of memberS S S ship, non-membership, and hesitancy degrees of u to A˜ S . For each, u ∈ U, if μ LA˜ = μUA˜ , (u), v AL˜ (u) = vUA˜ (u) and μ LA˜ (u) = μUA˜ (u) then an intervalS S S S S S valued spherical fuzzy set reduces to a single-valued spherical fuzzy set. For an interval-valued spherical    fuzzy set, the pair   U L μ A˜ (u), μ A˜ (u) , v AL˜ (u), vUA˜ (u) , μ LA˜ (u), μUA˜ (u) is called an S S S S S S interval-valued spherical fuzzy  number. For  convenience, the pair   μ LA˜ (u), μUA˜ (u) , v AL˜ (u), vUA˜ (u) , μ LA˜ (u), μUA˜ (u) is denoted by S S S S S S α˜ = [a, b], [c, d], [e, f ] where [a, b] ⊂ [0, 1], [c, d] ⊂ [0, 1], [e, f ] ⊂ [0, 1]; b2 + d 2 + f 2 ≤ 1. Note that, α˜ + = [1, 1], [0, 0], [0, 0]; α˜ − = [0, 0], [1, 1], [0, 0] and α˜ ∗ = [0, 0], [0, 0], [1, 1] are the largest, smallest, and moderate interval-valued spherical fuzzy numbers, respectively. Definition 2 Basic operations of interval-valued spherical fuzzy sets are given below: Addition; ⎧  1/2  1/2  ⎫ ⎪ ⎪ ⎪ (a1 )2 + (a2 )2 − (a 1 )2 (a2 )2 , (b1 )2 + (b2 )2 − (b1 )2 (b2 )2 ,⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪   ⎪ ⎪ ⎪ ⎪ ⎨ c1 c2 , d1 d2 ⎬ ⎡ ⎤  1/2 α˜ 1 ⊕ α˜ 2 = 2 (e )2 + ((1 − (a )2 (e )2 − (e2 )(e )2 ⎪ ⎪ ) , 1 − (a ⎪⎢ ⎪ 2 1 1 2 1 2 ⎪ ⎪ ⎪ ⎪ 1/2 ⎥ ⎪ ⎪ ⎣  ⎦ ⎪ ⎪ ⎪ ⎪ 2 2 2 2 2 2 ⎩ ⎭ 1 − (b ) ( f ) + ((1 − (b ) ( f ) − ( f )( f ) 2

Multiplication;

1

1

2

1

2

(1.3)

6

A. Menekse and H. Camgoz Akdag

 ⎧ 1/2  1/2  ⎫    ⎪ ⎪ ⎪ a a ,⎪ , (d1 )2 + (d2 )2 − (d 1 )2 (d2 )2 (c1 )2 + (c2 )2 − (c1 )2 (c2 )2 ⎪ ⎪ ⎪ 1 2 , b1 b2 , ⎪ ⎪ ⎪ ⎨ ⎡ ⎬ ⎤ 1/2 2 2 2 2 2 2 α˜ 1 ⊗ α˜ 2 = , 1 − (c2 ) (e1 ) + ((1 − (c1 ) (e2 ) − (e1 )(e2 ) ⎪ ⎪ ⎥ ⎢   ⎪ ⎪ 1/2 ⎦ ⎪ ⎪ ⎪⎣  ⎪ ⎪ ⎪ ⎩ ⎭ 1 − (d 2 )2 ( f 1 )2 + ((1 − (d 1 )2 ( f 22 ) − ( f 12 )( f 22 )

(1.4)

Multiplication by a scalar λ; λ > 0  ⎫ ⎧     1/2   1/2  λ λ  2 λ 2 λ ⎪ ⎪ ⎪ ⎪ , c ,d , , 1− 1−b ⎬ ⎨ 1− 1−a  λ.α˜ =  λ  λ 1/2  λ  λ 1/2 ⎪ ⎪  ⎪ ⎪ ⎭ ⎩ 1 − a 2 − 1 − a 2 − e2 , 1 − b2 − 1 − b2 − f 2 (1.5) λ th power α;λ ˜ >0   ⎫ ⎧      1/2   1/2 λ λ 2 λ 2 λ ⎪ ⎪ ⎪ ⎪ , , 1− 1−d ⎬ ⎨ a ,b , 1 − 1 − c λ  α˜ =  λ  λ 1/2  λ  λ 1/2 ⎪ ⎪  ⎪ ⎪ ⎭ ⎩ 1 − c2 − 1 − c2 − e2 , 1 − d2 − 1 − d2 − f 2 (1.6) Definition 3 Interval-valued spherical fuzzy I V S F W G M operator is defined as follows: w

w

weighted

geometric

mean

w

I V S F W G Mw (α˜ 1 , α˜ 2 , . . . , α˜ n ) = α˜ 1 1 ⊗ α˜ 2 2 ⊗ ... ⊗ α˜ n n = ⎫ ⎧⎡ ⎤ ⎡⎛ ⎞1/2 ⎛ ⎞1/2 ⎤ n n n  n  ⎪ ⎪ w w    ⎪ ⎪ wj  wj ⎪ ⎪ ⎢ j j 2 2 ⎪ ⎪ ⎣ ⎦, ⎣⎝1 − ⎠ , ⎝1 − ⎠ ⎥ , a b 1 − c 1 − d ⎪ ⎪ ⎦ ⎪ ⎪ j j j j ⎪ ⎪ ⎪ ⎪ ⎬ ⎨ j=1 j=1 i=1 i=1 ⎡⎛ ⎤ ⎞1/2 ⎛ ⎞1/2 ⎪ ⎪ ⎪ ⎪ n  n  n  n  ⎪ ⎪ w w w w     ⎪⎢ ⎥⎪ ⎪ ⎪ j j⎠ j j⎠ ⎪ ⎝ − , ⎝1 − − 1 − c2j 1 − c2j − e2j 1 − d 2j 1 − d 2j − f j2 ⎪ ⎪ ⎦⎪ ⎪ ⎪⎣ 1 − ⎭ ⎩ i=1

i=1

i=1

i=1

(1.7) Definition 4 Score and accuracy functions are defined as follows: Scor e(α) ˜ =

a 2 + b2 + c2 + d 2 − (e/2)2 − ( f /2)2 2

Accuracy(α) ˜ =

a 2 + b2 + c2 + d 2 + e2 + f 2 2

(1.8)

(1.9)

1 A Novel Interval-Valued Spherical Fuzzy …

7

1.3 Methodology In this section, the classical EDAS method is extended in an interval-valued spherical fuzzy environment. The steps of the proposed method are as follows: Define an MCDM problem with a set of alternatives and the most related criteria. Let Ai = (A1 , A2 , . . . , Am ) be the alternatives and C j = ( A1 , A2 , . . . , An ) be the selected criteria set. Step 1. The experts evaluate the alternatives with respect to criteria by utilizing the readily given linguistic scale in Table 1.1. Step 2. Convert linguistic evaluations of alternatives to interval-valued spherical fuzzy numbers in a matrix form, and aggregate all matrices obtained from decisionmakers by utilizing Eq. 1.7. Now we get one unique collective matrix which is named as interval-valued spherical fuzzy alternative evaluation matrix. Step 3. Since each criterion may have a different significance level, the decisionmakers assign an importance level to the criteria by utilizing the readily given linguistic terms that are given in Table 1.1. Step 4. Convert linguistic evaluations of criteria to interval-valued spherical fuzzy numbers in a matrix form, and aggregate all matrices obtained from decision-makers by utilizing Eq. 1.7. In this step, we get another collective matrix and call it an interval-valued spherical fuzzy criterion weight matrix. Step 5. An interval-valued spherical fuzzy decision matrix is obtained by multiplying the interval-valued spherical fuzzy alternative evaluation matrix by the intervalvalued spherical fuzzy criterion weight matrix. Utilize Eq. 1.4 for the multiplication operation. Step 6. Defuzzify interval-valued spherical fuzzy decision matrix by utilizing the score function given in Eq. 1.8.

Table 1.1 Linguistic terms and corresponding interval-valued spherical fuzzy numbers Linguistic term

μL , μU

vL , vU

πL , πU

Absolutely more importance (AMI)

[0.85, 0.95]

[0.10, 0.15]

[0.05, 0.15]

Very high importance (VHI)

[0.75, 0.85]

[0.15, 0.20]

[0.15, 0.20]

High importance (HI)

[0.65, 0.75]

[0.20, 0.25]

[0.20, 0.25]

Slightly more importance (SMI)

[0.55, 0.65]

[0.25, 0.30]

[0.25, 0.30]

Equally importance (EI)

[0.50, 0.55]

[0.45, 0.55]

[0.30, 0.40]

Slightly low importance (SLI)

[0.25, 0.30]

[0.55, 0.65]

[0.25, 0.30]

Low importance (LI)

[0.20, 0.25]

[0.65, 0.75]

[0.20, 0.25]

Very low importance (VLI)

[0.15, 0.20]

[0.75, 0.85]

[0.15, 0.20]

Absolutely low importance (ALI)

[0.10, 0.15]

[0.85, 0.95]

[0.05, 0.15]

8

A. Menekse and H. Camgoz Akdag

Step 7. Calculate the average solution, which is called the interval-valued spherical fuzzy average solution. Utilize the aggregation operator given in Eq. 1.7 to calculate the average solution. Step 8. Defuzzify interval-valued spherical fuzzy average solution by utilizing the score function given in Eq. 1.8. Step 9. The attributes above the interval-valued spherical fuzzy average solution are assigned as positively positioned attributes. In the same way, the attributes below the interval-valued spherical fuzzy average solution are assigned as negatively positioned attributes. Note that the crisp values in steps 6 and 8 are evaluated only for obtaining the positions of the attributes according to the average solution. Step 10. For each alternative (Ai ), calculate the total distance between positively positioned attributes and the interval-valued spherical fuzzy average solution and call it the positive distance to the average solution (P D ASi ) for that alternative. In the same way, calculate the total distance between negatively positioned attributes and the interval-valued spherical fuzzy average solution and call it the negative distance to the average solution (N D ASi ) for that alternative. Use normalized Euclidean distance [28, 29] given in Eq. 1.10 to the total distance of each alternative to the average solution.   d X i j , X ∗j

⎛ &  & & & & & ⎞ & L 2  ∗ 2 & & U 2  ∗ 2 & & L 2  ∗ 2 & n μ − μ − μ − v + μ + v & & & & & &+⎟ $ i j i j i j i j i j i j 1 ⎜ = ⎝ && 2  2 && && 2  2 && && 2  2 && ⎠, ∀i 4n j=1 & vU − v ∗ & + & π L − π ∗ & + & π U − π ∗ & ij ij ij ij ij ij (1.10)

where X i j is the jth attribute of ith calculate alternative and X ∗j is the jth attribute of the average solution. Step 11. Normalize P D ASi and N D ASi as follows: N or mali zed P D ASi =

P D ASi max(P D ASi )

N or mali zed N D ASi = 1 −

N D ASi max(N D ASi )

(1.11) (1.12)

Step 12. Obtain appraisal score (Si ) for each alternative by utilizing Eq. 1.13 and rank all alternatives according to descending values of appraisal scores. The alternative with the highest appraisal score is the best choice among the candidate alternatives. Si =

1 (N or mali zed P D ASi + N or mali zed N D ASi ) 2

(1.13)

1 A Novel Interval-Valued Spherical Fuzzy …

9

1.4 Application Our proposed interval-valued spherical fuzzy EDAS methodology is implemented to an IT auditor selection problem. For this purpose, four IT auditor candidates (A1, A2, A3, and A4) are evaluated with respect to five selected criteria, which are: Using information technology (C1), apply control system designs and procedures (C2), apply internal auditing technologies, and procedures (C3), apply laws and regulations (C4), and documentation of internal audit work (C5). In the evaluation process, three decision-makers (DM1, DM2, and DM3) are included with the same experience level having equal weight (1/3). The numerical solution of the proposed model is given in a step-by-step form as follows: Step 1. Linguistic evaluations of candidates with respect to selected criteria by three decision-makers are presented in Table 1.2. Step 2. Linguistic evaluations of three decision-makers are converted to their corresponding spherical fuzzy forms, and three matrices are obtained. Then, these matrices are aggregated, and the interval-valued spherical fuzzy alternative evaluation matrix is obtained in Table 1.3. Step 3. Linguistic evaluations of criteria are obtained as in Table 1.4. Step 4. Linguistic evaluations of three decision-makers are converted to their corresponding spherical fuzzy forms, and three matrices are obtained. Then, these matrices are aggregated, and one collective interval-valued spherical fuzzy criterion weight matrix is obtained as in Table 1.5. Steps 5–8. The interval-valued spherical fuzzy decision matrix, the average solution, and their crisp forms are given in Table 1.6.

Table 1.2 Linguistic evaluations of alternatives C1 DM1

DM2

DM3

C2

C3

C4

C5

A1

VHI

SMI

HI

LI

HI

A2

SMI

EI

SMI

VLI

VLI

A3

SMI

HI

EI

HI

SMI

A4

HI

SMI

SLI

AMI

EI

A1

HI

VHI

EI

SMI

VHI

A2

VHI

AMI

VHI

SMI

SLI

A3

EI

VHI

HI

HI

HI

A4

HI

VHI

SMI

VHI

SMI

A1

SMI

EI

HI

EI

VHI

A2

EI

AMI

SMI

SLI

VLI

A3

AMI

SMI

SMI

VHI

VHI

A4

EI

EI

EI

HI

HI

10

A. Menekse and H. Camgoz Akdag

Table 1.3 Interval-valued spherical fuzzy alternative evaluation matrix C1

C2

C3

C4

C5

A1

([0.64, 0.75], [0.20, 0.25], [0.21, 0.25])

([0.59, 0.67], [0.32, 0.39], [0.25, 0.33])

([0.60, 0.68], [0.31, 0.39], [0.24, 0.33])

([0.38, 0.45], [0.49, 0.59], [0.25, 0.32])

([0.72, 0.82], [0.17, 0.22], [0.17, 0.22])

A2

([0.59, 0.67], [0.32, 0.39], [0.25, 0.33])

([0.71, 0.79], [0.28, 0.36], [0.19, 0.29])

([0.61, 0.71], [0.22, 0.27], [0.22, 0.27])

([0.27, 0.34], [0.58, 0.69], [0.21, 0.26])

([0.18, 0.23], [0.70, 0.80], [0.18, 0.23])

A3

([0.62, 0.70], [0.31, 0.38], [0.24, 0.32])

([0.64, 0.75], [0.20, 0.25], [0.21, 0.25])

([0.56, 0.64], [0.32, 0.40], [0.26, 0.34])

([0.68, 0.78], [0.18, 0.23], [0.19, 0.23])

([0.64, 0.75], [0.20, 0.25], [0.21, 0.25])

A4

([0.60, 0.68], [0.31, 0.39], [0.24, 0.33])

([0.59, 0.67], [0.32, 0.39], [0.25, 0.33])

([0.41, 0.48], [0.44, 0.53], [0.27, 0.34])

([0.75, 0.85], [0.16, 0.20], [0.15, 0.21])

([0.56, 0.64], [0.32, 0.40], [0.26, 0.34])

Table 1.4 Linguistic evaluations of criteria C1

C2

C3

C4

C5

DM1

AMI

VHI

SMI

LI

AMI

DM2

VHI

VHI

HI

SMI

AMI

DM3

AMI

AMI

VHI

EI

VHI

Table 1.5 The interval-valued spherical fuzzy criterion weight matrix C1

C2

C3

C2

C3

([0.82, 0.92], [0.12, 0.17], [0.10, 0.17])

([0.78, 0.88], [0.14, 0.18], [0.13, 0.19])

([0.64, 0.75], [0.20, 0.25], [0.21, 0.25])

([0.56, 0.64], [0.32, 0.40], [0.26, 0.34])

([0.82, 0.92], [0.12, 0.17], [0.10, 0.32])

Steps 9–11. Normalized positive distance to the average solutions and normalized negative distance to the average solutions are calculated as in Table 1.7. Step 12. Appraisal scores of alternatives and corresponding rankings are obtained as in Table 1.8. The appraisal scores based on the proposed decision support model indicate that the best candidate is A3.

1.5 Discussion In this section, the proposed model is further discussed through sensitivity and comparative analyses. To check the robustness and consistency, two different sensitivity analyses are presented by means of testing the effect of the criteria and decision-maker weights.

1 A Novel Interval-Valued Spherical Fuzzy …

11

Table 1.6 Interval-valued spherical fuzzy decision matrix C1

C2

C3

C4

C5

([0.53, 0.68], [0.24, 0.30], [0.22, 0.30])

([0.46, 0.59], [0.34, 0.43], [0.27, 0.36])

([0.38, 0.50], [0.37, 0.45], [0.30, 0.38])

([0.21, 0.29], [0.57, 0.67], [0.32, 0.39])

([0.58, 0.75], [0.21, 0.27], [0.19, 0.27])

0.281

0.109

0.001

-0.354

0.376

([0.48, 0.62], [0.33, 0.42], [0.26, 0.35])

([0.56, 0.70], [0.31, 0.40], [0.23, 0.33])

([0.39, 0.53], [0.30, 0.37], [0.29, 0.35])

([0.15, 0.22], [0.64, 0.75], [0.29, 0.33])

([0.14, 0.21], [0.70, 0.81], [0.19, 0.24])

0.137

0.253

0.080

-0.472

-0.556

A3

([0.50, 0.64], [0.33, 0.41], [0.25, 0.35])

([0.50, 0.66], [0.24, 0.31], [0.24, 0.30])

([0.36, 0.48], [0.38, 0.46], [0.31, 0.39])

([0.38, 0.50], [0.37, 0.45], [0.30, 0.38])

([0.53, 0.68], [0.24, 0.30], [0.22, 0.30])

0.167

0.247

-0.028

0.001

0.281

A4

([0.49, 0.62], [0.33, 0.42], [0.26, 0.35])

([0.46, 0.59], [0.34, 0.43], [0.27, 0.36])

([0.26, 0.35], [0.48, 0.57], [0.32, 0.38])

([0.42, 0.55], [0.36, 0.44], [0.29, 0.37])

([0.46, 0.59], [0.34, 0.43], [0.27, 0.36])

0.143

0.109

-0.213

0.049

0.104

([0.50, 0.64], [0.31, 0.39], [0.25, 0.34])

([0.49, 0.63], [0.31, 0.39], [0.25, 0.34])

([0.35, 0.46], [0.39, 0.47], [0.31, 0.38])

([0.27, 0.36], [0.51, 0.61], [0.30, 0.37])

([0.38, 0.50], [0.45, 0.55], [0.22, 0.30])

0.180

0.175

-0.050

-0.242

-0.073

A1

A2

Av

Table 1.7 Normalized PDAS and normalized NDAS Alternative

PDA

NDA

Normalized PDA

Normalized NDA

A1

0.063

0.020

0.878

0.759

A2

0.023

0.081

0.319

0.000

A3

0.072

0.002

1.000

0.976

A4

0.051

0.028

0.715

0.651

Table 1.8 Appraisal scores and final rankings of alternatives

Alternative

Appraisal score

Ranking

A1

0.818

2

A2

0.159

4

A3

0.988

1

A4

0.683

3

In the first study, one-at-a-time sensitivity analysis is conducted with respect to each criterion. For this purpose, reference criterion weight values, namely absolutely more importance (AMI), equally importance (EI), and absolutely low importance (ALI), are defined to see the effect of the change in the criterion weights on the final rankings. The proposed model is executed by assigning each reference value to each

12

A. Menekse and H. Camgoz Akdag

criterion, and the scores and the resulting rankings are calculated. The ranking of alternatives obtained in this way is presented in Fig. 1.1. As can be seen, alternative A3 ranks first in 14 of the 15 scenarios. Likewise, alternative A2 is the last in all 15 scenarios. It is also seen that the ranking of alternative A4 is stable except for two of the 15 scenarios, and alternative A1 has a lower and an upper rank in three of the scenarios; however, its ranking is generally stable. The second analysis is conducted to check the effect of decision-maker weights on the final rankings. In this context, 10 different scenarios are generated, provided that the sum of the weights of three decision-makers is one. The proposed model is executed for these 10 scenarios by assigning extreme values to the decision-maker weights as much as possible. The ranking of alternatives obtained in this way is presented in Fig. 1.2. It is seen that alternative A3 is in the first place in all scenarios except for one scenario, while alternative A2 is in the last place in all weight distributions. On the other hand, the ranking values of alternatives A1 and A4 do not change except for one scenario, and the rankings are well-balanced in general. These sensitivity analyses results show that the proposed interval-valued spherical fuzzy EDAS method gives relatively stable results for decision-making problems. For comparing the validity of our method, we compare the appraisal scores of the proposed method with six other generally accepted MCDM approaches, which are developed in spherical fuzzy and intuitionistic fuzzy environments. The selected MCDM models and the appraisal scores of alternatives obtained from these models are presented in Table 1.9.

Fig. 1.1 Sensitivity analysis results for criterion weights

1 A Novel Interval-Valued Spherical Fuzzy …

13

Fig. 1.2 Sensitivity analysis results for decision-maker weights

Table 1.9 Appraisal scores of alternatives for different MCDM approaches MCDM method

A1

A2

A3

A4

Interval-valued spherical fuzzy EDAS

0.818

0.159

0.988

0.683

Spherical fuzzy TOPSIS

0.680

0.216

0.79

0.647

Spherical fuzzy EDAS

0.632

0.316

1.000

0.427

Spherical fuzzy CODAS

0.178

-0.419

0.167

0.095

Intuitionistic fuzzy TOPSIS

0.610

0.243

0.758

0.601

Intuitionistic fuzzy EDAS

0.825

0.171

0.917

0.725

Intuitionistic fuzzy CODAS

0.094

-0.220

0.103

0.037

The ranking results of the proposed model are pretty similar to other fuzzy MCDM models, and it can be said that our model is consistent with other state-of-the-art decision-making methods.

1.6 Conclusion and Future Remarks The aim of this study is to develop a new EDAS based MCDM model with intervalvalued spherical fuzzy sets and make use of the advantages of both mathematical approaches. To illustrate this new model, an IT auditor selection problem is handled, and four candidates are evaluated with respect to five criteria by three decisionmakers, and a numerical solution of the problem is presented in a step-by-step manner in accordance with the order given in the methodology section.

14

A. Menekse and H. Camgoz Akdag

The main implications of our study can be drawn as follows: (i) Although the classical EDAS technique can cope with many MCDM problems successfully and gives precise results, it cannot handle the vagueness in the problem alone. In this study, a new model is developed to address this issue more comprehensively than most of the previously developed models. (ii) Spherical fuzzy sets are already known to provide large and independent space for modeling the membership, non-membership, and hesitancy degrees; on the other hand, recently developed interval-valued spherical fuzzy sets provide an increased area for modeling the above-mentioned degrees. (iii) The robustness and consistency of the method are shown through sensitivity analyses for decision-maker and criterion weights; and extensive comparative analysis with six other MCDM models. (iv) The proposed model is simple and convenient and can be used as a practical tool for IT auditor selection problems. Moreover, since the presented model has a flexible structure, it can be applied to any other MCDM type of problem by utilizing simple mathematical software. While the proposed methodology provides some novelties, the following comment can be considered a limitation: The criteria presented in this study are in a qualitative form, and the evaluations of the alternatives with respect to these criteria are made according to the subjective evaluations of the experts. However, in other problems, there may also be quantitative criteria such as distance, weight, volume and etc., that our model may encounter, and these criteria should be reflected in the problem separately in an objective manner. For future studies, the following are recommended: (i) Other MCDM techniques can be extended with interval-valued spherical fuzzy sets, and its applicability can be tested in other fields, including education, engineering, health and etc. (ii) Different types of spherical fuzzy sets, such as triangular spherical fuzzy sets, trapezoidal spherical fuzzy sets or hesitant spherical fuzzy sets can also be developed with various MCDM techniques. (iii) The proposed model can be analyzed in terms of rankreversal phenomena as well as can be deepened by using a wider set of criteria and including more decision-makers. (iv) Machine learning algorithms such as logistic regression, decision tree, or naive Bayes can be integrated into the proposed framework for making use of available data within the institution. (v) Apart from this, readily available software can be developed for the proposed method so that it can be performed by users easily.

References 1. Aggarwal L, Goswami P, Sachdeva S (2021) Multi-criterion intelligent decision support system for COVID-19. Appl Soft Comput 101:107056. https://doi.org/10.1016/j.asoc.2020.107056 2. Ashraf S, Abdullah S, Almagrabi AO (2020) A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19. Soft Comput 1–17.https://doi.org/10.1007/ s00500-020-05570-8 3. Aydogdu A, Gul S (2020) A novel entropy proposition for spherical fuzzy sets and its application in multiple attribute decision-making. Int J Intell Syst 35(9):1354–1374. https://doi.org/10. 1002/int.22256

1 A Novel Interval-Valued Spherical Fuzzy …

15

4. Budak A, Kaya I, Karasan A, Erdogan M (2020) Real-time location systems selection by using a fuzzy MCDM approach: an application in humanitarian relief logistics. Appl Soft Comput 92:106322. https://doi.org/10.1016/j.asoc.2020.106322 5. Chen Y, He L, Li J, Zhang S (2018) Multi-criteria design of shale-gas-water supply chains and production systems towards optimal life cycle economics and greenhouse gas emissions under uncertainty. Comput Chem Eng 109:216–235. https://doi.org/10.1016/j.compchemeng.2017. 11.014 6. Chen Z, Ming X, Zhou T, Chang Y (2020) Sustainable supplier selection for smart supply chain considering internal and external uncertainty: an integrated rough-fuzzy approach. Appl Soft Comput 87:106004. https://doi.org/10.1016/j.asoc.2019.106004 7. Deveci K, Guler O (2020) A CMOPSO based multi-objective optimization of renewable energy planning: case of turkey. Renew Energy 155:578–590. https://doi.org/10.1016/j.renene.2020. 03.033 8. Dotoli M, Epicoco N, Falagario M (2020) Multi-criteria decision making techniques for the management of public procurement tenders: a case study. Appl Soft Comput 88:106064. https:// doi.org/10.1016/j.asoc.2020.106064 9. Ghorabaee MK, Amiri M, Zavadskas EK, Antucheviciene J (2018) A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Arch Civil Mech Eng 18(1):32–49. https://doi.org/10.1016/j.acme.2017.04.011 10. Gul S (2020) Spherical fuzzy extension of DEMATEL (SF-DEMATEL). Int J Intell Syst 35(9):1329–1353. https://doi.org/10.1002/int.22255 11. Karasan A, Kahraman C (2018) A novel interval-valued neutrosophic EDAS method: prioritization of the United Nations national sustainable development goals. Soft Comput 22(15):4891–4906. https://doi.org/10.1007/s00500-018-3088-y 12. Kahraman C, Oztaysi B, Cevik Onar S (2020) Single & interval-valued neutrosophic AHP methods: Performance analysis of outsourcing law firms. J Intell Fuzzy Syst 38(1):749–759. https://doi.org/10.3233/jifs-179446 13. Karasan A, Bolturk E, Gundogdu FK (2021) Assessment of livability indices of suburban places of Istanbul by using spherical fuzzy CODAS method. In: Decision making with spherical fuzzy sets. Springer, Cham, pp 277–293. https://doi.org/10.1007/978-3-030-45461-6_12 14. Keshavarz Ghorabaee M, Zavadskas EK, Olfat L, Turskis Z (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3):435–451. https://doi.org/10.15388/informatica.2015.57 15. Kutlu Gundogdu F, Kahraman C (2019) Spherical fuzzy sets and spherical fuzzy TOPSIS method. J Intell Fuzzy Syst 36(1):337–352. https://doi.org/10.3233/jifs-181401 16. Kutlu Gundogdu F, Kahraman C (2019) A novel fuzzy TOPSIS method using emerging intervalvalued spherical fuzzy sets. Eng Appl Artif Intell 85:307–323. https://doi.org/10.1016/j.eng appai.2019.06.003 17. Li J, Luo L, Wu X, Liao C, Liao H, Shen W (2019) Prioritizing the elective surgery patient admission in a Chinese public tertiary hospital using the hesitant fuzzy linguistic ORESTE method. Appl Soft Comput 78:407–419. https://doi.org/10.1016/j.asoc.2019.02 18. Liang D, Zhang Y, Xu Z, Jamaldeen A (2019) Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry. Appl Soft Comput 78:583–594. https://doi.org/10.1016/j.asoc.2019.03.006 19. Liu P, Zhu B, Wang P, Shen M (2020) An approach based on linguistic spherical fuzzy sets for public evaluation of shared bicycles in China. Eng Appl Artif Intell 87:103295. https://doi. org/10.1016/j.engappai.2019.103295 20. Lo HW, Shiue W, Liou JJ, Tzeng GH (2020) A hybrid MCDM-based FMEA model for identification of critical failure modes in manufacturing. Soft Comput 24(20):15733–15745. https:// doi.org/10.1007/s00500-020-04903-x 21. Mahmood T, Warraich MS, Ali Z, Pamucar D (2021) Generalized MULTIMOORA method and Dombi prioritized weighted aggregation operators based on T-spherical fuzzy sets and their applications. Int J Intell Syst. https://doi.org/10.1002/int.22474

16

A. Menekse and H. Camgoz Akdag

22. Mathew M, Chakrabortty RK, Ryan MJ (2020) A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Eng Appl Artif Intell 96:103988. https://doi.org/10.1016/j.engappai.2020.103988 23. Onar SC, Oztaysi B, Kahraman C, Ozturk E (2020) Evaluation of legal debt collection services by using hesitant pythagorean (intuitionistic type 2) fuzzy AHP. J Intell Fuzzy Syst 38(1):883– 894. https://doi.org/10.3233/jifs-179456 24. Oztaysi B, Onar SC, Kahraman C (2020) A fuzzy pricing model for mobile advertisements by using spherical fuzzy AHP scoring. In: ˙International conference on intelligent and fuzzy systems. Springer, Cham, pp 142–150. https://doi.org/10.1007/978-3-030-51156-2_18 25. Oztaysi B, Onar SC, Gundogdu FK, Kahraman C (2020) Location based advertisement selection using spherical fuzzy AHP-VIKOR. J. Multiple-Valued Logic Soft Comput 35 26. Oztaysi B, Cevik Onar S, Seker S, Kahraman C (2019) Water treatment technology selection using hesitant pythagorean fuzzy hierachical decision making. J Intell Fuzzy Syst 37(1):867– 884. https://doi.org/10.3233/jifs-181538 27. Pamucar D, Deveci M, Canitez F, Bozanic D (2020) A fuzzy full consistency method-dombibonferroni model for prioritizing transportation demand management measures. Appl Soft Comput 87:105952. https://doi.org/10.1016/j.asoc.2019.105952 28. Peng X, Yang Y (2016) Fundamental properties of interval-valued pythagorean fuzzy aggregation operators. Int J Intell Syst 31(5):444–487. https://doi.org/10.1002/int.21790 29. Peng X, Yang Y (2016) Pythagorean fuzzy Choquet integral based MABAC method for multiple attribute group decision making. International Journal of ˙Intelligent Systems 31(10):989–1020. https://doi.org/10.1002/int.21814 30. Sánchez-Lozano JM, Rodríguez ON (2020) Application of fuzzy reference ideal method (FR˙IM) to the military advanced training aircraft selection. Appl Soft Comput 88:106061. https://doi.org/10.1016/j.asoc.2020.106061 31. Sharaf IM (2021) Evaluating geothermal energy systems using spherical fuzzy promethee. ˙In: Decision making with spherical fuzzy sets. Springer, Cham, pp. 375–397. https://doi.org/ 10.1007/978-3-030-45461-6_16 32. Tas MA, Cakir E, Ulukan Z (2021) Spherical fuzzy swara-marcos approach for green supplier selection. 3C Tecnologia 10. https://doi.org/10.17993/3ctecno.2021.specialissue7.115-133 33. Zaim S, Sevkli M, Camgoz-Akdag H, Demirel OF, Yayla AY, Delen D (2014) Use of ANP weighted crisp and fuzzy QFD for product development. Expert Syst Appl 41(9):4464–4474. https://doi.org/10.1016/j.eswa.2014.01.008

Chapter 2

Supply Forecasting for Lebanon After Beirut Blast Nabil Nehme, Khaled Shaaban, and Roy Bou Nassif

Abstract The economic situation in Lebanon is abominable, vast supply chain materials are being cut, and businesses are closing. A huge bulk of products in Lebanon are imported from overseas. In this paper, two forecasting techniques are used to forecast future demand potentially imported from countries around the world to Lebanon. The first suggested technique is simple exponential smoothing, and the second technique is Winter’s method. Keywords Supply chain · Simple exponential method · Winter’s method · Beirut blast · Lebanon

2.1 Introduction Over one million people dead, many more suffering from serious illness, and world economies were struggling. This can be considered the headline for the impacts the year 2020 had on the world. However, in Lebanon, the situation was even worse. Starting from October 2019 and until today, Lebanon has been experiencing a compounded crisis. In October 2019, the financial crisis started because of a sudden stop in capital cash inflows to the country. This also caused banking and debt problems to unveil, which in turn leads to the devaluation of the Lebanese Lira (LL) compared to US Dollars (USD) exchange rates. By March 2020, the Lebanese government forced a lockdown as a measure to decrease the COVID-19 cases in the country. It ended up with a massive explosion on August 4, 2020, which devastated the capital’s main port and dense residential area, Beirut [9]. All these shocks lead to a miserable socio-economic situation in the country. The GDP was greatly contracting, public debts were at their highest levels, amounting to 126,582 LBP Billion in May 2020 as reported by the Ministry of Finance Public Debt Directorate, and the fiscal deficit increased. These factors lead to disruptions in several supply chains in Lebanon. N. Nehme (B) · K. Shaaban · R. Bou Nassif Department of Industrial and Mechanical Engineering, Lebanese American University, Byblos, Lebanon e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_2

17

18

N. Nehme et al.

In this paper, a forecast for the imports after the explosion will be made to predict what might happen in the long term and how this will affect supply chains in Lebanon.

2.2 Literature Review With globalization promising supply chains with great opportunities to increase revenue and decrease cost, supply chains became lengthy and complex. Apparel goods supply chains are a great example of how globalization can help in cost reduction. Given that clothing can easily be transported and the high labor cost, the United States decided to shift clothing manufacturing to China, where labor costs are lower ([3], p.155). According to the World Bank, 35.55% of US clothing and textile imports in 2018 were from China. In this way, both countries have benefited from cost reduction as a result of globalization. This can also be noticed from the annual trade of intermediate goods, which has been estimated to be worth $10 trillion since 2000 [14]. However, tremendous opportunities are always accompanied by high risks. A study done by Accenture in 2006 shows that half of the executives surveyed have experienced supply chain risks because of their globalization strategies [1]. McKinsey classified these risks into four different categories in a study published in 2020 [11]. The four categories are the following: foreseeable catastrophes, unanticipated catastrophes, foreseeable disruptions, and unanticipated business disruptions. Catastrophes are events that could cause losses of trillions of dollars if they occurred. On the other hand, disruptions are also severe events, but they can cause fewer losses compared to catastrophes. Catastrophes and disruptions are categorized as foreseeable once they have a lead time that ranges from one week to several months. When the lead time is less than one week, the catastrophe or disruption is said to be unanticipated. Several examples of events that happened in the past years had severe impacts on supply chains. The Japanese earthquake and tsunami in 2011 was an unanticipated catastrophe that affected several companies worldwide. Japan was and is still the world’s third-largest economy based on GDP. It is also considered to be the major supplier for several industries worldwide like computers, electronics, and automobiles [10]. The shutdown of the factories in Japan is the ripple effect of the earthquake that was felt globally. General Motors at that time suspended production at their facility in Louisiana, where the Chevrolet Colorado and GMC Canyon are produced. The reason behind this is the shortage of some parts that were supplied from Japan [13]. Another example was the volcanic eruption in Iceland in 2010. This foreseeable catastrophe affected millions of air travelers and time-sensitive air shipments [2]. The coronavirus disease 2019 (COVID-19) pandemic is the greatest foreseeable catastrophe that shocked supply chains all over the world [12]. This virus was first noticed in Wuhan, China, in 2019. It belongs to the family of viruses that cause illnesses like the common cold, Severe Acute Respiratory Syndrome (SARS), and

2 Supply Forecasting for Lebanon After Beirut Blast

19

Middle East Respiratory Syndrome (MERS). This virus began to spread quickly to other countries affecting many people worldwide. In March 2020, the World Health Organization announced that we are dealing with a global pandemic. By that time, the approximate number of confirmed cases around the world was 180,000, and the number of deaths was 7500. Governments responded to this critical situation by enforcing full lockdowns. Supply chain production and distribution activities were interrupted. Main companies suffered from this situation. Bloomberg reported that Apple warned retail employees that there would be shortages in iPhones replacements due to supply chain constraints. Those constraints were mainly because Apple’s suppliers located in countries like Malaysia, South Korea, and Europe were affected by the lockdowns and the shortage of parts supplies from their own sub-suppliers [4]. Moreover, Foxconn, the main Apple products assembler, was working below capacity [4]. The COVID-19 pandemic, being able to cross countries’ borders easily, affected several components of the supply chain sequentially or concurrently [5]. Manufacturing plants, distribution centers, logistics, and markets can suddenly stop or hopefully be functioning at low capacity. According to the World Food Program (WFP), Lebanon is a net food importer at a deficit of USD 2.1 billion in 2019. Given the increase in public debt, the increase in fiscal deficit, and the decrease in GDP [6], the country had to decrease imports [7]. Comparing the first half of 2020 with the first half of 2019, we notice a 41% decrease in the volume of total imports [15]. We also notice that the imports of food (excluding cereals) diminished by 17.6%. On the other hand, we see a growth in cereal imports by 2.7% [16]. This growth is because cereals are still currently subsidized by the Banque de Liban (BDL). Therefore, we can deduce that non-food-related raw materials and manufactured products are the categories that are greatly impacted by the current situation [8] (see Table 2.1). Many supply chains in Lebanon rely on importing non-food-related raw materials and manufactured products, which are essential in manufacturing their own product. For example, light manufacturing companies in Lebanon import LED modules to be used in the manufacturing of their lighting product. With the current situation in Lebanon, importing has become difficult; thus, supply chains relying on imported material were disrupted. Assessments done by the WFP in August showed that 22% of contracted retailers had experienced disturbances in the supply of ordered food commodities. Given the high LL to the USD exchange rate in July, this percentage increased to reach 38% (see Fig. 2.1). Table 2.1 Cereal, food, and total imports for Lebanon in H1 2019 versus H1 2020 in mt

Imports

H1 2019

H1 2020

% Change

Cereal

641,422

658,512

2.70

Food (excluding cereals)

949,316

781,922

−17.60

Total Imports

10,356,778

6,085,929

−41.20

20

N. Nehme et al.

Fig. 2.1 US Dollar to LL exchange rate from October 2019 to August 2020

In 2020, Lebanon experienced two foreseeable catastrophes (Pandemic and Financial Crisis) and one unanticipated business disruption (Man-made Disaster). These events caused a decrease in imports, which caused disruption in the Lebanese supply chains. Having these events happening together made the situation even worse because it limited the options for supply chains to survive. As a result, several known retail stores, such as Alshaya Group, closed.

2.3 Methodology 2.3.1 Data Collection The first step in this paper is to collect data from formulating the forecast model. Data was acquired from “Trading Economics.” Total Monthly imports into Lebanon from around the world were shown. On trading economics, the data was shown per month, in yearly bulks. The data collected was in units of USD Million, and the dates retrieved were from January 2015 until August 2020. After Beirut’s explosion on August 4, no data was further entered on any website online. Therefore, assumptions were made for the months ranging from September 2020 until February 2021. Assumptions were made based on three factors. The first factor, the Beirut explosion, disrupted all internal and external supply chains. The second factor, the possible decision of BDL to remove subsidies on many products such as wheat, oil, and medicine, as reported by the WFP VAM update. The third factor, drastic inflation, led to disruption of the external importing from outside Lebanon due decreased purchasing power. Imports from external sources shall be exponentially increased due to a large amount of money in millions of dollars received and managed by non-profit and international organizations to help and support individuals affected by the explosion. This caused a decrease in the exchange rate of the Lebanese Lira compared to the US dollar, as illustrated in Fig. 2.1, which boosted the demand for the essential survival materials imported from outside Lebanese and paid by the US dollar. These insights

2 Supply Forecasting for Lebanon After Beirut Blast

21

explain the increase of the demand by 300% in August 2020. The demand level returns to normal in October, and it gradually decreases through November, December, and January. The sweeping decrease in February and March was assumed because BDL is planning to gradually remove subsidies on many products. Specific products like pharmaceuticals and petroleum were searched for, but the data available for Lebanon is very minimal. Yearly total imports were available for these two products, but forecasting with only yearly data will not give accurate upcoming forecasts. Seasonality was not available at all with respect to the case we are working on. Therefore, it was predictable that the forecasts were not going to be highly accurate. The results stated could only convey an overview of what to expect in the coming years. Two methods are used to estimate the forecast: The simple Exponential method and Winter’s method.

2.3.2 Simple Exponential Method Simple exponential method (SEM) was used since demand has no observable trend or seasonality. An initial level value was deduced by averaging the total demand data available. The below Eqs. (2.1–2.3) were used in our SEM forecast:

L t+1 =

Ft+l = L t

(2.1)

L t+1 = α Dt+1 + (1 − α)L t

(2.2)

t−1 

α(1 − α)n Dt+1−n + (1 − α)t D1

(2.3)

n=0

where α L T Dt Ft

smoothing constant 0 < α < 1 estimate of initial level estimate of trend actual demand observed in period t forecast of demand for period t

2.3.3 Winter’s Method When calculating the predictable demand for the next two years using Winter’s Method, the below Eqs. (2.4–2.6) were used to find the Level (L), Trend (T), and Seasonal Factors (S).

22

N. Nehme et al.

 L t+1 = α

Dt+1 St+1

 + (1 − α)(L t + Tt )

Tt+1 = β(L t+1 − L t ) + (1 − β)Tt  St+ p+1 = γ

Dt+1 L t+1

(2.4) (2.5)

 + (1 − γ )St+1

(2.6)

where: α β γ

level smoothing constant trend smoothing conteant seasonal factor smoothing constant.

The above three constants were determined using an excel solver to minimize total errors.

2.4 Results 2.4.1 Simple Exponential Method To find the optimal alpha, a trial-and-error technique was used. Obviously, as alpha increases, the forecast will abide by the shape of the previous demand. Due to the absence of seasonality, in this case, an alpha below 0.5 was used to enhance the model. A value of alpha between 0.3 and 0.4 was used in the model, as illustrated in Fig. 2.2. The last two periods are the same, so only one period was targeted. After March 2021, the imports slightly increased then decreased.

Fig. 2.2 Forecast for Lebanon imports for the coming two years using Simple Exponential method

2 Supply Forecasting for Lebanon After Beirut Blast

23

Fig. 2.3 Forecast for Lebanon imports for the coming two years using Winter’s method

2.4.2 Winter’s Method The Winter’s Method was conducted using an excel solver. Results showed alpha equal to zero during beta and gamma one. Figure 2.3 illustrates the forecast for demand imported. This model is much more accurate because it shows that the demand is diminishing just as predicted before proceeding with the forecast. An increase in demand every December (month 73) is also noticed, which can be justified by the high imports around the holiday season.

2.5 Recommendations Many aspects of supply chain management related to importing products worldwide into Lebanon are absent. These aspects are compulsory for an overall successful supply chain management. The first recommendation is to compress cycle time, the time required for a product to reach a customer from the supplier. Cycle time can be reduced by importing from countries closer to Lebanon, further reducing the transportation cost. The second recommendation is the supply chain segmentation; expensive suppliers may be filtered out to increase cost-efficiency. In addition, the Lebanese government may be able to sign a treaty with another country to minimize total transportation costs. The third recommendation is the integration of supply chain technologies and artificial intelligence tools to monitor and control all imported materials, which will enhance forecast models and minimize forecast errors. Hidden seasonality may be derived for specific products. Therefore, it allows local companies

24

N. Nehme et al.

to apply just-in-time manufacturing. The fourth recommendation is quality control assurance, where local quality control authorities shall set standards for Lebanese manufacturers.

2.6 Conclusion Many factors are affecting the consuming behavior of Lebanese society. Economic crisis, Beirut’s explosion, and Coronavirus are key aspects of the perfect explanation of a major supply chain with no seasonality. Seasonality relieves business owners and retailers since they can easily forecast the future, allowing them to adopt a pull-based supply chain rather than a wasteful push-based one. The developed forecast reveals an exponential decrease in the demand (imports), and thus Lebanese supply chains should be altered for the coming months. An optimistic forecast was stated in the WFP report on August 2020, stating that the economic recovery will start by 2022. However, until that time, every decision related to the supply chains strategy in the coming time horizon is vital for the survival of the Lebanese society.

References 1. Accenture (2006) Keeping ahead of supply chain risk and uncertainty. http://www.oracle.com/ us/018575.pdf 2. Chopra S, Sodhi M (2014) Reducing the risk of supply chain disruptions. MIT Sloan Manag Rev 55(3):72–80 3. Chopra S, Meindl P (2016) Supply chain management: strategy, planning, and operation, 6th edn. Pearson Prentice Hall 4. Gurman M (2020) Apple Warns stores about a shortage of replacement iPhones. Retrieved from https://www.bloomberg.com/news/articles/2020-03-04/apple-warns-storesabout-shortage-of-replacement-iphones 5. Ivanov D, Das A (2020) Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note. Int J Integr Supply Manage 13(1):90–102 6. Lebanon: Economic Indicators, Historic Data & Forecasts: CEIC (1970) Retrieved from https:// www.ceicdata.com/en/country/lebanon 7. Lebanon Imports1993–2020 Data: 2021–2022 Forecast: Historical: Chart: News (n.d.) Retrieved from https://tradingeconomics.com/lebanon/imports 8. Lebanon trade statistics (2020) Retrieved from https://wits.worldbank.org/CountryProfile/ en/LBN 9. Lebanon—Explosions Fact Sheet #2 (August 12, 2020) [EN/AR] - Lebanon (n.d.) Retrieved from https://reliefweb.int/report/lebanon/lebanon-explosions-fact-sheet-2-august12-2020-enar 10. Lohr B (2011) “Supply chain still working without links in Japan; effects of earthquake and Tsunami on World Industry Appear Limited.” International Herald Tribune 11. McKinsey Global Institute (2020) Risk, resilience, and rebalancing in global value chains. http://www.mckinsey.com/business-functions/operations/our-insights/risk-resilienceand-rebalancing-in-global-value-chains?cid=eml-web 12. Person (2020) Lebanon import woes deepen as supply chains buckle under Coronavirus. Retrieved from https://www.arabnews.com/node/1655646/business-economy

2 Supply Forecasting for Lebanon After Beirut Blast

25

13. Rooney A (2011) Reminiscing about Japan. Patriot-news Harrisburg, Pa 14. United States Textiles and Clothing Imports by country (2020) Retrieved from https://wits. worldbank.org/CountryProfile/en/Country/USA/Year/LTST/TradeFlow/Import/Partner/bycountry/Product/50-63_TextCloth 15. World Food Program (2020) Beirut port explosion: impact on key economic and food security indicators. https://docs.wfp.org/api/documents/WFP-0000118691/download/ 16. World Food Program (2020) Lebanon: VAM update on food price trends. https://docs.wfp.org/ api/documents/WFP-0000119637/download/

Chapter 3

Multi-criteria Fuzzy Decision-Making Techniques with Transaction Cost Economy Theorem Perspectives in Product Launching Process Cagatay Ozdemir and Sezi Cevik Onar Abstract Many studies have been conducted in the area of transaction costs. However, these studies have generally focused on testing which decisions companies take in case of a shock or transformation and how the transaction cost affects this. However, the literature study showed that when companies need to undergo a certain transformation and strategically create a product/project, there is a lack of decisionmaking technique in the literature that reveals whether they should do this job with internal or external resources according to the conditions. With our digitalizing age, technology companies need this decision-making technique. The main purpose of this paper is to create a decision-making method in which technology companies will decide with which source to do this work at the stage of product or project development. In line with the study, the cost of infrastructure and development and the cost of uncertainty and time emerged as the most important cost items to determine the decision. The importance levels of all cost items were also determined with this study. In this way, companies will be able to make more effective decisions. Keywords Transaction cost · Decision making techniques · Multi-criteria model · Fuzzy set · Hybrid fuzzy decision method · Digital product management · Multi-criteria decision making · Magnetic resonance imaging · Fuzzy analytical hierarchy process

3.1 Introduction The fact that the transaction cost issue is an important paradigm has been provided by the studies and researches developed by Coase and then Williamson [6, 11]. There are 3 dimensions in the field of Transaction cost economies. These dimensions are C. Ozdemir · S. C. Onar (B) Industria l Engineering Department, Management Faculty, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] C. Ozdemir e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_3

27

28

C. Ozdemir and S. C. Onar

uncertainty, asset specificity, and frequency of order [12]. Transaction cost occurs when these dimensions encounter a certain behavior (bounded rationality) or opportunism [7]. Transaction cost economy is defined as the total delta cost arising from the subtraction of the cost resulting from outsourcing a job from the cost of doing it with internal resources. In the case of doing this job with external resources, the costs of researching the outsourced company that will do the job, making the contract, and monitoring this work arise. If companies carry out these works with internal resources, the company has production costs. These costs and structures are affected by the 3 dimensions given above [9] (Fig. 3.1). One of the most important problems is that the effect of cost items included in the transaction cost structure on the total costs calculated is not in scalar form. This situation affects the decision-making mechanism of the companies. A new decisionmaking method is needed to determine the coefficient of cost items that affect the total transaction costs and to determine the strength of each cost item’s effect. The main purpose of this article is to reveal this method. With this method to be revealed, it is to provide support to the company during the decision whether the company should do it with internal or external resources. This article reveals all the costs that the company has to bear in the case of internal production or outsourcing. These costs were added to the decision-making technique by categorizing them into 3 main dimensions. With this technique, companies will easily see which decision to take in which situations. Within the scope of this article, some of the variables determined to be used in the decision-making model are known numerically, but some of them cannot be known and predicted during the productization process. For the purpose of this study, it also expresses linguistically those that cannot be defined by value. In addition, it is to create a decision model by using value-based variables and linguisticbased variables in the same model. In line with this publication, the prioritization and weighting of all the variables, which is the first stage of the planned study, will be done linguistically. In the next stage, the article will be expanded with the establishment of the planned decision-making model in line with this output.

Fig. 3.1 Conceptual framework of transaction costs

3 Multi-criteria Fuzzy Decision-Making Techniques …

29

3.2 Background Many studies have been conducted in the field of transaction cost in the literature. In some of these studies, from the perspective of transaction cost economics, which decisions companies take in the event of a strategy or innovation shock have been examined. These investigations will show that the tendency to accept a higher transaction cost rate will be higher to quickly move to a new position in response to the shock. This situation will occur if the cost of moving late to the new position exceeds the contribution of early action [1]. In addition, according to the study, firms that are already repositioned are more likely to prefer to do these jobs with internal sources to reduce transaction costs as competitive pressure increases. From another point of view, it is stated that during the innovation shock, companies interested in innovation have a higher ability to imitate compared to other companies and can use outsourced resources to imitate this innovation quickly at the beginning [3]. In this study, it has been observed that as the competition increases and the imitations increase, production returns through internal resources. In another study conducted in terms of transaction costs, it is revealed that retail companies are more motivated to turn to private branding instead of using the manufacturer’s brand in product marketing [5]. It is stated that they use this situation to provide the commitment to the outsourcing company to do the work. Optimization and operation studies were also carried out in the area of transaction costs. Examples of these studies are the selection of the areas that bring the investment decision to the highest profitability and portfolio optimization area by using the transaction cost theory [2, 8]. In addition, this theorem led by Coase and Williamson is also used in decision-making situations. These analyzes were provided by regression, chi-square analysis, OLS, and other statistical analyses (Table 3.1). As mentioned above, the studies in the transaction cost area are spread over many areas. Generally, it aims to determine which decision companies take when they need strategic change and the variables that affect this decision. In this direction, it was examined what is important when making decisions. It also examined the change in transaction costs before and after these decisions. In line with this information, it is also determined that which cost items affect the transaction cost and in which Table 3.1 Transaction cost literature review based on scopus library (method breakdown)

Year

Transaction cost

Transaction cost & Transaction cost decision making & fuzzy

Transaction cost & fuzzy & decision Making

Paper

Paper

Paper

Article

Article

Paper

Article

Article

1950–1999

3.608

2.939

129

111

8

4

1

0

2000–2009

9.738

6.714

503

321

67

34

17

10

2011–2021

15.030

10.347

804

574

190

133

26

21

Total

28.376

20.000

1.436

1.006

265

171

44

31

30

C. Ozdemir and S. C. Onar

case they provide production with internal resources, in which case they operate with outsourcing. The literature study in the field of transaction costs revealed the broad research perspective mentioned above. However, in these studies, there are very few studies involving a decision-making technique in line with the transaction cost perspective of whether the company should do a job with internal or external resources. In line with the research conducted within the scope of this paper, a decision-making technique that is created in line with the transaction cost dimensions (asset specificity, uncertainty, frequency, and behavioral assumption) will be formulated during the production and development of a strategic business. For this reason, it is necessary to use a fuzzy set because the effect of cost items in the determined dimensions is relative and not clear. The number of articles with both transaction cost and decision making and fuzzy is also limited in the literature. With this study, the transaction cost theorem and the multi-criteria fuzzy decision-making method were established. In this way, the research conducted will help the firm determine whether it should do the job with internal resources or outsourcing if it makes a strategic decision. In addition, this deficiency in the literature will be contributed.

3.3 Case Study The main purpose of technology companies is to increase their own income and meet the needs of customers by developing new digital application products and projects. If the changing needs of customers are met with new products, the chance of achieving customer loyalty will increase. Higher income and profitability are also obtained from loyal customers. In this direction, it is very important for technology companies to develop and launch products/projects that will meet the needs of customers and the market. In addition, technology companies must launch these products and projects at the right time and most optimal cost. It is one of the most important problems they need to solve, whether it should provide this product/project with internal or external resources. With the parameters obtained and determined within the scope of this article, a decision-making diagram will be established in which the source to proceed during the development of the digital product in the technology sector is determined. In this way, it will be possible to observe which variables affect the decision diagram and in which cases it is necessary to proceed with internal resources or external resources.

3.4 Data Sources One of the biggest problems in the process of product extraction in the technology sector is that some of the costs incurred in the development of the product or project are uncertain and not clearly determined. Although it is difficult to clarify the cost

3 Multi-criteria Fuzzy Decision-Making Techniques …

31

parameters within the scope of this study, a working group consisting of product manager experts working in large-scaled technology companies has been formed. In line with this working group, many product and project developments of product managers who have launched digital products have been examined in detail. The cost parameters that emerged during these developments were determined. Within the scope of this application, the required cost items and parameters in digital product and project development stages were determined by using the product development process capability of large-scale technology service providers. These technology companies include companies with more than 500 employees and companies that release digital products to meet customer needs. In addition, these companies actively use digital tools while producing products. In addition, the following cost parameter types are accessed, cleared, and corrected for analysis. Tables 3.2 show the variable categories and data types in detail.

3.5 Data Models With digital transformation, the need to determine whether the planned products or projects should be made with internal resources or outsourced has increased. The importance of making this decision right has become even more crucial, as it is the goal of technology companies to produce the fastest and most cost-effective products that will meet the needs of the market and customer with this transformation. So far, companies have made this decision by looking at the total delta cost. However, if the importance of all cost items is taken equal, it is possible to make wrong decisions. For this reason, the cost items determined in the above section have been categorized and included in the total delta cost formula below. T otal Cost = (T echnical E f f iciency Cost) + (Agency E f f iciency Cost) (3.1) (T echnical E f f iciency Cost) = (H R) + (I D) + (S A) (Agency E f f iciency Cost) = (P A) + (U T )

(3.2) (3.3)

(H R) = I H R PC + I P MC + I T C + I T BC + I K I C − O H R PC − O P MC − O T C − O T BC − O K I C (3.4) (I D) = I I MC + I DT C + I DSC − O I MC − O DT C − O DSC

(3.5)

(S A) = I D MC + I S MC + I ASC + I T AC − O D MC − O S MC − O ASC − O T AC

(3.6)

32

C. Ozdemir and S. C. Onar

Table 3.2 Total cost variables (cost category breakdown) HR

EC

SA

PA

UT

Human Resources

Infrastructure & Development

Support Activities

Process & Approval

Uncertainty & Time

InsourceHuman Resources Personal Cost

InsourceInfrastructure & Maintenance Cost

Insource- Design & Market Research Cost

InsourceApproval Process Cost

Insource- Internal Uncertainty Cost

InsourceProject Management Cost

InsourceDevelopment & Test Cost

Insource- Sales & Marketing Cost

InsourceIntegration Cost

Insource- Time Cost

InsourceTraining Cost

Insource- Device Insource- After & Supply Chain Sales & Support Cost Cost

InsourceBusiness Continuity Cost

InsourceSearching Cost

InsourceOutsourceTeam Building Infrastructure & Cost Maintenance Cost

Insource- Tax Cost OutsourceOutsourceContracting Cost Searching Cost

InsourceKnow How & IP Cost

OutsourceDevelopment & Test Cost

Outsource- Design Outsource& Market Monitoring Cost Research Cost

OutsourceHuman Resources Personal Cost

OutsourceOutsource- Sales Device & Supply & Marketing Cost Chain Cost

OutsourceApproval Process Cost

OutsourceAdaption Cost

Outsource- Time Cost

OutsourceProject Management Cost

Outsource- After Sales & Support Cost

OutsourceIntegration Cost

OutsourceTraining Cost

Outsource- Tax Cost

OutsourceCoordinating Cost

OutsourceTeam Building Cost

OutsourceExternal Uncertainty

OutsourceBusiness Continuity Cost

OutsourceKnow How & IP Cost

(P A) = I A PC + I I C + I BCC − OCC − O MC − O A PC − O I C − OC OC − OBCC (U T ) = I I U C + I T I C − O SC − O EU C − O AC − O T I C

(3.7) (3.8)

3 Multi-criteria Fuzzy Decision-Making Techniques … Table 3.3 Prioritization scale

33

Value

Numbers (ai j )

Equal

(1,1,1)

Equal to moderate

(1,2,3)

Moderate

(2,3,4)

Moderate to strong

(3,4,5)

Strong

(4,5,6)

Strong to very strong

(5,6,7)

Very strong

(6,7,8)

Very strong to extremely strong

(7,8,9)

Extremely strong

(9,9,9)

The effect of each of the determined cost items on the job output and the decision does not have the same weight. The fuzzy analytic hierarchy process (FAHP) has been applied in this study in order to learn the order of importance and weight of these cost items. The order of importance and weights of cost items were determined by the fuzzy analytical hierarchy process. In this study, the fuzzy analytical hierarchy process (FAHP) has been applied to find the importance and weight of cost items. The significance weight revealed by using FAHP will be added to the delta cost formula. The following steps have been applied to find and prioritize the weights of cost items with FAHP [10]. The first step of FAHP is to give an important value to the features with respect to the Prioritization Scale table. After the prioritization process of features, the pairwise comparison matrix is established based on the ai j . Moreover, Buckley’s Fuzzy AHP Method was applied to find normalized weights of features [4] (Table 3.3).  Step 1. Each element (a 12 ) of the pairwise comparison matrix A˜ is a fuzzy number corresponding to its prioritization scale [4]:

  1    a 21 ˜ A =  .  ..   a n1

   a 12 · · · a 1n    1 . . . a 2n  .. ..  . ... .    a n2 . . . 1 

(3.9)



a 12 = (a12l , a12m , a12u ) .. .

(3.10)



a 1n = (a1nl , a1nm , a1nu ) Step 2. The consistency of each fuzzy comparison matrix is examined. Pairwise comparison values are defuzzified by the graded mean integration approach in order to check the consistency of the fuzzy pairwise comparison matrices. According to

34

C. Ozdemir and S. C. Onar

the graded mean integration approach, a triangular fuzzy number can be transformed into a crisp number by employing the equation below [4]: A=

l + 4m + u 6

(3.11)

Step 3. The fuzzy geometric mean for each row of matrices is computed to weigh the criteria and alternatives [4]. a1l = [1 × a12l × . . . × a1nl ]1/n a2l = [a21l × 1 × . . . × a2nl ]1/n ... ail = [an1l × an2l × . . . × 1]1/n

(3.12)

b1l = [1 × b12l × . . . × b1nl ]1/n b2l = [b21l × 1 × . . . × b2nl ]1/n ··· bil = [bn1l × bn2l × . . . × 1]1/n

(3.13)

c1l = [1 × c12l × . . . × c1nl ]1/n c2l = [c21l × 1 × . . . × c2nl ]1/n ··· cil = [cn1l × cn2l × . . . × 1]1/n

(3.14)

Assume that the sums of the geometric mean values in the row are a1s for lower parameters,a2s for medium parameters,a1s for upper parameters    a1l b1m c1u   a3s , a2s , a1s     a2l b2m c2u   a3s , a2s , a1s   r˜i j =   ..   .    ail bim ciu   a3s , a2s , a1s 

(3.15)

Step 4. The fuzzy weights and fuzzy performance scores are aggregated as follows: U˜ i =

n 

w˜ j r˜i j , ∀i.

(3.16)

j=1

Step 5. The center of area method can be applied for defuzzification: B N Pi =

(u i − li ) + (m i − li ) + li , ∀i 3

(3.17)

3 Multi-criteria Fuzzy Decision-Making Techniques …

35

Step 6. The best alternative is determined as in the Fuzzy AHP

3.6 Data Models Evaluation In the data analysis used in determining the weights of cost items, the steps (3.1)– (3.6) stated above were followed. Prioritization scale was applied for the Variable Category, and the subcategories were evaluated with FAHP. Table 3.4 shows the prioritization matrix of the main cost variables of transaction cost in line with FAHP analysis. Transaction Cost main cost items are broken down into 6 categories. For the subgroups of these 6 categories, the FAHP prioritization matrices mentioned above were created separately. In order to take up less space in this article, only the prioritization table of the subgroups of the first categories is shown in Table 3.5. After Pairwise Comparison Matrices were generated for corresponding Categories, normalized weights were calculated. The following weights and normalized weights for each category are in ascending order (Tables 3.6 and 3.7). The consistency of the prioritization matrix created within the scope of FAHP was examined in accordance with step 2. Since the consistency rate of the given prioritization values is less than 10% for each matrix, it has been shown that the study is consistent. Table 3.4 Scale table of main cost HR HR

(1,1,1)

ID

(4,5,6) 1 1 1 4, 3, 2

SA PA

(1,2,3)

UT

(2,3,4)

ID 1

1 1 6,5,4



(1,1,1) 1 1 1 8, 7, 6 1 1 1 6, 5,4 1 1 1 3, 2, 1

SA (2,3,4) (6,7,8)

PA 1 1 1 3, 2, 1

UT 1 1 1 4, 3, 2

(4,5,6) 1 1 1 6, 5, 4

(4,5,6)

(1,1,1)

(1,2,3) 1 1 1 8, 7, 6 1 1 1 6, 5, 4

(6,7,8)

(4,5,6)

(1,1,1)

(1,1,1)

Table 3.5 Scale table infrastructure and development (ID) IIMC IIMC

(1,1,1)

IDTC 1 1 1 5, 4, 3

IDTC

(3,4,5)

(1,1,1)

IDSC 1 1 1 4, 3, 2 1 1 1 4, 3, 2

IDSC OIMC

(2,3,4) 1 1 1 4, 3, 2

ODTC

(2,3,4)

ODSC

(1,2,3)

(2,3,4) 1 1 1 5, 4, 3 1 1 1 3, 2, 1 1 1 1 3, 2, 1

(1,1,1) 1 1 1 4, 3, 2 1 1 1 4, 3, 2 1 1 1 3, 2, 1

OIMC (2,3,4)

ODTC 1 1 1 4, 3, 2

ODSC 1 1 1 3, 2, 1

(3,4,5)

(1,2,3)

(1,2,3)

(2,3,4) (1,1,1)

(2,3,4) 1 1 1 3, 2, 1

(1,2,3)

(1,1,1)

(1,2,3) 1 1 1 6, 5, 4 1 1 1 3, 2, 1

(4,5,6)

(1,2,3)

(1,1,1)

36 Table 3.6 Weights of category of main variables

Table 3.7 Weights of category of ID variables

C. Ozdemir and S. C. Onar Feature

Weights

Normalized weights

HR

0.10

0.09

ID

0.47

0.44

SA

0.04

0.04

PA

0.12

0.12

UT

0.34

0.31

Feature

Weights

Normalized weights

IIMC

0.10

0.08

IDTC

0.26

0.23

IDSC

0.37

0.31

OIMC

0.06

0.05

ODTC

0.15

0.13

ODSC

0.22

0.19

3.7 Conclusion and Future Research In line with the FAHP study, the importance of all group and subgroup variables has been determined linguistically in terms of cost. In this direction, when we examine the main cost group results, the Human Resources cost weight is 9%, the Infrastructure and Development cost weight is 44%, the Support Activities cost weight is 4%, the Process and Approval cost weight is 12%, and the Uncertainty and Time cost weight is 31%. In this direction, as a result of the points given by the experts, the cost of infrastructure and development and the cost of uncertainty and time became the most important costs. While the cost of infrastructure and development was the most important cost among technical efficiency, the cost of uncertainty and time was the most important cost among agency efficiency. In addition, the following results arose in the subgroups of infrastructure and development cost, which are examined in more detail within the scope of the study. Insource device and supply chain costs were the most important cost with a 31% weight rate. This cost was followed by Insource Development and Test costs, with a weight rate of 23%. On the outsourcing side, the device and supply chain cost have the highest importance with a 19% weight rate, while the development and testing cost has the second-highest importance with a 13% weight rate. For both outsource and insource, the cost of infrastructure and maintenance is less important. This study has been graded in detail for all subgroups, and the importance coefficient has been revealed. In line with the importance coefficients here, it will be possible to calculate the total transaction cost more effectively by using the formulas between 1 and 8.

3 Multi-criteria Fuzzy Decision-Making Techniques …

37

In line with all these studies, the order of priority and importance weight values of both main cost variables and subgroup variables were found among themselves. The aim of this study is to generate the groundwork for the decision-making structure that will be created by using both the variables defined by value and the linguistically defined variables in the same model. Moreover, this publication is to be the initial study of this decision-making structure. The paper will be expanded in line with the outputs from here. In line with this paper, we will create a decision model by using both value-based variables and linguistically -defined variables in the same model in the next stage. With the studies to be developed and detailed, it is planned that companies will be able to decide more clearly with which source they should use while developing the product.

References 1. Argyres N, Nickerson J (2019) Strategic responses to shocks: comparative adjustment costs, transaction costs, and opportunity costs. Strateg Manag J 40(3):357–376 2. Best MJ, Hlouskova J (2005) An algorithm for portfolio optimization with transaction costs. Manage Sci 51(11):1676–1688 3. Bigelow L, Nickerson J (2019) When and how to shift gears: Dynamic trade-offs among adjustment, opportunity, and transaction costs in response to an innovation shock. Strateg Manag J 40(3):377–407 4. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(1):233–247 5. Chen S-F (2010) Transaction cost implication of private branding and empirical evidence. Strateg Manag J 31(4):371–389 6. Coase RH (1937) The nature of the firm, Economica. Economica 4:368–405 7. Englander EJ (1988) Technology and oliver williamson’s transaction cost economics. Econ Behav Organ 10:339–353 8. Gerard G, Alan J (1994) Investment strategies under transaction costs: the finite horizon case. Manage Sci 40(3):385–404 9. Klaus B (1992) R&D cooperation between firms-a perceived transaction cost perspective. Manage Sci 38(4):514–524 10. Ozdemir C, Onar SC, Bagriyanik S, Kahraman C, Akalin BZ, Öztay¸si B (2021) Estimating shopping center visitor numbers based on a new hybrid fuzzy prediction method. J Intell Fuzzy Syst 1–14 11. Williamson OE (1975) Markets and hierarchies: analysis and antitrust implications. The Free Press, New York 12. Williamson OE (1981) The economics of organization: the transaction cost approach. Am J Sociol 87(3):548–577

Chapter 4

Assessment of Risk Attitudes of Generations: A Prospect Theory Approach M. Cagri Budak, Ayberk Soyer, and Sezi Cevik Onar

Abstract Understanding the behaviors and attitudes of different generations is crucial not only for individuals but also for the organizations. Utility Theory is a well-known and well-accepted behavioral economics theory that explains the human decision-making process, yet it has some limitations. Prospect Theory has improved Utility Theory by eliminating the contradictions in its fundamental assumptions. Prospect theory can be very beneficial for understanding the difference between generations. In this study, we use Prospect Theory’s certainty and reflection effects for understanding the reactions of different generations (i.e., Gen X, Gen Y, Gen Z) to the same conditions. We also analyze the risk attitudes of generations by using Cumulative Prospect Theory’s questions. A questionnaire including 28 questions with 145 participants is used for collecting data. The results show that the predicted certainty effect is valid for different generations; however, Gen X and Gen Z do not show the predicted reflection effect. The results also show that different generations reacted differently in some of the gain and loss gambles when compared to the Cumulative Prospect Theory’s findings. Keywords Prospect theory · Cumulative prospect theory · Risk attitude · Generation

M. C. Budak (B) · A. Soyer · S. Cevik Onar Industrial Engineering Department, Faculty of Engineering and Architecture, Beykent University, Istanbul, Turkey e-mail: [email protected]; [email protected] A. Soyer e-mail: [email protected] S. Cevik Onar e-mail: [email protected] Industrial Engineering Department, Faculty of Management, Istanbul Technical University, Istanbul, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_4

39

40

M. C. Budak et al.

4.1 Introduction Understanding human behaviors has always been an important research field. Contrary to the classical behavioral economics approaches, Kahneman and Tversky [7] claimed that a human makes his/her decisions irrationally. Based on this fundamental idea, they have introduced Prospect Theory (PT). By this theory, individual or organizational level risk attitudes and decision-making processes can be explained [3]. Making comparisons among various experimental groups is one of the most common research topics in this field. In this context, generations are suitable for making such comparisons. Differences between generations have been studied in many areas by many researchers. However, to the limit of our knowledge, there is no study in the literature focusing on the risk preference differences among generations using PT. Therefore, this study aims to address this gap by trying to explain the risk preferences of Gen X, Gen Y, and Gen Z generations according to the value function of PT. The generations of the participants are determined by their birth year. Individuals born between 1964 and 1980 are considered Gen X, born between 1981 and 1996 as Gen Y, and born from 1997 onward as Gen Z [9]. The remainder of the paper is organized as follows. In Sect. 4.2, a general overview of PT and Cumulative Prospect Theory (CPT) is explained briefly. Section 4.3 introduces the methodology, hypotheses, and the purpose of the study, followed by Sect. 4.4, in which the analysis of the generations’ risk attitudes and comparisons with PT and CPT results are given. Finally, Sect. 4.5 summarizes the conclusions and limitations of this study together with the recommendations for future research.

4.2 Literature Review Expected Utility Theory (EUT) has dominated the decision-making process under risk for a long time. This dominant theory has some axioms, which are proved to be violated by PT [7]. After showing these violations, PT draws three main conclusions: (i) reference point-oriented change of wealth, (ii) S-shaped value function, and (iii) perceived probability [10]. One of the most popular conclusions of PT receiving attention in the literature is the s-shape value function. Kahneman and Tversky [7] have established the value function for gains and losses, which is concave for gains and convex for losses. Concave shape indicates risk aversion, and convex shape indicates risk seeker behavior. Figure 4.1 demonstrates the illustrative value function of PT. As can be seen in Fig. 4.1, the value function is concave for gains and convex for losses. Hence, generally, people are risk-takers for gains and risk-averse for losses. One of the other remarkable points for value function, as can be seen in Fig. 4.1, is that the nonlinear line for losses is steeper than the nonlinear line for gains. Generally, people tend to avoid losses, and this phenomenon is called loss aversion [8]. The value function is built as:

4 Assessment of Risk Attitudes of Generations …

41

Fig. 4.1 Value FUNCTION OF PROSPECT THeory [7]

 V (x) =

xα if x ≥ 0 −λ(−x)β i f x < 0

where the reference point is accepted as 0, λ indicates the loss aversion parameter, and α and β indicate curvature parameters. Kahneman and Tversky [8] calculated α and β as 0.88 and estimated λ as 2.25 in their study. Some researchers tested different values of α, β, and λ parameters. For instance, Balcombe et al. [2] asserted that α and β parameters should not be symmetric, and under this circumstance, they tested the weighting function and estimated the λ parameter. Likewise, Bromiley [3] tested the value and weighting function under different values of curvature and loss aversion parameters, and he claimed that the value function is almost equal to a risk-neutral structure for huge losses and huge gains. Kahneman and Tversky [8] extended PT in several aspects, however, the major novelty of the new theory (i.e., CPT) is the cumulative decision weights. Their claim upon s-shape value function remained unchanged. Although the s-shape value function is commonly accepted in the literature, some researchers reject this claim of PT. After performing some empirical tests using the stochastic dominance approach, Levy and Levy [11] spurn the s-shape value function of PT and CPT. However, Wakker [15] claimed that [11] study had actually proven PT. In classical PT, the weight of probability is indicated with π(p). People generally tend to overestimate low probabilities, which is shown as π (p) > p, and underestimate high probabilities, which is shown as π (p) < p [7]. In CPT, weights are shown with w− and w+. Gains and losses situations have different weighting functions, but the fundamentals of probabilities under and overestimates are the same [8]. CPT builds the weighting function as: w + ( p) =

pγ ( p γ + ((1 − p)γ ))

1 γ

and w − ( p) = 

pδ  1  p δ + (1 − p)δ δ

42

M. C. Budak et al.

PT moots several aspects upon decision making processes of individuals, such as (i) certainty effect, which indicates people tend to choose lower outcome under exact gain condition; (ii) reflection effect, which shows individuals’ choices are risk-averse for gains and risk-seeking for losses under the same prospect; (iii) isolation effect which says that people tend to ignore similarities between outcomes [7]. One of the other aspects of PT is the framing effect. The framing effect claims that individuals react differently to the same problem depending on how the problem is presented to them. One of the most known phenomena for demonstrating the framing effect is the Asian disease problem, where people choose different options for the same issue under different frames [6]. An individual generally cannot perceive the same thing from different perspectives simultaneously, and therefore, s/he decides under his/her point of view [5]. Besides these theoretical studies directly related to PT, there are several additional studies applying PT in different areas. For instance, Sivakumar and Feng [14] developed a product improvement model according to consumer attributes. Using the value function of PT, they generated improvement scenarios based on product quality. Using PT’s domains, Ahrens et al. [1] established a model for price adjustment under the circumstance of stocks fluctuations. Hermann et al. [4] applied the PT value function formula in the game theory approach to measuring voter turnout to the elections. These kinds of researches could be extended. To sum up, PT is extensively used in the literature as it can be used for any decision-making process. Additionally, there are some researches that compare various groups/situations as in our study. For example, Rieger et al. [13] compared risk preferences among countries by using the CPT aspect and estimated CPT parameters. Similarly, Marshall et al. [12] compared western and eastern culture risk preferences.

4.3 Hypothesis and Methodology In this study, a survey replicating [8] CPT questions and including 28 questions in total under the losses and gains conditions separately was formed. The main purpose of this survey was to investigate whether there are any differences among generations and to see whether the answers of the relevant generations differ from CPT estimations. Besides including losses and gains gambles, the survey also aimed to test the certainty and reflection effects by using the original questions of PT [7]. The developed survey consisted of simple gains and losses monetary prospect questions that included two outcomes. One of the outcomes was zero, and on the other hand, the other outcome indicated a monetary gain or loss. In order to evaluate the understandability of the survey questions, a pilot study was carried out with 14 different people before the field application of the survey. The survey was finalized by making minor corrections in some of the question statements according to the feedback received from this pilot study and adding example questions in the survey. Questions were built by taking a leaf out of [13] framework. Table 4.1 shows the simple demonstration of a winning gamble. Losses gambles were also built similarly.

4 Assessment of Risk Attitudes of Generations … Table 4.1 Winning gamble demonstration [13]

43

%99 chance

Win 400

%1 chance

No winning

“I am willing to pay to this lottery at most ___ Turkish Lira (TL)”

In total, 145 people have participated in the survey. Forty-three of these participants belonged to Gen X, 67 to Gen Y, and 35 to Gen Z. Incomplete 31 surveys were excluded from the study (15 from Gen X, 14 from Gen Y, and 2 from Gen Z, respectively), resulting in a total of 114 valid surveys for analysis. 60% of the participants were male, 40% were female. %89 of the participants held B.Sc. and above degrees. When the professions of the participants are examined, it is seen that they were engineers, teachers, psychologists, architectures, lawyers, pilots, journalists, economists, film directors, entrepreneurs, merchants, academicians, students, etc. It can be said that the sample data reflects a considerable part of society.

4.4 Analyses The main purpose of this study was to find the generation’s risk attitude differences using the CPT questions [8] and to examine the certainty and reflection effects by using the questions of PT. In order to test the certainty effect, questions of PT were adapted. In this context, two questions were asked to the participants. The first question was: “There are two options below. In option 1, you will earn 4000 TL with 80% probability, or you may earn nothing with 20% probability. In option 2, you will earn 3000 TL with 100% probability. Please select one of the options below.” The second question was, “There are two options below. In option 1, you will earn 4000 TL with 20% probability, or you may earn nothing with 80% probability. In option 2, you will earn 3000 TL with 25% probability, or you may earn nothing with 75% probability. Please select one of the options below.” In both questions, option 1 has a higher certainty equivalent. Option 1 of the first question has 3200 TL certainty equivalent, while option 2 has 3000 TL. In the same way, the first option of the second question has 800 TL certainty equivalent, while option 2 has 750 TL. Kahneman and Tversky [7] found that 80% of the participants selected option 2 for the first question, however, option 1 has been selected by 65% of the participants for the second question; and the reason for this difference has been explained with certainty effect. This phenomenon was tested in this study in a similar manner to [7] study. According to the results, 86% of Gen X participants have selected option 2 for the first question, while 63% of them have selected option 1 for the second question, 83% of Gen Y participants have selected option 2 for the first question, while 74% of them have selected option 1 for the second question; and 75% of Gen Z participants have selected option 2 for the first question, while 82% of them have selected option

44

M. C. Budak et al.

1 for the second question. These results are consistent with PT’s results; i.e., people tend to choose specific options that have lower outcomes. According to these results, it can be concluded that all generations are affected by the certainty effect. One of the other phenomenon examined in this study is the reflection effect, which indicates that people tend to behave oppositely for gains and losses [7]. In order to test the reflection effect among generations, another two questions of PT were adapted. These two questions have been constructed, including the same monetary outcomes and probabilities with the questions formed to examine certainty effect, however, gain conditions in these questions were changed to lose conditions. PT found that 92% of people have chosen 4,000 monetary loss with 80% probability (option 1) for the first question, and 58% of them have chosen 3000 monetary loss with 25% probability (option 2) for the second question. The results of this study demonstrate that 96% of Gen X participants have selected option 1 for the first question and 74% of them have selected option 1 for the second question; 95% of Gen Y participants have selected option 1 for the first question, while 55% of them have selected option 2 for the second question; and 79% of Gen Z participants have selected option 1 for the first question and again 58% of them have selected option 1 for the second question. The findings for Gen Y participants are consistent with PT; however, the results of the second question for Gen X and Gen Z participants are not consistent. These controversies may be due to the relatively small sample size of the survey. CPT indicates that people tend to be risk-takers with small probabilities for gain conditions, and they become risk-averse when gaining probability increases. On the contrary, under the loss conditions, people are risk-averse with small probabilities, and they become risk-takers as loss possibility increases. Table 4.2 demonstrates the survey results (i.e., the median values of the responses for all gambles, probabilities, and generations). For instance, the median values of the responses for (0, 50) gain outcome with 10% probability are 5 for each generation. These results indicate that each participant from different generations settles with a 5 TL median value than the lottery of 50 TL winning chance with 10% possibility. Although the median values are the same for each generation, mean values seem different. For different probabilities of some specific gain or loss values, some generations have the same median values, although their mean values may differ. For instance, Gen X’s median value for losing gamble of (0, 200) is 10 under the probabilities of 1%, 10%, 50%, and 99%, respectively. However, the mean values for these probabilities are all different. For instance, for (0, 50) gain gamble with 10% probability, mean values are 4.64, 3.81, 4.6 with (1–10), (1–10), (1–20) ranges for Gen X, Gen Y, and Gen Z respectively. Similarly, for (0, −50) loss gamble with 10% probability, mean values are −7.46, −6.43, −7.93 for Gen X, Gen Y, and Gen Z, respectively. These results show that all generations are not behaving as risk seekers even for the small amounts of money with small probabilities for a gain gamble, and they avoid risk. For only small probability values and a small amount of money, they behave as risk-neutral. According to the results of this study, it can be concluded that people from all generations do not like risk for gains. These results do not match up with CPT’s results. Undoubtedly these results can be because of different conditions,

4 Assessment of Risk Attitudes of Generations …

45

Table 4.2 Results of the questionnaire (median values) Probabilities 0.01 Outcomes

X

0.05 Y

Z

X

0.1 Y

Z

0.25

X

Y

Z

X

0.5 Y

Z

X

Y

Z

(0, 50)

5

5

5

10

10

7

(0, −50)

5

5

5

7.5

10

20

(0, 100)

3

3

2

10

5

10

20

15

20

(0, −100)

10

5

5

10

20

15

10

20

35

(0, 200)

1

1

1

5

5

5

20

20

25

10

15

20

10

50

50

(0, −200)

10

5

2

(0, 400)

2.5

4

3

(0, −400)

10

10

5

0.75 X

0.9 Y

Z

0.95

X

Y

Z

(0, 50)

10

20

20

(0, −50)

10

25

20

Outcomes

(0, 100)

22.5

20

25

(0, −100)

15

35

50

0.99

X

Y

Z

30

35

50

10

50

70

X

Y

Z

(0, 200)

35

50

50

85

100

100

(0, −200)

12.5

100

75

10

120

100

(0, 400)

75

150

200

(0, −400)

10

200

250

such as economic conditions, cultural issues, etc. On the other hand, the results of this study do not match up with the results of CPT for losing condition as well, for a small amount of money under low probability. In other words, the participants behave as risk-taker and risk-neutral for a small amount of money under low probability values. The most similar results with CPT obtained in this study belong to the participants of Gen Z for losing condition. The reason for this similarity may be due to the characteristics of the sample, as [7] used university students’ responses and as most of the Gen Z members are university students in our sample as well. Putting aside the fact that these results do not match up with the results of CPT, differences among each generation can be observed for most of the gambles. The overall tendency is to be risk-averse for gain gamble and risk-taker for losing gamble for small probability values.

46

M. C. Budak et al.

4.5 Conclusion This study aimed to find differences between generations’ reactions towards risk using PT and CPT approaches. CPT’s gamble questions were adapted, and data from Gen X, Gen Y, and Gen Z participants were collected with this purpose. Additionally, some of the questions of PT, which are related to the certainty and reflection effects, were also adapted. Collected data do not come from a specific professional group. In this context, this study is different from other studies in the literature. Regarding the certainty effect, similar results with PT were obtained for all generations, indicating that all generations are affected by the certainty effect. On the contrary, regarding the reflection effect, although Gen Y participants behaved similarly to the results of PT, Gen X and Gen Z participants did not. In addition, the results of this study are not fully consistent with the results of CPT, i.e., generations always behave as risk-averse even for the gambles consisting of small amounts of money. Only Gen Z participants behave similarly to the results of CPT. One of the main limitations of this study is the sample size. Therefore, the study can be repeated with a larger sample size to make healthier interpretations. For further research, these risk attitude differences or similarities can be used in any study interested in individuals, and CPT’s value function and weight function parameters can be estimated using these results.

References 1. Ahrens S, Pirschel I, Snower DJ (2017) A theory of price adjustment under loss aversion. J Econ Behav Organ 134:78–95 2. Balcombe E, Bardsley N, Dadzie S, Fraser I (2019) Estimating parametric loss aversion with prospect theory: recognising and dealing with size dependence. J Econ Behav Organ 162:106– 119 3. Bromiley P (2010) Research notes and commentaries looking at prospect theory. Strateg Manag J 31:1357–1370 4. Hermann O, Jong-A-Pin R, Schoonbek L (2019) A prospect-theory model of voter turnout. J Econ Behav Organ 168:362–373 5. Kahneman D (2003) A perspective on judgment and choice mapping bounded rationality. Am Psychol 58(9):697–720 6. Kahneman D, Teversky A (1981) The framing of decisions and the psychology of choice. Science 211:453–458 7. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–269 8. Kahneman D, Tversky A (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5:297–323 9. Kurz CJ, Li G, Vine DJ (2019) Are millennials different? Handbook of US Consumer Economics (s. 193–232). Academic Press, San Diego 10. Levy H, Levy M (2002) Experimental test of the prospect theory value function: a stochastic dominance approach. Organ Behav Hum Decis Process 89:1058–1081 11. Levy H, Levy M (2002) Prospect theory: much ado about nothing? Manage Sci 48(10):1334– 1349

4 Assessment of Risk Attitudes of Generations …

47

12. Marshall R, Huan TC, Xu Y, Nam I (2011) Extending prospect theory cross-culturally by examining switching behavior in consumer and business-to-business contexts. J Bus Res 64:871–878 13. Rieger OM, Wang M, Hens T (2017) Estimating cumulative prospect theory parameters from an international survey. Theor Decis 82:567–596 14. Sivakumar K, Feng C (2019) Patterns of product improvements and customer response. J Bus Res 104:27–43 15. Wakker PP (2003) The data of Levy and Levy (2002) “Prospect theory: much ado about nothing?” actually support prospect theory. Manage Sci 49(7):979–981

Chapter 5

A Literature Review on Human-robot Collaborative Environments Considering Ergonomics Busra Nur Yetkin and Berna Haktanirlar Ulutas

Abstract Repetitive motions, lifting tasks, and work pace may cause ergonomic risks for the workers since they constitute a basis for the growth of work-related musculoskeletal disorders (WMSD) that adversely affect the efficiency of manufacturing. With the development of technology, several ways to prevent these risks are possible. Recently, robots that collaborate with humans assist workers by undertaking heavy and repetitive tasks. However, in order to achieve this benefit, many issues such as design, integration, control, task assignment, scheduling of robots must carefully be considered. Therefore, new research topics in this area, along with the need for new methods and tools, have arisen. This study contributes to the Human-Robot Collaboration (HRC) literature by evaluating the studies that deal with ergonomics in design and planning together. High-quality articles from 2009 to the beginning of 2021 are included in this literature review. The main intent of this paper is to highlight gaps and contradictions of previous studies. Keywords Collaborative robot · Human–robot collaboration · Ergonomics · Collaborative assembly

5.1 Introduction The fourth industrial revolution, Industry 4.0 (I4.0), is known to direct companies with the need of faster delivery times, more efficient and automated processes, high quality and customized products. Automation and Industry 4.0 for manufacturing and assembly systems is really appealing due to increased quality, reduced cycle times, high traceability, and the reduced ergonomic risks on the workers [1]. They can be

B. N. Yetkin (B) · B. H. Ulutas Industrial Engineering Department, Engineering Faculty, Eskisehir Osmangazi University, Eskisehir, Turkey e-mail: [email protected] B. H. Ulutas e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_5

49

50

B. N. Yetkin and B. H. Ulutas

used in different areas, e.g., packaging, carrying, loading/unloading, and specifically in assembly lines. A system that consists of the cooperation of humans and robots is called the human-robot collaborative (HRC) system. A collaborative robot (cobot) is designed to execute a predefined task in a workspace and is constructed to interact physically and collaborate with human workers safely. The main attention at the beginning was on safety, then ease of programming. A lightweight design and deployment of flexibility would also be needed if a robot was to be fully collaborative (URL1). Cobots embody Industry 4.0 concepts in means of technical assistance, information transparency, and interoperability (URL2). The production activities under certain processes may cause workers to expose awkward postures, excessive physical workload, and strain. According to the European Agency for Safety and Health at Work (EASHW) report, exposure to workrelated musculoskeletal disorders (WMSD) risk factors based on repetitive hand or arm movements have increased from 51.9% (2014) to 65% (2019) and lifting or moving people or heavy loads from 47% (2014) to 55.8% (2019) (URL3). The risk of WMSD may result in an inability to work, early retirement, replacement of workers, and loss of production and productivity in manufacturing systems. Improving working conditions and reducing the risk of WMSD can be handled and solved in different ways by organizations, and collaborative robots are one of the promising solutions. Human factors and ergonomics are fundamental components of an HRC system [2]. Cobots benefit workers’ physical and cognitive conditions besides their contributions to manufacturing. And also, the aging of the population is gaining importance because of its consequences on manufacturing systems. Therefore, cobots are an opportunity for both employers and workers in means of productivity and health [3]. On the other hand, robots are costly, and integration to manufacturing systems and operating them may be highly difficult. This paper aims to provide insights to what extent, what type of and how ergonomics applications are considered in HRC systems, especially assembly lines, and also investigates optimization techniques with the help of a systematic literature review. Also, it provides an easy introduction to readers with high-quality, relevant, and valid articles. It is important to reduce, if possible, eliminate repetitive and too many forceful movements in manufacturing environments. To overcome this problem, cobots present suitable solutions. However, some manufacturing processes require high customization and depend on skilled workers. Therefore, cobots and humans must work side by side, but they have unique characteristics so that the task can be assigned regarding these differences. Humans have cognitive skills like problemsolving capability, experience, dexterity, flexibility, and creativity. Cobots have high process speed, the capability to apply any force, involve awkward postures, and are indefatigable. There is a need for new models to meet these constraints in this circumstance, and there are opportunities for new applications.

5 A Literature Review on Human-robot Collaborative Environments …

51

The organization of this paper is as follows: In Sect. 5.2, collaboration types of humans and cobots are presented. Section 5.3 surveys the literature on HRC systems, and finally, the study ends with presenting main conclusions in Sect. 5.4.

5.2 HRC Configuration Types in Assembly Works Assembly lines can be described as systems that transfer materials in line by workforce or an appropriate handling system such as conveyors. Through these lines, required operations are processed along the line in sequence. In manual assembly lines, operators mostly use their upper body and arms when they fulfill assembly processes. Also, workers may need to walk among stations or in the warehouse. Main assembly activities include similar and repetitive tasks with short cycle times and demanding physical force-required tasks. As can be expected, inappropriate body postures rise during these activities. Therefore, automation applications and intelligent assist devices in assembly lines have gained attention in recent years. Cobots are mostly used in the assembly process in the literature. According to system requirements in assembly lines, there are different collaboration types between humans and cobots. These categories differ in programming, workplace design, and processing area. The important criteria for ergonomic consideration in this collaboration are based on how the worker and cobot interact. El Zaatari et al. [4] have defined four types of interaction as independent, simultaneous, sequential, and supportive. Independent interaction is defined when worker and cobot work on a different piece and different processes separately. Cobot is positioned in the same workspace but with no barrier, and it knows of workers’ existence and works safely. In simultaneous interaction, worker and cobot work on the same piece but do different processes concurrently. The process times and processes are not dependent, but the robot is aware of the worker’s process requirements and considers his/her space. The other interaction type is sequential, where worker and cobot operate different processes on the same piece, but processes have time dependency. Supportive interaction is the definition for the case when cobot and worker operate jointly on the same piece and do the same process. Cobot understands human intention and task requirements. Also, it helps the worker at jobs that are not ergonomic. Another workplace and the time-sharing system is presented by Krüger et al. [5]. Humans and cobots can perform handling and assembly tasks separately or together. When the cobot executes an assembly task in the first category, the human executes a handling task. The other category is the opposite position. The other categories are the human and cobot is executing a handling task together or an assembly task. Different collaboration types can be preferred on the complexity of the task, its ergonomics, the process variability, and the devices available [6]. Different problems, methods, and solution approaches are used according to category type. Therefore, several future research possibilities have emerged in this field.

52

B. N. Yetkin and B. H. Ulutas

5.3 Findings from the Literature Review The HRC field has gained more attention in recent years. It has common analogies with some of the current research in the field of robotic assembly lines. In addition, collaborative assembly lines can take advantage of two-sided assembly lines or multimanned assembly lines in terms of algorithm and design applications. This study contributes to the HRC literature by evaluating the studies dealing with ergonomics in design and planning. High-quality articles from 2009 up to date are included in this literature review. The main intent of this paper is to highlight gaps and contradictions of previous studies. The beginning of the study is to develop research methodology and criteria to access the related studies and evaluate them in depth. With a view to determine and analyze the most relevant articles, the search words: “Assembly line,” “Ergonomics,” and “Human-Robot Interaction/ Cobot” were used in their title, abstract, and keywords. These keywords were searched in the Science Direct and Scopus databases during February and March 2021. “Book chapters,” “articles,” and “conference papers” written in English were analyzed in an accessible area. In the first step, the studies were evaluated, and a number of papers that didn’t fit the purpose were excluded from this study. There are two studies that handle a similar literature review but in a different manner. Hashemi-Petroodi et al. [7] investigated operation management challenges in HRC systems and mentioned main challenges like ergonomics, safety, time, and flexibility. Gualtieri et al. [8] assessed the design of ergonomic and safe collaborative robotic work cells. These studies confirmed that the most mature research field on HRC is safety. Reducing or totally removing repetitive movements and/or heavy lifting duties are considered as the most important benefits for humans in an HRC system. In terms of ergonomics, real-life applications, the problems considered, and solution methods still need to be studied in a structured manner. Continuing improvement of cobots is an active field of research in operation management. The vision of humans and robots working together emerges questions of how such systems should be designed and managed. Answering those questions will require understanding how workers accept and interact with cobots in repetitive processing settings [9]. Research in this area should not be limited to safety, design, or control. There are many issues to be addressed. Table 5.1 summarizes the basic benefit, opportunities, costs, and risks for HRC in manufacturing systems. To radically prevent WMSD in an HRC environment, ergonomic assessments and rules must be considered during the design process. If not, reducing WMSD in the next stages may be more troublesome and costly. The design and integration of cobots in a human-centered way were studied in the literature. Colim et al. [10] defined design stages for HRC workstations with ergonomics criteria and safety requirements and contributed to the literature with an innovative framework that combines several ergonomic assessment methods. Revised strain index method for risk assessment and evaluated self-reported physical exertion with

5 A Literature Review on Human-robot Collaborative Environments …

53

Table 5.1 Basic benefit, opportunity, cost, and risks for HRC Human

Robot

Benefits

*Reduce/remove repetitive movement *Reduce/remove heavy lifting tasks

*Reduce energy consumption *Dealing with short product life cycles

Opportunity

*Performance improvement *New models for task assignment (qualifications, age, experience) *New wearable technology design

*Design *New models for robot selection *New models for robot assignment *New models for task allocation * User-friendly software development

Cost

*Total production costs *WMSD related costs (absentisim, surgery, compensation, sue)

*Installation cost (fixed) *Operationg cost (variable) *Set up time

Risk

*Loss of job

*Enabling safety

Category Ratio-10 method was applied. For the dimensioning of workstations such as workbench height, the software Jack-Siemens was applied to try different heights according to anthropometric data. After dimensions and task allocation are finished, a motion capture system is used to estimate WMSD risk for an entire work cycle. Krüger et al. [5] presented a survey about HRC forms and available technologies that support the collaboration. The main disadvantages and obstacles of using robot applications were also discussed. It is proven that tact time and physical strain on workers are highly reduced with a hybrid system compared to the automated or manual solutions by allocating heavy loads to the robot. Michalos et al. [1] focused on potential gains with cobot adoption and defined the main facilitators for a safe collaboration as the evolution of intuitive interfaces, building safety equipment and strategies, not using any barrier, new methods to allocate and plan tasks adequately. Their case study showed that cobots bring important savings in means of productivity and operator’s working conditions. It is crucial to provide good communication between humans and cobots in the integration of the robotic system for a fully collaborative environment. New wearable devices enable better interaction. Michalos et al. [11] used augmented reality glasses and smartwatches to provide better safety and communication. The ergonomic strain on workers was assessed by the MURI analysis. The tasks with the highest effect were assigned to the cobot. An improvement of about %40 was achieved compared to the manual case. When designing an HRC environment, it is necessary to evaluate the ergonomic benefits offered by robots. Maurice et al. [12] introduced a method for automatically selecting the related ergonomic indicators for a specific task to be performed with a cobot. Thirty ergonomic indicators for task phases were defined, and risks were estimated by a virtual human simulation. An ergonomic assessment method for the adoption of HRC was suggested. A holistic approach is crucial to form organization factors and obtain safe, ergonomic, and efficient collaborative assembly workstations. Gualtieri et al. [13]

54

B. N. Yetkin and B. H. Ulutas

proposed prerequisites and design guidelines based on international standards from the point of view of safety, ergonomics, and assembly efficiency. Physical ergonomics (biomechanical overload, static, and awkward body postures) and cognitive ergonomics were addressed at a basic level, and checklists were created. The main objective of cobots is considered to reduce the workload on workers. A direct and effective tool to analyze fatigue may be to use EMG signals and different sensory systems. Kim et al. [14] presented a control approach to HRC that considers the ergonomics of the workers during dexterous operations. A monitoring system for workers, which measures the worker body configurations and uses a human dynamic model to predict the body’s overloading joint torques, was utilized. An optimal model was developed to estimate the optimal configuration for the arm. EMG was used to evaluate the muscular arm effort during the task execution with different arm configurations. Results showed that the control framework was capable of designing ergonomic and task-optimized HRC. The application of EMG can be impractical, and EMG can’t reach muscles under the surface for some applications. Peternel et al. [15] proposed a machine learning technique to learn the arm configuration and muscle forces to solve these limitations. The novel fatigue management system in the HRC environment contributed to the literature where the robot estimated and adjusted its behavior to reduce human muscle fatigue. Most research on HRC tries to optimize robot’s positioning and behaviors and adjust robots to humans. Similar to this idea, [16] tried to find comfortable human body posture when executing a task. For this purpose, the REBA method was integrated into a planning framework to provide motion’s ergonomic score. Then with an interface, feedback was sent to the user about his/her posture risk. In an assembly line balancing problem (ALBP), tasks and workers are assigned to stations according to precedence constraints. The ALBPs continue to be the topic that draws great attention from both research scholars and practitioners, as revealed by the recent number of publications [17]. Task assignment for humans and robots, task scheduling problems, and assignment of workers and robots to stations are crucial to integrating cobots in HRC environments. These problems are very important because they directly affect the ergonomic condition of the human worker. Pearce et al. [18] introduced an approach that handles time and ergonomics simultaneously in HRC systems. The hierarchical Task Analysis (HTA) method was used to divide tasks into work elements, and this data provided a work element unit for both scheduling and Strain Index (SI) analysis. Then a MILP that assigns and schedules work elements between human and robot was proposed, and SI of human physical stress and discussed tradeoffs in assigning different priorities to time and ergonomics was calculated. Methods that allocate the task to the human or the robot are based on ergonomics and time constraints. For this problem, [19] developed a framework that first determines work elements using the manipulation taxonomy and then identifies capable agents for work according to the capability evaluation module. In addition to an automatic human postural assessment, REBA ergonomic evaluation was executed. Tasks

5 A Literature Review on Human-robot Collaborative Environments …

55

were allocated according to maximum allowable workload and maximum allowable REBA score. Requirements like human ergonomics improvement, minimum required workspace, the robot capabilities for a task, total time, and overall cost were determined by Tsarouchi et al. [20] for work cell layout and task allocation problems. A mechanism based on a decision-making concept was proposed. An analytical model and simulation were used to estimate requirements. By this means, users were enabled to calculate criteria values and visualize the results in a simulation environment, then can check workplace layout and task allocation schemes. For the ergonomics evaluation, the average human muscles strain percentage was estimated during the simulation of a task. Not only task planing but also motion planning and sequence of actions are important for ergonomics. Busch et al. [21] presented a new approach to planning tasks and motion in an HRC environment. A differentiable REBA was introduced to optimize posture and confirmed that dREBA leads better human postures. Task allocation problems may be harder when the system has multi-human and multi-robot interaction. Malvankar-Mehta and Mehta [22] described a model that enables optimal task allocation in these environments. If human factors are considered in addition to economic metrics like cost, performance, and time, then the interaction can be enhanced for multi-human and robot systems. To consider ergonomics, operator-specific workload thresholds were used. Incorporating task allocation to a human-robot environment requires identifying the skills of each agent and allocating tasks regarding skills. Johannsmeier and Haddadin [23] moved differences of humans and robots into cost functions to behave humans and robots as same resources. Workload and ergonomic factors were defined as a cost function. Then a framework for task allocation considering a given cost function in collaborative assembly planning was proposed. Unlike classical balancing problems, Antonelli and Bruno [24] discussed a classification for all tasks as executed by the human, robot, or both. Tasks were classified based on the weight of the part, displacement, dexterity requirements, and accuracy requirements. The assignment was done by task classification, durations, and precedence constraints along with a Gantt process. Beyond task allocation problems in assembly stations, further challenges arise in ALBP with HRC. Studies addressing this problem are very limited. Mura and Dini [25] considered the cobots for the input data set of the ALBP and developed a software tool for designing and balancing collaborative assembly lines. Tasks requiring high skills were grouped and aimed to minimize the number of highly skilled workers. Their other aim was to minimize assembly line cost, which was evaluated based on the number of workers and robots. Finally, energy expenditure and minimization of energy load differences among workers were considered. The problem was solved by the use of a genetic algorithm. ALBP can be enriched by resource/equipment selection and scheduling tasks. Weckenborg et al. [26] presented a mathematical optimization model that minimized cycle time in a given station number and developed a hybrid genetic algorithm. The number of robots, their ability to perform certain tasks, and resulting processing

56

B. N. Yetkin and B. H. Ulutas

times were considered. It is concluded that the productivity of lines can be improved by deploying robots, but ergonomic conditions were not in focus. Besides productivity, cobots can be an economical option for assembly lines. Weckenborg and Spengler [27] developed a mathematical model with scheduling and ergonomics constraints. Humans and cobots may be assigned to stations, and the model tried to find cost-efficient system configurations. According to the concept proposed by Price [28], the energy expenditure of workers was taken into consideration and added to constraints. Digital Human Modelling (DHM) is widely used in HRC since it reduces development time and cost. However, there is a lack of simulation tools special for the HRC environment. Ore et al. [29] presented simulation software. Productivity and biomechanical loads on the human body were evaluated by the RULA method. Verified results showed that HRC systems improve productivity and reduce ergonomic loads compared to manual assembly. Table 5.2 summarizes the papers considered in this research according to their objective and problems handled.

5.4 Discussion and Conclusion Nowadays, manufacturing is characterized by the complementarity between humans and automation. Therefore, new methods and tools are needed to design and implement collaborative workplaces regarding safety, ergonomics, and efficiency. In an HRC context, humans and cobots work together for the purpose of efficiency. While the cobot performs dangerous, demanding, and speedy tasks, human manages and controls manufacturing. In this way, humans can work beyond their limitations. Robots can be costly applications. Therefore, their integration into manufacturing systems is a process that requires significant effort and investment. In this regard, the purpose of this study is to identify research fields and challenges about ergonomics, especially for assembly works in the HRC context. The results show that there is considerable literature on ergonomics for HRC systems, but they mainly concentrate on the cobot technological aspects like sensory systems, positioning, and recognition systems. However, besides these aspects, there are more problems to be discussed on the human worker side. Ergonomics is generally considered a secondary safety application in studies. The literature on HRC systems currently focuses on design, implementation, and safety issues. However, it must be kept in mind that ergonomic assessments and rules must be taken into account during the design stage. Several papers on this subject have been studied in this review. These papers generally present a holistic framework that combines ergonomics, safety, and technological aspects. Ergonomics have been addressed in different ways; while some authors evaluated this directly through sensors for energy expenditure, others implicitly considered it as avoiding awkward postures and heavy loads by assigning them to robots.

5 A Literature Review on Human-robot Collaborative Environments …

57

Table 5.2 Summarized research on HRC considering ergonomics References

Problem

Main focus

Ergonomic evaluation methodology

[5]

Planning tools for integration and support workers

Define organizational, economic and ergonomic aspects of HRC

No evaluation method

[12]

Finding ergonomic assessment method for adoption of HRC

Provide a method for automatically selecting the relevant ergonomic indicators

Virtual human simulation

[1]

Combining skills for safe HRC

Integration of cobots for assembly operations

No evaluation method

[29]

Simulation of different cases

Present software for simulation, visualization and evaluation of HRC

RULA

[22]

Optimal task allocation

Maximize performance minimize cost and time considering ergonomics

No evaluation method

[23]

Combining capabilities of human and cobot in an optimal way

Propose a framework for task allocation

No evaluation method

[20]

Workcell layout and task allocation

Develop a decision-making framework

Average human muscles strain

[24]

Task allocation between human and cobot

Propose a method for classification of tasks

No evaluation method

[16]

To find optimal body posture during interaction

Introduce a postural optimization framework, minimize WMSD risk

REBA

[11]

Implementing a HRC assembly cell

Enable safe collaboration and reduce physical and cognitive ergonomics

MURI, smartwatch, augmented reality glassess

[18]

Assign and schedule tasks between human and cobot

Optimize makespan and ergonomics in HRC

Strain Index

[21]

To find optimal task sequence and motion planning

Present an approach to simultaneously plan task and motion

Differential REBA

[15]

Online estimation of individual muscle forces in the human arm

Propose a novel fatigue management system

Developed machine learning method

[19]

Identify capable agents for each task and ergonomic evaluation

Develop framework for task REBA allocation considering ergonomics

[25]

Design and balance Minimize high skilled collaborative assembly lines workers and costs

Energy expenditure (continued)

58

B. N. Yetkin and B. H. Ulutas

Table 5.2 (continued) References

Problem

Main focus

Ergonomic evaluation methodology

[27]

Assign tasks, workers and cobots to stations

Developed a mathematical model with scheduling and ergonomics constraints

Energy expenditure

[26]

Resource selection and scheduling tasks

Develop mathematical optimization model

No evaluation method

[13]

Determine prerequisites and Implement ergonomic design guidelines solutions and manage risks

Created physical abd cognitive checklists

[14]

Estimate and reduce overloading torques

Present a control approach considering ergonomics

EMG

[10]

Design framework for ergonomic HRC

Physical ergonomic improvement and safe design

Revised strain index Category ratio-10 Digital human modelling

Similar to the extensive ergonomic assembly line balancing literature, studies such as mathematical models and handling ergonomics with constraints have not been conducted. The most effective application, which takes into account both the efficiency of the line and the ergonomic risk level, is to consider economic factors such as cycle time and the number of stations, as well as ergonomic risk factors during assembly line balancing. In an assembly line with cobots, these techniques can be used. Optimization techniques are generally used in robot’s positioning and body posture configurations and in the task allocation between robot and human. More research is still required in this field. Manufacturers also appreciate cobots as they can relieve workers from physically intensive tasks. Consequently, aspects of ergonomics must be considered in system configuration and robot design. In addition, the focus should be on identifying robot types and economically determining the optimal number of robots. Therefore, future approaches should broaden the scope of the research and be based on multi-objective formulations. This research has examined accessible, up-to-date research in the field of HRC by prioritizing ergonomics. It has been concluded that ergonomics has been the main focus in a limited number of studies. Cobots can be ergonomic and economical in manufacturing systems as they improve productivity and working conditions. Future studies must develop new methodologies and tools in this newly emerging field by also considering other opportunities, benefits, costs, and risks related to HRC environments. As with any research, this study comes with some limitations. There could be a subjective bias in the reading and selection of papers. There may be papers omitted that are published in journals that are not listed in Science Direct and Scopus at the time of the search.

5 A Literature Review on Human-robot Collaborative Environments …

59

References 1. Michalos G, Makris S, Spiliotopoulos J, Misios I, Tsarouchi P, Chryssolouris G (2014) ROBOPARTNER: seamless human-robot cooperation for intelligent, flexible and safe operations in the assembly factories of the future. Procedia CIRP 23:71–76 2. Botti L, Mora C, Regattieri A (2017) Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Comput Ind Eng 111:481–491 3. Costa Mateus JE, Aghezzaf EH, Claeys D, Limère V, Cottyn J (2018) Method for transition from manual assembly to human robot collaborative assembly. IFAC-PapersOnLine 51:405–410 4. El Zaatari S, Mohamed M, Weidong L, Zahid U (2019) Cobot programming for collaborative industrial tasks: an overview. Robot Autom Syst 116:162–180 5. Krüger J, Lien TK, Verl A (2009) Cooperation of human and machines in assembly lines. CIRP Ann 58(2):628–646 6. Akella P, Peshkin M, Colgate JE (1999) Cobots for the automobile assembly line. In: Proceedings 1999 IEEE international conference on robotics and automation, Detroit, MI, USA, pp 728–733 7. Ehsan H-P, Thevenin S, Kovalev S, Dolgui A (2020) Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey. Annu Rev Control 49:264–276 8. Gualtieri L, Rauch E, Vidoni R (2021) Emerging research fields in safety and ergonomics in industrial collaborative robotics: a systematic literature review. Robot Comput-Integr Manuf 67:1–30 9. Olsen TL, Tomlin B (2020) Industry 4.0: opportunities and challenges for operations management. Manufact Serv Oper Manage INFORMS 22(1):113–122 10. Colim A, Faria C, Cunha J, Oliveira J, Sousa N, Rocha LA (2021) Physical ergonomic improvement and safe design of an assembly workstation through collaborative robotics. Safety 7:1–14 11. Michalos G, Kousi N, Karagiannis P, Gkournelos C, Dimo las K, Koukas S, Mparis K, Papavasileiou A, Makris S (2018) Seamless human robot collaborative assembly—an automotive case study. Mechatronics 55:194–211 12. Maurice P, Schlehuber P, Padois V, Measson Y, Bidaud P (2014) Automatic selection of ergonomie indicators for the design of collaborative robots: a virtual-human in the loop approach. In: 2014 IEEE-RAS international conference on humanoid robots, Madrid, Spain, pp 801–808 13. Gualtieri L, Rauch E, Vidoni R, Matt DT (2020) Safety, ergonomics and efficiency in human-robot collaborative assembly: design guidelines and requirements. Procedia CIRP 91(2020):367–372 14. Kim W, Peternel L, Lorenzini M, Babiˇc J, Ajoudani A (2021) A human-robot collaboration framework for improving ergonomics during dexterous operation of power tools. Robot Comput-Integr Manuf 68:102084 15. Peternel L, Fang C, Tsagarakis N, Ajoudani A (2019) A selective muscle fatigue management approach to ergonomic human-robot co-manipulation. Robot Comput-Integr Manuf 58:69–79 16. Busch B, Maeda G, Mollard Y, Demangeat M, Lopes M (2017) Postural optimization for an ergonomic human-robot interaction. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2778–2785 17. Chutima P (2020) Research trends and outlooks in assembly line balancing problems. Eng J 24(5):93–134. https://doi.org/10.4186/ej.2020.24.5.93 18. Pearce M, Mutlu B, Shah J, Radwin R (2018) Optimizing makespan and ergonomics in integrating collaborative robots into manufacturing processes. IEEE Trans Autom Sci Eng 15:1772–1784 19. El Makrini I, Merckaert K, De Winter J, Lefeber D, Vanderborght B (2019) Task allocation for improved ergonomics in human-robot collaborative assembly. Interact Stud 20:103–134 20. Tsarouchi P, Matthaiakis A-S, Makris S, Chryssolouris G (2017) On a human-robot collaboration in an assembly cell. Int J Comput Integr Manuf 30(6):580–589

60

B. N. Yetkin and B. H. Ulutas

21. Busch B, Toussaint M, Lopes M (2018) Planning ergonomic sequences of actions in humanrobot interaction. In: IEEEInternational conference on robotics and automation (ICRA), pp 1916–1923 22. Malvankar-Mehta MS, Mehta SS (2015) Optimal task allocation in multi-human multi-robot interaction. Optim Lett 9:1787–1803 23. Johannsmeier L, Haddadin S (2017) A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robot Autom Lett 2:41–48 24. Bruno G, Antonelli D (2017) Dynamic task classifica-tion and assignment for the management of human-robot col-laborative teams in workcells. Int J Adv Manuf Technol 98(9–12):2415– 2427 25. Mura MD, Dini G (2019) Designing assembly lines with humans and collaborative robots: a genetic approach. CIRP Ann 68:1–4 26. Weckenborg C, Kieckhäfer K, Müller C, Grunewald M, Spengler TS (2020) Balancing of assembly lines with collaborative robots. Bus Res 13(1):93–132. Springer, German Academic Association for Business Research 27. Weckenborg C, Spengler TS (2019) Assembly line balancing with collaborative robots under consideration of ergonomics: a cost-oriented approach. IFAC-PapersOnLine 52(13):1860– 1865 28. Price ADF (1990) Calculating relaxation allowances for construction operatives—part 1: metabolic cost. Appl Ergon 21(4):311–317 29. Ore F, Hanson L, Delfs N, Wiktorsson M (2015) Human industrial robot collaborationdevelopment and application of simulation software. Int J Hum Factors Model Simul 5(2):64–185 30. https://info.universalrobots.com/hubfs/Enablers/White%20papers/The%20role%20of%20c obots%20in%20industry.pdf. Last access: 17 Apr 2021 31. https://www.essentracomponents.com/. Last access: 17 Apr 2021 32. https://osha.europa.eu/en/publications/work-related-musculoskeletal-disorders-research-pra ctice-what-can-be-learnt/view. Last access: 17 Apr 2020

Chapter 6

The Use of Gamification in Sales: The Technology Acceptance Model Cigdem Altin Gumussoy, Nilay Ay, Kubra Cetin Yildiz, and Aycan Pekpazar

Abstract In recent years, many companies have aimed to increase the motivation and performance of their employees by using gamification to reach their business goals. This study investigates the factors affecting the sales employees’ adoption of gamification by extending the Technology Acceptance Model (TAM). A research model is constructed by including subjective norms from Planned Behavior Theory (PDT), perceived competence from Self-determination Theory (SDT), gamification, and job relevance constructs to TAM. A survey was conducted on the demo of the gamified sales system, and 277 questionnaires were collected. The data were analyzed with the Structural Equation Modeling (SEM) technique. According to the results, perceived usefulness, subjective norms, and perceived ease of use affect the intention to use gamification. Furthermore, job relevance and perceived competence have positive effects on the users’ usefulness perception of gamification in sales. Furthermore, gamification has a positive direct effect on perceived ease of use, which in turn indirectly affects intention to use gamification in sales. Keywords Gamification · Subjective norms · Perceived competency · Job relevance · Technology acceptance model

6.1 Introduction Recently, gamification has been commonly used in business, marketing, and education fields as games become more popular and attractive. Gamification can be defined C. A. Gumussoy (B) · N. Ay · K. C. Yildiz Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] K. C. Yildiz e-mail: [email protected] A. Pekpazar Department of Industrial Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_6

61

62

C. A. Gumussoy et al.

as “the use of game design elements and game thinking in a non-game context” [11]. Zichermann and Cunningham [66] define gamification as “the process of gamethinking and game mechanics to engage users and solve problems”. Game mechanics such as points, levels, leaderboards, badges, challenges/quests, and rewards are used to create a gamified experience. Gamification is used in various fields such as health [50], marketing [38], sports [57], education [21, 32], energy [30], and tourism [63] to increase engagement, loyalty, motivation and performance of individuals. In the literature, several studies investigated the factors affecting the adoption of gamification [4, 7, 9, 15, 23, 24, 34, 39, 42, 43, 49, 53, 57–59, 68]. While some of these studies use gamification as a distinct factor, others directly focus on the determinants of the intention to use gamified systems or technologies. Among these studies, Herzig et al. [23], Korn et al. [34], Suh et al. [53], Zikos et al. [68], Höllig et al. [24], Mitchell et al. [39], and Vanduhe et al. [58] focus on the adoption of gamification by employees and workers. Herzig et al. [23] used the job demand-resource model, psychological capital, and TAM theories to explore the determinants affecting behavioral intention of gamification in the workplace. They compared the gamified ERP system with a standard ERP by investigating the relationships between interactivity, telepresence, content/interface, flow, enjoyment, perceived usefulness, ease of use, and behavioral intention. The results show that behavioral intention is affected by perceived usefulness and ease of use. Furthermore, flow has an indirect effect on behavioral intention mediated by perceived usefulness. Korn et al. [34] investigated how gamification can be integrated into the business processes of the automotive industry. The results show that special conditions such as implicit interaction, error prevention, stress detection, and ethical issues are important in the acceptance of gamification in modern automotive production. Suh et al. [53] investigated the factors affecting continuance intention to use gamified information systems of employees in a consulting company. The results show that flow and aesthetic experience explain the variance of continuance intention to use. Furthermore, they investigated the relationships between gamification affordance (rewards, status, competition, self-expression) with aesthetic and flow experiences. Flow experience is affected by status and competition, while aesthetic experience is affected by status, competition, and self-expression affordances. Zikos et al. [68] evaluated a gamified knowledge-sharing platform by using different criteria: usability, knowledge integration, working experience, user acceptance, and overall impact. They collected data from employees working in two manufacturing industries. Based on the feedback from employees, they proposed a guideline that can be used for developing similar gamified platforms. Mitchell et al. [39] investigated the effects of intrinsic and extrinsic motivation on the behavioral intention of gamification among employees by using self-determination theory. They conducted a survey study with 291 employees working in different industries as information, education services, finance, and insurance. The results show that behavioral intention is affected by intrinsic motivation. Cognitive evaluation theory (CET) (autonomy and competency) and relatedness need have no direct effects on intention to use gamification. However, CET needs have an indirect effect on behavioral intention mediated by intrinsic motivation. Furthermore, CET needs satisfaction is

6 The Use of Gamification in Sales …

63

affected by identified and external regulations. Vanduhe et al. [58] investigated the important factors that may affect the intention to use gamification for training by using TAM with external variables: task-technology fit, social influence, and social recognition. They conducted a survey study with 375 instructors working at a university in Cyprus. The results show that perceived usefulness is affected by social influence, social recognition, and perceived ease of use. Höllig et al. [24] investigated the effect of personal trait competitiveness, leaderboard design, and enjoyment on usage intention of a mixed group of users, including students, employed and unemployed people. They conducted two survey studies. The results show that trait competitiveness indirectly affects usage intention mediated by perceived enjoyment. Furthermore, leaderboard design strengthened the relationship between trait competitiveness and usage intention mediated by perceived enjoyment. The studies [23, 24, 34, 39, 53, 58, 68] show that several factors may affect intention to use gamified systems among employees. However, compared to the number of studies investigating the adoption of gamification by students and customers, there is a restricted number of studies focusing on the behavioral intention to use such systems by employees. Therefore, to explore the determinants of intention to use a gamified system by employees, this study proposes an extension of the Technology Acceptance Model (TAM), which is one of the most widely used and important models in the investigation of factors affecting the acceptance of a technology [16, 37], by adding Planned Behavior Theory (PDT) determinant (subjective norms) and Selfdetermination Theory (SDT) determinant (perceived competence). In the context of the proposed research model, the relationships between gamification, job relevance, subjective norms, perceived competence, and TAM are examined. This study contributes to the literature in several ways: (1) Different from the studies [23, 24, 34, 39, 53, 58, 68] investigating the factors important in the adoption of gamification by employees, this study used TAM enhanced with PDT and SDT and examined the effects of different external factors such as gamification, job relevance and perceived competence on intention to use a gamified system, (2) as far as our knowledge, this is the first study focus on the behavioral intention of a gamification system by sales personnel.

6.2 Research Model and the Hypotheses In this study, to develop the research model, we used an extension of TAM enhanced with PDT and SDT by using the factors: job relevance, gamification, perceived competence, subjective norms, perceived ease of use, perceived usefulness, and intention to use. The research model, a total of eight hypotheses, are proposed, shown in Fig. 6.1, is constructed to understand the significant factors affecting intention to use gamification in sales.

64

C. A. Gumussoy et al.

Perceived ease of use

H5

H3

Gamification

H2 H4

Job relevance

H6

Perceived usefulness

Intention to use of gamification in sales

H1

H8

H7

Perceived competence

Subjective norms

Fig. 6.1 A research model for a gamified sales platform

6.2.1 Perceived Usefulness Perceived usefulness (PU) is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” [10]. In different models, perceived usefulness is defined as performance expectation, relative advantage, outcome expectation, and extrinsic motivation [65]. Within the scope of TAM, perceived usefulness is assumed to be a direct determinant of the behavioral intention to use information technology [40]. Many studies on technology acceptance have revealed the relationship between perceived usefulness and intention to use [8, 15, 19, 20, 41, 64]. Furthermore, gamified systems have a utilitarian dimension. As the gamified system becomes more useful, then employees will intend to use this system more. Therefore, it is necessary to examine the relationship between usefulness and intention to use. Thus, we propose: H1: PU has a positive effect on the intention to use the gamified sales system.

6.2.2 Perceived Ease of Use Perceived ease of use (PEU) is “the degree to which a person believes that using a particular system would be free of effort” [10]. Sánchez-Mena et al. [49] showed that student teachers who found gamification easy to use have the intention to use gamification in the future. Furthermore, Al Amri and Almaiah [1], investigating the factors affecting utilization of mobile gamification of e-learning systems, found that perceived ease of use has a positive effect on both perceived usefulness and intention to use. Several studies showed that perceived ease of use affects perceived usefulness

6 The Use of Gamification in Sales …

65

[31, 36, 44, 46] and intention to use [28, 36, 44] positively. Therefore, if the gamified system is easy to use, employees will find this system useful, and they will intend to use it in the future. Thus, we propose: H2: PEU has a positive effect on the intention to use gamified sales system. H3: PEU has a positive effect on perceived usefulness.

6.2.3 Gamification Gamification (GMF) has been defined as the use of game mechanics and game elements such as points, badges, and prizes in non-game contexts to engage people, encourage learning, motivate actions, and solve problems [33]. Corporate gamification can be defined as the adaptation of enjoyable game elements to business and job training processes [52]. Since gamification uses basic human needs and characteristics such as achievement, reward, status, and competition, it is an effective method that can be used to increase the performance and motivation of employees in businesses [56]. Rodrigues et al. [48], who investigated the factors affecting intention to use gamified e-banking systems of customers, found that gamification significantly affects perceived ease of use and intention to use. In addition, gamification has a significant positive effect on behavioral intention [15]. The effects of gamification on perceived ease of use and intention to use have been confirmed by many studies [4, 7, 42, 48, 67]. Thus, we propose: H4: GMF has a positive effect on the intention to use. H5: GMF has a positive effect on perceived ease of use.

6.2.4 Job Relevance Job relevance (JR) is defined as “an individual’s perception regarding the degree to which the target system is applicable to his or her job”. Furthermore, job relevance is an indicator of the importance of the tasks supported by the system within a person’s job. The variable of job relevance exists in the extension of TAM as a cognitive instrumental process that directly affects perceived usefulness [60]. Hu et al. [25], Hart and Porter [22], Baker et al. [3], Izuagbe and Popoola [29] showed the significance of job relevance for the usefulness of gamified sales systems. Therefore, as the relevancy of the gamification system to the employees’ job increase, it is expected that the usefulness perception among sales personnel will increase. Thus, we propose: H6: JR has a positive effect on perceived usefulness.

66

C. A. Gumussoy et al.

6.2.5 Perceived Competence Perceived competence (PC) can be defined as the degree to which an individual feels confident and effective while interacting with a technology to perform certain job-related tasks [47, 55]. This concept comes from Self-Determination Theory, which is related to motivation. According to SDT, intrinsic and extrinsic motivational factors motivates people. Furthermore, people have psychological needs, including competence [47]. If employees do not believe that they can use the gamified system effectively and confidently to perform their tasks, they cannot benefit from the system. Roca and Gagné [47], Teo et al. [55], and Lu et al. [36] found that perceived competence is a significant factor affecting perceived usefulness. Thus, we propose: H7: PC has a positive effect on perceived usefulness.

6.2.6 Subjective Norms Subjective norms (SN) are a “person’s perception that most people who are important to him think he should or should not perform the behavior in question” [13]. Subjective norms have been seen as an important variable influencing behavior in many studies. Hung et al. [27] found that subjective norms significantly affect attitudes towards e-government use, while Hsu and Chiu [26] found a similar result in the e-service use. Taylor and Todd [54] found that for both experienced and inexperienced users, subjective norms were positively associated with behavioral intention in using a computing resource center. Furthermore, Schepers and Wetzels [51] analyze the studies investigating the effect of subjective norms by using TAM. The results showed that subjective norms have a significant positive effect on behavioral intention to use new technologies. Employees may think to use a gamified system if their colleagues or managers, who are important to them, are willing to use this system. For this reason, the following hypothesis is developed: H8: SN has a positive effect on the intention to use of gamified sales system.

6.3 Methodology In this study, we used a survey methodology to explore the factors affecting the adoption of gamification in sales. Before the survey, the gamified sales platform developed by Salesmot company was explained and a link for access to the gamified sales platform has been added to the survey so that participants can experience the system. A sum of 277 questionnaires was amassed from sales personnel. In the first part, there are questions regarding demographic characteristics such as age, marital status. Among the respondents, 55.6% are male, and the majority of the

6 The Use of Gamification in Sales … Table 6.1 Demographic characteristics of the respondents (%)

Age

67 Gender

≤20 age: 1.4

Female: 44.4

21–25 age: 10.1

Male: 55.6

26–30 age: 31.8

Marital status

31–35 age: 26.4

Married: 57.4

≥36 age: 30.3

Single: 41.6

Education

Number of games played actively

Primary school: 0.7

I never play: 38.3

High school: 20.6

1–5: 53.4

College: 8.7

More than 5: 8.3

Bachelor degree: 54.5

Number of games saved on the phone

Master’s degree or above: 15.5

I never play: 35 1–5: 54.5 More than 5: 10.5

respondents are between the age of 26–30. A high percentage of respondents—54.5% have a bachelor’s degree, 15.5% have a master’s or above degree. The respondents’ demographic profile is given in Table 6.1. After the demographic questions, as shown in Table 6.2, a total of 27 questions adapted to the context of gamified sales system were asked to sales employees on a five-point Likert scale (1: Strongly disagree…5: Strongly agree).

6.4 Results The structural equation modeling technique is applied by using IBM SPSS Amos 26. First, the validity and reliability of the measurement model are tested. Then, the relationships defined with the research model are analyzed with the structural model.

6.4.1 Measurement Model The measurement model includes 27 items defining seven constructs. The model was tested with the confirmatory factor analysis. First, we examined the values of the fit indices: χ 2 /df (Normed Chi-square), RMSEA (The Root Mean Square Error Of Approximation), GFI (Goodness of Fit), CFI (Comparative Fit Index), NFI (Normed Fit Index). The fit indices values (χ 2 /df = 2.069 ≤ 3; RMSEA = 0.062 ≤ 0.08; GFI = 0.860 ≥ 0.80; CFI = 0.956 ≥ 0.90; NFI = 0.918 ≥ 0.80) pass the thresholds.

68

C. A. Gumussoy et al.

Table 6.2 Constructs and the items Items

Construct Gamification [7]

Job relevance [35]

Perceived competence [6]

Perceived ease of use [61]

Perceived usefulness [12]

GMF1

“If the gamified sales system was more enjoyable, I probably use it more often”

GMF2

“If using gamified sales system would give me points, rewards, and prizes, I probably use it more often”

GMF3

“If gamified sales system was more fun, I probably recommend it to other sales consultants”

GMF4

“I enjoy selling and playing sales-like games”

GMF5

“I enjoy being challenged by achievements and leaderboards”

JR1

“The use of gamified sales system is important in my job”

JR2

“It would be difficult to do my job without gamified sales system”

JR3

“Using gamified sales system is relevant to my work”

PC1

“My colleagues tell me I am good at using the gamified sales system”

PC2

“I have been able to learn interesting new skills related to the gamified sales system”

PC3

“I do feel competent when using the gamified sales system”

PEU1

"My interaction with the gamified sales system is clear and understandable."

PEU2

“Interacting with the gamified sales system does not require a lot of mental effort”

PEU3

“I find the gamified sales system to be easy to use”

PEU4

“I think the gamified sales system will make it easier for me to make sales and control my sales”

PU1

“I will accomplish my tasks more quickly using the gamified sales system”

PU2

“I will improve my performance in my work using the gamified sales system”

PU3

“I will increase my productivity in my work using the gamified sales system”

PU4

“I will enhance my effectiveness in my work using the gamified sales system”

PU5

“I will make it easier to do my job using the gamified sales system” (continued)

6 The Use of Gamification in Sales …

69

Table 6.2 (continued) Construct Subjective norms [5]

Intention to use [62]

Items PU6

“I find the gamified sales system useful in my job”

SN1

“People (my colleagues and managers) important to me supported my use of the gamified sales system”

SN2

“People who influenced my behavior will want me to use gamified sales system”

SN3

“People whose opinions I valued will prefer that I use the gamified sales system”

IU1

“Assuming I have access to the gamified sales system, I intend to use it”

IU2

“Given that I have access to the gamified sales system, I predict that I would use it”

IU3

“If I have access to the gamified system, I want to use it as much as possible”

Then, the relationships between the items and constructs are tested with p-values. As shown in Table 6.3, the results show that all factor loadings except GMF5 exceed the value of 0.7 with a p-value less than 0.001 [18]. However, the factor loading of GMF5 is greater than the threshold value of 0.5 [18]. Therefore, we kept this item for further analysis. Besides, average variance extracted values of the constructs range between 0.69 and 0.8, which are greater than the value of 0.5 [14], and the Cronbach’s alphas are greater than the value of 0.70. This result shows us that constructs can be measured with observed variables [18]. Furthermore, composite reliability scores are greater than 0.7, revealing that constructs can explain the observed variables [17]. For discriminant validity, the χ 2 difference tests were calculated for each pair, such as subjective norms and perceived usefulness. As a result of the analysis, the χ 2 difference for each construct pair was greater than the threshold value of 3.84 at the 95% significance level [2]. Therefore, the discriminant validity of the constructs is satisfied.

6.4.2 Structural Model The structural model examines the hypotheses defined in the research model. The fit indices values (χ 2 /df = 2.504 ≤ 3; RMSEA = 0.074 ≤ 0.08; GFI = 0.830 ≥ 0.80; CFI = 0.936 ≥ 0.90; NFI = 0.899 ≥ 0.80) are in the desired ranges. The results with standardized path coefficients and exploratory power of the constructs are shown in Fig. 6.2. The results show that all hypotheses except H3 and H4 are accepted. When we look at the R2 values of the dependent variables, the R2 value for perceived usefulness is found to be 0.84, and the remaining ones-perceived ease of use and intention to use were found as 0.68 and 0.85, respectively. Furthermore, indirect and total effects were measured as shown in Table 6.4.

70

C. A. Gumussoy et al.

Table 6.3 Measurement model Latent variable Gamification (GMF)

Job relevance (JR)

Observed variables

Factor loadings

p-value

Composite reliability

AVE

Cronbach’s alpha

0.87

0.68

0.88

0.87

0.73

0.89

0.89

0.77

0.87

0.87

0.62

0.86

0.95

0.79

0.96

0.87

0.70

0.91

0.92

0.81

0.93

GMF1

0.838

*

GMF2

0.863

*

GMF3

0.872

*

GMF4

0.717

*

GMF5

0.613

*

JR1

0.842

*

JR2

0.856

*

JR3

0.871

*

PC1 Perceived competence (PC) PC2

0.830

*

0.857

*

PC3

0.827

*

PEU1

0.792

*

Perceived ease of use (PEU)

Perceived usefulness (PU)

Subjective norms (SN)

Intention to use (IU)

*p

PEU2

0.827

*

PEU3

0.743

*

PEU4

0.795

*

PU1

0.881

*

PU2

0.873

*

PU3

0.901

*

PU4

0.887

*

PU5

0.898

*

PU6

0.889

*

SN1

0.838

*

SN2

0.915

*

SN3

0.884

*

IU1

0.903

*

IU2

0.894

*

IU3

0.909

*

< 0.001

As shown in Fig. 6.2 and Table 6.4, although gamification has an insignificant direct effect on the intention to use, it has a significant indirect effect through perceived ease of use on intention to use. Furthermore, perceived ease of use directly affects intention to use, and its total effect through perceived usefulness is also significant. In addition, all the constructs defined in the research model significantly affect the intention to use. Therefore, any improvement on the constructs-gamification, jobrelevance, perceived competence, perceived ease of use, perceived usefulness, and

6 The Use of Gamification in Sales …

Perceived ease of use

71

0.82*

Gamification

2

R =0.68 0.06

Job relevance

0.43*

0.19*

Perceived usefulness

0.03

Intention to use of gamification in sales

0.47*

R2=0.84

R2=0.85 0.34*

0.47*

Perceived competence

*p2015

2

Boc-Bow. Dart Box

2015=>2017

0 2013=>2015

Malmquist 6 5

Hard of Healing

Modern Pentatlon

4

Teakwando

3

Boc Bow Dart

2 1 0 2013=>2015

2015=>2017

Physical disability Billard

Boc-Bow. Dart Box

1

Visually impaired Wrestling

Badminton

Equestrianis m Bicyecle

Bicyecle

2

Athletics

2015=>2017

Visually impaired

Shooting Athletics Badminton Physical disability Billard Equestrianism Bicyecle Boc-Bow. Dart Box Visually impaired Wrestling Weightlifting Hard of hearing Judo and Kurash Karate Kick Box Rowing Table Tennis Modern Pentathlon Motorcycle Chess

Fig. 7.1 Changes of the Malmquist index and its components in periods

2013–2015, it increases in 2015–2017. The wrestling federation increases more in 2013–2015 than in the next period. Technological efficiency is an evaluation of the approach to the efficient frontier. As for technological efficient change, the number of federations that increase in technological efficiency in the 2013–2015 period is 20, while 4 federations decrease and 1 federation does not change. During the 2015–2017 period, 18 federations decrease, and 6 federations increase. The number of federations that increase during the period of 2013–2015 is more than 3 times the number of federations that increase in the period of 2015–2017. In addition, while the average technological efficiency value increases in the period of 2013–2015, it decreases in the period of 2015–2017.

88

M. Murat et al.

In other words, the effective production limit of most of the federations in the 2013– 2015 period has shifted upwards, and they have achieved more output with the same input amount by showing technological development in this period. As can be seen from the figure, Modern Pentathlon has been the federation which showed the biggest increase in the 2013–2015 period but showed the highest decrease with the decrease in the next period. Taekwondo has been the federation with the highest increase from 2013–2015 to 2015–2017. Malmquist’s total factor productivity (TFP) is found by using technical efficiency and technological efficiency values. According to these values, 13 federations increase, 11 federations decrease, and 1 federation does not change during the 2013–2015 period. Although 8 federations increase during the 2015–2017 period, 15 federations decrease in total factor productivity. The increase in the average TFP in the period 2013–2015 is about 2 times the average increase in the period of 2015–2017. As can be seen from the figure, the Hard of Hearing Federation and the Modern Pentathlon are the highest-increasing federations in 2013–2015, while these federations become the federation with the highest decrease in TFP in the following period. Boc-Bow and Taekwondo are the federations with the highest increase in TFP in 2015–2017 from 2013–2015. Wushu, Athletics and Wrestling, some of the most medal-winning federations, showed a decline in the TFP from the period 2013–2015 to 2015–2017. It is seen that the majority of the increase in Total Factor Productivity in the period of 2013–2015 is due to technological development. In the Modern Pentathlon, the major part of the increase in 2013–2015 is the technological change. Therefore, the decline in 2015–2017 from the period of 2013–2015 is the reason for the decrease in technological progress. A significant portion of Boc-Bow’s increase in TFP from the period 2015–2017 is due to technical development (5.39 * 0.55 = 2.99). A significant portion of Taekwondo’s increase in TFP from the period 2015–2017 is due to technological development (1 * 3.77 = 3.77). It is tried to understand the changes and causes of the total factor productivity of the federations periodically. Hence, Table 7.3 is formed, showing the mean values of the components of TFP according to periods. In the last row of the table, the geometric averages of corresponding components are calculated so that inferences can be more robust. According to these values, in the 4-year period, the average total factor productivity of federations has increased by 50.9%. It can be said that technological progress has been caused more by this increase. According to the Malmquist index, federations with the highest increase in efficiency are the Modern Pentathlon and the Hard of Hearing Sports Federations. Table 7.3 Average of components periodically

Technical change

Technological change

Malmquist TFP

2013–2015

1.21

1.71

2.21

2015–2017

1.14

0.98

1.03

Geo. avg.

1.174

1.294

1.509

7 Efficiency Evaluation of Turkish Sports Federations …

89

Badminton, Visually Impaired, Shooting-Hunting are among the federations that have the most decrease in efficiency. It is seen that the highest effectiveness increase is between 2013 and 2015.

7.5 Conclusion According to the Malmquist index between the years 2013–2017, the overall efficiency of Turkish Sports Federations can be said to be developing. Especially in 2013–2015, there is a great improvement. Since Malmquist’s total factor productivity index consists of two components, we can say that technical efficiency is caused by 17.4%, and technological efficiency is 29.4%. In the study, the total revenue of 6 federations (Billiards, Weightlifting, Hard of Hearing Impaired, Kick Boxing, Modern Pentathlon, and Swimming) decreases over the years, whereas total factor productivity increases. In general, while the total revenue of federations has decreased over the years, the increase in their efficiency shows that federations’ performances are not dependent on financial resources exclusively. Although the highest increase in total factor productivity is observed in the 2013–2015 period, it is observed that the number of athletes decreases in this period for 11 federations. It can be concluded that the number of athletes exclusively will not be sufficient in the evaluation of the federations’ performance. Since the federations with at least one medal awarded are considered, every federation could not be included in the model in the present study. More federations than required ones according to this formula (2 * (m + n)) that proposed for the minimum number of decision-making units are used [6]. This paper has some potential contributions to the related literature. The efficiency of the Sports Federations in Turkey is analyzed. To the authors’ best knowledge, there is no study that analyzed the efficiency of the Turkish Sports Federations. In this study, the total revenue per athlete is used as input in reference to the per capita GDP used in the studies where the effectiveness analysis is performed on a country level. This variable is considered as a measure of the performance of federations in competitions. Another important contribution of the study to the literature is the use of the number of trainers per athlete as input. Thus, the education factor is taken into consideration in the evaluation of the federations’ performance. The study includes a comparison of the performances of both the old federations and the newly established federations. As for future studies, it is being planned that the efficiency change analysis for years can be applied by considering different importance degrees of the gold, silver, and bronze medals. In addition, as the number of medals cannot be expressed in noninteger numbers, the DEA model can be arranged as an integer optimization. Determining the maximum number of medals that each federation can win in the competitions and the maximum number of athletes for each federation that can be participated in the competitions will enable the efficiency analysis to reflect the reality more. In the present study, financial resource allocation from public or private funds

90

M. Murat et al.

could not be made due to a lack of available data. Public or private financial source allocation may be performed to reveal which type of financial funds get better results. The findings are not examined in terms of team sports versus individual sports since the federations such as basketball, volleyball, and handball are not included in the study due to lack of available data. Examining the effect of team sports and individual sports on the success of federations is another future study topic.

References 1. Angulo M, Pescarini V, Soares de Mello JCCB (2015) Assessing the efficiency of sports in using financial resources with DEA models. Proc Comput Sci 55:1151–1159 2. Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30(9):1078–1092 3. Barros CP (2003) Incentive regulation and efficiency in sport organisational training activities. Sport Manag Rev 6:33–52 4. Barros CP, Santos A (2003) Productivity in sports organisational training activities: a DEA study. Eur Sport Manag Q 3(1):46–65 5. Bernard AB, Busse MR (2004) Who wins the olympic games: economic resources and medal totals. Rev Econ Stat 86(1):413–417 6. Bowlin WF (1987) Evaluating the efficiency of US Air Force real-property maintenance activities. J Oper Res Soc 38(2):127–135 7. Caves DW, Christensen LR, Diewert WE (1982) Multilateral comparisons of output, input, and productivity using superlative index numbers. Econ J 92(365):73–86 8. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444 9. Charnes A, Cooper WW, Huang ZM, Sun DB (1990) Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. J Econom 46(1–2):73–91 10. Churilov L, Flitman A (2006) Towards fair ranking of olympics achievements: the case of sydney 2000. Comput Oper Res 33:2057–2082 11. De Bosscher V, Bingham J, Shibli S, van Bottenburg M, de Knop P (2008) The global sporting arms race: an international comparative study on sports policy factors leading to international sporting success. Meyer & Meyer Sport, Aachen 12. De Carlos P, Alén E, Pérez-González A (2017) Measuring the efficiency of the Spanish olympic sports federations. Eur Sport Manag Q 17(2):210–225 13. Egilmez G, McAvoy D (2013) Benchmarking road safety of US states: a DEA-based Malmquist productivity index approach. Accid Anal Prev 53:55–64 14. Estache A, de la Fe BT, Trujillo L (2004) Sources of efficiency gains in port reform: a DEA decomposition of a Malmquist TFP index for Mexico. Utilities policy 12(4):221–230 15. Fare R, Färe R, Fèare R, Grosskopf S, Lovell CK (1994) Production frontiers. Cambridge University Press 16. Farrell MJ (1957) The measurement of productive efficiency. J Roy Stat Soc Ser A (Gen) 120(3):253–281 17. Lei X, Li Y, Xie Q, Liang L (2015) Measuring olympics achievements based on a parallel DEA approach. Ann Oper Res 226(1):379–396 18. Li Y, Liang L, Chen Y, Morita H (2008) Models for measuring and benchmarking olympics achievements. Omega 36(6):933–940 19. Li Y, Lei X, Dai Q, Liang L (2015) Performance evaluation of participating nations at the 2012 London summer olympics by a two-stage data envelopment analysis. Eur J Oper Res 243(3):964–973

7 Efficiency Evaluation of Turkish Sports Federations …

91

20. Lins MPE, Gomes EG, Soares de Mello JCCB, Soares de Mello AJR (2003) Olympic ranking based on a zero sum gains DEA model. Eur J Oper Res 148:312–322 21. Lozano S, Villa G, Guerrero F, Cortés P (2002) Measuring the performance of nations at the summer olympics using data envelopment analysis. J Oper Res Soc 53:501–511 22. Malmquist S (1953) Index numbers and indifference surfaces. Trab Estad 4(2):209–242 23. Roll Y, Cook WD, Golany B (1991) Controlling factor weights in data envelopment analysis. IIE Trans 23(1):2–9 24. Sami, El-Mahgary Risto, Lahdelma (1995) Data envelopment analysis: visualizing the results. Europ J Operat Res 83(3):700–710. https://doi.org/10.1016/0377-2217(94)00303-T 25. Thompson RG, Langemeier LN, Lee CT, Lee E, Thrall RM (1990) The role of multiplier bounds in efficiency analysis with application to Kansas farming. J Econ 46(1–2):93–108 26. Titko J, Jureviciene D (2014) DEA application at cross-country benchmarking: Latvian vs Lithuanian banking sector. Proc Soc Behav Sci 110:1124–1135 27. Torres L, Martin E, Guevara JC (2018) The gold rush: analysis of the performance of the Spanish olympic federations. Cogent Soc Sci 4(1):1446689 28. Vagenas G, Vlachokyriakou E (2012) Olympic Medals and demo-economic factors: novel predictors, the ex-host effect, the exact role of team size, and the ‘population-GDP’ model revisited. Sport Manag Rev 15:211–217 29. Wu H, Chen B, Xia Q, Zhou H (2013) Ranking and benchmarking of the Asian games achievements based on DEA: the case of Guangzhou 2010. Asia Pac J Oper Res 30(06):1350028 30. Wu J, Liang L, Yang F (2009) Achievement and benchmarking of countries at the summer olympics using cross efficiency evaluation method. Eur J Oper Res 197(2):722–730 31. Wu J, Zhou Z, Liang L (2010) Measuring the performance of nations at Beijing summer olympics using integer-valued DEA model. J Sports Econ 11(5):549–566

Chapter 8

On the Terrain Guarding Problems: New Results, Remarks, and Directions Haluk Elis¸

Abstract A guard is defined as an entity capable of observing the terrain or sensing an event on the terrain. By this definition, relay stations, sensors, watchtowers, military units, and similar entities are considered as guards. Terrain Guarding Problem (TGP) is about locating a minimum number of guards on terrain such that points on the terrain are guarded by at least one of the guards. Terrains are generally represented as triangulated irregular networks (TIN), and TINs are also referred to as 2.5 dimensional (2.5D) terrains. TGP on 2.5D terrains is known as 2.5D TGP. 1.5D terrain is a profile of a 2.5D terrain, and the guarding problem on a 1.5D terrain is referred to as 1.5D TGP. This paper presents an example that illustrates that the set of vertices in TIN does not necessarily contain an optimal solution, which implies that an optimal solution is yet to be found for 2.5D TGP. We show that a finite dominating set (FDS) found earlier for 1.5D TGP is optimal in the sense that no other FDS has a smaller cardinality. Keywords Location theory · Finite dominating sets · Terrain guarding problem · Set-covering

8.1 Introduction A guard is defined as an entity capable of observing the terrain or sensing an event on the terrain. In real life, numerous guards are used for a variety of purposes. For example, forest fires are detected by watchtowers located on terrains [12], where watchtowers are considered as guards since, with the appropriate equipment, they can guard the terrain (to detect fires). It is important for military units to prevent any intrusion into their region of deployment. Military units achieve this by locating watch-posts on the terrain such that no dead zone exists [6]. In order to maintain effective communication, relay stations need to be placed on the terrain such that each station is visible from at least one other station [10]. As defined in Eli¸s [8], H. Eli¸s (B) Part Time Academic Staff, Department of Industrial Engineering, Faculty of Engineering, Ya¸sar University, ˙Izmir, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_8

93

94

H. Eli¸s

the goal in the Terrain Guarding Problem (TGP) is to locate a minimum number of guards on the terrain such that each point on the terrain is visible or guarded by at least one of the guards. Real three-dimensional terrains need to be represented as mathematical objects to be able to solve TGP and to perform similar terrain-related analyses. A mathematical or a digital representation of a real terrain surface is known as a Digital Elevation Model (DEM) [9, 15]. Triangulated irregular network (TIN) is a preferred DEM since some important terrain features are preserved [9]. As described in Eli¸s [8], a TIN is obtained as follows. Suppose that S ∈ R 3 is a real terrain surface. Points are sampled from S that we assume represent S sufficiently. Let P = {p1 , …, pn } be the set of such points with x, y, and z coordinates. Let pi ∈ R 2 be the projection of pi onto the x–y plane, and P = { p1 , . . . , pn } be the set of such points. The points sampled from S and their projections are referred to as vertices. The vertices in P become the vertices of the triangles on the plane after the triangulation is performed [4]. Let us denote the triangulation by T . Next, each point pi ∈ P is elevated to its real height together with the edges. The object obtained as such is a TIN (Fig. 8.1). In the following, we largely adopt the notation used in Eli¸s [6–8]. Let us denote TIN by T. We may assume that T is in the nonnegative orthant without loss of generality. Let g((x, y)) denote the height of the terrain at (x, y) and V be the visible region, i.e. V = {(x, y, z): (x, y) ∈ T and z ≥ g((x, y))}. The region below T is denoted by F. Let p1 and p2 ∈ R3 such that their projection is in T . The line segment LS(p1 , p2 ) ≡ {p1 + λ (p2 − p1 ): λ ∈ [0, 1]} connects p1 and p2 . We say that p2 is visible from/guarded by p1 if LS(p1 , p2 ) is a subset of V, and not visible from p1 if

Fig. 8.1 Triangulated irregular network (as illustrated in Church [2])

8 On the Terrain Guarding Problems: New Results …

95

LS(p1 , p2 ) ∩ F /= ∅. A visibility function is defined as follows, )

(

(

)

v p1 , p2 = v p2 , p1 =



) ( 1 if LS p1 , p2 ⊆ V 0 otherwise

Let VS(x) denote the “viewshed” of p, i.e. V S(x) = { y ∈ T : v(x, y) = 1}. Let X = {x1 , …, xk } be a set of points on T. We say that X guards or covers T if every point on T is guarded by at least one of the guards located at points in X. A formal definition is given by the following function,  V I S( y, X) =

( ) 1, if ∃ x j ∈ X, such that v y, x j = 1 0, otherwise

In the Terrain Guarding Problem (2.5D TGP), the goal is to find a set X such that X guards T and has the minimum cardinality. A formal definition is given as follows, (2.5D TGP) Minimize |X| Subject to V I S( y, X) = 1, ∀ y ∈ T X ⊆T For 1.5 D terrains, we adopt the notation used in Eli¸s [7]. 1.5D terrain (T′ ) is a profile of a 2.5D terrain along a line and is characterized by a piecewise linear curve (Fig. 8.2). The definition of 1.5D TGP is the same as 2.5D TGP except that the terrain is a 1.5-dimensional surface. 1.5D TGP has applications where street lights or security sensors are placed along roads, communication networks are constructed [1], or cameras/posts are located on the borderline [7]. As shown in Fig. 8.2, the terrain surface T′ is denoted by h(x), which gives the height of x ∈ [0, L]. Visibility and covering of a given point by another point or by a set of guards are defined as in 2.5D case. The formal definition of 1.5D TGP is the same as 2.5D except that the terrain is 1.5 dimensional.

T′

Fig. 8.2 A 1.5 dimensional terrain

V h(x)

F

0

x

L

96

H. Eli¸s

8.2 Literature Review 8.2.1 2.5D TGP The 2.5D TGP was first investigated by De Floriani et al. [5]. They showed that a set-covering formulation could be used to solve the TGP. In their study, the vertices of the triangles were used as potential guard locations, among which an optimal solution is sought. We note that in order for the solution obtained to be optimal, it is not sufficient to use an exact algorithm. In addition to using an exact algorithm, one also needs to prove that the set of guard locations is a finite dominating set (FDS). An FDS is a finite set of points that contains an optimal solution to an optimization problem with (possibly) an uncountable feasible set. Perhaps, the best-known FDS is the set of extreme points in linear programming. The concept of FDS was used in his seminal paper by Hakimi [13] and others thereafter in location problems. Yet, the term ‘finite dominating set’ is due to Hooker et al. [14]. In order to solve the TGP to optimality, an FDS must be identified, and then an exact algorithm (such as branch and bound) must be used. In the next section, we present an example where an optimal solution is not necessarily a vertex of a triangle. In other words, we show that the set of vertices is not a finite dominating set for the 2.5D terrain guarding problem. As discussed in Eli¸s et al. [6] and in more depth in ReVelle and Eiselt [16], in location problems, there are customers whose demand must be met by a number of facilities to be located on a surface. The goal is to minimize the number of facilities such that the demand of each customer is met. In this respect, TGP is also a location problem since guards, similar to facilities, are located on terrain to guard each point on the terrain. In a sense, each point on the terrain has a demand, which is being guarded, that needs to be met by the guards. 2.5D TGP is NP-Hard, as shown in Cole and Sharir [3]. When potential guard locations are identified, 2.5D TGP can be solved by Location Set Covering Problem (LSCP) formulation. LSCP is a set-covering problem within a location context. LSCP was introduced by Toregas et al. [17], and the problem formulation can be used for solving 2.5D TGP as follows, Minimize

n Σ

yj

j=1

Subject to

n Σ

ai j y j ≥ 1, ∀ i = 1, . . . , m.

j=1

y j ∈ {0, 1}, ∀ j = 1, . . . , n. where yj is 1 if a guard is located at site j, and 0 otherwise. Each part of a terrain surface (part of a triangle) seen by each potential guard location is considered a

8 On the Terrain Guarding Problems: New Results …

97

demand element to be covered in the formulation. aij is 1 if the guard location j covers region i and 0 otherwise.

8.2.2 1.5D TGP We note that 1.5D TGP can be solved to optimality by LSCP formulation as in 2.5D TGP once the set of guard locations is an FDS. Friedrichs et al. [11] presented the first FDS, and thus an optimal solution for 1.5D TGP. The FDS they have found has a size of O(n2 ). The regions covered by each potential guard location are taken to be a “witness set” such that guarding the elements of the witness set implies guarding of the terrain. The witness set in Friedrichs et al. [11] has a size O(n3 ). Ben-Moshe et al. [1] showed that there exists a witness set of size O(n2 ) before Friedrichs et al. [11]. Later, Eli¸s [7] showed a smaller finite dominating set and a smaller witness set, each of which has a size of O(n). Friedrichs et al. [11] posed the question of whether any optimal discretization exists, that is, whether an optimal FDS and an optimal witness set exist. A set is referred to as optimal if it has the minimum cardinality. In the next section, we show that the FDS found in Eli¸s [7] is optimal; that is, no other FDS for the problem has a smaller cardinality than O(n).

8.3 Analysis and Remarks 8.3.1 2.5D TGP Consider the terrain shown in Fig. 8.3. The terrain is shaped like a football stadium such that the rectangle ABCD is at ground level, and the terrain rises from the edges of the rectangle. The horizontal lines passing through points 1–8 are the edges of tilted planar surfaces such that every cross-section of the terrain across the edges of AB and CD is like a staircase. Note that point P is inside a triangle and not a vertex. Consider, for example, the cross-section of the terrain along with the points 1-P-8 (Fig. 8.4). In each such cross-section, P (and possibly other nonvertex points) can guard the cross-section. Thus, P is an optimal solution (there may be alternative optimal points). We conclude that the set of vertices is not a finite dominating set and a finite dominating set needs to be identified in order to solve TGP to optimality by LSCP formulation.

98

H. Eli¸s

1 2 3 4

Fig. 8.3 A 2.5-dimensional terrain

B

A

P D

C

5 6 7 8 8

1 7

2 3

4

P

6 5

Fig. 8.4 Cross-section of the terrain along with the points 1-P-8

8.3.2 1.5D TGP We use the same definitions and notations that are used in Eli¸s [7], which we give in the following. T denotes the terrain surface. Convex region: Function h(x) may be convex in some intervals. The part of h(x) that is composed of maximally connected edges is referred to as a convex region. The set of convex regions is denoted by ‘CR’. Convex point: A vertex where two convex regions intersect is referred to as a convex point. The two endpoints of T are included in the set of convex points, and the set of convex points is denoted by ‘C’, |C| = k. If k is the number of convex points, then the number of convex regions is k − 1 and vice versa. There are 11 vertices, 10 edges, 6 convex points, and 5 convex regions in Fig. 8.5. The part of T that is between convex points 5 and 6 is a convex region that has 4 edges. Dip point: A point p on T “fully covers” N if N ⊆ VS(p). A point that is not a convex point and fully covers at least one convex region to its left and right, which are both

8 On the Terrain Guarding Problems: New Results …

99

convex points

Fig. 8.5 Convex points and convex regions on T

6 2

3

4

5

1

0

L

Q Fig. 8.6 Illustration of a dip point Q

different from the convex region it is in, is called a dip point. A dip point is illustrated in Fig. 8.6. The set of dip points and convex points is referred to as critical points. Eli¸s [7] has shown that the set of critical points is an FDS and has a cardinality of O(n), and that the number of dip points is bounded by k − 1. Eli¸s [8] showed that there is even , and this bound is tight. a smaller bound on the number of dip points, given by (k−2) 2 In the following theorem, we show that the FDS of critical points is optimal in the sense that no other FDS for the problem can have a smaller cardinality. Theorem The FDS of critical points is optimal for 1.5D TGP in the sense that the number of points in any FDS is at least the number of critical points. Proof We note that an FDS is a set of points with predefined properties such that the points in FDS must be identifiable for all instances of TGP. Let X be an arbitrary FDS. Suppose, for example, that the points in X are defined such that the ith convex point is not in X. One can build an instance such that the ith convex point is the only optimal point. This can be done easily by constructing an example in which the height of the ith convex point is set at a value such that it is an optimal solution, i.e., the only point that can see both of its sides. Suppose, alternatively, that X does not contain a dip point. This implies that X, by construction, contains no nonconvex point that fully covers convex regions to its left and right, since if it contained, there would exist a dip point by definition. However, as shown in Fig. 8.6, there may exist an instance in which a dip point is the only optimal solution. Thus, X must contain

100

H. Eli¸s

Fig. 8.7 A comparison of the FDS of Friedrichs et al. [11] to the optimal FDS of Eli¸s [7]

convex points and dip points. Since X is arbitrary, together with the fact that the set of critical points is an FDS, it follows that the FDS of critical points is optimal. We note that the points in an FDS correspond to the decision variables in the LSCP formulation. Thus, fewer points in the FDS imply that there will be fewer columns in matrix A(aij ) of the formulation, which, in turn, implies that the solution time will be shorter. The following example compares the number of points in the FDS found in Friedrichs et al. [11] to that in Eli¸s [7], which is shown to be optimal by the above theorem. As illustrated by the terrain in Fig. 8.7, the number of points in the FDS of Friedrichs et al. [11] is more than three times the number of points in the FDS of Eli¸s [7].

8.4 Conclusions and Directions for Future Research We have shown by a counter-example that the set of vertices is not a finite dominating set for 2.5D TGP. This implies that solving TGP to optimality is an open problem. Considering the important application areas of 2.5D TGP, future research may focus on finding an FDS for this problem. The type of points we have shown in the counterexample suggests that such points might belong to an FDS. We have provided a partial answer to the research question posed in Friedrichs et al. [11] and shown that FDS of critical points found in Eli¸s [7] is optimal in the sense that no FDS exists with a cardinality smaller than O(n). Thus, future research efforts can be directed towards investigating an optimal witness set.

8 On the Terrain Guarding Problems: New Results …

101

References 1. Ben-Moshe B, Katz MJ, Mitchell JSB (2007) A constant-factor approximation algorithm for optimal 1.5D terrain guarding. SIAM J Comput 36(6):1631–1647 2. Church RL (2002) Geographical information systems and location science. Comput Oper Res 29(6):541–562 3. Cole R, Sharir M (1989) Visibility problems for polyhedral terrains. J Symb Comput 7(1):11–30 4. De Berg M, Cheong O, Van Kreveld M, Overmars M (1997) Computational geometry, algorithms and applications. Springer, Heidelberg 5. De Floriani L, Falcidieno B, Pienovi C, Allen D, Nagy G (1986) A visibility-based model for terrain features. In: Proceedings 2nd international symposium on spatial data handling, pp 235–250 6. Eli¸s H, Tansel B, O˘guz O, Güney M, Kian R (2021) On guarding real terrains: the terrain guarding and the blocking path problems. Omega 102:102303 7. Eli¸s H (2017b) A finite dominating set of cardinality O(k) and a witness set of cardinality O(n) for 1.5D terrain guarding problem. Ann Oper Res 254(1–2):37–46 8. Eli¸s H (2017a) Terrain visibility and guarding problems. Ph.D. dissertation. Bilkent University 9. De Floriani L, Magillo P (2003) Algorithms for visibility computation on terrains: a survey. Environ Plann B Plann Des 30(5):709–728 10. De Floriani L, Marzano P, Puppo E (1994) Line-of-sight communication on terrain models. Int J Geogr Inf Syst 8(4):329–342 11. Friedrichs S, Hemmer M, Schmidt C (2014) A PTAS for the continuous 1.5D terrain guarding problem. 26th Canadian conference on computational geometry (CCCG), Halifax, Nova Scotia 12. Goodchild MF, Lee J (1989) Coverage problems and visibility regions on topographic surfaces. Ann Oper Res 18(1):175–186 13. Hakimi SL (1964) Optimum locations of switching centers and the absolute centers and medians of a graph. Oper Res 12(3):450–459 14. Hooker JN, Garfinkel RS, Chen CK (1991) Finite dominating sets for network location problems. Oper Res 39(1):100–118 15. Li Z, Zhu Q, Gold C (2004) Digital terrain modeling: principles and methodology. CRC Press 16. ReVelle CS, Eiselt HA (2005) Location analysis: a synthesis and survey. Eur J Oper Res 165(1):1–19 17. Toregas C, Swain R, ReVelle C, Bergman L (1971) The location of emergency service facilities. Oper Res 19(6):1363–1373

Chapter 9

Retention Prediction in the Gaming Industry: Fuzzy Machine Learning Approach Ahmet Tezcan Tekin, Ferhan Cebi, and Tolga Kaya

Abstract Traditional machine learning algorithms may not produce satisfactory results on high-dimensional and imbalanced datasets. Therefore, the popularity of the concept of ensemble learning has increased, especially in recent years. Standard machine learning algorithms try to learn a single hypothesis from the training dataset, while ensemble-learning algorithms create a set of hypotheses and try to combine them. In this way, they can produce better results than the common machine learning algorithms. The fuzzy logic concept is also used in machine learning problems, especially in clustering problems in recent years. The fuzzy logic approach, by its nature, is used to solve problems that do not have a definite result, such as real-life problems, brings this approach to the fore, especially in machine learning problems. In this paper, we survey the latest status of ensemble learning and fuzzy clustering methods. Also, we proposed a new approach that combines fuzzy clustering and ensemble learning. This approach is applied in a case study, and results are compared with existing ensemble learning algorithms in the methodology section. Keywords Ensemble learning · Fuzzy clustering methods · Fuzzy ensemble learning · Fuzzy classification · Retention prediction

9.1 Introduction In solving a problem, we encounter daily, we may not be sure of a single answer. To be sure, we ask the same question to more than one person and act according to the A. T. Tekin (B) · F. Cebi · T. Kaya Department of Management Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] F. Cebi e-mail: [email protected] T. Kaya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_9

103

104

A. T. Tekin et al.

majority of the votes, or we weigh the votes. Ensemble learning algorithms also act in this way. They run base algorithms multiple times to solve a problem and develop a hypothesis to vote for the results. Thanks to this approach, ensemble learning algorithms have recently achieved great success in solving complex data problems. With the advancement of technology daily, a severe performance increase has been realized in the tough problem-solving times of ensemble learning algorithms. Ensemble learning algorithms have recently been applied in both academia and private sector problems, and these studies are pretty common in the literature. Private sector problems generally consist of various fields such as production, marketing, finance, etc. Although the sector is different, the aim is to predict the unpredictable events that will occur in the future and take precautions against these situations. Therefore, establishing a successful prediction model is very crucial. There are many methods in the literature to establish a successful model. The number of these methods is increasing day by day. Among these methods, one of the proven successful approaches is ensemble learning approaches. The primary purpose of ensemble learning algorithms is to combine multiple algorithms or algorithm results to close their weaknesses and create a more robust model. In this way, it is aimed to reduce the error rate in prediction. Since the day it was first introduced in the literature, fuzzy logic [27] has been used in many fields such as control, optimization, and data analysis. Fuzzy logic approaches have also started to be used in machine learning over time, and these studies can be found in the literature in many different fields [18]. Fuzzy logic is generally used to extend machine learning and data mining studies in the literature. For this purpose, fuzzy logic approaches are seen, especially in clustering and association rule mining studies. The fuzzy machine learning approach is applied to fuzzy sets. This, of course, necessitates the extension of corresponding learning algorithms, which usually assume crisp data. Although the number of literature studies in fuzzy machine learning increases, these studies are generally carried out without clarifying the actual meaning of fuzzy observation. The datasets used in machine learning problems that are tried to be solved also contain fuzziness. Modeling objects with different characteristics in the same data set can negatively affect prediction success. At this point, using clustering approaches and machine learning models together and modeling items with similar characteristics in similar groups can increase prediction success. As a clustering approach, fuzzy clustering approaches can also be preferred, unlike hard clustering approaches like K-Means Clustering [17]. In this study, we aim to predict the retention value of attributed users in a mobile game. Naturally, the genders, countries, age groups, and game behaviors of the users who play this game differ. These differences affect many variables, such as when the user stays in the game and how long he will play the game. In this case, clustering users is a good approach, but a user can also have the characteristics of two or more clusters at the same time. For this reason, we proposed a method that combines the fuzzy clustering method and ensemble learning method. In this way, we aimed to increase the overall prediction success in the modeling phase compared to using ensemble

9 Retention Prediction in the Gaming Industry …

105

learning methods alone. In addition, since each algorithm—parameter group can have different success rates on different clusters, we aimed to make the prediction more successful by weighting the most successful algorithm—parameter groups with the prediction success rates for each cluster. The paper addresses the literature review of ensemble learning and fuzzy methods of machine learning in Sect. 9.2. Our proposed approach, which ensembles fuzzy clustering and ensemble learning algorithms and modeling data, are illustrated in Sect. 9.3. Finally, the study’s results are briefly outlined, and the last section presents potential work for the future.

9.2 Literature Review 9.2.1 Ensemble Learning In machine learning problems, a single-week learning algorithm is very dependent on the training set, which can lead to overfitting [31]. It aims to close these weaknesses by aggregating more than one weak algorithm to deal with this problem. This method is called ensemble learning [30]. Ensemble learning approaches, whose importance and use have increased considerably in recent years, are used in many studies in the literature. They have been very successful in real-life applications in machine learning and machine learning challenges such as Kaggle competitions [1, 16, 25]. One of the most important reasons for the ensemble learning approach’s success is the generalization ability [12]. Ensemble learning methods are divided into two groups as parallel and sequential methods. Weak learners are generated in parallel with the bagging approach in the parallel ensemble learning method. In the sequential ensemble learning method, weak learners are generated sequentially with the boosting approach [30]. Bagging and boosting approach principles are introduced briefly below. (1)

Bagging

Bagging, which stands for bootstrap aggregating, is a technique that trains various homogeneous weak learners separately from each other in parallel and then combines them using a deterministic averaging procedure to achieve the final prediction or classification [8]. In this method, while creating regression trees, a training set of the same size as the original data set is created based on the “bootstrap” approach. Some items can be left out in this training set, while others can also be used repeatedly. Breiman stated that for the created bags to be effective, the observation data in the bag is unstable; that is, it depends on the rate of response to changes in the training data [4]. Random Forest is an example of bagging type ensemble learning algorithms.

106

(1.1)

A. T. Tekin et al.

Random Forest

Random Forest (RF) comprises many individual decision trees that work together to form an ensemble. RF is an ensemble of Classification and Regression Trees (CART) [3] trained on datasets of the same size as the training set, known as bootstraps generated by random resampling on the training set. In RF, the prediction is based on each tree’s predictions. So, the most votes become the prediction. The random forest philosophy is that many autonomous decision tree models working as a group outperform any single decision tree model. After constructing a tree, a set of bootstraps do not contain any specific record from the original dataset. Because out-of-bag samples are used as the test dataset. RF model can be represented as m(x) ˆ =

1  mˆ j(x) M j

(9.1)

where mˆ j denotes an individual tree, and the prediction is based on the averaging of each tree’s prediction. (2)

Boosting

Boosting, which stands for sequential ensemble learning approach, can be used for both classification and regression. It produces a weak prediction model at each stage, which is then weighted and applied to the overall model, reducing variance and bias and improving model efficiency. Adaptive Boosting (Adaboost), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Catboost and Light Gradient Boosting Machine (Light GBM) are the most popular boosting type ensemble learning algorithms. These algorithms are explained briefly below. (2.1)

Adaboost

Adaboost is an ensemble-type boosting algorithm that Freund and Shapire proposed in 1995 [9]. Adaboost is an iterative algorithm, and it generates a robust model from a set of weak models. In each generation, it tries to minimize the sum of the training error. Adaboost model can be represented as Et =

   E Ft−1 (xi ) + at h(xi )

(9.2)

i

Ft−1 (x) is the previous learner, E is an error function, and at h(x) is the weak learner, contributing to the stronger learner [10]. (2.2)

LightGBM

The Light Gradient Boosting algorithm is a new version of the GBDT algorithm. It is widely used in a wide range of modeling problems, including classification and regression. LightGBM employs two new strategies to accommodate many data

9 Retention Prediction in the Gaming Industry …

107

instances and functions: gradient-based one-side sampling and exclusive function bundling [13]. Compared to base gradient boosting strategies or Extreme Gradient Boosting, LightGBM extends the decision tree vertically, whereas others extend it horizontally. This feature enhances LightGBM’s ability to process large amounts of data. (2.3)

Catboost

Catboost is a new proposed version of the gradient boosting type algorithm, which Prokhorenkova proposed in 2018 [22]. Catboost works reliably with categorical features with the least amount of information loss. CatBoost is distinct from other gradient boosting algorithms such as Extreme Gradient Boosting and LightGBM. It employs ordered boosting, an effective modification of gradient boosting algorithms, to address target leakage [7]. Furthermore, Dorogush claims that Catboost attempts to avoid overfitting issues created by gradient boosting algorithms by executing random permutations to change leaf values when deciding on a tree structure. Catboost model can be represented as Z = H (xi ) =



  J j = 1c j 1 x ∈ R j

(9.3)

H (xi ) is a decision tree function of the explanatory variables xi , and R j is the disjoint region corresponding to the tree leaves [22]. (2.4)

XGBoost

XGBoost is a well-known gradient boosting algorithm suggested by Chen and Guestrin [6]. XGBoost is an improved GBDT algorithm that utilizes many decision trees and is commonly used in classification and regression. XGBoost incorporates a regularization principle to optimize the size of the tree’s classification function in order to make it more reproducible. Furthermore, regularization aids in the prediction of feature value, which is critical in big data problems. XGBoost can be represented as  Z = F(xi ) = Tt = 1 f t (xi ) (9.4) where xi denotes the explanatory variables and f t (xi ) is the output function of each tree. (2.5)

Gradient Boosting

Gradient Boosting is the base version of XGBoost, Catboost, and LightGBM, and it was proposed by Friedman [11]. It is a tree-based machine learning algorithm that applies the gradient boosting method effectively and scalably. It is also one of the most common machine learning algorithms, and it has been stated in the literature that it performs very well in both regression and classification problems. Gradient

108

A. T. Tekin et al.

Boosting attempts to minimize bias variation by using the base learner as a weighted total and reweighting misclassified results. Besides, it employs decision trees as base learners to reduce the loss function. Gradient Boosting can be represented as Z = F(xi ) =



M j = 1β j h

(9.5)

  where the function h x; b j is the base learner, x is the explanatory variables, β j is the expansion coefficients, and b j is the parameters of the model.

9.2.2 Evaluation of Performance Metrics In classification-type machine learning problems, we apply several machine learning algorithms for choosing the best appropriate model. For algorithm performance comparison, we consider some metrics such as accuracy, precision, AUC, F1 score, etc. These metrics are explained briefly below. Accuracy: It is a metric that is one of the most popular metrics for evaluating classification-type machine learning problems. This score indicates the algorithm’s performance by showing the true value ratio of the predicted label. So, it shows the algorithm’s overall performance. Precision: It is a metric that shows us prediction’s power. Precision is defined as the proportion of correctly predicted positive observations to all predicted positive observations. Recall: Recall is named as sensitivity also. It is the ratio of correctly predicted positive observations to all observations in the actual class. It shows us the effectiveness of the algorithm in a single class. AUC: AUC stands for Are Under the ROC Curve. ROC (Receiver Operation Characteristics) is a curve that shows the performance of a classification model. AUC is a metric that aggregates output overall classification thresholds. AUC can be translated as the model’s chance to score a random positive example higher than a random negative example. F1 Score: It is a metric that the weighted average of Precision and Recall. F1 Score is considered both false positives and negatives. F1 score can be more useful, especially in imbalanced datasets [23].

9.2.3 Fuzzy Clustering Techniques Fuzzy logic, an essential place in the literature, is often widely used in machine learning problems. One of the primary reasons for this is that the answers to real-life

9 Retention Prediction in the Gaming Industry …

109

problems do not consist of only 0 and 1 singular values, as is the case with machine learning approaches that address real-world problems. Fuzzy Logic [27–29] was discovered by Lofti Zadeh in 1965. Zadeh believed that all real-world problems could be solved using analytical and computer-based methods [15]. In 1964, he revealed the “Fuzzy Set Theory”. Some academic communities have criticized this theory for its uncertainty, but it is used in many different areas today. A fuzzy set is defined by a function that maps objects in the respective domain to the membership value in the set [15]. Basically, Fuzzy Logic is a method that allows defining intermediate values between traditional evaluations such as true/false or yes/no. The fuzzy logic theorem is also frequently encountered in the literature with clustering problems, one of the unsupervised learning problems. Clustering algorithms can generally be considered in two classes as hard and soft clustering [19]. In the hard clustering method, each observation in the test data set belongs to only one cluster, while in the soft clustering method, an observation may belong to more than one cluster [24]. The soft clustering method is also included in the literature as fuzzy clustering. In the fuzzy clustering method, the membership level is calculated for each observation value. This level lies between 0 and 1. There are different approaches for fuzzy clustering suggested in the literature. These approaches are Fuzzy C-Means Clustering (FCM), Possibilistic C-Means Clustering (PCM), Fuzzy Possibilistic C-Means Clustering (FPCM), Possibilistic Fuzzy C-Means Clustering (PFCM). These algorithms are explained briefly below. Fuzzy C-Means Clustering (FCM): FCM is a clustering technique in which each data point belongs to a cluster to a degree determined by a membership degree. Jim Bezdek proposed this approach in 1981 [2]. This approach is an advancement to previous clustering approaches. FCM groups data points as populating multidimensional space to a set number of different clusters. The primary advantage of FCM is that it allows data points for memberships of each cluster with degrees between 0 and 1. FCM is based on a minimization objective function. This function can be represented as Jm =

N  C 

u imj xi − c2j , 1 ≤ m < ∞

(9.6)

i=1 j=1

where u i j is the degree of membership xi in the cluster, xi denotes that ith of ddimensional measured data, c j is the dimension center of the cluster [5]. Possibilistic C-Means Clustering (PCM): This method, first proposed by Krishnaparum and Keller [14] in 1996, enables the detection of outliers in the data set [24]. In this method, low typicality values are produced for outliers, and these outliers are automatically eliminated. This method is also sensitive to initial assignments and can assign an observation value to more than one cluster at the same time because it exhibits a flexible clustering approach. Furthermore, typicalities can be extremely sensitive to selecting the additional parameters required by the PCM model.

110

A. T. Tekin et al.

Fuzzy Possibilistic C-Means Clustering (FPCM): This method, proposed by Pal et al. [20] in 1997, consists of combining the FCM and PCM approaches. This method has been tried to obtain more successful results y combining the typical values of the FCM clustering method with the typical values of the PCM clustering method. FPCM normalizes the possibility values such that the number of all data points in a cluster equals 1. Although FPCM is much less susceptible to the problems that FCM and PCM cause, the probability values become very small as the data set size increases. Possibilistic Fuzzy C-Means Clustering (PFCM): This method was suggested by Pal et al. [21] in 2005, They aimed to exhibit a more successful clustering approach by eliminating the lack of noise sensitivity of the FCM algorithm, the random clustering problem of the PCM approach, and the row sum constraints of the FPCM clustering approach. Pal et al. derive the first-order necessary conditions for PFCM objective function extrema and use them as the foundation for a typical alternating optimization approach to finding PFCM objective functional local minima. Soft and hard clustering methods are used quite frequently in machine learning problems, and although they are used on their own, they are also used as a precursor in regression or classification problems. When the points in the data set are grouped according to their behavioral characteristics, making individual model trials for these groups can increase the success of the general model.

9.3 Proposed Methodology Churn prediction is one of the most popular topics in classification problems. Instead of churned customers, companies have a significant focus on their retained users. More than half of the revenue, especially in the game industry, comes from users who continue to play after the day they downloaded the game. While the revenues from the users who churn from the first day cannot cover their costs, retained users have an essential place in the established revenue model. The dataset used in this study is related to a crossword puzzle game published in Google Play Store and App Store. This dataset consists of users’ first 24 h of gameplay data and session information. In this study, we aim to predict customers are retained or not after the first 24 h. Base features that are used in this study are shown in Table 9.1. The dataset consists of 24 features and 356,603 rows, indicating each customer’s first 24-h activity summary. Firstly, data cleaning, data preprocessing, missing value elimination were administered to the dataset. After that, one hot encoding technique was applied to the dataset for the categorical values, and all categorical values were converted to numerical values. Then, we used the min–max scaling technique for the numerical values, and all of the data were scaled. Traditional machine learning algorithms and boosting type ensemble learning algorithms were applied to the dataset with default parameters to detect the most successful algorithms. The results can be shown in Table 9.2. The results indicate that boosting type ensemble learning algorithms are more

9 Retention Prediction in the Gaming Industry …

111

Table 9.1 Base features used in retention prediction Feature

Description

Type

Range

Missing value ratio (%)

session_cnt

The number of sessions started for the user in the first 24 h

Numerical

[1, 82]

0.00

session_length

The total duration of sessions started for the user in the first 24 h

Numerical

[0, 17,540]

0.00

app_version

Application version of the game

Categorical

13 different values

0.00

Language

The language which users played in the first 24 h

Categorical

30 different values

0.00

max_level_no

The maximum level number which the user reached

Numerical

[1, 459]

0.00

gameplay_duration

The total duration of levels for the user in the first 24 h

Numerical

[1, 14,760]

0.00

bonus_cnt

The number of bonuses which the user used

Numerical

[0, 109]

0.00

hint_cnt

The number of hints which the user used

Numerical

[0, 124]

0.00

repeat_cnt

The number of repeating the levels which users used for completing levels

Numerical

[0, 1086]

0.00

gold_cnt

The final gold amount Numerical which the user has at the end of the first 24 h

[0, 525,461]

0.00

banner_cnt

The number of banner-type advertisements that users display in the first 24 h

[0, 1679]

0.00

interstitial_cnt

The number of Numerical interstitial type advertisements which users display in the first 24 h

[0, 444]

0.00

Numerical

(continued)

112

A. T. Tekin et al.

Table 9.1 (continued) Feature

Description

Type

Range

Missing value ratio (%)

rewarded_video_cnt

The number of rewarded video type advertisements that users display in the first 24 h

Numerical

[0, 25]

0.00

Revenue

The revenue amount Numerical is acquired from users in the first 24 h

[0, 10.82]

0.00

max_session_length

Maximum session duration for users in the first 24 h

Numerical

[0, 6060]

0.00

avg_session_length

Median session duration for users in the first 24 h

Numerical

[0, 4149]

0.00

campaign_name

The information from Categorical which campaign the user attributed

158 distinct values

2.33

Partner

Campaign provider information

Categorical

5 different values

2.33

Ecpi

The acquisition cost of the user

Numerical

[0, 2.02]

0.00

Os

The operating system information of the user

Categorical

2 different values

0.00

Country

The country information of the user

Categorical

149 different values

0.00

Retention

Is the customer retained or not?

Label

2 different values

0.00

Table 9.2 Algorithm performances with default parameters Model

Accuracy

AUC

Recall

Prec

F1

XGB

0.828

0.913

0.709

0.881

0.786

LGBM

0.826

0.910

0.711

0.877

0.785

CB

0.818

0.897

0.755

0.835

0.793

GBC

0.803

0.884

0.707

0.835

0.766

Adaboost

0.779

0.856

0.723

0.787

0.754

9 Retention Prediction in the Gaming Industry …

113

Table 9.3 Fuzzy cluster details Cluster a

Cluster a–b

Cluster a–c

Cluster b

Cluster b–c

Cluster c

110,286

43,601

31,574

76,957

35,833

58,352

successful than traditional algorithms, and XGBoost is the most accurate prediction algorithm. The main contribution of this study is in the modeling section. In the modeling phase, instead of directly applying traditional machine learning algorithms to the final data we have, firstly, similar users were grouped with the fuzzy clustering approach. The Fuzzy C-Means Clustering method was preferred instead of the KMeans clustering approach. The degree of belonging of a user to each cluster was calculated separately, and users above a certain value were included in more than one cluster at the same time. In this way, after the clustering approach, the dimensions of the clusters shrank less than the K-Means clustering approach, but still, the users were grouped with similar users (Table 9.3). In the Fuzzy Clustering part, different k initial seed values were tested for FPC [26] ratings. Also, different “m” fuzzifier parameters 1.2, 1.5, 2, 2.5 were tested to find optimum cluster structure. For a fuzzy cluster with a 0.85 FPC ranking, c = 3 and m = 2 were selected as the initial seed value and fuzzifier parameters. Table 9.3 shows us some of the users have characteristics of more than one group. So, they involve more than one group at the same time. After that, the three most successful algorithms, XGBoost, Catboost, and LightGBM were applied to each fuzzy cluster with their different hyperparameters. The results show us each algorithm with different parameters has different success ratios in each cluster. So, we can say that there is no best algorithm for the entire dataset. For each observation, the prediction outcomes were saved in separate data frames. The output results of each algorithm were then examined for each cluster. The performance results of the algorithms for each cluster are summarized in Table 9.4. In our proposed method, we chose accuracy as a performance metric that is popular in classification problems. The steps of our proposed method have been explained briefly below. (1) (2)

(3)

(4)

Data preprocessing and feature engineering technics that are necessary for modeling are applied to the dataset. FCM is applied to the processed dataset, and a membership value threshold is set to cluster objects. According to this threshold value, objects can be assigned to more than one cluster. Candidate models with different parameters are applied to all clusters separately, and the best models are chosen according to the selected success criteria for each cluster. At this stage, 70% of the dataset is used as learning and 30% as a test set. The success rate is based on the accuracy rate of the test set. The weighted average method is used to ensemble the outcomes of the best models. The weight calculation is defined by Eq. (9.7).

114

A. T. Tekin et al.

Table 9.4 Algorithm performances with different parameters Model

Parameters

Cluster a

Cluster a–b

Cluster a–c

Cluster b

Cluster b–c

Cluster c

XGB

md:6,lr:0.3

0.81

XGB

md:6,lr:0.1

0.8

0.79

0.78

0.8

0.82

0.83

0.8

0.83

0.77

0.77

XGB

md:8,lr:0.3

0.82

0.81

0.79

0.79

0.76

0.81

0.8

XGB

md:8,lr:0.1

CB

d:4,lr:0.01

0.8

0.78

0.8

0.82

0.82

0.83

0.79

0.82

0.8

0.76

0.81

CB

0.81

d:8,lr:0.01

0.79

0.81

0.76

0.78

0.81

0.79

CB

d:4,lr:0.1

0.78

0.8

0.77

0.8

0.78

0.79

CB

d:8,lr:0.1

0.78

0.78

0.81

0.79

0.8

0.78

LGBM

lr:0.1,ne:200

0.82

0.81

0.77

0.79

0.76

0.81

LGBM

lr:0.1:ne:100

0.81

0.79

0.79

0.81

0.82

0.8

LGBM

lr:0.3,ne:200

0.83

0.82

0.8

0.83

0.81

0.79

LGBM

lr:0.3:ne:100

0.82

0.82

0.83

0.81

0.79

0.81

accci W eightci = n j=1 accc j

(9.7)

where ci denotes each classifier and accci is the accuracy of ci , j is the number of classifiers in the ensemble group and accc j denotes the jth classifier. As a result, combining the top three models with their optional parameters based on their accuracy is a more feasible solution for better prediction. The ensembled prediction was then made, and its output was compared to the performance of the other model and parameter groups. Table 9.5 shows the comparison in detail. The findings show us our proposed ensemble solution, a combined version according to the fuzzy cluster distribution of existing models in the literature, has better success at the predictive level than the model–parameter tuples individually. Table 9.5 Overall prediction results with each model and parameters (accuracy) Model

Parameters

Overall accuracy

Model

Parameters

Overall accuracy

XGB

md:6,lr:0.3

0.81

CB

d:8,lr:0.1

0.80

XGB

md:6,lr:0.1

0.80

LGBM

lr:0.1,ne:200

0.80

XGB

md:8,lr:0.3

0.80

LGBM

lr:0.1:ne:100

0.81

XGB

md:8,lr:0.1

0.81

LGBM

lr:0.3,ne:200

0.82

CB

d:4,lr:0.01

0.80

LGBM

lr:0.3:ne:100

0.82

CB

d:8,lr:0.01

0.79

Ensembled model

CB

d:4,lr:0.1

0.79

0.85

9 Retention Prediction in the Gaming Industry …

115

9.4 Conclusion This study aims to predict whether mobile game users will stay in the game after the first 24 h. This prediction affects the user-based marketing strategies of gaming companies in the upcoming period. It is of great importance in the investment processes of these companies, which have received severe investments recently. For this prediction, we proposed a new method combining fuzzy Logic and community learning steps. This method aims to identify one or more clusters to which they belong with fuzzy clustering logic rather than evaluate the users within a single cluster and model them separately. It is aimed to establish a more successful model by combining the results of the most successful models among the models created independently, in direct proportion to their accuracy. In this study, the game movements of the users of a mobile game company in the first 24 h and the data of the advertisements they see while downloading the game to their phones are used. First, the missing data in the available data set was filled, and then the categorical data for the modeling stage were digitized. In the next step, the entirely digitized data were scaled. In the modeling phase, the data set we have was modeled with the default parameters of various algorithms, and the results were examined. In the next step, the first step of the proposed method, fuzzy clustering, was applied, and the data was separated into three main clusters and sub-components of these clusters. Thanks to this stage, users who could belong to more than one group simultaneously were determined, and these users were evaluated in separate clusters. After the clustering process, XGBoost, Catboost, and LightGBM, the three most successful models in the first modeling process, were applied separately to the groups with various hyperparameter combinations. It was observed that different groups were different models in each group that could be more successful. The estimates of each group’s three most successful model combinations were combined with the weighted average method at the last stage. The weights were determined by the accuracy rates of the models. When the ensemble model’s success rates are compared with the success achieved when the models are used one by one, an increase in performance was observed in the results. In future work, it is aimed to apply this proposed method in different fields and different datasets. Besides, other fuzzy clustering approaches suggested in the literature will be added to the algorithm-cluster combinations in the modeling clustering stage to achieve more successful results.

References 1. Ahneman DT, Estrada JG, Lin S, Dreher SD, Doyle AG (2018) Predicting reaction performance in C-N cross-coupling using machine learning. Science 360(6385):186–190 2. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, USA

116

A. T. Tekin et al.

3. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press, Boca Raton, FL 4. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140 5. Bora DJ, Gupta AK (2014) A comparative study between fuzzy clustering algorithm and hard clustering algorithm. Int J Comput Trends Technol (IJCTT) 10(2) 6. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system, pp 785–794 7. Dorogush AV, Ershov V, Gulin A (2018) CatBoost: gradient boosting with categorical features, pp 1–7 8. Feng D, Wang W, Mangalathu S, Hu G, Wu T (2021) Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements. Eng Struct 235. https://doi.org/10.1016/J.ENGSTRUCT.2021.111979 9. Freund Y, Shapire R (1995) A decision-theoretic generalization of online learning and application to boosting. In: Proceedings of the second European conference on computational learning theory, pp 23–27 10. Freund Y, Shapire R (1999) A short introduction to boosting. J Jpn Soc Artif Intell 14(5):771– 780 11. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat, 1189–1232 12. Hu G, Kwok KCS (2020) Predicting wind pressures around circular cylinders using machine learning techniques. J Wind Eng Ind Aerodyn 198:104099 13. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T (2017) LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st international conference on neural information processing systems (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, pp 3149–3157 14. Krishnapuram R, Keller JM (1996) The possibilistic C-means algorithm: insights and recommendations. IEEE Trans Fuzzy Syst 4(3):385–393 15. Kumar M, Misra L, Shekhar G (2015) A survey in fuzzy logic: an introduction. IJSRD Int J Sci Res Dev 3(06) 16. Lee K, Jeong H-O, Lee S, Jeong W-K (2019) CPEM: accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network. Sci Rep 9(1):1–9 17. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Cam LML, Neyman J (eds) Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. University of California Press, pp 281–297 18. Mirzakhanov VE (2020) Value of fuzzy logic for data mining and machine learning: a case study. Expert Syst Appl 162. https://doi.org/10.1016/j.eswa.2020.113781 19. Nasibov E, Ordin B (2019) An incremental fuzzy algorithm for data clustering problems. J Balıkesir Üniv Fen Bilim Enst Derg 21:169–183 20. Pal NR, Pal K, Bezdek JC (1997) A mixed c-means clustering model. In: IEEE international conference fuzzy systems, pp 11–21 21. Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy cmeans clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530. https://doi.org/10.1109/tfuzz.2004.840099 22. Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) Catboost: unbiased boosting with categorical features. Adv Neural Inf Process Syst, 6638–6648 23. Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar A, Kang B (eds) AI 2006: advances in artificial intelligence. AI 2006. Lecture notes in computer science, vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_114 24. Sujamol S, Ashok S, Kumar UK (2017) Fuzzy based machine learning: a promising approach. CSI Commun Knowl Dig IT Commun 41(8):21–25 25. Triguero I, del Río S, López V, Bacardit J, Benítez JM, Herrera F (2015) ROSEFW-RF: the winner algorithm for the ECBDL’14 big data competition: an extremely imbalanced big data bioinformatics problem. Knowl Based Syst 87:69–67

9 Retention Prediction in the Gaming Industry …

117

26. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847 27. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-995 8(65)90241-X 28. Zadeh LA (1968) Fuzzy algorithms. Inf Control 12:94–102 29. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes 30. Zhou Z-H (2012) Ensemble methods: foundations and algorithms. Chapman and Hall/CRC 31. Zhou Z-H (2015) Ensemble learning. Encycl Biometr, 411–416

Chapter 10

Sentiment Analysis on Public Transportation During Covid-19: An Exploratory Study Busra Buran

Abstract Public transportation is a backbone for cities. During Covid-19, public transportation became one of the sectors that did not stop due to the transport of goods and people. As a result, it is a critical issue for sustainable mobility. Due to improving the service quality of public transportation, passenger demands need to be investigated. This study presents sentiment analysis on public transportation as an exploratory study to take into account public demands. R programming is used to extract and analyze tweets about public transportation. Data and network analysis are conducted along with the sentiment analysis. The results show that there is a positive perception of public transportation despite the pandemic. In addition, wearing a mask, the Covid-19 test, and vaccination are seen as important issues. The study shed light on public opinions and perceptions about public transportation. Also, this work presents several significant results which might be critical to consider by decision-makers and practitioners. Keywords Public transportation · Sentiment analysis · Network analysis · Covid-19 · Tweet

10.1 Introduction It is critical for public transportation to provide quality services taking into account passenger demands. In the technology era, it is straightforward to achieve it. There are many social media tools for companies to find out what are the main demands for the customers and how to increase satisfaction, such as Facebook, Instagram, Linkedin, Twitter, Youtube, Pinterest, Tumblr, TikTok this. From these, Twitter is one option to measure the perception of customers and what their priorities are. Twitter has been used effectively by both private and public companies for these issues. With the development of technology and digitization, big data studies are conducted in the literature [4, 5, 45]. Sentiment analysis is one of them that is B. Buran (B) Management Engineering Department, Management Faculty, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_10

119

120

B. Buran

preferred due to its ease of application, cost reduction, speed, and ease of analysis, effectively [29]. Sentiment analysis (SA) is used with textual data, which converts data into knowledge. There are different studies about healthcare, transportation, policy, tourism, education, agriculture, and finance for sentiment analysis in the literature [2, 9, 27, 36, 41, 43] (Garcia et al. 2018). SA comprises Natural Language Processing (NLP), computational linguistics, and machine learning (ML) [24]. The main purpose of SA is to extract people’s thoughts from textual data to measure perception. When the classification can be varied by used methods, categorization can be based on three main classes such as negative, neutral, and positive [35]. SA is widely used in the literature that is summarized in Sect. 10.2 as a literature review. One of the sectors affected by Covid-19 is public transportation. When life comes to a standstill, transportation has become more important than before due to the transport of goods and people. During the virus, people will hesitate to use public transportation. To understand perception about it, we extracted tweets that were related to public transportation and performed sentiment analysis. The major contributions of this work are as follows: • • • •

to measure the perception of public transportation during Covid-19, to understand passenger demands and monitor trends, to define priority issues for improving the service quality of public transportation, to contribute to the literature about public opinion.

The rest of the paper is organized as follows. Section 10.2 represents the literature review for sentiment analysis. The methodology is introduced with subtitles such as data collection, pre-processing, data analysis, network analysis, and sentiment analysis in Sect. 10.3. Findings and discussion are shared in Sect. 10.4. Finally, the conclusion and future works are presented in Sect. 10.5.

10.2 Literature Review on Sentiment Analysis In the healthcare domain, Srivastava and his colleagues presented a hybrid model that consisted of Naive Bayes and random forest approaches to make sentimental analysis for healthcare. With the study, they aimed to understand recent developments in the healthcare system [41]. Thomas and his colleagues surveyed brachytherapy by analyzing related tweets from patient and professional perspectives. Patient tweets were examined before and after the treatment. According to the results, when patients’ tweets have negative sentiment, professionals have neutral sentiment and focus on research [43]. During the pandemic, different studies have been conducted to find out its effect on our life. Chakraborty et al. [10] analyzed Covid-19 tweets to bring out their impact on people. They performed a fuzzy rule-based model to classify tweets as positive or negative. The study also revealed that people think the World Health Organization (WHO) failed to provide precise information for dealing with the disease. Singh et al. [38] studied the Covid-19 effect in social life using the BERT model. They

10 Sentiment Analysis on Public Transportation During Covid-19 …

121

collected two data sets. When one of them is from all over the world, the other one is from India. The results show that Indian people have relatively more positive communication on Twitter. Samuel et al. [37] aimed to examine Coronavirus-specific Tweets and analyzed them using R software. Also, they applied machine learning methods that are logistic regression and Naive Bayes. Results show that when both methods showed weaker performance for long tweets, the logistic regression method represented a reasonable accuracy of 74% with shorter tweets. From the policy side, Kinyua et al. [27] analyzed Trump’s tweets to find out the effect on DJIA and S&P stock market performance. They applied Random forest, Decision tree, and logistic regression to make a sentimental analysis. For prediction, regression analysis was performed. Results showed that tweets have a strong effect on market performance. In the education policy domain, Saini et al. [36] wondered about the behavioral response of Indian citizens to national education policy 2020. They aimed to improve the existing education system and measure the perception of education policy 2020. They indicated that policymakers would use the results of the study to correct negative emotions. Although English tweets can be analyzed with existing algorithms and packages such as R, Python, different language tweets can be examined with customized algorithms. To improve Sentiment Analysis of Arabic Tweets, One-way ANOVA is performed by Qamar and Alassaf [35]. They used Qassim University’s tweets to analyze the proposed model. Results show that the best combination is Support Vector Machine and Naïve Bayes with one-way ANOVA. Nasim and Haider [32] developed three different algorithms: K-Means, Bisecting K-Means, and Affinity Propagation algorithms to make sentiment analysis for Urdu tweets. Ayata et al. [3] developed an algorithm for sentiment analysis using Support Vector Machine (SVM) and decision tree methods. Soumya & Pramod looked into Malayalam tweets using machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) [40]. Ozturk and Ayvaz [33] analyzed related refugee tweets to find out public opinions about Syrian refugees. They examined both English and Turkish tweets and categorized them into three classes positive, negative, and neutral. The authors indicated that Turkish tweets have a higher ratio of positive sentiment than English ones with 35%. Different machine learning techniques are conducted for sentiment analysis in the literature. Agarwal et al. [1] discussed using unigram, a feature-based, and a tree kernel-based model to examine sentiment analysis on Twitter data. They compared the methods to each other according to their performance. Lal et al. [21] examined crime tweets to identify needed policy attention. Tweets classified into crime and not-crime class. The authors indicated that the Random forest classifier gave the best results with an accuracy of 98.1%. García-Díaz et al. [17] investigated Sentiment Analysis on Spanish Financial Tweets using machine learning methods. They applied 48, Bayes-Net, and Sequential Minimal Optimization (SMO) to compare results. According to the results, SMO gives the best accuracy with an F-measure of 73.2%. Kharde and Sonawane [25] reviewed a survey of techniques for sentiment analysis of Twitter data. They applied various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine. They used Stanford University’s

122

B. Buran

dataset to perform methods and get comparison results. Su et al. [42] developed a sentiment analysis approach using a supervised machine learning technique to improve reliability when analyzing social media content. The method assures to classify individual content and also estimated proportions of the content categories of interest. When Fuzzy logic is used in many areas, it is also used in sentiment analysis due to its vagueness [10, 36]. Kandasamy et al. [24] is preferred to Multi Refined Neutrosophic Sets (MRNS), which have seven memberships, namely strong positive, positive, positive indeterminate, indeterminate, negative indeterminate, negative and strong negative. The comparative study of the sentiment analysis is carried out with the three methods that are Single Valued Neutrosophic Set (SVNS), Triple Refined Indeterminate Neutrosophic Set (TRINS), and Multi Refined Neutrosophic Sets (MRNS). In the tourism sector, halal tourism is a growing segment. Sulaiman and his colleagues extracted Halal tourism tweets for content and sentiment analysis. According to the study, Japan is seen as a popular location for Halal tourism [2]. Tweet analysis is not used only in the private sector but also is utilized by the public sector like transportation planning, citizen engagement, service quality, behavior issues, and information dissemination, etc. To find out the effectiveness of information dissemination, Kocatepe et al. [28] examined tweets of the Florida Department of Transportation (FDOT) within three distinct. The authors advised that authorities should share tweets regarding related factors like time to post to increase the effectiveness and reach of their social media accounts. Cases & Delmelle used Twitter data to measure the perception BRT system in the city of Cali, Colombia. According to the results, safety, the infrastructure of the system, and behavioral topics are raised issues [9]. Cottrill et al. [12] did a study about analyzing social media strategies for transport information management during a large event. Glasgow Commonwealth Games (2014) case study was conducted for exploration. Results demonstrated that twitter data is Twitter beneficial to both service providers and customers. Additionally, in 2012 during the London Olympics Transport for London (TfL) managed travel demand using Twitter data [8]. Lock and Pettit (2020) used Twitter data for transportation planning. The study forms a case study collecting data from Sydney, Australia. Authors collected transport performance big data and surveyed data together. Thus, they provided a holistic study about apprised of the current opportunities and challenges for transport planning. During natural disasters like earthquakes, Hara [18] examined tweets using the support vector machine to bring into the open decision-making behavior for choosing the mode of transportation. The author indicated that social media data provides an effective tool for travel behavior analysis. Siyam et al. [39] examined government tweets to identify and predict citizen engagement. Dubai government tweets analyzed with techniques of Random Forest and Adaboost. Results compared to Naïve Bayes and K-Nearest Neighbor. According to the results, Random Forest and Adaboost produced more accurate predictions. Also, the authors showed that government tweets help to increase citizen engagement. Welch and Widita [47] reviewed big data in public transportation in terms of

10 Sentiment Analysis on Public Transportation During Covid-19 …

123

sources and method perspectives. Results indicated that big data has largely been used to find out travel behavior and to evaluate public transit service quality. In summary, the literature review on sentiment analysis is summarized in Table 10.1 with domains such as healthcare, Covid-19, policy, language, social media, fuzzy logic, tourism, and public.

10.3 Methodological Framework This section provides methodology steps with explanations that are summarized in Fig. 10.1.

10.3.1 Dataset Collection Twitter provides an effective tool with Twitter Application Programming Interface (API) to examine shared tweets. There are different programming languages to extract data from Twitter-like Python [11], Jason [13], C-family, R [23]. I preferred to use R due to its user-friendly interface. In the R programming language, tweets are extracted using the ‘rtweet’ package. Including public and transportation keywords, tweets were collected from Twitter during May 2021. After the retrieving process, the dataset contained 3.443 tweets. Before sentiment analysis, the dataset is cleaned with pre-processing.

10.3.2 Pre-processing Pre-processing is the tweet cleaning step. Tweets may include emojis, punctuation, URLs, special characters, etc. They prevent making text analysis cleanly. Also, this step contains the removal of numbers, whitespaces, and stopwords that includes the, and, of, or, am, is, are, etc. Converting letters from upper to lower and stemming is applied in this stage. Due to the increasing performance of tweet analysis, the below steps are performed, respectively [2, 35]. • • • • • • • •

Removal of Emojis. Removal of Punctuations. Removal of URLs. Removal of Stopwords. Removal of Numbers. Removal of Whitespaces. Removal of Stemming. Removal of Tolover.

124

B. Buran

Table 10.1 Literature review Domain

Year

Authors

Method

Healthcare

2018

Thomas et al.

Consensus Coding

2019

Srivastava et al.

Naive Bayes and Random Forest

Covid-19

2020

Chakraborty et al.

Fuzzy Rule-Based Model

2021

Singh et al.

BERT Model

Policy

Language

Social media

Fuzzy logic

2020

Samuel et al.

Logistic Regression and Naive Bayes

2021

Kinyua et al.

Random Forest, Decision Tree, and Logistic Regression

2017

Ayata et al.

Support Vector Machine and Decision Tree Methods

2018

Ozturk and Ayvaz

Developed a Sentiment Lexicon

2020

Qamar et al.

Support Vector Machine and Naive Bayes with one-way ANOVA

2020

Nasim and Haider

K-Means, Bisecting K-Means, and Affinity Propagation Algorithms

2020

Soumya and Pramod

Naive Bayes, Support Vector Machine, and Random Forest

2011

Agarwal et al.

Unigram, a Feature-based, and a Tree Kernel-based Model

2016

Kharde and Sonawane

Naive Bayes, Max Entropy, and Support Vector Machine

2017

Su et al.

Hybrid Method

2018

Garcia et al.

48, Bayes-Net, and Sequential Minimal Optimization

2020

Lal et al.

Naive Bayesian, Random Forest, J48 and ZeroR

2020

Kandasamy et al.

Single Valued Neutrosophic Set, Triple Refined Indeterminate Neutrosophic Set, and Multi Refined Neutrosophic Sets (MRNS)

Tourism

2020

Ainin et al.

MD5 Hash Algorithm

Public

2015

Hara

Behaviour Inference Method and Behavioural Model

2017

Cottrill et al.

a modified version of the approach described in Naaman et al. [31]

2017

Casas and Delmelle

Methodology proposed by Hargittai [19]

2018

Kocatepe et al.

Machine Learning and Naive Bayesian Techniques

2020

Lock and Pettit

Machine Learning and Natural Language Processing

2020

Siyam et al.

Random Forest, Naive Bayes, and K-Nearest Neighbor

10 Sentiment Analysis on Public Transportation During Covid-19 …

125

Fig. 10.1 Flowchart of the proposed methodology

10.3.3 Data Analysis Data analysis comprises four main steps for this study that are post day, post hour, post location, and word cloud. Data analysis allows researchers to understand which hour, which day and which location is mostly used for tweets. Also, the word cloud is mostly preferred to show frequently used words [2]. For word cloud representation, the word cloud package is used in R programming.

126

B. Buran

10.3.4 Network Analysis A network system includes nodes, edges, and weights of edges that connect each other [46]. Elements of network analysis are as follows [26]. Node is an individual word that can be a verb, noun, and adjective extracted from a tweet post. An edge connects two words in a sentence. It means that there is an edge between two words. Edge weight, Edges are often associated with different weights due to their strength, intensity, or capacity [6]. Using the network approach, words are represented as a network to describe the relationship between them. For word network analysis, the i-graph package is used in R programming.

10.3.5 Sentiment Analysis Although there are different packages for sentiment analysis in R like syuzhet, RSentiment, Sentimentr, SentimentAnalysis, and Meanr package is mostly preferred [30]. Using the Syuzhet package, which was developed by Jockers [16], sentiment analysis is performed. According to the scores, there are ten classes such as anger, anticipation, disgust, fear, joy, negative, positive, sadness, surprise, and trust.

10.4 Results and Discussion Data were extracted with public and transportation keywords from Twitter in May 2021. The data set included 3.443 tweets, including user_id, status_id, created_at, screen_name, text, source, which were shared in English. According to Fig. 10.2, which represents the hour of tweets is seen starts to increase from 10 am to 6 pm. When word cloud was performed, that is represented in Fig. 10.3. Public transportation words dominated presentation due to keywords of search. The highlights can be summarized as follows. Bus, train, and plane are seen as public transport. Public transportation can be accepted as a key issue for schools, hospitals, and works. Instead of public transportation, private cars and bikes can be preferred due to safe travel. To protect from Covid-19 wearing masks, tests, and vaccination issues are prominent in public transportation and indoor places such as restaurants, airports, prisons, facilities. Each word represents a node. When two words are connected, they are called a biagram. The weight of the edge expresses the number of times the biagram comes insight into the network. For network analysis, Fig. 10.4 comes up. According to Fig. 10.4, public and transportation have a higher appearance in the corpus due to keywords of search. The difference of word network analysis from the word cloud is to give detailed information due to showing the relationship of links between words.

10 Sentiment Analysis on Public Transportation During Covid-19 …

Fig. 10.2 The trend of public transportation tweets according to the hour

Fig. 10.3 Word cloud for public transportation tweets

127

128

B. Buran

Fig. 10.4 Network analysis with 30 weight threshold taking into account the number of appearances

When public word from place perspective includes restaurants, grocery, and store, public from transportation perspective comprises plane, bus, and trains. Wearing a mask is seen as a requirement, mandate, or need in public areas. Fully vaccinated people come up in the network. Public health is seen as a long-term issue. In R programming, there are ten classes based on scores for sentiment analysis. After the pre-processing, sentiment analysis was performed, which is presented in Fig. 10.5. According to the results, there was a positive perception of public transportation for 3.443 tweets. Public transportation is an indispensable part of the cities. During the pandemic, it has been more important for mobility. Although there are Covid-19 studies about transportation in the literature, such as [15, 20, 34, 44, 48], there is a lack of public opinion about it. To measure public opinion, sentiment analysis is the preferred approach [9, 28, 36, 39]. There are studies in the literature that are about public transportation during Covid19, showing that if the people travel according to the rules which are wearing masks,

10 Sentiment Analysis on Public Transportation During Covid-19 …

129

Fig. 10.5 Sentiment analysis for public transportation

social distance and [7, 14, 44], it is safe. In summary, this paper introduces the following findings. • There was a positive perception of public transportation for retrieving 3.443 tweets. • People will do not hesitate to use public transportation during the pandemic when the rules are followed, e.g., wearing the mask, social distance. • To satisfy public demands, safety travel is seen as a critical topic. • Wearing masks, tests, and vaccination are seen as important issues. While word of crowded, ill, covid, and fully yield negative perception, vaccinated, mask, safe, and safety cause positive perception for sentiment analysis. The frequency of use by word cloud affects the sentiment score of the analysis. Searching with keywords that are public and transportation has a positive perception for retrieving the tweets.

10.5 Conclusion, Limitation and Future Works This paper presents an exploratory study analyzing public transportation tweets via Twitter. The extracted data set is examined according to the day, hour, and location of tweets. For detailed analysis, word cloud, sentiment, and network analysis are conducted.

130

B. Buran

To understand people’s opinions during the pandemic, sentiment and network analysis are applied during the pandemic. The results show that people have positive thoughts about public transport. Their expectations are wearing masks during travel, and also, tests and vaccination are seen as important issues for protection. The study contributes to both academics and practitioners for increasing the service quality of public transportation. From the literature side, it fills the gap about public opinions for public transportation during Covid-19. From the application side, it allows decisionmakers to understand public demands about transportation. The number of extracted tweets can be seen as a limitation of this study. To generalize results, more tweets are needed. For solving this problem, customized Twitter accounts will be built up. For future studies, sentiment analysis will be improved with different machine learning methods. The data set will be enlarged, taking into account different keywords.

References 1. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau RJ (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on language in social media (LSM 2011), pp 30–38 2. Ainin S, Feizollah A, Anuar NB, Abdullah NA (2020) Sentiment analyses of multilingual tweets on halal tourism. Tourism Manage Perspect 34:100658 3. Ayata D, Saraclar M, Ozgur A (2017) Turkish tweet sentiment analysis with word embedding and machine learning. In: 2017 25th signal processing and communications applications conference (SIU). IEEE, pp 1–4 4. Al-Barashdi H, Al-Karousi R (2019) Big data in academic libraries: literature review and future research directions. J Inf Stud Technol (JIS&T) 2018(2):13 5. Baum J, Laroque C, Oeser B, Skoogh A, Subramaniyan M (2018) Applications of big data analytics and related technologies in maintenance—literature-based research. Machines 6(4):54 6. Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci 101(11):3747–3752 7. Betkier I (2020) Safety of urban transport users during the Covid-19 pandemic. Eur Res Stud 23:99–115 8. Burnap P, Housley W, Morgan J, Sloan L, Williams ML, Avis N et al (2012) Working paper 153: social media analysis, Twitter and the London Olympics 2012 9. Casas I, Delmelle EC (2017) Tweeting about public transit—gleaning public perceptions from a social media microblog. Case Stud Transp Policy 5(4):634–642 10. Chakraborty K, Bhatia S, Bhattacharyya S, Platos J, Bag R, Hassanien AE (2020) Sentiment analysis of COVID-19 tweets by deep learning classifiers—a study to show how popularity is affecting accuracy in social media. Appl Soft Comput 97:106754 11. Chandio MM (2019) Twıtter sentiments analysis. Doctoral Dissertation, Near East Unıversıty 12. Cottrill C, Gault P, Yeboah G, Nelson JD, Anable J, Budd T (2017) Tweeting transit: an examination of social media strategies for transport information management during a large event. Transp Res Part C: Emerg Technol 77:421–432 13. Cuesta Á, Barrero DF, R-Moreno MD (2014) A framework for massive Twitter data extraction and analysis. Malays J Comput Sci 27(1):50–67 14. Darsena D, Gelli G, Iudice I, Verde F (2020) Safe and reliable public transportation systems (SALUTARY) in the COVID-19 pandemic. arXiv preprint arXiv:2009.12619

10 Sentiment Analysis on Public Transportation During Covid-19 …

131

15. Du Z, Wang L, Cauchemez S, Xu X, Wang X, Cowling BJ, Meyers LA (2020) Risk for transportation of coronavirus disease from Wuhan to other cities in China. Emerg Infect Dis 26(5):1049 16. Jockers M (2017) Package ‘syuzhet’. https://cran.r-project.org/web/packages/syuzhet 17. García-Díaz JA, Salas-Zárate MP, Hernández-Alcaraz ML, Valencia-García R, Gómez-Berbís JM (2018) Machine learning based sentiment analysis on Spanish financial tweets. In: World conference on information systems and technologies. Springer, Cham, pp 305–311 18. Hara Y (2015) Behaviour analysis using tweet data and geo-tag data in a natural disaster. Transp Res Procedia 11:399–412 19. Hargittai E (2003) Blog types. Retrieved 12 Mar 2007 20. Huang J, Wang H, Fan M, Zhuo A, Sun Y, Li Y (2020) Understanding the impact of the COVID19 pandemic on transportation-related behaviors with human mobility data. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3443–3450 21. Lal S, Tiwari L, Ranjan R, Verma A, Sardana N, Mourya R (2020) Analysis and classification of crime tweets. Procedia Comput Sci 167:1911–1919 22. Lock O, Pettit C (2020) Social media as passive geo-participation in transportation planning– how effective are topic modeling & sentiment analysis in comparison with citizen surveys? Geo-spatial Inf Sci 23(4):275–292 23. Lund BD (2020) Assessing library topics using sentiment analysis in R: a discussion and code sample. Public Serv Q 16(2):112–123 24. Kandasamy I, Vasantha WB, Obbineni JM, Smarandache F (2020) Sentiment analysis of tweets using refined neutrosophic sets. Comput Ind 115:103180 25. Kharde V, Sonawane P (2016) Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971 26. Kim J, Jeong D, Choi D, Park E (2020) Exploring public perceptions of renewable energy: evidence from a word network model in social network services. Energ Strat Rev 32:100552 27. Kinyua JK, Mutigwe C, Cushing DJ, Poggi M (2021) An analysis of the impact of President Trump’s tweets on the DJIA and S&P 500 using machine learning and sentiment analysis. J Behav Exp Financ 29:100447 28. Kocatepe A, Ulak MB, Lores J, Ozguven EE, Yazici A (2018) Exploring the reach of departments of transportation tweets: what drives public engagement? Case Stud Transp Policy 6(4):683–694 29. Mazidi A, Damghanijazi E (2017) A sentiment analysis approach using effective feature reduction method. Int J Comput Appl 975:8887 30. Misuraca M, Forciniti A, Scepi G, Spano M (2020) Sentiment analysis for education with R: packages, methods and practical applications. arXiv preprint arXiv:2005.12840 31. Naaman M, Boase J, Lai CH (2010) Is it really about me? Message content in social awareness streams. In: Proceedings of the 2010 ACM conference on computer supported cooperative work, pp 189–192 32. Nasim Z, Haider S (2020) Cluster analysis of Urdu tweets. J King Saud Univ-Comput Inf Sci 33. Ozturk N, Ayvaz S (2018) Sentiment analysis on Twitter: a text mining approach to the Syrian refugee crisis. Telematics Inform 35(1):136–147 34. Parr S, Wolshon B, Renne J, Murray-Tuite P, Kim K (2020) Traffic impacts of the COVID19 pandemic: statewide analysis of social separation and activity restriction. Nat Hazard Rev 21(3):04020025 35. Qamar AM, Alassaf M (2020) Improving sentiment analysis of Arabic Tweets by one-way ANOVA. J King Saud Univ-Comput Inf Sci 36. Saini M, Singh M, Kaur M, Kaur M (2021) Analysing the tweets to examine the behavioural response of Indian citizens over the approval of national education policy 2020. Int J Educ Dev 82:102356 37. Samuel J, Ali GG, Rahman M, Esawi E, Samuel Y (2020) Covid-19 public sentiment insights and machine learning for tweets classification. Information 11(6):314

132

B. Buran

38. Singh M, Jakhar AK, Pandey S (2021) Sentiment analysis on the impact of coronavirus in social life using the BERT model. Soc Netw Anal Min 11(1):1–11 39. Siyam N, Alqaryouti O, Abdallah S (2020) Mining government tweets to identify and predict citizens engagement. Technol Soc 60:101211 40. Soumya S, Pramod KV (2020) Sentiment analysis of malayalam tweets using machine learning techniques. ICT Express 6(4):300–305 41. Srivastava A, Singh V, Drall GS (2019) Sentiment analysis of twitter data: a hybrid approach. Int J Healthc Inf Syst Inf (IJHISI) 14(2):1–16 42. Su LYF, Cacciatore MA, Liang X, Brossard D, Scheufele DA, Xenos MA (2017) Analyzing public sentiments online: combining human-and computer-based content analysis. Inf Commun Soc 20(3):406–427 43. Thomas J, Prabhu AV, Heron DE, Beriwal S (2018) Twitter and brachytherapy: an analysis of “tweets” over six years by patients and health care professionals. Brachytherapy 17(6):1004– 1010 44. Tirachini A, Cats O (2020) COVID-19 and public transportation: current assessment, prospects, and research needs. J Public Transp 22(1):1 45. Vanani IR, Majidian S (2019) Literature review on big data analytics methods. Soc Media Mach Learn 46. Wasserman S, Faust K (1994) Social network analysis: methods and applications 47. Welch TF, Widita A (2019) Big data in public transportation: a review of sources and methods. Transp Rev 39(6):795–818 48. Zheng R, Xu Y, Wang W, Ning G, Bi Y (2020) Spatial transmission of COVID-19 via public and private transportation in China. Travel Med Infect Dis 34:101626

Chapter 11

Car Rental Prediction Using Segmented and Unsegmented Customer Data Basar Oztaysi, Aydeniz Isik, and Elmira Farrokhizadeh

Abstract Car rental is one of the most important industries worldwide in terms of tourism and professional travel. However, due to high initial costs and inadequate planning brings causes capacity problems which lead to potential revenue loss. Considering the high volatility in tourism and the high costs of facilitating a car rental location, forecast models and decision support systems are gaining importance. In this study, we aim to design a forecast model using different algorithms to estimate the upcoming car rental demands. To this end, two approaches are used, first holistic demand data is used to make a forecast, and in the second approach, customers are divided into segments, and segmented demand is used in the study. The results show that ARIMA and Holtwinters techniques provide the best results for the study. The results of the study show that a short-term forecast can be beneficial in the industry for making car supply decisions. Keywords Time series analysis · Car rental · Demand forecast

11.1 Introduction In the past decades, the car rental industry had a significant role in the business area and became a popular research area. Car rental or car hire agencies are agencies that rent automobiles for a short period at a certain cost. This service is often organized with many local branches, generally located near airports or busy areas in the city, and complemented by a website to allow online reservations. Factors such as the rise in the trend of on-demand transportation services and the low rate of car ownership among millennials drive the growth of the car rental market [24]. So predicting the demand for the car rental industry has a large impact on revenue management. Trying B. Oztaysi (B) · E. Farrokhizadeh Industrial Engineering Department, Management Faculty, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] A. Isik Naryaz Yazilim, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_11

133

134

B. Oztaysi et al.

to keep a good customer satisfied is better than finding new customers [20]. As a result, recognizing customers and anticipating their needs is critical to the survival of companies in today’s competitive world. Naryaz Software has been developing software solutions for the car rental industry since 1986 and is Turkey’s leader in car rental software. Naryaz provides Rent-ACar Software and Fleet Leasing Software services to many companies in the car rental sector, from small and medium-sized car rental companies to large and corporate operational leasing companies. Naryaz provides services that can be accessed anywhere and anytime with its cloud-based, easy-to-use, responsive, and mobilecompatible interface and advanced technology platform. Naryaz has developed innovative software solutions that can make cost calculations and second-hand forecasting by using Machine Learning in previous R&D projects. In this study, we design the forecast model for Naryaz Software to estimate the weekly demand of customers. For this goal, we suggested two states of the model. In the first one, we predict the demand based on different time series prediction methods. And the second state that the first segments the customers with the RFM method into four categories: Premium, Gold, Risky, and Inactive customers and then predicts the weekly demands based on historical data. Based on experts’ opinions, annual demands are divided into winter and summer data. Generally, we focus on short-term prediction for a car rental using different time series approaches such as Holt_Winter, ARIMA, TBATS, Artificial Neural Network (ANN), Support Vector Machine (SVM). Accuracy and results of models are examined with RMSE and MAPE methods. Results show the efficiency of ARIMA and Holt_Winter methods. The structure of the study is organized as follows; the first part includes an introduction that describes the subject of study. Segmentation and prediction methods are defined and investigated in the methodology section. In the problem definition section, the case study of the model is defined, and the results are presented. Finally, the directions for future research and conclusions are provided in the conclusion part.

11.2 Methodology In this study, we apply customer segmentation to classify our customers in car rental firms and then try to predict the weekly demand of the customer in each segment by different prediction methods. Then, to compare the accuracy of the different prediction methods, we use comparison indicators.

11.2.1 Segmentation Segmentation is an efficient method that categorizes the enterprise’s target market into groups of potential customers with similar behaviors and needs [18]. Customer segmentation, introduced by Smith in 1991, has been applied in vast areas such as

11 Car Rental Prediction Using Segmented and Unsegmented …

135

retail, wholesales, e-business, small and medium enterprise, financial service, health care service, IT, etc. [8]. Due to today’s competitive world, companies need to know more about their environment to survive. Segmentation helps enterprises to have a well understanding of the requirement and interests of the customers and know the potential customers. They can customize marketing plans, identify trends, plan product development, advertising campaigns, and deliver relevant products using segmentation [6]. So it can increase the profit of enterprises by determining the customer’s wishes. Due to the Pareto principle, 20% of customers have more profit to the company than the rest [19]. So recognizing these customers and wishes is better than finding new customers. One of the critical steps in customer segmentation is the data collection step. Generally, data in customer segmentation are classified into two main groups: 1— Internal data, which include demographic data and purchase history, and 2—External data, which contain cookies and server logs [18]. Based on past researches and articles, customer segmentation can be categorized into four main classes: 1— Simple technique, 2—RFM technique, 3—Target technique, and 4—Unsupervised technique, as you can see in Fig. 11.1 [18]. RFM is one of the famous and powerful segmentation techniques to explain customer’s purchase behavior that compares three main components: Recency (when was the last time that the customer makes a purchase?), Frequency (How many times Fig. 11.1 Classification of customer segmentation

136

B. Oztaysi et al.

Table 11.1 Customer description based on the score

Score

Description

5

Potential

4

Promising

3

Can’t lose them

2

At-risk

1

Lost

did the customer purchase?), and Monetary (How much money did the customer spend?). Recency is known as the most important component in the RFM method [22]. Based on the meaning of these three factors, so the most valuable customer must have the highest frequency and monetary and has the lowest recency [8]. Therefore, RFM can help organizations recognize valuable customers and provide an effective marketing strategy for profit organizations and non-profit organizations such as government agencies [22]. The structure of the RFM technique is introduced as follow [6]: Step 1:

Step 2:

Step 3:

Sort the dataset based on each Recency, Frequency, and Monetary component and take the score 5 for highest quantile to 1 for lowest quantile. The scores can be described, as given in Table 11.1. Gather every 3 scores in one cell. Finally, all customers are represented the same as 555 (the most valuable), 554, 553, …, 112, 111 (the worst customer group). Customers can be grouped into segments based on the assigned RFM behavior scores, and their profitability can be analyzed. Approximately 88% of studies used clustering, and the rest used CLV (Customer Life Value) to segment the customers. Segmentation based on clustering has more accuracy than the CLV methods [12]. The CLV value is a single RFM score counted by multiplying each RFM value and its weight. In addition to using equal weight, some researchers used other methods such as the Analytical Hierarchy Process (AHP), Fuzzy AHP, Fuzzy Analytical Network Process (F-ANP), and Entropy method [8]. In addition to the original RFM, in some papers, due to the nature of the application area, RFM variables can be changed, or other variables are added to a simple RFM model [17]. For example, the RFM method was modified in the insurance sector by adding the factors related to risk, diversity of types of insurance purchased, time, number, and amount of expenses of an insurance claim [8]. In other studies, M (Monetary) factor was replaced by C (College) for library users segmentation [23]. One of the classic RFM method disadvantages is that customers’ personal and demographic information did not consider. Some studies added these critical factors to their RFM model [8].

11 Car Rental Prediction Using Segmented and Unsegmented …

137

11.2.2 Prediction Prediction systematically estimates the future value of one unknown variable based on a historical dataset. Due to the unknown nature of forecasting value, there are many different possible values that the predictor must select the best and most probable one [14]. There are various prediction methods, but finding the best and fit method for the relative dataset is critical. Different prediction models exist in literature which is categorized in Fig. 11.2. In this study, seven models were practically used. An analytical description of the models can be found below. Exponential Smoothing Exponential smoothing was introduced with Robert G. Brown, who worked as an OR analyst in US Navy during world war II. During the 1950s, Brown extended simple exponential smoothing to discrete data [10]. Exponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component. The exponential smoothing methods, widely used for demand forecasting in business, are relatively simple but robust approaches to predicting [9]. Three basic variations of exponential smoothing are commonly used: simple exponential smoothing, double exponential smoothing, and Holt–Winters’ method. Simple exponential smoothing is used when data has no trend and no seasonal pattern. This method uses weighted moving averages with exponentially decreasing weights [4]. The single exponential smoothing formula is given by Eq. 11.1: st = αxt + (1 − α)st−1 = st−1 + α(xt − st−1 )

(11.1)

where st is smoothed statistic (the simple weighted average of current observation xt ), st−1 is a previous smoothed statistic, α is the smoothing factor (0 < α < 1), and t is the time period. Double exponential smoothing or Holt’s Trend corrected exponential sothing is used when data has linear trend and no seasonality [11]. Double exponential smoothing is formulated as follow:

Fig. 11.2 Classification of prediction methods [21]

138

B. Oztaysi et al.

s1 = x 1

(11.2)

b1 = x1 − x0

(11.3)

st = αxt + (1 − α)(st−1 + bt−1 )

for t > 1

(11.4)

βt = β(st − st−1 ) + (1 − β)bt−1

for t > 1

(11.5)

where st is smoothed statistic (simple weighted average of current observation xt ), st−1 is previous smoothing statistic, α is the smoothing factor (0 < α < 1), t is the time period, bt is the best estimate of the trend at time t, and β is the trend smoothing factor (0 < β < 1). Holt-Winters method is used when data has both linear trend and seasonality factors. ˙In this method, exponential smoothing is applied three times, so it is also called triple exponential smoothing. This technique is one of the exponential smoothing methods that decrease the fluctuations in data to provide a transparent perspective of time series data. This method has three smoothing factors to update the smoothing data in time t such α, β, and γ . All of these factors must be between 0 and 1 [7]. Holt-Winter method formulas are shown as follows: s0 = x 0 st = α

xt + (1 − α)(st−1 + bt−1 ) ct−s

bt = β(st − st−1 ) + (1 − β)bt−1

(11.6) (11.7) (11.8)

xt + (1 − γ )ct−s st

(11.9)

Ft = (st−1 + bt−1 )ct−1

(11.10)

Ft+m = (st + bt m)ct−s+m

(11.11)

ct = γ

where st is smoothed statistic (simple weighted average of current observation xt ), st−1 is previous smoothing statistic, α is the smoothing factor (0 < α < 1), t is the time period, bt is the best estimate of the Trend at time t, β is Trend smoothing factor (0 < β < 1), ct is the sequence of seasonal correction factor at time t, γ is seasonal change smoothing factor (0 < γ < 1), Ft is forecast value of a period ahead, Ft+m is the monthly forecasting time period, m is the forecast period, and s is the seasonal duration.

11 Car Rental Prediction Using Segmented and Unsegmented …

139

11.2.3 ARIMA ARIMA is one of the popular methods of prediction introduced by Box and Jenkins in 1976 [3], and it is used for forecasting data that has stationary. It means that the trend and seasonality of data must be eliminated before using the ARIMA method. The other name of the ARIMA method is the Box-Jenkins method. ARIMA has three main parts: AR (refers to the autoregressive), I (refers to integral), and MA (refers to moving average). AR is the model that uses a dependent relationship between an observation and some number of lagged observations. I explain the difference of raw observation to make the stationery. MA is a model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations [13]. The generalized model of the Box-Jenkins model is represented as ARIMA (p, d, q), where p shows the number of lag observations, d shows the degree of differencing, and q shows the size of the moving average window. The variable’s future value in the ARIMA model has a linear combination of past values and past errors. This relation is represented as follows [1]: Yt = ϕ0 + ϕ1 Yt−1 + ϕ2 Yt−2 + . . . + ϕ p Yt− p + εt − θ1 εt−1 − θ2 εt−2 − . . . − θq εt−q (11.12) where Yt represents the actual value in time t, εt explains the random error at time t, ϕi and θ j are the coefficients, p refers to the AR, and q refers to the MA.

11.2.4 TBATS TBATS is one of the exponential smoothing methods suggested by Hyndman et al. handles complex seasonalities. It is a strong method that can satisfy all requirements (Bajpai et al. n.d.). This model is preferable when the seasonality changes over time. TBATS is a short form of key features such as Trigonometric seasonality, Box-Cox transformation, ARIMA errors, Trend, and Seasonal components. TBATS is more robust against increasing forecast periods than other forecasting models (such as ARIMA) (Bajpai et al. n.d.).

11.2.5 Support Vector Machine Support vector machine is one machine learning method that learns from historical datasets to assign objects to related sets [16]. SVM is quadratic mathematical programming that finds the global optimal value and, based on Vapnik–Chervonenkis theory, has a tradeoff between minimum training set error and margin. It has been successfully applied in various applications such as decision-making,

140

B. Oztaysi et al.

forecasting malaria transmission, pattern classification, image recognition, medical diagnosis, and text analytics [5]. C (penalty parameter) and γ (smoothing parameter) have a significant role in the SVM method. C parameter has sustained the balance between the minimization of fitting error and model complexity, and γ is concluded the nonlinear mapping from the input space to the high-dimensional feature space. SVM model loses its efficiency if the value of C and γ have a distance from a global optimum point. Many researches study the proper value for the c and γ but more precisely, the search for two parameters is limited to 1.215 ≤ c ≤ 215 and1.25 ≤ γ ≤ 25 . SVM can be divided into two class of linear and nonlinear. Suppose that T = {(x1 , y1 ), (x2 , y2 ), . . . , (xl , yl )}, yi ∈ {−1, 1} and xi ∈ R d , then linear Support Vector Machine is formulated as follow [5]: l Σ 1 Min |w|2 + C εi 2 (i=1)

( ) Subject to: yi w T xi + b ≥ 1 − εi

(11.13)

∀i = 1, 2, . . . , l

(11.14)

where w is a normal vector, C is the penalty parameter, l denotes the number of elements in the train set, εi states positive slack variables, yi is a designed class or label, xi is the data point, and b is the scalar quantity. Equation 11.13 can be transformed to Eq. 11.15 by its unconstrained dual form: ⎧ l ⎨Σ

⎫ l ⎬ 1 Σ Max αi − αi α j yi y j xi x j ⎩ ⎭ 2 i, j=1 i=1 0 ≤ αi ≤ C

∀i = 1, 2, . . . , l,

l Σ

αi yi = 0

(11.15)

(11.16)

i=1

Equation 11.15 is quadratic programming and can solve by quadratic programming techniques and the Karush–Kuhn–Tucker condition. The results can be represented as follow: b=

Nsv 1 Σ (wxi − yi ) Nsv i=1

(11.17)

W expressed as a linear combination of the training vectors, and the b can be expressed as the average of all support vectors where Nsv is the number of support vectors.

11 Car Rental Prediction Using Segmented and Unsegmented …

141

11.2.6 Artificial Neural Networks An artificial neural network (ANN) is a machine learning method that simulates the human brain to analyze and process data. It has the self-learning ability to solve problems that can not solve by human or statistical standards. The number of train data set has a significant impact on the efficiency of the ANN model. This model is a powerful method to solve the problems with the larger train dataset and a complex nonlinear relationship between input and output [15]. In forward ANN, unlike the backward ANN, the information is transformed from one layer to the next, and the weights of connections are updated to minimize errors in each run. If all nodes in one layer are connected to all nodes in the next one, that model is called Fully Connected ANN. Each Artificial Neural Network is a network of the input layer, output layer, nods, and optionally hidden layer. One of the popular algorithms to calculate the weights is the backpropagation algorithm. In the prediction case of ANN, input layers must be equal to the entered variables. The number of layers and nodes play an important role in the ANN method. Adding too many layers and nodes can cause overfitting [14].

11.2.7 Comparison of the Methods To evaluate the accuracy and validity of the prediction methods, different statistical indicators such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (RMSE), Directional Accuracy (DA), and Relative Root Mean Squared Error (RRMSE) which are formulated as follows: ⎤ ⎤ m 1 Σ ⎤⎤ (Yt − Yˆt ) ⎤⎤ (11.18) M AP E = ⎤ ⎤ ⎤ ⎤ m Yt (t=1)

⎡ ⎤ m ⎛ ⎞2 ⎤1 Σ Yt − Yˆt R M S E = √ m t=1 / RRMSE = DA =

1 m

Σm ⎛ t=1 1 m

Yt − Yˆt

Σm t=1

(11.19)

⎞2

Yˆt

m 1 Σ at × 100% m (t=1)

× 100

(11.20)

(11.21)

142

B. Oztaysi et al. Δ

where m denotes the number of samples in test data, Yt and Y t represent respectively the actual and predicted values at time t, and at is a binary parameter that if ⎛ ⎞ (Yt+1 − Yt ) Y t+1 − Yt ≥ 0, then at is equal to 1 and otherwise is equal to 0. Δ

11.3 Real-World Case Study In this study, we focus on the short-term prediction of car rental data using the time series approach.

11.3.1 Problem Definition NarYaz Software supplies software solutions to car rental companies. In this context, it has been determined that an important development area for car rental companies is vehicle planning. By adding an estimation module to the software, it has developed, the company aims for the search booking companies to make accurate demand forecasts and make the proper vehicle planning accordingly. This study includes forecasting studies carried out in this context. In the first step of the study, the data structures available for the forecasting study are examined. It is decided that the most appropriate data that can be used for this purpose is the previous period’s demand data. After the initial forecasting studies were examined, it was observed that the forecasting errors were very high. Analyzing the data with industry experts, the annual demand data was divided into Summer Term and Winter Term. After that, customer segments were created by adapting the RFM method to the car rental sector. At the end of the segmentation study, four customer segments were created, these are: (a) (b) (c) (d)

Premium Customers: The customers with the highest monetary value frequently rent a car with high costs. Gold Customers: The customers, which have high value for the company. Risky Customers: The customers are frequent buyers but have not rent-a-car recently. Inactive Customer: The customers who are not renting a car for a long time.

The forecasting study is carried out with two different approaches. In the first approach, all demand data is used for forecasting. In the second approach, the demands of each customer segment are extracted, and separate forecasting models are created for each, and demand forecasting is realized by combining these forecasts. In the studies, Holt-Winters, ARIMA, TBATS, Support Vector Machines, and Artificial Neural Networks methods on the R package program are used with time series approach, and the results were summarized according to RMSE and MAPE error measures.

11 Car Rental Prediction Using Segmented and Unsegmented …

143

300 250 200 150 100 50 1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 177

0

Premium

Gold

Risky

Inactive

Total

Fig. 11.3 Time series data of the car rental company

11.3.2 Results The data used in the study is given in Fig. 11.3. As mentioned before, the main goal of the study is to predict the total demand. To this end, two approaches are used. In the first approach, the total demand data is used to predict the future demand. The segmented demand data is used as separate time-series data in the second approach, and the forthcoming segmented demand is forecasted. Various forecasting tests are applied with the current data. Starting from day 30, the demand for the next seven days is forecasted, and the forecasting results are measured. This approach is applied until day 175, which means 145 separate forecasts are made. As a result, the average of the error measures is obtained. The average MAPE values are shown in Table 11.2, and the average RMSE values are reported in Table 11.3. According to these results, we can see that Risky and Inactive segments Table 11.2 The average MAPE values for segment demand Exp.Sm

HoltWinters

ARIMA

Tbats

SVR

ANN

Premium

30.731

32.114

27.846

28.794

30.727

29.727

Gold

31.585

32.179

29.461

29.603

31.591

31.331

Risky

13.179

13.418

12.279

13.393

13.240

13.140

Inactive

15.189

15.572

15.102

15.571

15.194

14.444

Tbats

SVR

ANN

Table 11.3 The average RMSE values for segment demand Exp.Sm

HoltWinters

ARIMA

Premium

9.416

9.959

8.703

8.972

9.420

9.230

Gold

9.200

9.456

8.578

8.579

9.210

8.380

Risky

7.163

7.240

6.833

7.378

7.180

6.760

14.359

14.371

12.387

14.095

14.371

13.851

Inactive

144

B. Oztaysi et al.

Table 11.4 The average MAPE values for overall demand Exp.Sm Total demand Segmented demand

HoltWinters

ARIMA

Tbats

SVR

ANN

9.486

9.379

8.441

9.393

9.486

9.714

39.080

39.096

38.721

38.996

39.101

39.117

Table 11.5 The average RMSE values for overall demand Exp.Sm Total demand Segmented demand

HoltWinters

ARIMA

Tbats

SVR

ANN

26.609

26.609

22.615

26.009

26.635

26.939

111.470

111.158

109.674

110.676

111.460

111.669

Table 11.6 The parameters of the best models Methods

Parameters

Exp.Sm

α = 0.468, β = 0.028 γ, = 0.560

HoltWinters

α = 0.601, β = 0.144

ARIMA

p = 3, d = 1, q = 1

SVR

radial basis function, C = 0.58, ε = 0.0034, γ = 0.020

ANN

Num. of hidden n. = 4, Learning Rate = 0.02, momentum factor = 0.6

are the segments that can be forecasted accurately. However, in the premium and Gold segments, the errors are very high. As the main focus of the study is to forecast the total demand, Table 11.4 shows the average MAPE values for total demand and segmented demand prediction. Total demand predictions use the total demand data, while segmented demand results use the predictions of the segments to find the total demand. The results of Tables 11.4 and 11.5 show that forecast results, which use the overall demand, outperform the segmented demand approach for all methods. When the methods are compared considering the overall demand, the best method is ARIMA, and HoltWinters and Support Vector Regression methods follow it. The parameters of the best models are shown in Table 11.6.

11.4 Discussion and Conclusions In this study, we use real-world car-rental demand data to compare different forecasting models. The segmentation results reveal four segments: Premium, Gold, Risky, and Inactive. The forecasting results show that the most predictable segments are Risky and Inactive segments. One of the main topics of the research is whether to use segmented data or unsegmented data. When the forecasting results are compared, all methods using the overall time-series demand data for forecasting outperform

11 Car Rental Prediction Using Segmented and Unsegmented …

145

the segment-based forecasting approach. When the methods are compared, the best models are ARIMA, Holt-Winters, and Support Vector Regression. This result reveals that using unsegmented data provides better predictability. Using RFM as a segmentation tool and the way we define the segments can be the reason for the result. In future studies, other segmentation approaches can be used to provide better prediction results. As mentioned in the study, the time-series approach is used in this study. The causal models can be built in further studies and compared with the results of this study. Acknowledgements This work is supported by TUBITAK (The Scientific and Technological Research Council of Turkey, Project Id: 3200643).

References 1. Ayo CK (2014) Stock price prediction using the ARIMA model. https://doi.org/10.1109/ UKSim.2014.67 2. Bajpai P, Olsen T, Edgar S, Mccurdy R, Enbody R (n.d.) BATSense: anomalous security event detection using TBATS machine learning. In: 2019 international conference on cyber security and protection of digital services (Cyber Security), pp 1–8. https://doi.org/10.1109/CyberSecP ODS.2019.8885079 3. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden Day, San Francisco 4. Brown RG (1959) Statistical forecasting for inventory control. McGraw-Hill, New York 5. Chao CF, Horng MH (2015) The construction of support vector machine classifier using the firefly algorithm. Computational intelligence and neuroscience. https://doi.org/10.1155/2015/ 212719 6. Christy AJ, Umamakeswari A, Priyatharsini L, Neyaa A (2018) RFM ranking—an effective approach to customer segmentation. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/ j.jksuci.2018.09.004 7. Elmunim NA, Abdullah M, Hasbi AM, Zaharim A (2015) Forecasting ionospheric delay during quiet and disturbed days using the Holt-Winter method. In: International conference on space science and communication, IconSpace, 2015-September, pp 132–135. https://doi.org/10.1109/ IconSpace.2015.7283758 8. Ernawati E, Baharin SSK, Kasmin F (2021) A review of data mining methods in RFM-based customer segmentation. J Phys Conf Ser 1869(1). https://doi.org/10.1088/1742-6596/1869/1/ 012085 9. Gardner ES (1985) Exponential smoothing: the state of the art. J Forecast 4(1):1–28. https:// doi.org/10.1002/for.3980040103 10. Gardner ES (2006) Exponential smoothing: the state of the art-Part II. Int J Forecast 22(4):637– 666. https://doi.org/10.1016/j.ijforecast.2006.03.005 11. Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast 20(1):5–10 12. Hosseini ZZ, Mohammadzadeh M (2016) Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: an empirical study in public health care services. Iranian J Pharm Res 15(1):355–367. https://doi.org/10. 22037/ijpr.2016.1827 13. Hyndman RJ, Athanasopoulos G (2017) Forecasting: principles and practice (2nd edn). Monash University, Australia. https://otexts.com/fpp2/index.html

146

B. Oztaysi et al.

14. Karabiber OA, Xydis G (2019) Electricity price forecasting in the Danish day-ahead market using the TBATS, ANN and ARIMA methods. Energies 12(5). https://doi.org/10.3390/en1205 0928 15. Mukhopadhyay S (2011) Artificial neural network applications in textile composites. Soft computing in textile engineering. Elsevier, pp 329–349. https://doi.org/10.1533/978085709 0812.4.329 16. Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565–1567. https:// doi.org/10.1038/nbt1206-1565 17. Peker S, Kocyigit A, Eren PE (2017) LRFMP model for customer segmentation in the grocery retail industry: a case study. Mark Intell Plan 35(4):544–559. https://doi.org/10.1108/MIP-112016-0210 18. Sari JN, Nugroho LE, Ferdiana R, Santosa PI (2016) Review on customer segmentation technique on ecommerce. Adv Sci Lett 22(10):3018–3022. https://doi.org/10.1166/asl.2016. 7985 19. Srivastava R (2016) Identification of customer clusters using RFM Model: a case. In: 8th International conference on business intelligence, analytics, and knowledge management. Singapore 20. Stormi K, Lindholm A, Laine T, Korhonen T (2020) RFM customer analysis for productoriented services and service business development: an interventionist case study of two machinery manufacturers. J Manag Gov 24:623–653 21. Wang C-C, Chien C-H, Trappey AJC (2021) On the application of ARIMA and LSTM to predict order demand based on short lead time and on-time delivery requirements. Processes 9.https://doi.org/10.3390/pr9071157 22. Wei J, Lin S, Wu H (2010) A review of the application of RFM model. Afr J Bus Manage 4(19):4199–4206 23. Weng CH (2016) Knowledge discovery of digital library subscription by RFC itemsets. Electron Library 34(5):772–788. https://doi.org/10.1108/EL-06-2015-0086 24. Yang Y, Ceder A, Zhang W, Tang H (2021) Unconstrained estimation of multitype car rental demand. Appl Sci 11(10):4506

Chapter 12

A Goal Programming Model for Optimizing the Reverse Logistics Network of Glass Containers and an Application ˙ Raci Berk Islim, Sule ¸ Itır Satoglu, ˘ and Hakan Durbaba Abstract Sustainable supply chains gained importance worldwide because of the increasing environmental concerns and new governmental legislations. Optimization of the reverse logistics networks takes an important place to achieve sustainability. Moreover, glass containers are one of the most environment-friendly packaging materials with almost 100% recyclable characteristic. In this study, we propose a goal programming model to optimize the designed reverse logistics network of glass containers. Financial loss and carbon dioxide emission minimization are the objectives of the model. The model concerns a single-product, multiple facilities, echelons, and periods and decides on the location of the glass-recycling facility, investments, waste transportation frequencies, and allocation of the resources. The model is solved with Augmented Epsilon Constraint Method (AUGMECON) and justified with a case study in Turkey, including real data. The results show that a remarkable amount of CO2 emission is avoided to be exposed in the nature by building the recycling facility. Besides the financial and environmental aspects, the proposed model also creates social value by considering the employment generated throughout the network and completely achieves sustainability. Keywords Reverse logistics · Glass container · Goal programming · Sustainability · Facility location · Carbon dioxide (CO2 ) emission

12.1 Introduction Increasing environmental concerns and governmental legislations forced the companies to transform their supply chain operations into sustainable ones. Moreover, Blackhurst et al. [5] stated that to ensure a supply chain to be sustainable, the measures of environmental and social dimensions should also be included alongside the financial profit and loss. This conceptualization is called the Triple Bottom R. B. ˙Islim (B) · S. ¸ I. Sato˘glu · H. Durbaba Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_12

147

148

R. B. ˙Islim et al.

Line (TBL) approach. For investigating sustainable supply chains, the concept of reverse logistics (RL) should also be clearly defined and internalized. According to Ferguson and Souza [10], “Reverse logistics focuses on the storage and movement of materials (and associated information) from the consumer or end-user back to the manufacturer, recycler, or other third party.” The European Container Glass Federation (FEVE) defines glass as a sustainable material since it is a 100%-recyclable without any quality deterioration, and it gives an opportunity to reuse the same container up to 40 times (Think Life Cycle, Think Glass, 2016). Moreover, Testa et al. [25] emphasized that the usage of one ton of melted cullet, which means crashed glass, in glass production saves 1.2-ton raw material and reduces CO2 emission by about 60%. Furthermore, glass recycling leads to a significant amount of energy-saving since the melting operation of cullet takes place at lower temperatures. Thereby, the RL of glass containers and the concept of recycling become very valuable for nature. In this study, to optimize the RL network of the glass containers, we propose a goal programming model in order to select the location of the new recycling facility, and to determine the optimum amount of flow between nodes in the network, allocation of resources, transportation frequencies and make other additional investment decisions. These decisions are both at operational and strategic levels. The economic objective of the proposed model includes revenues and costs, which are mainly investment, labor, rent, transportation, operational and purchasing costs. To the best of our knowledge, the environmental objective of the proposed model is the unique aspect of this study. All CO2 emissions that stem from transportation activities, recycling operations, remanufacturing operations, manufacturing operations without recycled materials, and CO2 emission savings thanks to the recycling of glass are considered in the environmental objective function. We expressed the real life-equivalent of the amount of CO2 saved, in terms of thousands-hectares of a forest absorbing that amount on an annual basis, to show the utility of our model. We also performed an application with a case study, which is composed of real data, to validate the proposed model and solve a real-life problem.

12.2 Literature Review We reviewed reverse and closed-loop supply chain studies as well as forward logistics studies considering sustainability aspects. In the context of forward logistics, BaudLavigne et al. [4] minimized the total cost of the supply chain design in which bill-ofmaterials of products are associated with environmental issues. Some of the forward logistics studies consider green-house or CO2 emission minimization besides the cost minimization [6, 24, 28]. Mota et al. [17] intended to increase social benefits, and Xifeng et al. [28] maximized service reliability as a third objective in addition to the economic and environmental objectives. So, there is a limited number of studies considering all three aspects of sustainability in forward logistics and supply chain literature.

12 A Goal Programming Model for Optimizing the Reverse …

149

Some goal-programming models were formulated where cost minimization and recycled or reused waste maximization were simultaneously aimed in the scope of the RL literature. The applications of these models concern recycling of paper [21], plastic [27], end-of-life vehicles (ELVs) [13], and waste electrical and electronic equipment (WEEE) [3]. Moreover, mixed-integer linear programming models with a single objective were developed in the context of the RL network design, which is profit maximization in the recycling of washing machines-tumble dryers [1], cost minimization for ELVs [8] and profit maximization for ELVs [9]. Most of the studies decided the number and location of recycling facilities and the amount of waste collected and transported to these facilities. Data-envelopment analysis for evaluating different types of closed-loop supply chain (CLSC) networks was employed by Neto et al. [18]. Moreover, several multiobjective models to design CLSC such as a model to decide the flow of the raw materials and products that are fully-, semi- or non-recyclable [20], a model to decide the facilities’ locations, capacities, and optimum quantity of shipments [14], and a model considering multiple plants and products, demand markets, and collection centers [2] were developed. Pedram et al. [22] decided the numbers and locations of distribution, collection, refurbishing, and recycling centers and the flows between them by considering an uncertain demand structure and return rates. Furthermore, a model that integrates used parts of the ELVs into the forward automotive supply chains [19] and another one for the CLSC network of glass materials [23] were developed. Although the latter paper is similar to our study, they did not consider any goals in their decisions for the facility location and the amount of flow between the facilities. To sum up, studies covering three aspects of sustainability are limited in the literature. Furthermore, economic, social, and environmental objectives were either converted into a single objective or treated from a financial point of view in most of these models. On the other hand, in terms of the solution methodology utilized to solve goal programming models, only a few of the studies employed exact solution algorithms that provide Pareto Optimal solutions, and the rest usually preferred the weighting method. Besides, there are a few studies for the RL network of glass materials. So, our study intends to fill the aforementioned gaps by proposing a novel mixed integer goal programming model (MIGP) for the RL network of glass containers. The proposed model completely achieves sustainability by also considering employment perspectives for the social pillar. To obtain several Pareto Optimal solutions, the Augmented Epsilon Constraint Method (AUGMECON) [16] was employed in this study.

12.3 Problem Definition and the Network Configuration In the current RL of glass containers in many countries, there are collection centers in each city center. Glass containers are shipped to these centers from various districts of that city after the collection. Then, they are sent to a glass recycling facility to be

150

R. B. ˙Islim et al.

Fig. 12.1 Proposed network design for the RL of glass containers

processed. After the recycling processes, the cullet is sent to glass manufacturers to be used in remanufacturing. There are many transportation activities along with the existing decentralized network. This is costly and damages the environment since the transportation is carried out by fossil-fuel trucks. The proposed network design for the RL of glass containers is shown in Fig. 12.1. There are three echelons in the proposed network configuration, whereas there were four in the existing one. The first layer is the consumption point or source of glass containers. Glass containers can be collected from glass recycling bins available in the districts, through HORECA, which means a hotel-restaurant-café project, and they can be bought from other waste recyclers. The glass recycling facility exists at the second tier of the network. This facility sorts glass containers out according to their colors and then breaks them to be shaped as a cullet. Small and special-equipped trucks collect glass containers from the sources and transport them to this facility. Finally, big trucks transport the cullet from the glass recycling facility to the glass manufacturer, where the actual production of glass is realized. Thus, the used glass containers can remain in the same region, and excessive distances to be traveled, which cause an intensive amount of CO2 emission, might be prevented.

12 A Goal Programming Model for Optimizing the Reverse …

151

12.4 Mathematical Formulation According to our proposed network design, we devised a MIGP model in order to find the location of the recycling facility. This model also gives certain insights into the operational side of the RL of glass containers. Thereby, integrated problems such as facility location and waste transportation are solved in a multi-objective manner. Assumptions of the model are as follows: • Facility location is decided at the beginning of the first period, and the opened facility cannot be closed in future periods. • Inner routes among the glass recycle bins in a district are omitted. • Trucks operate at their full truck loads. • The glass manufacturer purchases all the recycled cullet due to legal issues. • There are two truck types. The first one is for collecting glass containers from glass recycle bins and through HORECA, whereas the second one transports the cullet to the glass manufacturer. The sets, parameters and decision variables are denoted in Table 12.1. Then, the MIGP model is presented. MinZ 1 =



b−p

(12.1)

p∈P

  n 1dr p .T C O21 .F T L 1 .D1dr

MinZ 2 =

p∈P d∈D r∈R

+

  n r2p .T C O22 .F T L 2 .D2r p∈P r∈R

+



yr p (R MC O2 + RC O2 − MC O2)

(12.2)

p∈P r∈R

Subject to 

fr = 1

r∈R

 



yr p ≥ L Pp .P R p

r∈R

PT Rp Pp

(12.3)  ∀p ∈ P

(12.4)

   + (yr p .S P p ) + B p + b− ( fr (I N Vr p − I N Cr p ) p − b p = W W p WR + 2.nta1 p + nta2 p +

r∈R

+

r∈R

      nt pt p .I N Vt p + ( fr .R E N Tr p ) + n 1dr p .D1dr .T C1 .D P p t∈T

+



r∈R

(nr2p .D2r .T C2 .D P p ) +

r∈R

 r∈R

or p +

 d∈D

d∈D r∈R

     or p PC p + RC p + RC p xdr p + h dr p

∀p ∈ P

(12.5)

d∈D r∈R

 xdr p + h dr p = yr p ∀r ∈ R, p ∈ P

(12.6)

R. B. ˙Islim et al.

152 Table 12.1 Notation of the mathematical model Sets p

Set of periods (p = 1, …, P)

d

Set of districts, i.e., sources of glass containers (d = 1,…, D)

r

Set of potential locations for the recycling facility (r = 1,…, R)

t

Set of truck types (t = 1,…, T )

Parameters RCO2

CO2 emission due to 1 ton of glass recycling operation

MCO2

CO2 emission due to 1 ton of glass container manufacturing

RMCO2

CO2 emission due to 1 ton of glass container manufacturing by using cullet

WR

Number of workers required for the operations in a glass recycling facility

CAP

The capacity of the glass recycling facility

WD

Number of days the facility operates in a year

Bp

Budget available in period p

WW p

Wage per worker in period p

INV rp

Initial investment cost to build a recycling facility at the location r in period p

INV tp

The purchasing cost of a truck type t in period p

INC rp

Incentive payment to build a recycling facility at the location r in period p

D1dr

Round-trip distance between district d and glass recycling facility r in kilometers

D2r

Round-trip distance between potential recycling facility r and the glass manufacturer in kilometers

TCO2t

CO2 emission of truck type t

FTL t

A full truckload of the truck type t

C dp

Amount of glass containers available at glass recycle bins in district d in period p

HORdp

Amount of glass containers available through HORECA at district d in period p

OT p

Amount of glass containers available for purchase from other waste recyclers in period p

PC p

Unit purchasing price of the cullet from other waste recyclers in period p

RC p

The unit recycling operation cost of glass containers in period p

TC t

Unit fuel consumption in liter per kilometer by truck type t

SPp

Unit selling price of the cullet in period p

Lp

Legal cullet usage ratio for the glass manufacturer in period p

PRp

Total domestic sales amount of the glass container manufacturer in period p

Pp

The population of the country in period p

PTRp

The population of the region in period p

DPp

Unit diesel price per liter in period p

RENT rp

Rent cost of the land in the location of potential recycling facility r in period p

Decision Variables fr

1 if the recycling facility r is opened, 0 otherwise (continued)

12 A Goal Programming Model for Optimizing the Reverse …

153

Table 12.1 (continued) Sets p

Set of periods (p = 1, …, P)

x drp

Amount of glass containers to be collected from district d and shipped to the facility r during period p

orp

Amount of glass containers to be purchased from other waste recyclers and brought to the recycling facility r during period p

hdrp

Amount of glass containers to be collected through HORECA from district d and shipped to the recycling facility r in period p

yrp

Amount of cullet to be sold to the glass manufacturer from recycling facility r in period p

ndrp 1

Number of times for truck type-1 to collect glass containers from district d and ship to the recycling facility r during period p

nrp 2

Number of times for truck type-2 to ship recycled glass containers to the glass manufacturer from recycling facility r in period p

ntptp

Number of trucks type-t to be purchased at the beginning of period p

ntatp

Number of trucks type-t must be available at the beginning of period p

bp –

Negative deviational variable for the budget in period p

bp

+

Positive deviational variable for the budget in period p

  xdr p ≤ fr .Cdp ∀d ∈ D, r ∈ R, p ∈ P

(12.7)

  or p ≤ fr .O T p ∀r ∈ R, p ∈ P

(12.8)

  h dr p ≤ fr .H O Rdp

∀d ∈ D, r ∈ R, p ∈ P

(12.9)

yr p ≤ fr .C A P ∀r ∈ R, p ∈ P

(12.10)

  n 1dr p ≥ xdr p + h dr p /F T L 1 ∀d ∈ D, r ∈ R, p ∈ P

(12.11)

n 2dr ≥ yr p /F T L 2 

∀r ∈ R, p ∈ P

n 1dr p ≤ W D.nta1 p

∀p ∈ P

(12.12) (12.13)

d∈D r∈R



n r2p ≤ W D.nta2 p

∀p ∈ P

(12.14)

r∈R

ntat1 = nt pt1 ∀t ∈ T

(12.15)

R. B. ˙Islim et al.

154

ntat p = ntat p−1 + nt pt p ∀ p ∈ P : p > 1, t ∈ T  n 1dr p ≤ fr

C AP F T L1

(12.16)

 ∀d ∈ D, r ∈ R, p ∈ P

(12.17)

xdr p , h dr p ≥ 0 ∀d ∈ D, r ∈ R, p ∈ P

(12.18)

or p , yr p ≥ 0 ∀r ∈ R, p ∈ P

(12.19)

b−p , b+p ≥ 0 ∀ p ∈ P

(12.20)

n 1dr p ≥ 0 and integer

∀d ∈ D, r ∈ R, p ∈ P

n r2p ≥ 0 and integer

∀r ∈ R, p ∈ P

(12.21) (12.22)

ntat p , nt pt p ≥ 0 and integer ∀t ∈ T , p ∈ P

(12.23)

fr ∈ {0, 1} ∀r ∈ R

(12.24)

Equation (12.1) shows the first objective function which minimizes the total annual deviations from the budget throughout the planning horizon. Equation (12.2) is the second objective function and minimizes CO2 emission resulted from the flow of trucks between the layers, glass container production by using cullet and recycling of glass containers. Besides, the amount of CO2 emission avoided by recycling is subtracted. Equation (12.3) is in line with our assumption that only one facility is opened at a location at the beginning of the first period. Equation (12.4) forces the glass manufacturer to buy the cullet at least as much as the amount required by the legal regulations. That amount is found by the multiplication of the legal ratio with its corresponding glass container consumption in the region, which is calculated by multiplying the estimated domestic sales amount with the rate of population. The left-hand side of Eq. (12.5) includes the summation of the budget and the revenue earned by sales of the recycled cullet. The right-hand side includes all costs, including the labor costs, investment costs of the facility and trucks, operational costs of the recycling facility including rent, unit processing cost, energy cost, transportation costs between the layers, and purchasing cost of the glass containers from other waste recyclers. Equation (12.6) states that all collected and bought glass containers are sold to the glass manufacturer. Equations (12.7), (12.8), and (12.9) satisfy that amounts of glass containers brought into the glass recycling facility from districts, from other waste recyclers, and through HORECA cannot exceed their available amounts respectively. Equation (12.10) implies that the processed amount of glass containers is less than or equal to the capacity of the opened recycling facility.

12 A Goal Programming Model for Optimizing the Reverse …

155

Equations (12.11) and (12.12) determine the travel frequencies of truck type-1 and 2 by considering full truckload, respectively. Equations (12.13) and (12.14) ensure that a truck can be operated as much as the maximum working days in a year. Since there is no truck available at the beginning, Eq. (12.15) ensures that the number of each truck type in period one is equal to the purchased amounts in that period. Equation (12.16) provides that the number of trucks in a period equals the number of trucks purchased in previous periods plus the number of trucks purchased in the current period. If a recycling facility is not open in the given location, Eq. (12.17) prevents travels from districts to that location for truck type-1. Equations (12.18)–(12.24) are the domain constraints.

12.4.1 Solution Methodology Since this is a bi-objective mathematical model, a solution methodology provides Pareto Optimal solutions is required. AUGMECON is an improved version of the traditional Epsilon-Constraint Method where some set of efficient or Pareto Optimal solutions are generated [16]. After the formulation of the bi-objective model, it is solved for each objective function separately. According to these objective functions’ values, the pay-off table is constructed. The range of the second objective function is computed through this payoff table, based on its maximum and minimum values and the intended number of solutions. Hence, the solution space is divided into grids with an equal length called “range.” Then, the objective function is reconstructed, and a constraint is added to the model for the second objective function. The righthand side of this newly added constraint is increased as much as the range value, and the re-formulated model is solved again until the intended number of solutions are obtained.

12.5 Case Study and Results 12.5.1 Case Study In this study, glass recycling in the Thrace Region, located on the northwest side of Turkey and includes cities of Tekirdag, Kirklareli, and Edirne, is considered. We also added Silivri and Catalca, districts of Istanbul, into this network since they are also part of the Thrace Region. Currently, used glass containers are brought to the collection centers located at the city centers from glass recycle bins in districts and provinces by third-party companies. Then, glass containers are transported from the collection centers to several recycling facilities in Istanbul, which is considerably far from the three cities mentioned above. Recycled glass containers are shipped to factories of the glass container manufacturer located in cities of Eskisehir or Bursa

156

R. B. ˙Islim et al.

that are close to the middle part of Turkey. Thereby, the trucks that carry cullet covers more than 400 km on average on the highways and emit an excessive amount of CO2 . Besides, the Thrace Region also has a great potential for collection through HORECA since it has a dense population of higher than two million, and its population tends to increase further in the future at the rate of 2.5% per year, which means possible increases in the usage of glass containers through years. Moreover, there is one float glass and one glassware factory within the region. Thereby, the Thrace Region can be considered a possible region for building up a recycling facility. Some districts of cities are combined into clusters since their populations are very low. By means of clusters, trucks are assumed to visit all districts of a cluster on the same day. The glass manufacturer is assumed to be a Glassware Factory in Kirklareli. The recycling facility location will be chosen among five possible candidates which were determined based on the locations’ proximity to the industrial sites, to the glass consumption points, and to the glass manufacturer. The information mentioned above is visualized in Fig. 12.2. The details of the data associated with the Case are stored at Mendeley [7].

Fig. 12.2 Representation of the case study

12 A Goal Programming Model for Optimizing the Reverse …

157

12.5.2 Computational Results and Discussion When the proposed goal programming model is formulated for the case study, it consists of 1535 variables, including 5 binary, 520 integer, and 1010 continuous variables, and 3561 constraints. The model is built on IBM ILOG CPLEX 12.9, and it is solved on a computer running Windows 10 OS with 2.40 GHz Intel Core i7 CPU and 16 GB of RAM. When we solve the model for the environmental objective, it takes 0.7 s to reach the optimum solution. Since we utilize the AUGMECON method, a payoff table must be created at the beginning. While solving the model according to the first objective function, the first objective function value is found as 29,680,000 whereas the second objective function value is −7,939,100. While solving the model according to the second objective function, the second objective function value is found as –24,100,000, whereas the first objective function value is 37,370,000. Thereby, these four values constitute the payoff table as discussed in Sect. 12.4.1. The range of the second objective function is divided into ten equal intervals, and thereby eleven grid points are created. The range of the second objective function can be found as follows: Range =

−7939100 − (−24100000) = 1,616,090 10

A new nonnegative slack variable, named “s2”, is introduced to the model, and the model is solved by reforming the first objective function and adding the second objective function as a constraint. Equation (12.25) shows the reformulated version of the first objective function in which the epsilon is equal to 10–5 . Equation (12.26) shows the form of the second objective function as a constraint with some changes. “Step” in the Eq. (12.26) starts from 0 and ends at 10 to achieve eleven grid points. Besides, the constraints between (12.3) and (12.24) are still applicable. Min Z 1 =





b−p − 10−5 .

p∈P

s2 Range

 (12.25)

Subject to Equation (12.3)–(12.24)    2  n 1dr p .T C O21 .F T L 1 .D1dr + nr p .T C O22 .F T L 2 .D2r p∈P d∈D r∈R

+



p∈P r∈R

yr p (R MC O2 + RC O2 − MC O2) + s2

.Step

p∈P r∈R

= −24,100,000 + 1,616,090. (12.26)

R. B. ˙Islim et al.

158 Table 12.2 Solution table Sol. #

Objective 1 [Turkish Liras (TL)]

Objective 2 (kg CO2 )

Location of recycling facility

s2 value

Pareto optimality

1

37,366,000

−24,100,000

Ergene

3.7825

No

2

35,529,000

−23,263,000

Ergene

Near 0

Yes

3

34,663,000

−22,255,000

Ergene

Near 0

Yes

4

34,013,000

−20,537,000

Ergene

Near 0

Yes

5

33,453,000

−18,798,000

Ergene

Near 0

Yes

6

32,155,000

−16,950,000

Ergene

Near 0

Yes

7

31,633,000

−15,273,000

Ergene

Near 0

Yes

8

31,153,000

−13,504,000

Corlu

Near 0

Yes

9

30,859,000

−11,674,000

Corlu

Near 0

Yes

10

30,650,000

- 9,606,900

Corlu

51,726

No

11

29,679,000

−7,939,100

Corlu

6.4346

No

Reaching the optimal solution for the environmental objective takes 1.14 s in this setting. The other 10 runs are also optimally performed with reasonably short computational times. The solutions are presented in Table 12.2. We should note that the value of s2 is expected to be 0 or very close to zero in order to fulfill the Pareto Optimality condition. Objective function 1 states the total monetary loss throughout the 5-year horizon. On the other hand, having negative values for objective function 2 is favorable since it indicates some CO2 savings. Because producing glass containers from virgin raw materials exposes a lot higher amount of CO2 than the cumulative amount resulting from recycling and transportation activities and production from cullet. Thus, absolute values of these CO2 amounts are avoided to be exposed to nature. In Table 12.3, recycled amounts of glass containers are shown each year for the solutions that purely minimize environmental impacts and financial losses, respectively. As the financial aspect is more concerned, the fewer amount of glass containers is collected, purchased, and processed. Furthermore, the increase in the amounts from the period-1 to 5 in both solutions is due to the expected population increase in the region since the population is the main driver of the consumption, and this also increases the legal minimum amount of glass to be collected. Table 12.3 Total glass containers to be recycled Sol. # \ Period

Collected and purchased glass containers (ton/period) 1

1 11

2 17,143

5592

3 17,657

5635

4 18,190

5680

Total

5 18,746

5727

19,332 5776

91,068 28,410

12 A Goal Programming Model for Optimizing the Reverse …

159

We made detailed explanations for two solutions. We have considered Pareto Optimality status and chosen solutions #2 and #9 in which the environmental and monetary objectives are optimized, respectively. Table 12.4 summarizes the results of solution #2. As the amount of collected glass containers increases, more CO2 emissions can be avoided. However, this creates a huge financial burden since more trucks should be purchased to collect and transport glass materials. The facility is built in Ergene, which is a district of Tekirdag. Almost all glass containers are collected from glass recycle bins and through HORECA, and purchased from other waste recyclers in each period. We also analyze solution #2 for its real-life equivalent. In 2005, 7.99% of 312.31 T-g (109 kg) CO2 was absorbed by the forests of Turkey [26]. On the other side, the forested land of Turkey was measured as 21,248,498 ha in 2005 [12]. By considering the aforementioned numerical values, the amount of CO2 saved in solution #2 corresponds to the amount absorbed annually by 19,809 hectares of forest, where this area is equivalent to the area occupied by an average national park. Since the recycling facility is a regional plant of the country, the amount of CO2 saved is remarkable. Table 12.5 summarizes the results of solution #9. The financial point of view is more concentrated on that solution. The facility is built to Corlu, which is again a district of Tekirdag. About 6 Million TL less loss is obtained in the first period in comparison with solution #2 whereas the total loss in future periods is almost the Table 12.4 Results of the Solution #2 Periods 1 Loss (TL)

2

3

4

5

31,972,000 523,430 1,577,500 660,980 795,080

# of Truck type-1

11

11

12

12

12

# of Truck type-2

3

3

3

3

3

Total cullet processed and sold (ktons) 16,587

16,631

18,050

18,090

18,148

Flow freq. b/w the RF and GM

666

722

724

726

664

Abbreviations: RF—Recycling facility, GM—Glass Manufacturer, Freq—Frequency

Table 12.5 Results of the Solution #9 Periods 1

2

3

4

5

Loss (TL)

25,958,000

1,080,900

1,198,900

1,277,300

1,363,700

# of Truck type-1

5

5

5

5

5

# of Truck type-2

2

2

2

2

2

Total cullet processed and sold (ktons)

8342

8385

8430

8477

8525

336

338

340

341

Flow freq. b/w the RF and GM 334

Abbreviations: RF—Recycling facility, GM—Glass Manufacturer, Freq—Frequency

160

R. B. ˙Islim et al.

same. In this solution, the glass recycler collects and purchases glass containers only a bit more than the legal constraint requires, which means there is a certain amount of glass containers remaining uncollected. Thereby, a smaller number of trucks is needed to cover transportation operations. Thus, the number of trucks to be purchased decreases by 50% when compared to that of solution #2. If two solutions are compared, the losses except the one in the first period is less in the environmentally favored solution #2. This indicates that collecting more glass containers helps reduce the monetary loss regardless of the first investment cost. Besides, in all solutions, the recycling facility is located very close to the main glass waste generation sources and glass manufacturer. Finally, if solution #9 is compared to solution #2, monetary loss reduces by 13.1%, but the amount of CO2 saved reduces three-fold more, i.e. 49.8%. We also made sensitivity analyses based on two parameters, namely the amount of glass containers from other waste recyclers, i.e. OT p , and the rental cost of the recycling facility, i.e. RENT p . For the first analysis, we increase the OT p based on the assumption that if the glass recycle ratio is low in a region, then other parties may have more glass containers among their wastes. On the other hand, there is a possibility within the scope of the government incentive that the required land for the recycling facility may directly be provided by the government. Thus, we assume that RENTp is 0 throughout the 5-year planning horizon in the scope of the second analysis. Even though parameter values are changed in both analyses, locations of the facility remain the same as in the results summarized in Table 12.2, and this validates that the model has certain robustness. Besides the numerical results, this study gives some managerial and social insights. Firstly, there are too many truck purchases in all solutions. Thus, having logistics service by a third-party company might be considered. Furthermore, since the cullet is melted at a lower temperature in furnaces when compared to virgin raw materials, less energy will be spent by the glass manufacturer. Moreover, the glass manufacturer purchases the cullet for cheaper prices when compared to the virgin raw materials used in production. The society also benefits from the proposed network. The created glass waste will be recycled and reused within the same region. Besides, employment opportunities will be provided to the local community by opening a new facility. Furthermore, thanks to a huge amount of CO2 emission saving, air quality in the region will improve and the greenhouse effect will decrease. Finally, the policies to make this business profitable may be searched by the governments.

12.6 Conclusion In this study, we designed an RL network for glass containers and formulated MIGP to optimize facility location, investment, and material flow decisions. Financial loss and CO2 emission minimizations constitute the objectives of the model. Although we only consider economic and environmental objectives, the model also contains an employment perspective. In this way, social value is also created, and sustainability

12 A Goal Programming Model for Optimizing the Reverse …

161

is achieved with all three pillars. It is evident that if the economical objective is more concerned, then the recycled amount of glass containers only passes the legal requirement, and many of the glass containers remain uncollected. On the other hand, when the recycled amount of glass containers increases, CO2 emission is more avoided, and more employment is generated. We should note that the rate of the increase in the amount of CO2 saved is much higher than the rate of the financial loss increase. Thanks to the usage of AUGMECON, any solution from Pareto Optimal Set can be used to determine the facility location. Hence, this model supports the decision process. In future studies, a social objective such as targeting an employment rate can be included in the model. Besides, a closed-loop supply chain network of glass containers can be designed in future studies. Furthermore, the model can be reformulated using stochastic programming techniques, based on the uncertain parameters of glass-waste disposal rates, or scenario analysis can be performed. Finally, a heuristic or meta-heuristic algorithm can be developed and applied for solving larger-size instances.

References 1. Alumur SA, Nickel S, Saldanha-Da-Gama F, Verter V (2012) Multi-period reverse logistics network design. Eur J Oper Res 220(1):67–78. https://doi.org/10.1016/j.ejor.2011.12.045 2. Amin SH, Zhang G (2013) A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Appl Math Model 37(6):4165–4176. https:// doi.org/10.1016/j.apm.2012.09.039 3. Bal A, Satoglu SI (2018) A goal programming model for sustainable reverse logistics operations planning and an application. J Clean Prod 201(1):1081–1091. https://doi.org/10.1016/j.jclepro. 2018.08.104 4. Baud-Lavigne B, Agard B, Penz B (2014) Environmental constraints in joint product and supply chain design optimization. Comput Ind Eng 76(1):16–22. https://doi.org/10.1016/j.cie. 2014.07.014 5. Blackhurst J, Cantor D, O’Donnell M (2012) Sustainable supply chains: a guide for small—to medium-sized manufacturers. Iowa State University Extension, Iowa 6. Chaabane A, Ramudhin A, Paquet M (2011) Designing supply chains with sustainability considerations. Prod Plann Control 22(8):727–741. https://doi.org/10.1080/09537287.2010. 543554 7. Islim RB, Durbaba H, Satoglu SI (2019) Data on a goal programming model for optimizing the reverse logistics network of glass containers, and an application in Turkey, Mendeley Data, vol 1. https://doi.org/10.17632/dkf65jd9fc.1 8. Demirel E, Demirel N, Gökçen H (2016) A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. J Cleaner Prod 112(Part 3):2101– 2113. https://doi.org/10.1016/j.jclepro.2014.10.079. 9. Ene S, Öztürk N (2015) Network modeling for reverse flows of end-of-life vehicles. Waste Manage 38(1):284–296. https://doi.org/10.1016/j.wasman.2015.01.007 10. Ferguson M, Souza GC (2010) Closed-loop supply chains: new developments to improve the sustainability of business practices. CRC Press, Boca Raton, FL 11. FEVE (2016) Think life cycle, think glass [Brochure]. Retrieved from https://feve.org/wp-con tent/uploads/2016/04/FEVE-presentation-brochure.pdf

162

R. B. ˙Islim et al.

12. Food and Agriculture Organization of the United Nations (2014) Global forest resources assessment 2015 country report: Turkey. Retrieved from http://www.fao.org/3/a-az358e.pdf 13. Harraz NA, Galal NM (2011) Design of sustainable end-of-life vehicle recovery network in Egypt. Ain Shams Eng J 2(3–4):211–219. https://doi.org/10.1016/j.asej.2011.09.006 14. Khajavi LT, Seyed-Hosseini S-M, Makui A (2011) An integrated forward/reverse logistics network optimization model for multi-stage capacitated supply chain. iBusiness 3(2):229–235. https://doi.org/10.4236/ib.2011.32030 15. Li S (2013) A 1488 approximation algorithm for the uncapacitated facility location problem. Inf Comput 222:45–58. https://doi.org/10.1016/j.ic.2012.01.007 16. Mavrotas G (2009) Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl Math Comput 213(2):455–465. https://doi.org/ 10.1016/j.amc.2009.03.037 17. Mota B, Gomes MI, Carvalho A, Barbosa-Povoa AP (2015) Towards supply chain sustainability: economic, environmental and social design and planning. J Clean Prod 105(1):14–27. https://doi.org/10.1016/j.jclepro.2014.07.052 18. Neto JQF, Bloemhof-Ruwaard JM, van Nunen JAEE, van Heck E (2008) Designing and evaluating sustainable logistics networks. Int J Prod Econ 111(2):195–208. https://doi.org/10.1016/ j.ijpe.2006.10.014 19. Ozceylan E, Demirel N, Çetinkaya C, Demirel E (2017) A closed-loop supply chain network design for automotive industry in Turkey. Comput Ind Eng 113(1):727–745. https://doi.org/10. 1016/j.cie.2016.12.022 20. Paksoy T, Ozceylan E, Weber GW (2010) A multi objective model for optimization of a green supply chain network. AIP Conf Proc 1239:311–320. https://doi.org/10.1063/1.3459765 21. Pati RK, Vrat P, Kumar P (2006) A goal programming model for paper recycling system. Omega-Int J Manage Sci 36(3):405–417. https://doi.org/10.1016/j.omega.2006.04.014 22. Pedram A, Yusoff NB, Udoncy OE, Mahat AB, Pedram P, Babalola A (2017) Integrated forward and reverse supply chain: a tire case study. Waste Manage 60(1):460–470. https://doi.org/10. 1016/j.wasman.2016.06.029 23. Pourjavad E, Mayorga RV (2018) An optimization model for network design of a closedloop supply chain: a study for a glass manufacturing industry. Int J Manage Sci Eng Manage 14(3):169–179. https://doi.org/10.1080/17509653.2018.1512387 24. Sazvar Z, Al-E-Hashem SM, Baboli A, Jokar MA (2014) A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products. Int J Prod Econ 150:140–154. https://doi.org/10.1016/j.ijpe.2013.12.023 25. Testa M, Malandrino O, Sessa MR, Supino S, Sica D (2017) Long-term sustainability from the perspective of cullet recycling in the container glass industry: evidence from Italy. Sustainability 9(10):1752. https://doi.org/10.3390/su9101752 26. Tolunay D (2011) Total carbon stocks and carbon accumulation in living tree biomass in forest ecosystems of Turkey. Turk J Agric For 35:265–279. https://doi.org/10.3906/tar-0909-369 27. Wongthatsanekorn W (2009) A goal programming approach for plastic recycling system in Thailand. Int J Ind Manuf Eng 37(1):513–518 28. Xifeng T, Ji Z, Peng X (2013) A multi-objective optimization model for sustainable logistics facility location. Transp Res Part D: Transp Environ 22:45–48. https://doi.org/10.1016/j.trd. 2013.03.003

Chapter 13

Risks in Supply Chain 4.0: A Literature Review Study Sevde Ceren Yildiz Ozenc, Merve Er, and Seniye Umit Firat

Abstract In recent years, the fourth industrial revolution started to transform business, processes, services, and products in many different manufacturing and service industries. Industry 4.0 describes the ongoing trend towards automation and data exchange and provides real-time interaction between things, machines, and human beings to develop digital and smart business systems. This digitalization is re-shaping the supply chains through the implementation and acceleration of Industry 4.0 technologies. Supply Chain 4.0 can be defined as the new generation digital supply chains that utilize Industry 4.0 technologies such as the internet of things, advanced robotics, and big data analytics to improve performance and customer satisfaction. Digitization in the supply chain provides companies various benefits, including improved productivity, profitability, agility, flexibility, and responsiveness. However, the transition from a traditional supply chain towards a digitalized supply chain comes up with some new risks, including lack of information, IT failures, computer security risks, and cyber-attack risks, etc. This paper aims to identify and categorize the Supply Chain 4.0 risks by providing a comprehensive and systematic literature review. For this purpose, 5-phases research methodology is defined, and related research questions are structured. Findings and discussions are evaluated in the frame of research questions. Keywords Supply Chain 4.0 · Industry 4.0 · Risks in Supply Chain 4.0 · Systematic literature review · Digital supply chains S. C. Yildiz Ozenc (B) Department of Industrial Engineering, Faculty of Engineering, Dogus University, Istanbul, Turkey e-mail: [email protected] M. Er Department of Industrial Engineering, Faculty of Engineering, Marmara University, Istanbul, Turkey e-mail: [email protected] S. U. Firat Department of Industrial Engineering, Faculty of Engineering, Istanbul Gedik University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_13

163

164

S. C. Yildiz Ozenc et al.

13.1 Introduction The fourth industrial revolution has a big impact on a supply chain as much as the manufacturing industry. Digitization of a supply chain is one of the great opportunities for organizations to improve the performance, business reputation, and growth possibility in the future. Digital supply chain (Supply Chain 4.0) is basically redesigning the supply chain (SC) with new innovative technologies of Industry 4.0 such as the internet of things (IoT), cyber-physical systems, big data technologies, advanced robotics, etc. [7, 10]. Adopting Industry 4.0 enabled technologies provides some advantages for SCs such as speed, flexibility, global connectivity, real-time inventory, and transparency [1]. An SC includes lots of risk sources inherently. Some of the most common risks are uncertain demand, supply interruptions, volatile exchange rates, political instability, dynamic consumer markets, and even unexpected events such as work accidents, natural disasters, and terrorism [5]. Additionally, new risk types arise with the adoption of new technologies. The risks in SCs that arise with the adaption of Industry 4.0 technologies are called Supply Chain 4.0 (SC 4.0) risks. These risks mainly yield information, connectivity, and security risks. Hence, cyber attacks, information security, and computer security risks may be some examples of Supply Chain 4.0 risks [41]. The main objective of this study is the identification of the main Supply Chain 4.0 risks. For this purpose, five-phased literature is performed to answer the identified four research questions. Then, a comprehensive and systematic literature review is conducted in order to identify the risks in Supply Chain 4.0. The current risks in the existing literature are determined and classified. Findings are evaluated in the frame of conducted research questions that focus on; (i) Supply Chain 4.0 risks, (ii) most addressed risks and risk management approaches, (iii) current state of the literature, and (iv) under-explored research areas and future research directions. The rest of the conference paper will explain the research methodology, background of Industry 4.0 and Supply Chain 4.0, literature on risks in Supply Chain 4.0, findings, discussions, and future works.

13.2 Research Methodology The research methodology of the literature review study is conducted within five phases in order to identify Supply Chain 4.0 risks. These phases, which are research planning, selection/elimination of papers, screening, reviewing the selected papers and results and recommendations, are explained in the following in detail, and the flow diagram of the research methodology that is adopted from [25] is given in Fig. 13.1.

13 Risks in Supply Chain 4.0: A Literature Review Study

165

Fig. 13.1 Flowchart of the research methodology for literature review (Adopted from [25])

13.2.1 Phase I: Research Planning First, detailed research planning is designed to execute a systematical approach. Research planning involves formulating the research objectives and the research questions. The main research questions are as follows;

166

S. C. Yildiz Ozenc et al.

RQ1: What are the risks included in Supply Chain 4.0? RQ2: What are the most addressed risks and risk management approaches in Supply Chain 4.0? RQ3: What is the current status of the literature on risks in Supply Chain 4.0? RQ4: What are under-explored research areas and future research directions for Supply Chain 4.0?

13.2.2 Phase II: Selection/Elimination of Articles After formulating the research questions, articles are found by searching the two databases (SCOPUS and Web of Science) using 2-level keyword formulation not to miss any articles in the literature. The first level of the formulation is “risks in Supply Chain 4.0”. The second level of keyword formulation includes the terms of Industry 4.0 components such as “Industry 4.0,”, “Blockchain,” “Big Data,” “Artificial Intelligent,” “3D Printers”, “Internet of Things,” “Augmented Reality,” “Cloud Computing”, “Cyber-Physical System” etc. The formulation of keywords is given in Table 13.1. Then, inclusion and exclusion criteria are determined and given in the following in detail. Inclusion criteria: Document types: journal articles, conference papers, and book chapters. Subject area: engineering, computer science, business management, and decision science. Publication period: 2015–2021. Exclusion Criteria: Exclude the irrelevant articles with respect to the subject. Exclude the articles which have not included specified search terms, either title, abstract, or keywords. Table 13.1 2-level keyword formulation Level 1 Risks in supply chain 4.0 Level 2 Industry 4.0, Blockchain, Big Data, Artificial Intelligence, Additive Manufacturing, 3D Printers, Data Analytics, Digital Storage, Big Data Analytics, Blockchain Technology, Digitalization, Internet of Things, Augmented Reality, Cloud Computing, Cyber-Physical System, Cyber Security, Industry 5.0

13 Risks in Supply Chain 4.0: A Literature Review Study

167

30 20 10 0 2021

2020

2019

2018

2017

Fig. 13.2 Number of papers by year

13.2.3 Phase III: Screening In the preliminary screening phase, a total of 121 papers (48 conference papers, 47 articles, 11 conference reviews, 7 reviews, 6 book chapters, 1 book, and 1 editorial) were found before applying inclusion and exclusion criteria. Journal articles, conference papers, and books were selected for further analysis because this topic is fairly new, and the publications are very limited in this area. Subject areas were selected as the engineering, computer science, business management, and decision making. By selecting these areas, the irrelevant subject areas have been eliminated, such as economics, econometrics, finance, medicine, social sciences, and art and humanities. Since our research topic gained growing acceleration after 2011 with the emergence of the Industry 4.0 paradigm, the publication period of all documents was between 2015 and 2021. Further, the articles which were not relevant to the objective have been eliminated, and consequently, 78 papers (44 conference papers, 29 articles, 5 book chapters) were selected for the final screening stage. The distribution of the number of papers by year is given in Fig. 13.2.

13.2.4 Phase IV: Reviewing the Selected Articles The most relevant documents that have been found in the final screening stage are analyzed in detail to identify, evaluate and classify the risks in Supply Chain 4.0. The most relevant articles were deeply reviewed in the frame of four research questions.

13.2.5 Phase V: Results and Recommendations Since Supply Chain 4.0 and related topics have recently become a popular research area, there are not enough sources to address adoption and risks. Also, most of the studies are review papers or focus only on the theoretical part of the study; there is a lack of empirical studies. This makes this topic open to future development. The most used Industry 4.0 technologies in SCs big data, cyber-physical systems, the internet of things, and cloud computing. There is a research gap for using some

168

S. C. Yildiz Ozenc et al.

technologies in SCs such as blockchain, augmented reality, etc., and this forms a promising research area for future studies.

13.3 Theoretical Background of Supply Chain 4.0 13.3.1 Industry 4.0 Over the last decades, the fourth industrial revolution, Industry 4.0, has had a crucial impact on our lives in many fields such as manufacturing, service sector, and daily life, and its growth accelerates globally. Industry 4.0 is described as conducting the real-time connection between physical and digital systems via a connected network where these systems can interact without any need of human beings [15]. The fourth industrial revolution comes up with new technological developments and concepts, including smart factories, cyber-physical systems, and self-organization [18]. In addition to these concepts, there are some other concepts such as the internet of things, internet of services, smart products, machine-to-machine, big data, and cloud computing [8, 9]. Cyber-physical systems, the internet of things, smart factory and the internet of services are the most commonly used technologies in Industry 4.0 [8, 9]. Nowadays, Industry 4.0 is continuously growing, especially in the manufacturing area with newer technologies such as augmented reality, big data, cloud computing, digital supply chain, internet of things, nanotechnology, omnichannel, robotics, sensor technology, SC operations reference, self-driving vehicles, unmanned aerial vehicle and 3D printing [1]. Additionally, vertical and horizontal integration is an integral part of Industry 4.0.

13.3.2 Supply Chain 4.0 Integration of smart technologies to an SC creates a digital supply chain (DSC), in other words, Supply Chain 4.0. There are several definitions of Supply Chain 4.0 (SC 4.0) in the literature. Most basically, SC 4.0 uses Industry 4.0 embedded technologies in an SC. Büyüközkan and Göçer [1] provided a comprehensive definition of DSC as “an intelligent best-fit technological system that is based on the capability of massive data disposal and excellent cooperation and communication for digital hardware, software, and networks to support and synchronize interaction between organizations by making services more valuable, accessible and affordable with consistent, agile and effective outcomes.” There is a need for technological adaptation of SC to increase efficiency, speed, transparency, and reliability [32]. Using innovative technologies of Industry 4.0 makes SCs faster, reliable, efficient, flexible, accurate, globally connected, agile, and transparent. Other benefits are realtime decision making, optimal resource allocation, higher productivity, improved

13 Risks in Supply Chain 4.0: A Literature Review Study

169

customer satisfaction, and enhancement of operational and business performance [12, 27, 28, 43]. Recently, global supply chains (GSCs) faced a major disruption called the COVID19 pandemic. Pandemic has a negative impact on all stages of GSCs, such as manufacturing, processing, transport, logistics, and demand shifting [38]. Guan et al. [11] found that SC losses depend on much more duration of lockdown than lockdown strictness. In this process, all sectors, especially education, went digital to minimize the negative effects of the pandemic. From our point of view, the pandemic highlighted the importance of digitalization in SCs.

13.3.3 Literature on Risks in Supply Chain 4.0 Risk in an SC can be defined as a failure, damage, or loss caused by any unforeseen events [44]. Harland et al. [13] categorized risks as strategic, operations, supply, customer, reputation, financial, etc. Jüttner et al. [16] gave three groups of risks, environmental risks, network-related risks, and organizational risks. Unlike these two groups, supply chain risks can be categorized mainly into two groups that are internal and external risks [21, 37]. They defined internal risks as capacity, operation, information system risks, and external risks as market risks, nature, political system. Likewise, Trkman and Mccormack [35] stated the risks in the supply chain in two categories: endogenous and exogenous risks. Endogenous risk is fluctuation in the market and technology. Exogenous risks are price changes, inflation rates, etc. In addition, Samvedi et al. [30] identified the risks in the supply chain as supply risks, demand risks, process risks, and environmental risks. Er and Fırat [4] provided the most comprehensive risk categorization, financial risks, organizational risks, supply risks, manufacturing risks, customer and market risks, logistics and transportation risks, technological risks, environmeZekhninintal risks, geopolitical risks, regulatory, legal, and bureaucratic risks, industry- and/or SC structure-specific risks. New risk types arise with the adaption of Industry 4.0 technologies. Basically, risks in Supply Chain 4.0 can be categorized as financial risks, supply risks, demand risks, operational risks, environmental risks, and Industry 4.0 risks [41]. This study focuses on Industry 4.0 risks in SCs. Industry 4.0 risks include cyber-attacks, information security risks, computer security risks, and technology adaption risks. Another risk factor for SC 4.0 is the human factor. Lack of skilled workers for using new technologies is a significant risk for the industry. Previous studies of the authors include measurement of awareness and knowledge level about Industry 4.0 technologies of undergraduate students [39, 40]. It is found that the students are lack awareness or have very little knowledge level in this area. This constitutes a risk for finding skilled graduates in this area. Generally, literature on Supply Chain 4.0 risks focuses on cyber security risks. The reviewed articles were categorized into three risk groups; cyber security risks, challenges of implementing digital SCs and other risk types.

170

S. C. Yildiz Ozenc et al.

Zekhnini et al. [41] considered new technologies like IoT and smart systems, and they stated the risks in SC 4.0 as financial, supply, demand, operational, environmental, and Industry 4.0 risks. They analyzed supply chain 4.0 risks by using the AHP approach and examined relationships among risks. They found that cyberattacks, products arrival delays, and information security risks are the first three most important risk factors. Zekhnini et al. [42] focused on new Industry 4.0 technologies and identified the SC 4.0 risks as cyber-attack, spyware, phishing, pharming, malware and loss of data, confidentiality risk, information security risks, transaction risk, risks to computer security. They also proposed a framework for efficient risk management for SC 4.0 risks. The proposed framework has 4 stages, identify SC 4.0 risks, analyze SC 4.0 risks, implement SC 4.0 risk management and review the SC 4.0 risk management system. Pandey et al. [22] focused on cyber-physical systems and identified SC 4.0 risks like cyber security risks, which include information theft, poor protection of cargo in transit, plant malfunctioning, counterfeit products, failure of its equipment, product specification fraud, manipulation of data, poor cryptographic decision, poor protection of cargo in transit, partners trust which occurs along with the end-to-end cyber SC. They categorized cyber security risks into three groups; supply risks, operational risks, and demand risks based on expert opinions and case studies. They also proposed a framework for cyber security attacks and mitigating these risks by using some mitigation strategies such as software assurance approach, data management, and demand-related information. Sobb et al. [32] considered Industry 4.0 components as 5G and wireless communication systems, cloud computing, IoT and Industry 4.0 systems, cyber-physical systems (cps), blockchain technology, smart contracts, artificial intelligence-enabled applications in terms of cyber security risks. They gave a comprehensive overview of cyber security challenges, solutions, and future direction of SC 4.0, especially in terms of military SC 4.0. Radanliev et al. [24] focused on new SC technologies such as the industrial internet of things, cloud technologies, etc., related to cyber risks. They provided a systematic literature review based on integrating new technologies in SCs and related cyber risks. They developed a transformational roadmap for the industrial internet of things enabled SC of SMEs. Tagarev [34] considered Industry 4.0 and Industry 5.0 technologies in cyber security. They provided a comprehensive study of governance needs, objectives, and requirements of collaborative networked organizations. They interviewed organizations, and the results addressed 16 governance issues such as profit orientation, geographical representation, and limitations, SC security, involvement of external stakeholders, etc. Raut et al. [27] considered RFID, IoT, cloud computing, big data analysis, and blockchain technology in the frame of cyber attacks in implementing blockchain technology. They provided a systematic review to evaluate Industry 4.0-enabling technology on SC management. Büyüközkan and Göçer [1] considered Industry 4.0 technologies as augmented reality, big data, cloud computing, digital supply chain, internet of things, nanotechnology, omnichannel, robotics, sensor technology, supply chain operations reference, self-driving vehicles, unmanned aerial vehicle, 3D printing in the frame of challenges and issues of implementing Digital Supply Chain (DSC) such as lack of planning, lack of collaboration, wrong demand forecast, lack of information sharing, silver

13 Risks in Supply Chain 4.0: A Literature Review Study

171

bullet chase, lack of knowledge, agility, and flexibility, high volatility, overconfidence on suppliers, lack of integration. They provided a comprehensive overview of digital SCs in terms of definition, features, components, benefits, and challenges of DSCs. They also proposed a framework to improve DSCs. Schlüter [31] focused on RFID and Big Data applications in SCRM based on real-time risk-related information transparency. They proposed a simulation-based approach for digitalization scenarios in SCRM. Ivanov and Dolgui [14] considered physical and cyber network SC and focused on disruption risks in SCs. They studied the digital twin of SCs to manage disruption risks using model-based and data-driven approaches. Dolgui et al. [3] proposed frameworks for digital cyber-physical SCs, SC risks, and control analytics. Diaz et al. [2] focused on Artificial Intelligence in ship-building and repair sub-tier SCs, and the following supplier risks: availability of component, material and service (also cyber risks), and business and operational risks, which are industrial sector enablers and barriers affected by the business sector. They proposed a simulation-based framework for ship-building risks using real-time data in order to analyze risk assessment and decision making. The stages of their study are as follows: (1) Mapping and simulating the SC, (2) Developing substitution and innovation effects, and (3) Embedding artificial intelligence tools. Kaur and Singh [17] considered disaster sensing using BDA, RFID/Barcodes, public–private partnership, smart contracts using blockchain, data visibility across the value chain, IoT infrastructure, shared platforms, resource efficiency using cloud computing, GIS/GPS enabled logistics, manufacturing flexibility, additive manufacturing, cyber security. They identified the following SC risks: forecast risks, procurement risk, quality & time problems of delivered items due to supplier price hikes, improper contract conditions or any inefficiency at supplier’s end, capacity risk, inventory risk, systems risk, which is in integrated cyber-physical systems. They developed a methodology for a global company for supplier selection and order allocation in the process of digital transition into Industry 4.0 by considering the minimization of disruption risks and disaster events. Lohmer et al. [19] focused on blockchain technology (BCT) with risk-related BCT scenarios. They developed scenarios for the potential application of blockchain technology in SCRM and investigated the influence of resilience strategy theoretically. Then, they made a quantitative simulative analysis of potential BCT technology, its benefits, and impacts. Vieira et al. [36] focused on big data integration and classified SCRM risks as macro risks: natural order (e.g., weather-related or earthquakes) or man-made (e.g., wars, terrorist attacks, political-related) and micro risks: demand, manufacturing, supply, and infrastructural risks. They reviewed the supplication of simulation methods in SCRM. They emphasized the data integration approaches and expressed data integration level (manual or advanced techniques such as big data technologies). Ralston and Blackhurst [26] focused on smart systems and considered capability loss as SC risk. They provided a case study to determine whether using smart systems of Industry 4.0 provides firms capability enhancement or capability loss in SCs. They conducted semi-structured interviews with 7 firms in the transportation and manufacturing industry. They found that, Industry 4.0 has a positive impact on firms’ performance, and Industry 4.0 does not cause any capability loss. Faridi et al. [6] focused on blockchain and IoT in the

172

S. C. Yildiz Ozenc et al.

textile industry. They identified the challenges of the textile industry as traceability and reliability because blockchain and IoT-based traceability may cause information explosion. They developed a blockchain and IoT-based system for traceability of the products in the textile industry. Milwandhari and Maniah [20] focused on cloud computing in logistic processes. They determined risks of implementing cloud technology in 13 logistic activities: customer service, demand forecasting, logistics communications, inventory control, material handling, order processing, parts, and service support, plant and service support, procurement, packing, reverse logistics, traffic, and transportation, warehousing and storage, and also determined data security risks in the cloud for data storage. They provided a risk analysis for cloud computing applications in logistic activities. They did an online survey of companies that use cloud technology and determined which logistic activities can utilize from the cloud technology. Results show that out of 13 activities, all activities can be implemented with cloud technology except Order Processing activities. It can be implemented after the packaging activity process is completed. Rifqi et al. [29] focused on big data. They provided an overview of Big Data for Lean Six Sigma (LSS). They discussed the hybrid model of Big Data and LSS. They also provided an LSS framework with big data analytics. They dedicated big data techniques for the LSS phases (DMAIC). Portna et al. [23] focused on new technologies within Industry 4.0 and identified the strategic challenges of the company as loss of control over technologies, uneven distribution of power, and loss of real and potential demand for the company’s products.

13.4 Findings, Discussion, and Conclusion Articles are analyzed in detail to answer the identified four research questions. Based on the literature review, SC 4.0 risks are grouped into three categories, as shown in Table 13.2. These risks are cyber security risks, risks of implementing DSC, and other risks. Within the scope of RQ2, the most addressed risk type in Supply Chain 4.0 is a cyber security risk. Percentages of the top 8 risks included in the reviewed publication are given in Fig. 13.3. Cyber security risks are threatening organizations all over the globe. For this reason, cyber security should be considered an integral part of the strategy, design, and operations for organizations to handle a competitive environment between companies in the Industry 4.0 context [22]. Cyber attacks on a physical system have become a trending issue in today’s digital world. According to Symantec [33] report, the manufacturing industry has been one of the most popular targets for attackers. Pandey et al. [22] proposed a framework to mitigate cyber security attacks. They stated the cyber security attacks like password sniffing/cracking software, spoofing attacks, denial of service attacks, direct attacks, malicious tampering, and insider threat. Further, they determined the risk mitigation strategies, software assurance approach, data management, and demand-related information. The software assurance approach aims to reduce the vulnerabilities of

13 Risks in Supply Chain 4.0: A Literature Review Study

173

Table 13.2 Summary of the risk types in Supply Chain 4.0 Types of risks

Risks

Cyber security

The cyberattack, Information security risks, Risks to computer-security, Spyware, Phishing, Pharming, Malware and loss of data, Confidentiality risk, Transaction risk, Information theft, Plant malfunctioning, Counterfeit products, Failure of IT equipment, Product specification fraud, Manipulation of data, Poor cryptographic decision, Poor protection of cargo in transit, Partners trust

Risks of implementing DSC

Lack of planning, Lack of collaboration, Wrong demand forecast, Lack of information sharing, Silver bullet chase, Lack of knowledge, Agility and Flexibility, High volatility, Overconfidence in suppliers, Lack of integration

Other risks

Supplier risks, Business and operational risks, Customer risks, Forecast risks, Procurement risk, Capacity risk, Inventory risk, System’s risk, Macro risks, Micro risks, Capability loss, Disruption risks

Fig. 13.3 Top eight risk types in SC 4.0

the system. Data management deals with two concerns which are information security and information access. Demand-related information which is stored in a cloud system might be accessible for all members of an SC. Although it is beneficial, cloud system has a particular risk of being attacked. In this situation the system and the data can become unavailable and unusable. Likewise, Dolgui et al. [3] proposed a framework for control analytics in risk management in a cyber-physical SC. It consists of four stages, risk analysis, modeling, control, and learning. In the risk analysis stage, descriptive and diagnostic analyses were performed regarding past time disruption analysis. Also, process analysis, performance and resilience analysis, and marketing analysis are carried out. In the modeling stage, simulation and optimization models are performed with real-time control that supplies real-time flow control, disruption identification, real-time performance, and recovery control. The control stage provides real-time control such as

174

S. C. Yildiz Ozenc et al.

manufacturing, inventory and shipment feedback control, risk control, and stability control. The learning stage includes risk mitigation learning, disruption recovery learning, and disruption pattern recognition. Within the scope of RQ3, existing literature mostly focuses on the development of frameworks, theoretical studies, reviews, and case study approaches. The articles that show the economic and operational advantages of Supply Chain 4.0 are very limited. Thus, there is a need for empirical studies to demonstrate the economic and operational benefits of using Industry 4.0 technologies in SCs. Another finding within the scope of RQ3 is the multitude of conference papers. Publications are dominated by conference papers. In this field, more comprehensive, systematic, practical, and empirical studies are needed instead of theoretical studies and reviews. Therefore, the managerial and operational impacts of new innovative technologies on an SC can be detected easily. Within the scope of RQ4, research gaps and future directions are determined. The main research gap includes the lack of using some innovative technologies such as blockchain and 3D printers in SC 4.0 to eliminate some risks. Blockchain and its decentralized structure provide SC speed, transparency, and security with smart contracts. 3D printers provide an opportunity to produce some parts and components of a product. This technology might help to reduce supplier risks. Using this kind of technology in SC 4.0 will be one of the most promising future research directions. Another finding in the scope of RQ4 is the lack of standard guidelines or roadmaps while implementing and managing digital SCs considering risks. Challenges, limitations, and implementation issues of SC 4.0 should be assessed in detail. Additionally, governments should focus on implementing digital SCs too. Finally, for under-explored research areas, a mind map was developed by taking an expert opinion with an interview, and the most promising future directions for SC 4.0 risks were determined. An overview of the promising future directions is given in Fig. 13.4. Digital SC has become a new promising research area recently due to the advancement of Industry 4.0 enabled technologies in both academy and business environments. The research areas of Supply Chain 4.0 and risks in Supply Chain 4.0 are still in their infancy stage. For a future study, the authors will cooperate with a company that is adopting digitalization strategies.

13 Risks in Supply Chain 4.0: A Literature Review Study

175

Fig. 13.4 Overview of promising future research directions

References 1. Büyüközkan G, Göçer F (2018) Digital supply chain: literature review and a proposed framework for future research. Comput Ind 97:157–177. https://doi.org/10.1016/j.compind.2018. 02.010 2. Diaz R, Smith K, Acero B, Longo F, Padovano A, Diaz R, Smith K, Acero B, Longo F, Padovano A, Pietro V (2021) Developing an artificial intelligence framework to assess shipbuilding and repair sub-tier supply chains risk. Procedia Comput Sci 180:996–1002. https://doi.org/10.1016/ j.procs.2021.01.363 3. Dolgui A, Sokolov B, Ivanova M, Ivanov D, Dolgui A, Sokolov B, Ivanov D, Dolgui A, Chantrerie L, Kastler A, Spiiras RAS (2019) Intellectualization of control: cyber-physical supply chain risk analytics. IFAC PapersOnLine 52(13):355–360. https://doi.org/10.1016/j.ifa col.2019.11.146 4. Er M, Fırat SÜ (2017) Supply chain risks: literature review and a new categorization. Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi 10(1):31–60 5. Er M, Fırat SÜ, Ghadge A (2020) A data mining-based framework for supply chain risk management. Comput Ind Eng 139:105570. https://doi.org/10.1016/j.cie.2018.12.017 6. Faridi MS, Ali S, Duan G, Wang G (2020) Blockchain and IoT based textile manufacturing traceability system in Industry 4.0. In: International conference on security, privacy and anonymity in computation, communication and storage, pp 331–344 7. Ferrantino MJ, Koten EE (2019) Understanding Supply Chain 4.0 and its potential impact on global value chains. Global Value Chain Development Report 8. Fırat OZ, Fırat SÜ (2017) Endüstri 4.0 Yolculu˘gunda Trendler ve Robotlar. ˙Istanbul Üniversitesi ˙I¸sletme Fakültesi Dergisi 46(2):211–223. https://doi.org/10.5152/iujsb.2017.005 9. Fırat SÜ (2017) Sanayi 4.0 Dönü¸sümü Nedir? Belirlemeler ve Beklentiler. Global Sanayici: Ekonomi ve ˙I¸s Dünyası Dergisi ÇOSB Yayını 10. Ghadge A, Er Kara M, Moradlou H, Goswami M (2020) The impact of Industry 4.0 implementation on supply chains. J Manuf Technol Manage 31(4):669–686 11. Guan D, Wang D, Hallegatte S, Davis SJ, Huo J, Li S, Bai Y, Lei T, Xue Q, Coffman DM, Cheng D, Chen P, Liang X, Xu B, Lu X, Wang S, Hubacek K (2020) Global supply-chain effects of

176

12.

13. 14.

15.

16.

17.

18. 19.

20.

21. 22.

23.

24.

25. 26.

27.

28.

29.

30.

S. C. Yildiz Ozenc et al. COVID-19 control measures. Nat Hum Behav 4(6):577–587. https://doi.org/10.1038/s41562020-0896-8 Gunasekaran A, Papadopoulos T, Dubey R, Fosso S, Childe SJ, Hazen B, Akter S (2017) Big data and predictive analytics for supply chain and organizational performance. J Bus Res 70:308–317. https://doi.org/10.1016/j.jbusres.2016.08.004 Harland C, Brenchley R, Walker H (2003) Risk in supply networks. J Purch Supply Manag 9(2):51–62. https://doi.org/10.1016/S1478-4092(03)00004-9 Ivanov D, Dolgui A (2020) A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Prod Plann Control, 1–14. https://doi.org/10.1080/095 37287.2020.1768450 Ivanov D, Sethi S, Dolgui A, Sokolov B (2018) A survey on control theory applications to operational systems, supply chain management, and Industry 4. 0 R. Annu Rev Control 46:134– 147. https://doi.org/10.1016/j.arcontrol.2018.10.014 Jüttner U, Peck H, Christopher M (2010) Supply chain risk management: outlining an agenda for future research. Int J Log Res Appl 6(4):197–210. https://doi.org/10.1080/136755603100 01627016 Kaur H, Singh SP (2021) Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies. Int J Prod Econ 231:107830. https:// doi.org/10.1016/j.ijpe.2020.107830 Lasi H, Fettke P, Kemper H-G, Feld T, Hoffman M (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242. https://doi.org/10.1007/s12599-014-0334-4 Lohmer J, Bugert N, Lasch R (2020) Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: an agent-based simulation study. Int J Prod Econ 228:107882. https://doi.org/10.1016/j.ijpe.2020.107882 Milwandhari S, Maniah (2020) Risk analysis of cloud computing in the logistics process. In: 2020 Third international conference on vocational education and electrical engineering (ICVEE), pp 1–5. https://doi.org/10.1109/ICVEE50212.2020.9243247 Olson DL, Wu DD (2010) A review of enterprise risk management in supply chain. Kybernetes 39(5):694–706. https://doi.org/10.1108/03684921011043198 Pandey S, Singh RK, Gunasekaran A, Kaushik A (2020) Cyber security risks in globalized supply chains: conceptual framework. J Glob Oper Strateg Sourcing 13(1):103–128. https:// doi.org/10.1108/JGOSS-05-2019-0042 Portna OV, Iershova NY, Tereshchenko DA, Kryvytska OR (2021) Economic business partnerships within Industry 4.0: new technologies in management. Montenegrin J Econ 17(1):151–163. https://doi.org/10.14254/1800-5845/2021.17-1.11 Radanliev P, Roure DD, Page K, Nurse JRC, Montalvo RM, Santos O (2020) Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and Industry 4. 0 supply chains. Cybersecurity 3:1–21 Rajagopal V, Venkatesan SP, Goh M (2017) Decision-making models for supply chain risk mitigation: a review. Comput Ind Eng 113:646–682. https://doi.org/10.1016/j.cie.2017.09.043 Ralston P, Blackhurst J (2020) Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss? Int J Prod Res 58(16):5006–5019. https://doi.org/ 10.1080/00207543.2020.1736724 Raut RD, Gotmare A, Narkhede BE, Govindarajan UH, Bokade SU (2020) Enabling technologies for Industry 4.0 manufacturing and supply chain: concepts, current status, and adoption challenges. IEEE Eng Manage Rev 48(2):83–102. https://doi.org/10.1109/EMR.2020.2987884 Raut RD, Kumar S, Narwane VS, Gardas BB, Priyadarshinee P, Narkhede BE (2019) Linking big data analytics and operational sustainability practices for sustainable business management. J Clean Prod 224:10–24. https://doi.org/10.1016/j.jclepro.2019.03.181 Rifqi H, Zamma A, Souda Ben S (2021) Lean 4.0, Six Sigma-big data toward future industrial opportunities and challenges: a literature review. Advances on smart and soft computing, pp 201–210. https://doi.org/10.1007/978-981-15-6048-4 Samvedi A, Jain V, Chan FTS (2013) Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS. Int J Prod Res 51(8):2433–2442. https://doi.org/10.1080/ 00207543.2012.741330

13 Risks in Supply Chain 4.0: A Literature Review Study

177

31. Schlüter F (2017) A simulation based evaluation approach for supply chain risk management digitalization scenarios, pp 1–5. https://doi.org/10.1109/ICIMSA.2017.7985579 32. Sobb T, Turnbull B, Moustafa N (2020) Supply Chain 4.0: a survey of cyber security challenges, solutions and future directions. Electronics 9(11):1–31. https://doi.org/10.3390/electronics9 111864 33. Symantec (2019) Internet security threat report, vol 24 34. Tagarev T (2020) Governance of collaborative networked organisations: stakeholder requirements. In: 2020 IEEE 11th International conference on dependable systems, services and technologies (DESSERT), pp 439–445 35. Trkman P, Mccormack K (2009) Supply chain risk in turbulent environments—a conceptual model for managing supply chain network risk. Int J Prod Econ 119(2):247–258. https://doi. org/10.1016/j.ijpe.2009.03.002 36. Vieira AAC, Dias LMS, Santos MY, Pereira GAB, Oliveira JA (2020) Supply chain data integration: a literature review. J Ind Inf Integr 19(July):100161. https://doi.org/10.1016/j.jii. 2020.100161 37. Wu T, Blackhurst J, Chidambaram V (2006) A model for inbound supply risk analysis. Comput Ind 57(4):350–365. https://doi.org/10.1016/j.compind.2005.11.001 38. Xu Z, Elomri A, Kerbache L, El Omri A (2020) Impacts of COVID-19 on global supply chains: facts and perspectives. IEEE Eng Manage Rev 48(3):153–166. https://doi.org/10.1109/EMR. 2020.3018420 39. Yildiz SC, Firat SU (2020) Türkiye’deki Üniversite Ö˘grencilerinin Endüstri 4.0 Bilgi Düzeylerinin Belirlenmesi. J Ind Eng 31, 1–16 40. Yildiz SC, Firat SU (2019) A study on Industry 4.0 awareness of undergraduate students by using structural equational modelling. International statistical congress, 4–8 October 2019, Bodrum, Mu˘gla, Turkey, pp 639–651 41. Zekhnini K, Cherrafi A, Bouhaddou I, Benghabrit Y (2020) Analytic Hierarchy Process (AHP) for Supply Chain 4.0 risks management. In: International conference on artificial intelligence & industrial applications, pp 89–102 42. Zekhnini K, Cherrafi A, Bouhaddou I, Benghabrit Y (2019) Supply Chain 4.0 risk management: bibliometric analysis and a proposed framework. In: International conference on integrated design and production, pp 322–332 43. Zelbst PJ, Green KW Jr, Sower VE, Baker G (2010) RFID utilization and information sharing: the impact on supply chain performance. J Bus Ind Market 25(8):582–589. https://doi.org/10. 1108/08858621011088310 44. Zsidisin GA, Ritchie B (2009) Supply chain risk management—developments, issues and challenges. In: Zsidisin GA, Ritchie B (eds) Supply chain risk. International series in operations research & management science. Springer, Boston

Chapter 14

Supply Chain Risk Prioritization and Supplier Analysis for a Footwear Retailer Esra Agca Aktunc, Simay Altintas, Bengisu Baytas, Nazli Dur, and Asli Zulal Ozokten Abstract Supply Chain Management (SCM) requires the alignment of the flow of material, information, and services in a network of suppliers, manufacturers, distributors, and customers. Timing, quantity, and quality should be streamlined by all the stakeholders. The efficiency of SCM operations is especially essential for the growing e-commerce businesses in an era where uncertainties and risks abound and expectations change dynamically. This study initially identifies and prioritizes SCM risk factors for a footwear retailer, which is in both the traditional and e-commerce market, using the Analytical Hierarchy Process (AHP). After the supplier selection risks are found to be the most important risks for the company, a detailed data analysis is conducted to compare the performances of three critical suppliers. Based on historical data, demand forecasting for the year 2021 was made using seasonal factors. Forecasts are then used as requirements in a supply simulation to identify the extent to which the demands will be met and whether there will be any delays in the procurement process. According to the data analysis, forecasting, and simulation results, recommendations for supplier selection and order timing are made. Keywords Supply chain management · Risk prioritization · Analytical hierarchy process · Supplier selection · Data analysis · Demand forecasting

14.1 Introduction Supply chain management (SCM) is the management of the entire production flow of goods and services, as well as the processes to transform raw materials into final products. Businesses need to streamline supply activities to maximize quality, timely delivery, customer experience, and profitability in the presence of a variety of uncertainties and disruptions. Effective SCM has become even more important in recent

E. Agca Aktunc (B) · S. Altintas · B. Baytas · N. Dur · A. Z. Ozokten Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_14

179

180

E. Agca Aktunc et al.

years, as the amount and range of required services are increasing due to a more interconnected and diverse business environment with the surge of e-commerce accelerated by the COVID-19 pandemic. There has been a significant shift from brickand-mortar retail to e-commerce globally as a result of the necessary confinement measures to fight the COVID-19 crisis. This shift resulted in the share of e-commerce in total retail rising from 11.8% in the first quarter to 16.1% in the second quarter of 2020 in the US and from 20.3 to 31.3% in the UK [16]. Similarly, in Turkey, the share of e-commerce in retail rose from 11.87% in the first quarter to 17.4% in the second quarter of 2020, and a 51.8% increase is observed in the share of e-commerce in the gross domestic product compared to 2019 [20]. Supply chain risk management to maintain reliability and resilience is especially crucial in e-commerce, where the market needs and customer expectations are dynamically changing. Supply chain risk can be defined as “any risk to the information, material and product flow from original suppliers to the delivery of the final product” [4]. To remain competitive, businesses must identify the risks involved in their supply chain and develop and implement solutions to minimize, if not eliminate, the impact of those risks. The focus of this study is the identification and assessment of the supply chain risks of a footwear retailer. This retailer both manufactures its own products and procures a group of its products from more than 300 suppliers to be shipped to its brick-and-mortar stores and to the warehouses for online sales. The company that has almost 500 stores in Turkey and over 100 stores in other countries entered the e-commerce market in recent years and would like to improve its online performance by making its supply chain more resilient. First, the main risks in the supply chain of the retailer are determined based on interviews with the company employees, and a risk assessment survey is conducted to select the most important risk to be addressed using the Analytical Hierarchy Process (AHP) method. After identifying supplier selection as the high priority risk, the performances of a subset of critical suppliers are analyzed and compared based on historical data. Finally, demand for the products procured from these suppliers is forecasted, and suggestions are made regarding the order timing and quantity decisions depending on the supplier performance results. Supply chain risk management (SCRM) is the collaborative effort of supply chain members to identify, evaluate, mitigate, and monitor macro-and micro-level risks for the supply chain using quantitative and qualitative risk management methodologies to reduce supply chain vulnerability [8, 24]. The macro-level risks can be seen as external risks, or discrete risks as suggested by Trkman and McCormack [25], such as natural or man-made disasters, war, terrorism, or political instability, whereas microlevel risks, or continuous risks, can be classified based on their source within the organization or the supply network. In this study, based on interviews and discussions with the company employees, six fundamental SCM risk areas are determined to be supplier selection, procurement planning, sample development and approval, raw material planning and purchasing, quality control, and logistics, all of which are internal or micro-level risks that are operational risks. For an extensive list of risk factors concerning supply chains and recent discussions on the SCRM literature, the reader is referred to the literature reviews of [5, 8, 17, 18].

14 Supply Chain Risk Prioritization and Supplier Analysis …

181

This study focuses on the supply chain risk identification and risk assessment stages of SCRM. Supply chain risk identification studies mostly applied qualitative methods such as conceptual models [25], vulnerability maps [2], value-focused process engineering methodology [14], hazard and operability analysis method [1], and knowledge-based system approach [10]. Only a few studies applied quantitative methods, including the AHP method [6, 26]. However, these studies do not prioritize the negative impact of risk types or risk factors. In this study, the AHP method is used to prioritize the identified SCM risks to focus on the risk which has the most negative impact. Quantitative methods used for supply risk assessment include mathematical programming and data envelopment analysis (DEA), multicriteria decision-making and AHP, Bayesian networks, decision trees, fuzzy-based failure mode, and effect analysis (FMEA) [8]. Supply chain objectives need to be prioritized to identify the risks that can hinder achieving those objectives, and the AHP and fuzzy AHP are widely accepted useful methods for such tasks. For example, Kahraman et al. [9] use fuzzy AHP for domestic supplier selection with a case study including criteria related to the supplier, product performance, and service performance. Chan and Kumar [3] use fuzzy extended AHP to deal with decision criteria, including cost, quality, service performance, and supplier’s risk profile involved in the selection of global suppliers. Kull and Talluri [11] propose a combination of AHP and goal programming for supplier selection considering risk measures and product life cycle. Viswanadham and Samvedi [27] use fuzzy AHP and fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) for supplier selection considering both performance criteria and risks from governments, political and social networks, resources, and delivery systems. Ho et al. [7] combine quality function deployment and AHP to ensure judgment consistency in supplier selection. Radivojevi´c and Gajovi´c [19] compare AHP and fuzzy AHP for risk assessment and show that although the values of vectors, the priority of risk categories, and risk levels are different for the two methods, they provide the same ranking of risk categories as well as the same ranking of the low, medium, and high total risk levels. AHP and the FAHP methods. Gaudenzi and Borghesi [6] apply the AHP method to prioritize supply chain objectives and then select risk indicators, assuming that the main objective is creating customer value through four sub-objectives: on-time delivery, order completeness, order correctness, and damage-free and defect-free delivery. In our study, the data obtained does not include any information on the correctness, damage, or defect of delivered products, therefore, supplier performances are analyzed according to the on-time delivery and order completeness objectives. The supplier analysis is based on the Procurement Process Report, Warehouse Entry Report, and Final Product Report provided by the company, where the data for 1.5 years is obtained, including the fall-winter season of 2019, the spring–summer season of 2020, and the fall-winter season of 2020. The raw data includes fields such as season, variety code, deadline, demand quantities, warehouse entry dates, and warehouse entry quantities. However, the provided data needed to be cleaned and reorganized for the analysis. After the data is pre-processed, measures such as percent of satisfying demand, delivery lead times, early/late delivery rates, and

182

E. Agca Aktunc et al.

quality control pass rates are computed and compared for the selected three critical suppliers of the sneaker and boot product groups. Finally, demand forecasts for 2021 for the selected product group are made based on historical data, and order planning suggestions are shared based on supplier performances.

14.2 Methodology 14.2.1 Risk Identification and Prioritization Using the AHP Method The type of supply chain risk that is deemed to have the highest priority by the company would be the focus of the study; therefore, initially, the most important type of risk had to be determined. The most relevant risks for the company supply chain were identified through interviews with the company employees. The flow diagram of the supply chain shown in Fig. 14.1 was developed and used to track the effects of risks in different stages on the other components. As a result of the interviews, six risk types that are used as alternatives (A1–A6) to be compared to each other were defined as follows: A1: Risks of the supplier selection process. A2: Procurement planning process risks. A3: Sample development and approval process risks. A4: Raw material planning and purchasing process risks. A5: Quality control process risks. A6: Logistics process risks.

Fig. 14.1 Supply chain flow diagram of the retail footwear company

14 Supply Chain Risk Prioritization and Supplier Analysis …

183

Table 14.1 The fundamental nine-point scale of the AHP method [22, 23] Importance intensity

Definition

Explanation

1

Equally important

Two activities contribute equally to the objective

3

Moderately important

Experience or judgment slightly favor one activity over another

5

Strongly important

Experience or judgment strongly favor one activity over another

7

Very Strongly important

One activity is favored very strongly over another

9

Extremely important

One activity is absolutely favored over another

2, 4, 6, 8

Intermediate values between the For compromise judgments two adjacent judgments between the above values

Reciprocals of the above values

If activity i is assigned one of the above positive values (c) when compared to activity j, activity j has the reciprocal (1/c) when compared to activity i

A questionnaire was prepared for the evaluation of these alternatives. This questionnaire was applied to a group of five expert engineers working in the company, who are able to answer questions about these risks since they are experienced employees in the supply chain department. In order to rank the supply chain risks, Saaty’s pairwise comparison is applied [21]. The experts were provided with the equivalent of a square pairwise comparison matrix in the online questionnaire. For each pair of risks, the experts are asked, “How important is the risk in this row compared to the risk in this column for the performance of the supply chain of your company?” The experts were asked to rate each risk using the nine-point scale shown in Table 14.1. The pairwise comparisons of experts (ai j ) were used to calculate the maximum eigenvalue (λmax ), consistency index (CI = (λmax − n)/(n − 1)), consistency ratio (CR ≤ 0.10 is acceptable), and priority weight for each alternative to obtain the ranking of risks. The outcomes are presented in the Results section.

14.2.2 Supplier Data Analysis The supplier analysis is based on the Procurement Process Report, Warehouse Entry Report, and Final Product Report provided by the company. Procurement Process Report includes data regarding the products, seasons for which the order is made (2019 fall-winter, 2020 spring–summer, 2020 fall-winter), brand groups, suppliers, assortment codes, model codes, demand level, deadline, canceled requests, and the

184

E. Agca Aktunc et al.

number of products entering the warehouse. Warehouse Entry Report includes the quantity and entry dates of the products, the number of times delivery is made, and the number of products fulfilled at each delivery. The final Product Report is also provided for review to ensure the information is accurate. The raw data needed to be cleaned and reorganized for the analysis. For instance, the data included products canceled by the retailer or supplier, which had to be deducted from the originally requested quantities. After the data is pre-processed, descriptive statistics are calculated, and measures such as percent of satisfying demand and delivery lead times are computed and compared for the selected three critical suppliers. Details of the supplier data analysis are provided in the Results section.

14.2.3 Demand Forecasting and Supply Simulation Monthly demand forecasts for 2021 for the selected sneaker and boot product group are made based on historical data, and order planning suggestions are shared based on supplier performances. The demand data is available for May 2019–December 2020 period (20 months) for Supplier A, January 2019–December 2020 (24 months) for Supplier B, and June 2019–December 2020 (19 months) for Supplier C. Due to the inherent seasonality of the demand for the selected product group and a different number of periods with available data for each supplier, monthly seasonality factors are calculated to forecast the demand in 2021 for each supplier using the following steps to determine the seasonality factors [13]: 1. 2. 3.

Sample mean, D, of all demand data is computed. Each observation, Dt , i = 1, 2, . . . , T , is divided by the sample mean to get the seasonal factors for each period, st = Dt /D. Factors are averaged for like periods within Σ each season to the seasonal Σget T T s / 1 factors for N periods (12 months), ci = t=1 12t−11 t=1 12t−11 , i = 1, 2, ..., 12, which are finally normalized.

Two forecasting methods are applied: (F1) seasonal forecast with the linear trend and (F2) seasonal forecast with aggregate-disaggregate intermittent demand approach (ADIDA) [15]. First, the linear trend forecasts for each month included in the demand data are multiplied by the corresponding seasonality factor to obtain the forecast. In the second method, a non-overlapping 2-month demand aggregation window is used, and the average of the aggregate demand is multiplied by the corresponding seasonality factor for each month. Forecasting errors are reported in terms of Mean Absolute Deviation (MAD), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE) based on the following equations.

14 Supply Chain Risk Prioritization and Supplier Analysis …

185

1 Σ |Actualt − For ecastt | N t=1 ⎡ | N |1 Σ R M S E = √ ( Actualt − For ecastt )2 N t=1 N

M AD =

M AP E =

⎞ N ⎛ 1 Σ |Actualt − For ecastt | ∗ 100 N t=1 Actualt

⎛ N 1 Σ M AS E = ⎛ N t=1

|Actualt − For ecastt | ⎞ ΣN 1 i=2 |Actuali − Actuali−1 | N −1

(14.1)

(14.2)

(14.3) ⎞ (14.4)

The forecasts for 2021 are used as requirements in a supply simulation is made based on the delivery size and lead time distributions of suppliers determined from the historical data. Demand forecasts and forecasting errors are provided in the Results section.

14.3 Results In the first step of the study, the application of the AHP method for ranking supply chain risks was carried out using both Microsoft Excel and Super Decisions software for validation purposes. According to the questionnaire responses of the five equivalently experienced experts, the pairwise comparisons were analyzed, and the CR value is calculated as 0.083 (CR < 0.10), so the responses are sufficiently consistent. Based on the priority weights shown in Table 14.2, the most important risks were in the supplier selection process (A1), and procurement planning process (A2), whereas the least important risks, were in the quality control (A5) and logistics processes (A6). The alternatives are classified as having high, medium, and low-risk levels according to the weights obtained. The only alternative with a significantly Table 14.2 Priority weights of alternatives Alternatives

Priority weight

Risk level

A1: Supplier selection risks

0.52

High

A2: Procurement planning risks

0.21

Medium

A3: Sample development and approval risks

0.12

Medium

A4: Raw material planning and purchasing risks

0.06

Low

A5: Quality control risks

0.05

Low

A6: Logistics risks

0.03

Low

186

E. Agca Aktunc et al.

Demand A

Supply A

Demand B

Supply B

Demand C

Nov-20

Oct-20

Sep-20

Aug-20

Jul-20

Jun-20

May-20

Apr-20

Mar-20

Feb-20

Jan-20

Dec-19

70 60 50 40 30 20 10 0 Nov-19

Quantity (1000 units)

high-risk level, supplier selection, is identified as the area to focus risk assessment efforts on. As the supplier selection risks are found to be highly important, the demand fulfillment characteristics of three critical suppliers (A, B, C) with high volumes of demand in the sneaker and boot product group are identified by data analysis. The quantities ordered from and delivered by each supplier monthly are shown in Fig. 14.2. Although the data covers September 2019–December 2020 period, there was no demand with delivery deadline in the months of September and October 2019 and May, June, and December 2020 due to the seasonality of footwear products. A summary of demand and supply data is provided in Table 14.3. It is observed that more than half the demand in the study period is requested from Supplier A which is also the only supplier with unmet demand. Out of 10 months when an order placed with Supplier A was due, delivered quantities were below the

Supply C

Fig. 14.2 Demand and supply quantities for Suppliers A, B, C (November 2019–November 2020)

Table 14.3 Demand and supply summary by the supplier Supplier A

Supplier B

Supplier C

Total demand

454,585

233,751

192,337

Share in total demand

51.62%

26.54%

21.84%

Total supply

450,815

236,215

192,986

Difference (Supply–Demand)

−3770

2464

649

Order fulfillment rate

99.17%

101.05%

100.33%

Number of separate deliveries per order (Min, Avg, Max)

(1, 3, 11)

(1, 2.9, 11)

(1, 2.7, 10)

Delay time (Days) (Min, Avg, Max)

(−25, 67, 351)

(−50, 73, 295)

(−48, 40, 283)

Early delivery rate

5%

8%

15%

On-time delivery rate

1%

3%

1%

Late delivery rate

94%

89%

84%

Quality control pass rate

80%

83%

93%

14 Supply Chain Risk Prioritization and Supplier Analysis …

187

80 Frequency

60 40 20 0 1

2

3

4

5

6

7

8

9

10

11

Number of deliveries per order A B C

Fig. 14.3 Number of separate deliveries per order for Suppliers A, B, C

expected values (by 614 units on average) in 7 months and above the expected values (by 176 units on average) in 3 months. Supplier B and C delivered an average of respectively 616 and 130 units more than the expected values in 4 and 5 months out of 8 months each. Although excess supply might be seen as an issue if it is in large quantities, the small average monthly proportions of excess supply from three suppliers (0.3%, 1%, 0.4%) are insignificant for the retailer, and excess units can be shipped to stores easily. The quality control pass rates for products delivered by the three suppliers are 80%, 83%, and 93%, respectively. Therefore, Supplier C has the best performance in terms of delivering high-quality products among the three. For large quantity orders, split deliveries are commonly preferred, and smaller quantities are shipped over a period of time to control inventory and logistics investments. In terms of the number of separate deliveries shown in Table 14.3 and the histograms in Fig. 14.3, all suppliers have similar results, but Supplier C has a slightly better performance with an average of 2.7 and a maximum of 10 split deliveries per order. However, the timing of deliveries should also be monitored to see if there are any late deliveries. It is assumed that the procurement of an order is completed when the last delivery is made. Therefore, the lead time of an order is the number of days from when the order is placed by the retailer with the supplier to the time of the last delivery for that order. The late (or early) deliveries are identified by taking the difference between the deadline set by the retailer and the last delivery time for an order. The histograms of delivery delay time in days are given in Fig. 14.4, for orders of 0–1000, 1000– 5000, and over 5000 units. It should be noted that early deliveries of up to 50 days are possible for all suppliers and the majority of all delays are less than 50 days. Supplier A has a higher frequency of delay for orders of 1000–5000 units, whereas more than half of delays of Suppliers B and C are for orders of up to 1000 units. Overall, the proportion of late deliveries are 94%, 89%, and 84% for suppliers A, B, and C. The proportion of on-time deliveries are merely 1%, 3%, and 1%, whereas the proportion of early deliveries are 5%, 8%, and 15%. Although Supplier B has the highest on-time delivery rate, it is still a low rate, and in terms of early/late delivery rates, the most reliable supplier would be Supplier C for the retailer.

Frequency (orders of 0-1000 units)

188

E. Agca Aktunc et al. 50

42

40 30

2321

20

17 12

13

10

6

6

10

17 11

10 6

6

4

022

1

10

000

100

0

Frequency (orders of 1000-5000 units)

[-50,0] 60

(50,100] (100,150](150,200](200,250](250,300](300,350](350,400]

51

50 40 30

20 13

20 10

23 17

15 8

7 3 5

1

4

7

3

0

2 2 2

5 3 2

1 0 0

0 [-50,0]

Frequency (orders of over 5000 units)

(0,50]

12

(0,50]

(50,100] (100,150] (150,200] (200,250] (250,300] (300,350]

10

10 7

8 6 4

5

4 2 2

6

2

2

3

4

3 0

0

1

0

1

0 0

1

0 0

0 [-50,0]

(0,50]

(50,100] (100,150] (150,200] (200,250] (250,300] (300,350] Supply delay in days for orders A B C

Fig. 14.4 Histograms of delay time in days for orders of 0–1000, 1000–5000, and over 5000 units

In the final step of the study, monthly demand forecasting is performed for the year 2021 based on historical data for each supplier to display the effect of the variability on forecast accuracy. The normalized seasonal factors are provided in Table 14.4. Demand data is available for the past 20, 24, and 19 months before January 2021 for suppliers A, B, and C, respectively. Demand forecasts for each supplier are obtained as the product of the seasonal factors and linear trend forecasts for each month included in the data in Forecast 1 (F1). Forecast 2 (F2) is obtained as the product of the seasonal factors and ADIDA with a non-overlapping 2-month aggregation window. The actual demand and forecasts are plotted in Fig. 14.5. The sample statistics and forecasting errors are shown in Tables 14.5 and 14.6, respectively.

14 Supply Chain Risk Prioritization and Supplier Analysis …

189

Table 14.4 Normalized seasonal factors for suppliers Month

A

B

C

Month

A

B

C

Jan

1.39

1.18

1.86

Jul

1.17

1.27

0.57

Feb

1.15

0.82

0.29

Aug

1.10

1.49

2.28

Mar

1.35

1.90

1.18

Sep

1.11

1.51

0.94

Apr

1.58

1.09

0.00

Oct

0.53

0.14

1.95

May

0.74

0.56

0.00

Nov

0.48

0.83

1.39

Jun

0.60

1.22

0.77

Dec

0.80

0.00

0.78

Supplier A 100000 80000 60000 40000 20000 0

Supplier B

50000 40000 30000 20000 10000 0

Supplier C

80000 60000 40000 20000 0

Actual Demand

Forecast 1

Forecast 2

Fig. 14.5 Forecast results compared to actual demand for Suppliers A, B, and C

190

E. Agca Aktunc et al.

Table 14.5 Sample statistics by the supplier Supplier A

Supplier B

Supplier C

Sample Size (# of months)

20

24

19

Sample mean

35,301.50

15,097.00

14,707.21

Sample St. Dev.

22,846.18

14,743.10

15,875.89

Demand forecast (F1)

396,963

239,466

207,356

Simulated supply

394,134

241,622

207,923

Fulfillment rate (%)

99.29%

100.90%

100.27%

Average delay time per unit (days)

67

78

44

Table 14.6 Demand forecasting errors by the supplier using Forecast 1 (F1) and Forecast 2 (F2) Supplier A Method

F1

Supplier B F2

F1

Supplier C F2

F1

F2

MAD

15,821.52

10,387.59

9,349.56

5,071.38

9,275.82

7,235.48

RMSE

18,613.19

13,402.18

11,300.85

7,906.38

12,138.30

9,467.84

30.84

24.04

47.13

34.48

19.94

22.75

0.87

0.57

0.67

0.36

0.63

0.50

MAPE (%) MASE

As shown in Table 14.5, demand variability is considerably high in terms of standard deviation since the footwear products have seasonal demand, and there may even be no orders in certain months. Therefore, this variability adversely affects forecast accuracy and, as shown in Table 14.6, results in high forecasting errors between 20 and 47% in terms of MAPE and between 0.36 and 0.87 in terms of MASE. However, it is observed that the forecast error can be reduced by using the second forecasting method with demand aggregation based on ADIDA. The main focus of the current study is not improving the accuracy of the demand forecast; therefore, as a future extension of this study, different forecasting methods that might be more suitable for intermittent demand can be applied. A sample supply simulation is made to see how the suppliers would perform, using the monthly forecasts for 2021 of F1 and supplier delivery characteristics based on historical data. The delivery size and delay time distributions provided in Table 14.7, Table 14.7 Delivery size and delay time distributions by the supplier

Supplier A

Supplier B

Supplier C

Delivery size (units)

12 + GAMM(2450, 0.898)

4 + 8040 * BETA(0.51, 1.44)

2+ WEIB(1330, 0.356)

Delay time (days)

– 25 + NORM(72.7, GAMM(62.8, 59.3) 1.47)

−48 + WEIB(92.9, 1.21)

14 Supply Chain Risk Prioritization and Supplier Analysis …

191

as well as quality control pass rates, are used in this simulation, where the forecasted monthly demand was used as an input. The resulting simulated total supply quantities and demand fulfillment rates are presented in Table 14.5. Overall, Supplier A lacks 1% of the demand, whereas Supplier B is delivering 1% more units than the demand. Supplier C is not fluctuating the delivered quantities, although there are late deliveries, like the other suppliers. It is evident that the suppliers would not be able to meet the majority of requests in time if orders were not placed earlier. Since the highest volume of orders is expected from Supplier A, the fact that it has the highest late delivery rate is a sign that special precautions must be taken. The retailer should consider either placing orders earlier or reallocating the demand among suppliers. Supplier C has the best performance in terms of meeting the demand in time; however, even its late delivery rate is 84%, which still requires attention. The delivery fulfillment performance ranking of suppliers is C > B > A. Therefore, the retailer should consider using the forecasts to place orders with the suppliers, especially A and B, earlier to allow enough time for in-time deliveries.

14.4 Discussion and Conclusion In the current global market, managing supply chains efficiently is essential for businesses, especially in e-commerce, due to the disruptions caused by recent developments such as the COVID-19 pandemic. While there are many possible risks and disadvantages to consider, the advantages are extremely rewarding if a company chooses the right suppliers. In this growing competitive business environment, operational partnerships with suppliers and customers as stakeholders in the supply chain are crucial to creating a reliable business. In this study, the supply chain processes of a footwear retailer have been examined. First, the supply chain process risks are determined, and a survey is administered to gather the pairwise comparisons from five supply chain experts from the company. As a result of the AHP method applied using the survey responses, among the identified risks, the most important one is determined to be the supplier selection process risks. In this regard, the data provided are analyzed for three major suppliers, A, B, and C. Supplier characteristics are described in terms of delivery quantities, order fulfillment rates, quality control pass rates, number of separate deliveries per order, and order fulfillment times. Then, on the basis of past data, demand forecasting is performed for the year 2021, considering the seasonality of the footwear products ordered from these suppliers. Based on demand forecasts, demand fulfillment and delay conditions were examined using a supply simulation model that is built according to the order fulfillment rates and delay time distributions of the suppliers. As a result of the analyses, order fulfillment rates are 99%, 101%, and 100%, and late delivery rates are 94%, 89%, and 84% for suppliers A, B, and C, respectively. Supplier A has a high likelihood of not being able to meet the demand completely and in time. On the other hand, Supplier B excessively meets the demand, and the majority

192

E. Agca Aktunc et al.

of deliveries are late, but delivering more than the desired amount may cause problems for the warehouse management of the company, especially if the excess amount is large. Therefore, the company should plan ahead for such uncertainties caused by suppliers A and B by placing orders early and monitoring the supply process more closely for accurate information exchange. Additionally, the quality control pass rates for products delivered by the suppliers are 80%, 83%, and 93%, respectively. Considering all performance measures, it is decided that the most efficient supplier for this group of footwear products is Supplier C, and in the future, more orders can be placed with this supplier provided that the supplier is capable of handling a larger volume of orders. The limitations of this study are the lack of data for years before 2019, which could have provided a better representation of supplier characteristics, and the use of a naïve seasonal demand forecasting method, which is used since the main objective of this study is not to obtain the most accurate forecast. As an extension of this study, different forecasting methods suitable for intermittent demand, such as ADIDA, can be applied with the aim of reducing error rates. Also, as the pandemic is ongoing, collecting more detailed supply chain data would enable more insightful analyses as to how the disruptions affect supplier performance and help the company in reaching its goals in e-commerce. The importance of choosing reliable suppliers as partners in an uncertain business environment, especially in the rapidly growing e-commerce market, is increasing. Conducting comprehensive supplier analyses as presented in this study is required to understand the characteristics and the performance of suppliers in detail. Only after such analyses can a retail company make informed decisions about when, how much, and from which supplier to order specific products. Supplier analysis is a crucial tool for managing the expectations from the suppliers, making proactive decisions to reduce supply chain risks, and, thus, gaining an edge over competitors by meeting dynamic customer expectations. Hence, quantitative supplier analysis is expected to retain its importance for the resilience of supply chains in the future.

References 1. Adhitya A, Srinivasan R, Karimi IA (2009) Supply chain risk identification using a HAZOPbased approach. Am Inst Chem Eng 55(6):1447–1463. https://doi.org/10.1002/aic.11764 2. Blos MF, Quaddus M, Wee HM, Watanabe K (2009) Supply Chain Risk Management (SCRM): a case study on the automotive and electronic industries in Brazil. Supply Chain Manag 14(4):247–252. https://doi.org/10.1108/13598540910970072 3. Chan FTS, Kumar N (2007) Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega 35(4):417–431. https://doi.org/10.1016/j.omega.2005. 08.004 4. Christopher M et al (2003) Understanding supply chain risk: a self-assessment workbook. Centre for Logistics and Supply Chain Management at the Cranfield School of Management, Cranfield University. https://dspace.lib.cranfield.ac.uk/handle/1826/4373

14 Supply Chain Risk Prioritization and Supplier Analysis …

193

5. Fan Y, Stevenson M (2018) A review of supply chain risk management: definition, theory, and research agenda. Int J Phys Distrib Logist Manag 48(3):205–230. https://doi.org/10.1108/IJP DLM-01-2017-0043 6. Gaudenzi B, Borghesi A (2006) Managing risks in the supply chain using the AHP method. Int J Logist Manag 17(1):114–136. https://doi.org/10.1108/09574090610663464 7. Ho W, Dey PK, Lockström M (2011) Strategic sourcing: a combined QFD and AHP approach in manufacturing. Supply Chain Manag 16(6):446–461. https://doi.org/10.1108/135985411111 71093 8. Ho W, Zheng T, Yildiz H, Talluri S (2015) Supply chain risk management: a literature review. Int J Prod Res 53(16):5031–5069. https://doi.org/10.1080/00207543.2015.1030467 9. Kahraman C, Cebeci U, Ulukan Z (2003) Multicriteria supplier selection using fuzzy AHP. Logist Inf Manag 16(6):382–394. https://doi.org/10.1108/09576050310503367 10. Kayis B, Karningsih PD (2012) SCRIS: a knowledge-based system tool for assisting manufacturing organizations in identifying supply chain risks. J Manuf Technol Manag 23(7):834–852. https://doi.org/10.1108/17410381211267682 11. Kull TJ, Talluri S (2008) A supply risk reduction model using integrated multicriteria decision making. IEEE Trans Eng Manage 55(3):409–419. https://doi.org/10.1109/TEM.2008.922627 12. Lewis I, Talalayevsky A (2004) Improving the interorganizational supply chain through optimization of information flows. J Enterp Inf Manag 17(3):229–237. https://doi.org/10.1108/174 10390410531470 13. Nahmias S (2005) Production and operations analysis, 5th edn. McGraw Hill, New York 14. Neiger D, Rotaru K, Churilov L (2009) Supply chain risk identification with value-focused process engineering. J Oper Manag 27(2):154–168. https://doi.org/10.1016/j.jom.2007.11.003 15. Nikolopoulos K (2021) We need to talk about intermittent demand forecasting. Eur J Oper Res 291(2):549–559. https://doi.org/10.1016/j.ejor.2019.12.046 16. OECD (2020) E-commerce in the time of COVID-19. https://www.oecd.org/coronavirus/pol icy-responses/e-commerce-in-the-time-of-covid-19-3a2b78e8/ 17. Pournader M, Kach A, Talluri S (2020) A review of the existing and emerging topics in the supply chain risk management literature. Decis Sci 51(4):867–919. https://doi.org/10.1111/ deci.12470 18. Queiroz MM, Ivanov D, Dolgui A, Wamba SF (2020) Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann Oper Res. https://doi.org/10.1007/s10479-020-03685-7 19. Radivojevi´c G, Gajovi´c V (2013) Supply chain risk modeling by AHP and Fuzzy AHP methods. J Risk Res 17(3):1–16. https://doi.org/10.1080/13669877.2013.808689 20. Republic of Turkey Ministry of Trade (2021) E-Commerce Info Platform. https://www.eticaret. gov.tr/istatistikler 21. Saaty TL (1980) The analytical hierarchical process. Wiley, New York 22. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26. https://doi.org/10.1016/0377-2217(90)90057-I 23. Saaty TL (1994) How to make a decision: the analytic hierarchy process. Interfaces 24(6):19– 43. https://doi.org/10.1287/inte.24.6.19 24. Stadtler H (2005) Supply chain management and advanced planning—basics, overview and challenges. Eur J Oper Res 163(3):575–588. https://doi.org/10.1016/j.ejor.2004.03.001 25. Trkman P, McCormack K (2009) Supply chain risk in turbulent environments—a conceptual model for managing supply chain network risk. Int J Prod Econ 119(2):247–258. https://doi. org/10.1016/j.ijpe.2009.03.002 26. Tsai MC, Liao CH, Han CS (2008) Risk perception on logistics outsourcing of retail chains: model development and empirical verification in Taiwan. Supply Chain Manag 13(6):415–424. https://doi.org/10.1108/13598540810905679 27. Viswanadham N, Samvedi A (2013) Supplier selection based on supply chain ecosystem, performance and risk criteria. Int J Prod Res 51(21):6484–6498. https://doi.org/10.1080/002 07543.2013.825056

Chapter 15

Autonomous Vehicle Travel Between Multiple Aisles by Intelligent Agent-Based Modeling Ecem Eroglu Turhanlar, Banu Yetkin Ekren, and Tone Lerher

Abstract With the recent increase in e-commerce, automated warehousing industries seek technology solutions providing high transaction rates with economic investment costs. In this context, the application of smart operational policies towards future smart factories’ concepts becomes a critical issue. With the help of recent IT and technological advancements towards Industry 4.0 developments, we study intelligent autonomous vehicle operation policies where vehicles can make decentralized decisions for their safe and flexible travels between multiple aisles in a warehouse. By that, instead of assigning vehicles within a dedicated zone, we allow vehicles to travel freely between multiple aisles. The advantage of such a travel policy might result in a reduced number of vehicle requirements in a warehouse compared to a dedicated path policy. However, the disadvantage of such a flexible travel policy might be the development of smart collision and deadlock control algorithms, and that travel of vehicles might result in increased travel time during their operation due to deadlock and collision cases. First, we develop a smart travel policy approach for the vehicles using an agent-based simulation modeling approach. Then, we apply a statistical method, analysis of variance (ANOVA), to identify which input design factors significantly affect the system performance. As a result, it is observed that the number of bays is the most significant factor affecting the performance of such a system. Keywords Autonomous vehicle · Agent-based simulation · Automated warehousing · Collision · Deadlock

E. E. Turhanlar (B) · B. Y. Ekren Industrial Engineering Department, Engineering Faculty, Yasar University, Izmir, Turkey e-mail: [email protected] B. Y. Ekren e-mail: [email protected] T. Lerher Faculty of Mechanical Engineering, University of Maribor, Maribor, Slovenia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_15

195

196

E. E. Turhanlar et al.

15.1 Introduction With the recent rapid e-commerce growth, warehouses face new challenges in meeting the recent order profile with the wide variety and fast response time requirements. Warehouses tend to invest in automated warehousing technologies towards Industry 4.0 developments to deal with that challenging order structure. With the help of those technologies, a safe and economical operating environment and increased system performance can be ensured. Autonomous vehicle-based storage and retrieval system solutions are such recent technologies providing great advantages in warehouses towards those demands. In practice, most warehouse solutions apply a dedicated travel policy for the autonomous vehicles because the fact that they would not require complex operational algorithms such as collision and deadlock prevention algorithms, and they can make travel within their pre-determined zones [3, 7, 8, 11]. In this paper, with the help of recent IT-based and technological developments, we study the flexible travel of autonomous robotic vehicles where those vehicles can make autonomous decisions for safe and efficient travel between multiple aisles in a warehouse. This work’s main motivation is the development of an alternative design for tier-captive shuttle-based storage and retrieval system (SBS/RS), where the average utilization of shuttles is very low due to having excess numbers of shuttles in the system. An SBS/RS is comprised of storage racks consisting of multiple tiers and aisles. Across each aisle, there is a lifting mechanism providing vertical travel for loads to be stored or retrieved to/from tiers. In a tier-captive SBS/RS, there is a dedicated autonomous vehicle (i.e., shuttle) in each tier of an aisle. We refer to this system as tier-captive SBS/RS, where the shuttle cannot change its aisle and tier. Namely, those shuttles can perform horizontal travel through a single axis for storage and retrieval of loads. Since automated warehouses are mostly designed with high-rise shelves, this issue creates an excess amount of shuttles in a tier-captive SBS/RS design. Also, since there is a single lifting mechanism at each aisle in this system, lifts mostly become bottlenecks in those systems. Thus, the average utilization of shuttles is very low compared to the average utilization of lifts. In an effort to overcome that unbalanced condition, we propose a system design where shuttles can travel between aisles within a tier. Namely, by that, instead of assigning shuttles within a dedicated tier of an aisle, we allow shuttles to travel freely between multiple aisles. The most important advantage of such a travel policy design might be that since shuttles can travel freely between aisles, with fewer shuttles in such a system, the same performance could be realized compared to a dedicated warehouse design. However, a flexible travel concept would require the development of smart collision and deadlock prevention algorithms not to cause increased average flow time per transaction in the system. In this paper, we focus on the development of a well-functioning collision and deadlock prevention policy in such a system design resulting in improved performance metrics. We utilize an agent-based simulation modeling approach to seek those smart operating policies. By defining the shuttles and transactions as agents in the system, they become smart, dynamic objects so that they can communicate

15 Autonomous Vehicle Travel Between Multiple Aisles …

197

with each other for efficient transaction processing. After developing the most proper collision and deadlock prevention algorithms, we apply an experimental design study under different velocity profiles of shuttles and racking designs and apply ANOVA to determine which design factors significantly affect the system performance. The simulation experiments are conducted for a single tier of the system design. Thus, the developed models are also applicable for any autonomous vehicle travel systems having multiple aisles in the warehouse environment. For instance, automated guided vehicle (AGV) systems running on the ground as well as autonomous vehicle-based storage and retrieval systems (AVS/RSs) [7, 8, 10, 11] can utilize the findings from the developed approaches. The organization of the paper is as follows: first, a literature review is presented. Then, the methodological approach, along with the operating procedures of the system, is explained. After then, the ANOVA results are interpreted. In the last section, we summarize the paper and the findings.

15.2 Literature Review Policies for collision and deadlock prevention are generally studied for AGVs. Hsueh [17] developed an AGV design called EX-AGV that provides load exchange between AGVs. The simulation study results show that the proposed system is robust and efficient in terms of its performance measures. Cossentino et al. [1] present a conservative path reservation policy for collision avoidance between AGVs. Their policy is applied on an agent-based simulation model where they offer the model for smart decision-making in a warehouse. Roy et al. [24] develop blocking prevention protocols for three types of the autonomous vehicle blocking cases. Their numerical studies show that delays caused by autonomous vehicle blocking cause a significant increase in transaction cycle time (10–20%). Lerher [20] studies an SBS/RS (Shuttle-based storage and retrieval system), including aisle-changing shuttle carriers. He proposes analytical travel time models and compares their performance with the simulation results. However, there is no procedure to prevent collision and deadlock in his models. Lienert and Fottner [21] offer a model utilizing the time window routing method for safe shuttle movement in a tier-to-tier and aisle-to-aisle SBS/RS configuration. Recently, Kuçukyasar et al. (2020) compare the performance of tier-captive and tier-to-tier SBS/RS configurations by average cycle time, energy consumption, and total investment cost performance metrics. The results indicate that a well-designed tier-to-tier system could result in lower investment costs while providing a reasonable throughput rate. Kuçukyasar et al. (2021) also study an energy-efficient design for tier-to-tier SBS/RS. A graph-based solution is presented by Ekren [4] to help practitioners find the right SBS/RS warehouse design based on their requirements. The number of bays, tiers, and aisles for the rack design and transaction arrival rate are considered as

198

E. E. Turhanlar et al.

design concepts in the system. Performance measures are represented by average utilization of lifts as well as average cycle time per transaction. A software tool is proposed by Ekren et al. [6] to estimate travel time and energy-related performance outputs from tier-captive SBS/RS under a variety of input parameters. That tool can also estimate the mean and variance values for travel time per transaction of lifts and shuttles separately. By utilizing that previous work’s outputs [5, 6] study tier-captive SBS/RS design by proposing an open queuing network-based model, including queuing performance metrics from the system design. Recent studies are presented by [12, 14]. Ekren [12] performs a statistical experimental design analysis to determine the significant factors affecting some critical system performance metrics from the studied SBS/RS. The results show that the number of aisles design factors affect the system performance significantly. Ekren [14] studies warehouse design by a multi-objective optimization procedure by considering both energy consumption and cycle time performance metrics in the system. Besides the SBS/RS cases, in literature, there are some other works on autonomous vehicle-based storage and retrieval systems, where the autonomous vehicles can travel between aisles [3, 9, 11, 16, 25]. That system design is frequently utilized to store or retrieve the heavy unit load. To the best of our knowledge, none of those works include any deadlock or collision prevention algorithms. Eroglu and Ekren [12] propose a tier-captive and aisle-to-aisle SBS/RS design also preventing deadlock and collisions of shuttles by agent-based model application. Three transaction selection procedures are compared for the proposed system, and it is observed that the bidding-based strategy works better. However, it is indicated that the performance of the system’s transaction selection policy and working principle could be improved more. Turhanlar et al. (2021) propose a tier-captive and aisle-toaisle SBS/RS design again and evaluate system performance under different velocity profiles. The results show that the proposed flexible aisle-to-aisle SBS/RS design works better than the dedicated system design. There are few papers in the literature examining deadlock and collision prevention of autonomous vehicles that can change aisles. Existing applications generally dedicate autonomous vehicles to specific zone areas preventing any deadlock possibilities. However, that dedicated design might cause an increased number of vehicles in the system. We propose an alternative flexible travel pattern for vehicles (i.e., shuttles) where they can travel between multiple aisles. We treat the shuttles as intelligent agents that can decide on which transaction to process on their own and which route to follow through their destination addresses under efficient deadlock and collision prevention algorithms. We detail the proposed systematic approach in the next section.

15 Autonomous Vehicle Travel Between Multiple Aisles …

199

15.3 Methodology 15.3.1 Agent-Based Simulation Modelling Approach Agent-based simulation modeling is suitable for complex and smart system modeling by using real-time information and allowing communication between agents. Agents having intelligent capabilities provide efficient operation in the systems. Since the proposed system is complex because of the autonomous decision-making of shuttles and the requirement of collision and deadlock prevention algorithms, an agentbased simulation modeling approach is considered to be appropriate. ARENA 16.0 commercial software developed by Rockwell Automation is utilized to simulate the system due to providing advanced and flexible modeling features. 1.

Communication Between Agents

The main elements of the system are shuttles and demands they process. Here, an available shuttle agent aims to select the most proper transaction waiting in the queue based on its goal. Shuttle agent makes the final decision for processing a transaction in the system as a result of the communication procedure defined. All agents can communicate with each other and track real-time information from the environment. Agent interactions are shown in Fig. 15.1. The environment here represents the main state information in the system. There exists two-way communication between one of the shuttle agents and all other agents. All agents can provide real-time information from their environment and their state status. A bidding procedure takes place through this communication of agents in decision-making. Shuttles make decisions depending on the outcome of that bidding process. There are pre-defined operating rules described in the following sections for the agent behaviors. The decision of each Fig. 15.1 Agent interactions

200

E. E. Turhanlar et al.

shuttle or each system state change affects the decision of all other shuttles. Continuous communication and dynamic decision revision feature of the system aims at an efficient (i.e., decreased average flow time per transaction) working system design.

15.3.2 Intelligent Operating Procedure of Agents for Transaction Selection Note that before a shuttle starts its travel, it first selects a transaction waiting in its queue. It becomes available immediately after a shuttle completes a storage or retrieval process. Then, it selects a waiting transaction from its queue. The goal of a shuttle is to result in system performance with decreased average flow time per transaction and decreased maximum flow time of transactions performance metrics. Here, flow time is the time between when a transaction request is created until it is disposed from the system. Reducing the values of average flow time per transaction as well as maximum flow time of transactions performance metrics would contribute to recent customer-focused supply chain requirements aiming to reduce response times for orders. Towards that goal, an available shuttle selects a transaction from its queue according to this rule. First, transactions waiting for more than the current average waiting time of all transactions are given priority. By that priority, the shuttle agent contributes to the aim of minimizing the maximum flow time of transactions in the system. Then, among those long waiting transactions, the one having the least traffic density through its address path and having the closest distance to the current location of the available shuttle is given the highest priority. After the shuttle selects the transaction, it starts its travel. If the selected transaction is a retrieval process, then the shuttle travels to the storage bay to pick up the load and bring it to the buffer location. Otherwise, it travels to the buffer location to pick up to load to store it to the storage location.

15.3.3 Intelligent Operating Procedure of Agents to Prevent Collision and Deadlock The system has been designed with intelligent operating policies so that shuttles have additional stopping points to decide, wait, or escape to prevent shuttles from colliding during their aisle-to-aisle travels. Remember that the main goal of the shuttle agents is to minimize the flow time performance metrics for transactions during the simulation run. Figure 15.2 shows the top view of the single-tier system design. “Buffer area” is the point where transactions are picked up or dropped off depending on storage or retrieval status, respectively. Shuttles make their aisle changes through “transitions.” The “decision points” where shuttle agents stop for decision-making about their travel

15 Autonomous Vehicle Travel Between Multiple Aisles …

201

Fig. 15.2 Top view of the system design

routes to their destination addresses are the basis of the travel procedure. Namely, a shuttle stops at a “decision point” to decide for its next destination point, which might be either another decision point or the destination bay location. In this design, the decision points that require the shuttles to stop to make a decision in order to prevent collision constitute a limitation. It has the effect of increasing the processing time. In the operating procedure, the shuttle travels from one decision point to the next one. However, if the shuttle agent decides that it is possible to cross two decision points without stopping at the same time, time is saved by traveling to the second decision point without stopping. The dwell point policy of shuttles is the closest decision point to their last locations. Triggering procedure is the basis for fluent travel of shuttles without any deadlock or collision possibility. Figure 15.3 shows the flowchart of the triggering procedure. Active shuttle (AS) is the shuttle triggering the deadlock shuttle (DS), which might cause a collision or deadlock. An example of the triggering procedure in which one shuttle makes another move to another location can be given as follows: If there is an idle shuttle at the decision point where another shuttle should arrive there, then the AS sends a signal to the idle one to let it travel to the connected waiting point. The idle shuttle waiting at the waiting point continues to stand there until it seizes a new transaction to process. After transaction selection according to the procedure in Sect. 15.3.2, it travels to the decision point on its way through the destination. Note that waiting points are located as connected to each decision point. “Escape points” are located across each waiting point that are other significant locations for shuttles. If a shuttle is planning to travel to a busy decision point and the waiting point on the path is also busy, then the shuttle being at the waiting point is triggered to the escape point while the shuttle at the decision point is triggered to the closest decision point. The shuttle at the escape point returns to the waiting point immediately after waiting there until the traffic jam is over. Deadlock and collision possibility of shuttles in the system are prevented by those operating rules.

202

E. E. Turhanlar et al.

Fig. 15.3 Flowchart of the triggering procedure

15.3.4 Simulation Model Assumptions The assumptions considered in the simulation modeling are as follows: • Mean arrival rate of transactions are adjusted so that we obtain 95% shuttle utilization by considering a Poisson distribution with an equal rate [2, 15, 22–24, 27]. • In an effort to improve system performance, transaction addresses are assigned randomly among the aisles having the least traffic density. • The required tote loading and unloading time onto/from the shuttle is assumed to be 3 s. [15]. • The maximum reachable shuttle velocity is assumed to be 2 m/s or 3 m/s based on the considered experiment. Theoretically, the maximum velocity that a shuttle can reach horizontally can take values up to 4 m/s [20]. • The acceleration and deceleration values for velocities are the same and equal to 1–3 m/s2 based on the pre-defined design.

15 Autonomous Vehicle Travel Between Multiple Aisles …

203

• The distance between all bays and points (i.e., buffers, decision, waiting, escape points) is assumed to be 0.5 m [2, 15, 23]. • It is considered that the system design has 12 aisles and 50 or 150 bays with double sides in a single-tier based on the considered experiment. • There are four numbers of shuttles in the system. • The simulation run length is one month with a one-week warm-up period decided by the eye-ball technique. • The model is run for five independent replications. • Shuttles do not break down during the simulation run.

15.4 Results Simulation models are run under a high average shuttle utilization level, specifically by adjusting their arrival rates to achieve a 95% value. The notations that are used in the experimentations are given in Table 15.1. The purpose of using the design of experiment (DOE) tool is to obtain the effect of determining factors on the determined performance measure by changing the factor values. It identifies the existence, direction, and degree of significance of factors. The pre-defined performance measure in this analysis is the average flow time of transactions which is the time between the creation and disposal of a transaction request. The pre-defined factor and levels of conducted DOE study are shown in Table 15.2. Two key design factors chosen are considered to be: the number of bays and velocity scenarios. The number of bays, B, under the bay scenario has two levels: low and high. Two design factors examined under the velocity scenario are the maximum velocity that a shuttle can reach in a long travel distance (V max ) and acceleration and Table 15.1 Notations that are used Notation

Description

B

Number of bays in an aisle (on a single side)

V max

The maximum velocity that a shuttle can reach in long travel distance

m/s

as

Acceleration value of shuttle

m/s2

ds

Deceleration value of shuttle

m/s2

λ

Mean arrival rate of transactions

transactions/month

T avg

The average flow time of transactions

s/transaction

T max

The maximum flow time of transactions

s

U avg

Average shuttle utilization

%

W avg

The ratio of average waiting time to average flow time of transaction

%

SD

The standard deviation of the flow time of transactions

s

Unit

204

E. E. Turhanlar et al.

Table 15.2 DOE table

# of Bays scenario Velocity scenario B

V max

1-Low (50)

1-Moderate (2 m/s) 1-Low (1 m/s2 )

as , d s

2-High (150)

2-High (3 m/s)

2-Moderate (2 m/s2 ) 3-High (3 m/s2 )

deceleration values of shuttles (as , d s ). There are two levels for the factor V max which are moderate and high, and three levels for the factor as , d s , which are low, moderate, and high. The simulation experiments and their results are shown in Table 15.3. The results are also summarized in Fig. 15.4 graphs. From Fig. 15.4, we understand how performance metrics are affected depending on bay and velocity scenarios. High V max and high as , d s result in not only the lowest T avg values but also the highest λ values for both bay scenarios. Three times increase in B causes about two times an increase in T avg , T max , and SD, and it causes a decrease in λ. It is also observed that an increase in acceleration and deceleration values affect all the performance metrics positively. The pre-defined performance measure to include in ANOVA is considered to be the average flow time of transactions, T avg . Minitab 17 statistical analysis software is used to do that analysis. Figure 15.5 shows the residual plots. According to Fig. 15.5, ANOVA accuracy conditions: residuals are normally distributed, they have nearly a straight line, they have a mean of zero, and they are independently distributed, are met. Hence, we interpret our results according to Fig. 15.6. Table 15.3 Results of simulation experiments Design

Scenario

System outputs

#

B; V max ; as , ds

λ

T avg

W avg (%)

T max

SD

1

1; 1; 1

328,420 ± 468

59.0 ± 0.2

50

799 ± 44

44.6 ± 0.3

2

1; 1; 2

387,330 ± 434

50.5 ± 0.2

50

699 ± 42

38.9 ± 0.2

3

1; 1; 3

421,970 ± 488

44.5 ± 0.2

48

591 ± 44

33.7 ± 0.2

4

1; 2; 1

350,590 ± 578

55.3 ± 0.3

50

749 ± 40

41.8 ± 0.5

5

1; 2; 2

447,360 ± 452

42.6 ± 0.2

50

600 ± 45

32.3 ± 0.5

6

1; 2; 3

476,350 ± 497

40.4 ± 0.2

50

595 ± 38

31.4 ± 0.4

7

2; 1; 1

176,520 ± 424

107.0 ± 0.6

49

1,307 ± 110

84.2 ± 0.9

8

2; 1; 2

204,260 ± 317

93.5 ± 0.4

49

1,189 ± 96

74.2 ± 1.0

9

2; 1; 3

221,710 ± 329

85.5 ± 0.4

49

1,115 ± 89

68.0 ± 0.8

10

2; 2; 1

196,570 ± 293

96.3 ± 0.3

49

1,213 ± 44

75.4 ± 0.7

11

2; 2; 2

259,560 ± 401

75.7 ± 0.3

50

1,029 ± 58

60.6 ± 0.6

12

2; 2; 3

270,350 ± 441

72.7 ± 0.3

50

972 ± 78

58.9 ± 0.9

15 Autonomous Vehicle Travel Between Multiple Aisles …

205

Fig. 15.4 Performance metrics based on bay and velocity scenarios

Residual Plots for Average Flow Time of Transactions Normal Probability Plot

Versus Fits 0.50

Residual

Percent

99.9 99 90 50 10 1 0.1

0.25 0.00 -0.25 -0.50

-0.8

-0.4

0.0

0.4

0.8

40

80

100

Versus Order

Histogram 0.50

12

Residual

Frequency

60

Fitted Value

Residual

9 6

0.25 0.00 -0.25

3

-0.50

0 -0.4

-0.2

0.0

0.2

Residual

Fig. 15.5 Residual plots

0.4

1 5

10 15 20 25 30 35 40 45 50 55 60

Observation Order

206

E. E. Turhanlar et al.

Fig. 15.6 ANOVA report

Figure 15.6 shows the ANOVA results obtained by Minitab. It is observed that all factors and interaction effects are significant on T avg . This is because all the p-values in Fig. 15.6 are less than 0.05 significance level (α). Figure 15.7 also shows the main effects plot for significant factors. It is observed that B is the most significant factor affecting the T avg value.

Main Effects Plot for Average Flow Time of Transactions Data Means Number of Bay

Velocity

Acceleration/Deceleration

90

Mean

80

70

60

50 1

Fig. 15.7 Main effects plot

2

1

2

1

2

3

15 Autonomous Vehicle Travel Between Multiple Aisles …

207

15.5 Conclusion This paper proposes both smart collision and deadlock prevention procedures and statistically significant factor analysis for SBS/RS where shuttles can travel flexibly between aisles. We are motivated by this work with the help of recent IT-based and technological developments in an effort to decrease too many numbers of autonomous vehicles in dedicated system designs. By treating the shuttles as intelligent agents, they can make decentralized decisions autonomously by considering their pre-defined goals. We observe several performance metrics from the system and observe that acceleration and deceleration values play a significant role in the average flow time performance metric. Also, the most significant factor affecting average flow time is the number of bays. One may focus on decreasing the number of bays and increasing acceleration and deceleration values in the warehouse to utilize such a system well. As future works, more experiments by also including the number of shuttle designs in the system could be conducted. Besides, a comparison of this flexible travel design can be completed with a dedicated one. Acknowledgements This work was supported by The Scientific and Technological Research Council of Turkey and Slovenian Research Agency: ARRS [grant number: 118M180].

References 1. Cossentino M, Lodato C, Lopes S, Ribino P (2011) Multi agent simulation for decision making in warehouse management. In: Proceedings of the 2011 federated conference on computer science and information systems (FedCSIS), pp 611–618 2. Eder M (2019) An analytical approach for a performance calculation of shuttle-based storage and retrieval systems. Prod Manuf Res 7(1):255–270 3. Ekren BY (2011) Performance evaluation AVS/RS under various design scenarios: a case study. Int J Adv Manuf Technol 55:1253–1261 4. Ekren BY (2017) Graph-based solution for performance evaluation of shuttle-based storage and retrieval system. Int J Prod Res 55(21):6516–6526 5. Ekren BY, Akpunar A (2021) An open queuing network based tool for performance estimations in a shuttle-based storage and retrieval system. Appl Math Model 89(2):1678–1695 6. Ekren BY, Akpunar A, Sari Z, Lerher T (2018) A tool for time, variance and energy related performance estimations in a shuttle-based storage and retrieval system. Appl Math Model 63:109–127 7. Ekren BY, Heragu SS (2010) Simulation based regression analysis for the rack configuration of autonomous vehicle storage and retrieval system. Int J Prod Res 48:6257–6274 8. Ekren BY, Heragu SS (2011) Simulation-based performance analysis of autonomous vehicle storage and retrieval system. Simul Model Pract Theor 19:1640–1650 9. Ekren BY, Heragu SS (2012) Performance comparison of two material handling systems: AVS/RS and CBAS/RS. Int J Prod Res 50(15):4061–4074 10. Ekren BY, Heragu SS, Krishnamurthy A, Malmborg CJ (2010) Simulation based experimental design to identify factors affecting performance of AVS/RS. Comput Ind Eng 58:175–185 11. Ekren BY, Heragu SS, Krishnamurthy A, Malmborg CJ (2014) Matrix-geometric solution for semi-open queuing network model of autonomous vehicle storage and retrieval system. Comput Ind Eng 68:78–86

208

E. E. Turhanlar et al.

12. Ekren BY (2020) A simulation-based experimental design for SBS/RS warehouse design by considering energy related performance metrics. Simul Model Pract Theor 98. https://doi.org/ 10.1016/j.simpat.2019.101991 13. Eroglu E, Ekren BY (2020) An agent-based simulation model for deadlock prevention in an aisle-to-aisle SBS/RS. In: Industrial engineering in the Internet-of-Things world: selected papers from the virtual global joint conference on industrial engineering and its application areas. Springer, pp 135–146 14. Ekren BY (2021) A multi-objective optimisation study for the design of an AVS/RS warehouse. Int J Prod Res 59(4):1107–1126. https://doi.org/10.1080/00207543.2020.1720927 15. Ha Y, Chae J (2019) A decision model to determine the number of shuttles in a tier-to-tier SBS/RS. Int J Prod Res 57(4):963–984. https://scholar.google.com/scholar?oi=bibs&hl=tr& cluster=8319588073035270211 16. Heragu SS, Cai X, Krishnamurthy A, Malmborg CJ (2011) Analytical models for analysis of automated warehouse material handling systems. Int J Prod Res 49:6833–6861 17. Hsueh CF (2010) A simulation study of a bi-directional load-exchangeable automated guided vehicle system. Comput Ind Eng 60:594–601 18. Küçükya¸sar M, Ekren BY, Lerher T (2020) Cost and performance comparison for tier-captive and tier-to-tier SBS/RS warehouse configurations. Int Trans Oper Res 28(4):1847–1863 19. Küçükya¸sar M, Ekren BY, Lerher T (2021) Energy efficient automated warehouse design. Solving urban infrastructure problems using smart city technologies, pp 269–292 20. Lerher T (2018) Aisle changing shuttle carriers in autonomous vehicle storage and retrieval systems. Int J Prod Res 56:3859–3879+ 21. Lienert T, Fottner J (2017) No more deadlocks—applying the time window routing method to shuttle systems. ECMS, pp 169–175 22. Marchet G, Melacini M, Perotti S, Tappia E (2013) Development of a framework for the design of autonomous vehicle storage and retrieval systems. Int J Prod Res 51(14):4365–4387 23. Ning Z, Lei L, Saipeng Z, Lodewijks G (2016) An efficient simulation model for rack design in multi-elevator shuttle-based storage and retrieval system. Simul Model Pract Theor 67:100–116 24. Roy D, Krishnamurthy A, Heragu SS, Malmborg CJ (2013) Blocking effects in warehouse systems with autonomous vehicles. IEEE Trans Autom Sci Eng 11:439–451 25. Roy D, Krishnamurthy A, Heragu SS, Malmborg CJ (2015) Stochastic models for unit-load operations in warehouse systems with autonomous vehicles. Ann Oper Res 231:129–155 26. Turhanlar EE, Ekren BY, Lerher T (2021) Aisle-to-aisle design for SBS/RS under smart deadlock control policies. In: Proceedings of the 11th annual international conference on industrial engineering and operations management Singapore, March 7–11, 2021, pp 3182–3193 27. Wu Y, Zhou C, Ma W, Kong XT (2020) Modelling and design for a shuttle-based storage and retrieval system. Int J Prod Res 58(16):4808–4828

Chapter 16

Scientometric Analysis of a Social Network Kadir Oymen Hancerliogullari, Emrah Koksalmis, and Gulsah Hancerliogullari Koksalmis

Abstract This research aims to conduct a quantitative analysis of academic literature by assessing the publications related to a social network, Instagram. The data needed is collected via Web of Science. “Instagram” was selected as the keyword for conducting the research, and the criteria of topic and title were used for identifying the publications. In this study, publications between 2010 and 2018 were taken into consideration, and totally there were 1019 publications. The number of studies reached 267 in 2018. Among all the document types, there were ten document categories for research on Instagram. Articles were the most used document type, while poetry and correction were the least used document type among others. Again, among all publications, the most productive country was the United States, and English was the most commonly used in all other document types with 942 publications in English. Furthermore, Computers in Human Behavior appeared as the most used distribution channel when source titles are considered, while computer science, communication, and engineering were the trendiest research areas. Keywords Instagram · Social media · Publication trends · Scientometric analysis

16.1 Introduction In the last ten years, social networking sites (SNSs) have become very famous, and Instagram is one of them, which was founded on October 6, 2010, by Kevin Systrom K. O. Hancerliogullari (B) Department of Pediatric Surgery, Faculty of Medicine, Giresun University, Giresun, Turkey e-mail: [email protected] E. Koksalmis Hezarfen Aeronautics and Space Technologies Institute, National Defense University, Istanbul, Turkey e-mail: [email protected] G. H. Koksalmis Industrial Engineering Department, Management Faculty, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_16

209

210

K. O. Hancerliogullari et al.

and Mike Krieger [9]. There are several social networking applications, and Instagram is one of the most popular ones worldwide. With roughly there are one billion monthly active users. People use Instagram to connect with their family members, friends, and even strangers through browsing their posts composed of photos, videos and today, even by their stories uploaded for twenty-four hours. In addition to keeping touch with people, expressing oneself freely, learning the news happening in and out of their country, and reflecting their personalities through sharing meaningful things for them. On April 9, 2012, it was sold to Facebook [6]. Before it was sold, Instagram had 30 million users, now it has reached 800 million monthly active users, and 500 million of them are active on a daily basis. Instagram has a really large user portfolio, and it is getting wider day by day. Demographically, almost 70% of Instagram users are females, 80% of users come from outside of the U.S., and 59% of the users between the ages of 18 and 29 [15]. There is much research on Instagram in the academic literature. Lee et al. [11] examined the motivations driving users to share photos on Instagram. A descriptiveexploratory study was conducted recently to explore the role of the social media Instagram in promoting traditional food into five-star quality fine dining [2]. Ramsey and Horan [16] examined the extent to which young women post sexualized photos of themselves on Instagram and if these photos get positive feedback in the form of “likes” and number of friends or followers. Social media has become ingrained in plastic surgery culture, the trends and content of plastic surgery residency-associated Instagram accounts were categorized [3]. Colliander and Marder [4] showed that photos with a snapshot aesthetic in social media produce higher brand attitudes and intentions to recommend others to follow the Instagram account. Salim et al. [18] analyze the impact of friendship-contingent self-esteem and fear of missing out on the self-presentation of Instagram users. Marine-Roig et al. [13] define and characterize the phenomenon of tourism user-generated events (UGEs) through social media around the user’s new empowered role and assess user-generated social media events’ online socialness. Similarly, to develop an understanding of how athletes use Instagram as a communication and marketing tool, Olympic athletes’ self-presentation on Instagram was studied and evaluated the user-generated social media events’ online socialness [7]. Apiraksattayakul et al. [1] provided an empirical study as to the key determinants of purchase intention towards clothing on Instagram. Drake et al. [5] studied a social media program to increase adolescent seat belt use in the United States, specifically, an Instagram account was created to serve as an educational tool to encourage it. Holmberg et al. [8] studied how adolescents communicate food images in a widely used social media-sharing application, Instagram. An overview of the various applications of Instagram in health and healthcare was explored using evidence from the literature and case reports [10]. The association between Instagram use and depressive symptoms through the mechanism of negative social comparison was studied [12]. McNely [14] explores a qualitative coding schema for understanding organizational implementations of Instagram within a prominent news organization, a non-profit, and a for-profit retailer. Roberts et al. [17] investigate the link between cell-phone activities, including Instagram, and addiction among male and female college students.

16 Scientometric Analysis of a Social Network

211

16.2 Data and Methodology The data needed is collected via Web of Science. “Instagram” was selected as the keyword, and the criteria of topic and title were used for identifying the publications. In this study, publications between 2010 and 2018 were taken into consideration, and 1019 Instagram-related publications were found in total. Since Instagram was founded in 2010, there was only 1 study conducted during that year. Consequently, for the lower time constraint of publications, 2010 was selected in this study. On the contrary, when the time frame of this study is considered, for the upper time constraint of publications, 2018 was chosen. In the website of Web of Science, the publications that fell in this space were assessed by the use of the filters of “publication year, document types, countries, language, source title, and research areas.” In order to analyze the data, Microsoft Excel was used.

16.3 Results Between 2010 and 2018, a total of 1019 Instagram-related publications were diagnosed. The distribution of research by years is provided in Fig. 16.1. Articles (628; 61.63%) were the most widely used type among all the document types. Articles were followed by proceedings papers (331; 32.48%), editorial material (24; 2.36%), review (17; 1.67%), meeting abstract (12; 1.18%), letter (5; 0.49%), news item (4; 0.39%), book review (2; 0.2%), correction (1; 0.1%) and poetry (1; 0.1%). The distribution of document types is shown in Fig. 16.2. The most productive nations are provided in Table 16.1. In the first place, there is the United States, as the most productive country with 326 (31.99%) publications, followed by Australia (82; 8.05%) and Spain (68; 6.67%). Among all 10 most productive countries, England’s, Indonesia’s, Republic of China’s, India’s, Brazil’s South Korea’s and Germany’s contributions were undeniable as well. In total, 92.41% of 1400 1200 1000 800 600 400 200 0

Fig. 16.1 Chronological distribution of research papers

Cumulative number of research papers Annual number of research papers

212

K. O. Hancerliogullari et al.

Article

32.48% 61.63%

1.18% 2.36% 3.53%

Proceeding Paper 0.39% 0.29% 1.67%

Editorial Review Abstract News Other

Fig. 16.2 Types of research

Table 16.1 Geographic and language distribution of publications Top 10 most productive countries

% Share in publication

USA

Top 10 language

% Share in publication 92.44%

31.99

English

Australia

8.05

Spanish

3.93%

Spain

6.67

Portuguese

1.77%

England

6.28

Russian

0.79%

Indonesia

4.12

Turkish

0.39%

Republic of China

3.83

Malay

0.29%

India

3.43

German

0.2%

Brazil

2.94

French

0.1%

South Korea

2.94

Polish

0.1%

Germany

2.75

–

–

all Instagram related publications were written in English (938; 92.44%), followed by Spanish (40; 3.93%) and Portuguese (18; 1.77%). In all publications, the other very common languages used were Russian, Turkish, Malay, and German. Table 16.1 summarizes the most preferred languages used in research. Table 16.2 summarizes the source distribution of research on Instagram. When the publication number is considered, Computers in Human Behavior (29; 2.85%) was the leading source which is followed by Lecturer Notes in Computer Science (23; 2.26%), Cyberpsychology Behavior and Social Networking (15; 1.47%), Social Media Society (13;1.28%) and New Media Society (12; 1.18%). Other sources such as Journal of Medical Internet Research, Advanced Science Letters Body Image, AEBMR Advances in Economics Business and Management Research had less than 1% share in all publications. Instagram’s effect was valued in many different research and publication areas such as computer science, communication, engineering, psychology, and business economics. In general, the majority of the Instagram-related studies were

16 Scientometric Analysis of a Social Network

213

Table 16.2 Top 10 source distribution Top 10 source distribution

Number of publications

% Share in publication

Computers in Human Behavior

29

2.85

Lecture Notes in Computer Science

23

2.26

Cyberpsychology Behavior and Social Networking

15

1.47

Social Media Society

13

1.28

New Media Society

12

1.18

Journal of Medical Internet Research

10

0.98

Advanced Science Letters

9

0.88

Advances in Economics Business and Management Research

8

0.79

Body Image

8

0.79

ACM Conference on Computer Supported Cooperative Work and Social Computing CSCW 2016

6

0.59

on computer science (289; 28.36%), followed by communication (150; 14.72%), engineering (110; 10.79%), and psychology (100; 9.81%).

16.4 Discussion and Conclusion The focus of the study conducted is the trend of publications related to Instagram. In order to analyze the trend, a total of 1019 Instagram-related publications published between the years 2010 and 2018 that were collected from the Web of Science’s database were taken into consideration. These outputs were categorized by “publication years, document types, countries, languages, source titles and research areas.” The results of this study enable valuable implications. For instance, in the last few years, the amount of research on social networks, specifically Instagram, increased significantly, showing the trend topics in academic literature and reflecting the interest of researchers. Moreover, according to the statistical analysis among all of them, articles were the ones that were mostly used. The United States and English are determined to be the most productive country and languages, respectively. In addition, the top research areas were computer science, communication, and engineering. As to conclude, similar to other studies, this study also has some limitations. Since only the Web of Science database is utilized to collect information, the data can be various when different databases are used. Furthermore, a totally different result can be achieved if a different assessment method is applied, such as an assessment done using the number of authors and institution origin. Last but not least, for future

214

K. O. Hancerliogullari et al.

research, the studies can be based on the predicted number of publications in the next years by determining a publication trend per year.

References 1. Apiraksattayakul C, Papagiannidis S, Alamanos E (2017) Shopping via Instagram: the influence of perceptions of value, benefits and risks on purchase intentions. Int J Online Market (IJOM) 7:1–20 2. Chairiyani RP, Nursanti TD, Sriyanto H (2018) The role of Instagram in promoting the shifting of traditional food into five-star quality fine-dining. Adv Sci Lett 24:7113–7116 3. Chandawarkar AA, Gould DJ, Stevens WG (2018) Insta-grated plastic surgery residencies: the rise of social media use by trainees and responsible guidelines for use. Aesthetic Surg J 1:8 4. Colliander J, Marder B (2018) ‘Snap happy’ brands: increasing publicity effectiveness through a snapshot aesthetic when marketing a brand on Instagram. Comput Hum Behav 78:34–43 5. Drake SA, Zhang N, Applewhite C, Fowler K, Holcomb JB (2017) A social media program to increase adolescent seat belt use. Public Health Nurs 34:500–504 6. Forbes (2012) Facebook Buys Instagram for $1 Billion. Smart Arbitrage. Available https://www.forbes.com/sites/bruceupbin/2012/04/09/facebook-buys-instagram-for-1-bil lion-wheres-the-revenue/#3097a1784b8a 7. Geurin-Eagleman AN, Burch LM (2016) Communicating via photographs: a gendered analysis of Olympic athletes’ visual self-presentation on Instagram. Sport Manage Rev 19:133–145 8. Holmberg C, Chaplin JE, Hillman T, Berg C (2016) Adolescents’ presentation of food in social media: an explorative study. Appetite 99:121–129 9. InstagramBlog (2017) Strengthening Our Commitment to Safety and Kindness for 800 Million. Available http://blog.instagram.com/post/165759350412/170926-news 10. Kamel Boulos MN, Giustini DM, Wheeler S (2016) Instagram and WhatsApp in health and healthcare: an overview. Future Internet 8:37 11. Lee CS, Bakar NABA, Dahri RBM, Sin S-CJ (2015) Instagram this! sharing photos on instagram. In: International conference on Asian digital libraries. Springer, pp 132–141 12. Lup K, Trub L, Rosenthal L (2015) Instagram# instasad?: exploring associations among instagram use, depressive symptoms, negative social comparison, and strangers followed. Cyberpsychol Behav Soc Netw 18:247–252 13. Marine-Roig E, Martin-Fuentes E, Daries-Ramon N (2017) User-generated social media events in tourism. Sustainability 9:2250 14. McNely BJ (2012) Shaping organizational image-power through images: case histories of Instagram. In: 2012 IEEE international on professional communication conference (IPCC). IEEE, pp 1–8 15. Omnicore (2018) Instagram by the Numbers: Stats, Demographics & Fun Facts. Available https://www.omnicoreagency.com/instagram-statistics/ 16. Ramsey LR, Horan AL (2018) Picture this: women’s self-sexualization in photos on social media. Pers Individ Differ 133:85–90 17. Roberts J, Yaya L, Manolis C (2014) The invisible addiction: cell-phone activities and addiction among male and female college students. J Behav Addict 3:254–265 18. Salim F, Rahardjo W, Tanaya T, Qurani R (2017) Is self-presentation of Instagram users influenced by friendship-contingent self-esteem and fear of missing out. Makara Hubs-Asia 21:70–82

Chapter 17

Assessing Multilevel Thinking Using Cognitive Maps to Measure Global Managers’ Cognitive Complexity in Addressing Management Issues Elif Cicekli Abstract For international businesses to thrive, global managers must have global mindsets, which require cognitive complexity. Complex thinking should include the ability to see and analyze issues from all levels of analysis, from the micro, meso, and macro levels. However, no studies measure multilevel thinking in cognitive complexity. Because of the complexities involved in international business, cognitive complexity is particularly important when global managers deal with multifaceted international business management issues like the management of organizational behavior in terms of, for instance, communication, motivation, leadership, conflict, and negotiation. Although global managers deal with these issues daily, cognitive complexity is not yet studied in relation to the level of complexity with which managers think about these issues. This paper argues the need to revise the conceptualization and operationalization of cognitive complexity to cover the full complexity of managerial thinking. Therefore, measurement of managers’ cognitive complexity as it relates to a particular issue should include analysis of their thinking at the micro, meso, and macro levels. The paper also argues that such measurement requires measurement methods like cognitive mapping, which has several advantages over other methods. This study fills a gap in the research by proposing an assessment of multilevel thinking using cognitive maps to measure managers’ cognitive complexity in addressing issues like the management of international organizational behavior. Keywords Cognitive complexity · Multilevel thinking · Macro, meso, and micro levels of analysis · International organizational behavior · Cognitive maps

E. Cicekli (B) Department of International Trade and Business, Faculty of Economics, Administrative and Social Sciences, Istinye University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_17

215

216

E. Cicekli

17.1 Introduction We live in a complex and rapidly changing world. Global managers are required to have global mindsets that are characterized by cognitive complexity and cosmopolitanism [21] to “enhance exceptional functioning and performance in a global environment” [1, p. 396]. A global mindset, which can be broadly defined as “an individual’s capability to influence others unlike themselves” [16, p. 145], is positively related to exhibiting leadership behavior [24], international networking, and knowledge acquisition activities [14], the number of foreign partners and customers, and the percentage of revenues from foreign markets [25]. Research conceptualizes a global mindset using the underlying dimensions of cognitive complexity and/or cosmopolitanism [21]. The cosmopolitanism dimension is related to cultural openness, which involves being open “toward divergent cultural experiences” and searching “for contrasts rather than uniformity” [13, p. 163]. The cognitive complexity dimension, which is the central issue of this paper, involves seeing multiple perspectives and considering ideas, people, and situations from various angles [4] instead of from a single perspective. Cognitive complexity is positively related to effective communication [12], effective leadership, organizational performance, and organizational commitment [35]. The two components of cognitive complexity are differentiation and integration [3, 5], or comprehensiveness and connectedness, as Calori et al. [6] call them. Differentiation is related to the number of dimensions or constructs an individual uses to describe a situation or issue, while integration refers to the number of links an individual makes among the differentiated dimensions [5]. Managers are expected to be skilled in many managerial matters, such as organizational behavior issues like communication, motivation, leadership, conflict, and negotiation. Studying the micro, meso, and macro levels of analysis of global managers’ cognitive complexity in relation to matters in which they are required to be skilled, such as organizational behavior issues, is important, as the issues become more complicated and multifaceted in international business. Cognitive mapping is one of the methods for measuring cognitive complexity. A cognitive map is a physical representation, a map, of an individual’s perceptions of a subject. It is composed of nodes that symbolize the factors and arrows that symbolize the causal relationships between the factors that the individual perceives [17]. Therefore, the definitions of the concept of cognitive complexity and the measurement tool of cognitive mapping match, with the differentiation and integration dimensions of cognitive complexity corresponding to the nodes and arrows, respectively, in cognitive maps. The many other methods for measuring cognitive complexity include Bieri’s repertory grid measure, which is based on Kelly’s Role Construct Repertory Test [26], Crockett’s Role Category Questionnaire [26], content analysis of documents written by respondents on specific issues [32], and Fletcher et al.’s [8] Attributional Complexity Scale. However, cognitive mapping is superior to these methods in several ways: It allows cognitive complexity to be measured not just in general

17 Assessing Multilevel Thinking Using Cognitive Maps to Measure …

217

terms but also in relation to specific issues, such as organizational behavior, it allows the complexity of thinking at the micro, meso, and macro levels to be measured; and it makes these in-depth research results visible and easily comprehensible as graphics. A review of the managerial cognitive complexity literature shows that: 1.

2.

3.

No studies yet focus on measuring managers’ cognitive complexity as it relates to the international organizational behavior issues in which they are required to be skilled. Few studies [6] use cognitive mapping while measuring cognitive complexity as a dimension of the global mindset, despite its benefits compared to other methods. No studies focus on measuring global managers’ cognitive complexity at the micro, meso, and macro levels, which is required because of the complexities of international business.

Therefore, this study proposes to assess managers’ cognitive complexity as it relates to matters like international organizational behavior, use cognitive mapping based on interviews in addition to other methods, and focus on the assessment of managerial thinking at the micro, meso, and macro levels.

17.2 Cognitive Complexity Cognitive complexity, which involves seeing multiple perspectives and considering ideas, people, and situations from various angles [4] instead of from a single perspective, is one of the underlying dimensions of the global mindset [21]. The concept of cognitive complexity is based on Kelly’s (1955, as cited in [26]) Personal Construct Theory, which suggests that individuals understand, predict, and control events like scientists and create their own systems of personal constructs—that is, cognitive templates—in understanding the world. Citing Kelly’s publication of Personal Construct Theory, Bieri (1955, as cited in [26]) proposes the concept of cognitive complexity based on the Personal Construct Theory and defines it in terms of the degree of differentiation, that is, the number of constructs in an individual’s construct system. Later another dimension of cognitive complexity, integration, which involves the “development of complex connections among the differentiated characteristics” [3, p. 274], was proposed by researchers [19]. Hence, when an individual views an issue from multiple perspectives and takes many characteristics or dimensions into account (differentiation), and perceives these characteristics or dimensions as operating not in isolation, but as linked to one another in multiple contingent patterns (integration) [32], then the individual has a high level of cognitive complexity.

218

E. Cicekli

17.3 Significance of Measuring Cognitive Complexity in Terms of Specific Management Issues Managers’ cognitive complexity should be measured in terms of the issues in which they are expected to be proficient. To illustrate the point, this paper uses organizational behavior issues, which are complex and multifaceted, and require the ability to consider many factors since all global managers are expected to be skilled in these areas. Measuring managerial cognitive complexity in terms of these issues can reveal how cognitive complexity is related to other important organizational variables, such as organizational commitment, employee engagement, creativity, and performance. Measuring cognitive complexity in these areas can provide feedback to managers regarding areas in which to improve and/or seek training. Managers can improve the differentiation dimension of cognitive complexity by increasing their knowledge about more dimensions of the issues in question and can improve the integration dimension by increasing their awareness of how these dimensions may be influencing each other. However, to improve managerial cognitive complexity in issues related to organizational behavior issues, we must be able to measure it. Studies explore the relationship between cognitive complexity and communication [12, 27], leadership [10, 23, 35], conflict [7, 9, 31], and negotiation [29], but they explore the influence of cognitive complexity on organizational behavior issues, not the individuals’ level of cognitive complexity as it relates to these issues. No studies measure global managers’ cognitive complexity as it relates to organizational behavior issues. One may argue that the reason for the lack of such studies is that measuring cognitive complexity as it relates to multiple issues is not needed, as we can measure it generally using content-free measures, regardless of the issue. However, cognitive complexity can be measured using content-free measures or by measures based on the content [6]. Calori et al. [6] is a rare study that measures managerial cognitive complexity based on the content of the topic in question. They explore CEOs’ understanding of the environment and CEOs’ cognitive complexity in relation to the scope of their organizations. To measure cognitive complexity, they ask managers to explain the changes they expect in their industries and their companies. Calori et al. [6, p. 440] consider that using measures based on the content of their research topic would result in a higher interest among CEOs and that “the interpretation of the relationships would be more instructive,” that is, convey more information about that issue. In short, cognitive complexity can be measured using content-free measures or with measures based on the content of the topic of the research [6]. Hence, we can measure managers’ cognitive complexity on issues that include international organizational behavior on the premise that doing so conveys more information.

17 Assessing Multilevel Thinking Using Cognitive Maps to Measure …

219

17.4 Operationalization of Cognitive Complexity 17.4.1 Methods for Measuring Cognitive Complexity Bieri (1955, as cited in [26]), who propose the concept of cognitive complexity and define it in terms of the degree of differentiation, modify Kelly’s (1955, as cited in [26]) Role Construct Repertory Test and use it to measure cognitive complexity. In Bieri’s repertory grid measure, individuals are asked to rate themselves and several others, such as a person they dislike, their mother, and a person they would like to help, on a 6-point scale using characteristics like being outgoing versus shy, decisive versus indecisive, and excitable versus calm [33]. The idea is that genuinely different characteristics “should be differentially applied to the persons being judged” and “the larger the number of ‘matches’ between constructs, … the lower the cognitive complexity” [26, p. 75]. The other measure of cognitive complexity that is used most often is Crockett’s Role Category Questionnaire, in which individuals describe up to eight people, such as a “liked peer” or a “disliked older person,” by writing down their “habits, beliefs, ways of treating others, mannerisms, and similar attributes” [26, p. 75]. The “total complexity score consists of the sum across the several descriptions, the higher the score, the more cognitively complex the subject is taken to be” [26, p. 75]. Scholars like Van Hiel and Mervielde [32] measure cognitive complexity by carrying out a content analysis of documents. The authors measure cognitive complexity in relation to political extremism through a content analysis of documents written by respondents about four political issues. Fletcher et al. [8, p. 875] study cognitive complexity as attributional complexity, that is, as “the complexity of attributional schemata for human behavior,” and measure it with their 28-item Attributional Complexity Scale. The scale measures aspects of attribution like the motivation to understand and explain human behavior and the preference for complex instead of simple explanations. The scale’s items include “Once I have figured out a single cause for a person’s behavior I don’t usually go any further,” “I think very little about the different ways that people influence each other,” and “I think a lot about the influence that society has on other people” [8, p. 879]. Arora et al. [1, p. 400] use Kefalas’ [18] framework, which defines the conceptualization dimension of the global mindset as involving “identification and explicit statement of the meaning of … main concepts that describe a phenomenon and … the main relationships that tie these concepts”, that is, cognitive complexity. They measure cognitive complexity using items (on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree”) like “I believe that in the next 10 years the world will be the same as it is today,” “We really live in a global village,” “In discussions, I always drive for a bigger, broader picture,” “I believe life is a balance of contradictory forces that are to be appreciated, pondered, and managed,” and “I find it easy to rethink boundaries, and change direction and behavior”.

220

E. Cicekli

Cognitive mapping, another method we can use to measure cognitive complexity, is explored in detail in the next section.

17.4.2 Cognitive Mapping as a Method of Measuring Cognitive Complexity A cognitive map, a physical representation of a person’s or a group of people’s perceptions of a subject, is constructed using information gathered in interviews with those persons or groups. A cognitive map is composed of nodes and arrows that symbolize the factors and causal relationships between these factors, respectively, that the interviewees perceive [17]. The differentiation dimension of cognitive complexity corresponds to nodes, and the integration dimension of cognitive complexity corresponds to arrows in cognitive maps. Two hypothetical examples of cognitive maps are illustrated in Figs. 17.1 and 17.2. Figure 17.1 depicts the result of a hypothetical interview in which a mid-level manager in a multinational company is asked, “What are the factors that affect employee motivation?” The constructs (or dimensions) the manager mentions as factors affecting employee motivation are nodes, and the causal relationships are connections between the nodes. Figure 17.1 shows that the manager mentions only rewards and punishment as factors that affect employee motivation. Figure 17.2 depicts the result of a hypothetical interview in which another midlevel manager in a multinational company answers the same question. The manager mentions many factors that may affect employee motivation, ranging from the employee’s individual characteristics to person-organization fit and leadership style. The number of dimensions or constructs the manager uses to describe the issue is much higher, so the manager’s differentiation dimension of cognitive complexity is higher than that of the first manager. The second manager makes many links among the differentiated dimensions by mentioning, for instance, that the subsidiary’s organizational culture affects the person-organization fit, the performance management practices used, opportunities for growth and recognition, relationships at work, and the manager’s leadership style, all of which affect employees’ motivation. Hence, the second manager’s integration dimension is more complex than that of the first manager. Therefore, the second manager is more cognitively complex in relation to the issue of what determines employee motivation. Of course, the first manager’s cognitive complexity may be greater in other areas. For instance, the first manager may be more cognitively complex on the technical aspects of the work.

Fig. 17.1 Example of a cognitive map of a manager with a low level of cognitive complexity on the issue of employee motivation

17 Assessing Multilevel Thinking Using Cognitive Maps to Measure …

221

Fig. 17.2 Example of a cognitive map of a manager with a high level of cognitive complexity on the issue of employee motivation

Decision-makers are boundedly rational and work to achieve “satisficing” solutions instead of finding the best answer that would maximize utility [30]. Because of the limitations of the human mind, time, and energy, no manager can gather and process all the information on the antecedents of motivation or any other topic. Although a manager may have a high level of cognitive complexity and mention many dimensions and links in a one-hour interview, in a real-life situation, such a manager would probably make a judgment about which factors are the most important in that situation and use them in making a decision. Even so, a high level of cognitive complexity would result in a larger pool of dimensions and links to choose from, which would make it less likely that the manager would ignore any important dimensions and links in making a decision.

17.4.3 Measuring the Extent of Multilevel Thinking in Cognitive Complexity The levels of analysis in research are the micro-level, which explores individuals; the meso level, which examines groups; and the macro level, which studies the

222

E. Cicekli

external macro-environment. These levels represent the lenses we use to perceive the world. For instance, in a study of the factors that affect an organization’s financial performance, the micro-level will focus on the influence of individuals, such as the characteristics of employees or managers; the meso level will focus on the influence of groups, including teams, units, departments, other organizations, and the relationships among these entities; and the macro level will focus on the influence of elements like the political, legal, economic, social, cultural, and technological environments. Meso-level research may also refer to research that links the micro and macro levels (Rousseau, 1985, as cited in [22]). A multilevel analysis features “relationships between independent and dependent variables that operate at different levels of analyses” [34, p. 882]. Hence, multilevel thinking refers to thinking at multiple levels of analysis when considering the relationships between variables. This paper focuses not on the degree to which research is multilevel but on the degree to which thinking is multilevel—more precisely, the significance of measuring the degree to which managerial thinking is multilevel in the process of assessing managerial cognitive complexity. The results of various studies show that managers use meso- and macro-level analysis on issues like the changes they expect in their companies and industries [6], corporate sustainability [11], and issues mentioned in letters to shareholders [2]. Moreover, in international business research, Levy et al. [21, p. 232] conceptualize the cognitive complexity dimension of the global mindset in relation to the “strategic complexity associated with globalization,” and Hitt et al. [15] associate strategic thinking with a macro-level approach. Thus, in global mindset research, managerial cognitive complexity is conceptualized only as strategic thinking, as a single level of complexity at the macro level. However, the issues managers face are rarely influenced by factors at a single level. Managers must also be skilled in the management of organizational behavior issues like communication, motivation, leadership, conflict, and negotiation, which require all managers, even those who work in domestic businesses, to analyze issues at all three levels. Of course, the requirement for multilevel thinking is not confined to organizational behavior issues since “[m]ost management problems involve multilevel phenomena” [15, p. 1385]. The issues become more complicated and multifaceted for managers in international business. For instance, a manager from country A may work in country B or in a subsidiary of a multinational company headquartered in country C and may work with subordinates, supervisors, peers, customers, and other stakeholders from countries D, E, and F. Therefore, they have cultural differences to consider, along with the other components of the environment, such as political, legal, social, economic, and technological developments that affect both businesses and individuals. No studies yet measure the cognitive complexity of global managers at the micro, meso, and macro levels. However, cognitive complexity involves seeing multiple perspectives and considering ideas, people, and situations from various angles [4] and levels using multiple lenses instead of using a single perspective to perceive the world. Thus, the level of complex thinking required in international business involves analyzing issues from the micro (i.e., individual), meso (i.e., teams and organizations), and macro (i.e., political, legal, social, economic, technological, and

17 Assessing Multilevel Thinking Using Cognitive Maps to Measure …

223

cultural environment) levels. Hence, it is important to study the extent of multilevel thinking when measuring managerial cognitive complexity in international business. To cover all the complexities of managerial thinking required in international business, the operationalization of cognitive complexity should include analyses of the differentiation and integration dimensions and the extent to which each of these dimensions is multilevel. Using dimensions or constructs from the micro, meso, and macro levels of analysis to describe a situation or issue would indicate having multilevel differentiation while making links among the differentiated dimensions from the micro, meso, and macro levels would indicate having multilevel integration. Operationalizing cognitive complexity based on this framework would give us richer and deeper information about a manager’s cognitive complexity.

17.4.4 Advantages of Cognitive Mapping Compared to Other Methods of Measuring Cognitive Complexity Cognitive complexity can be measured using content-free measures or measures based on the content of the research topic [6]. When cognitive complexity is considered in relation to a specific area, knowledge in that area becomes important since “without adequate knowledge, an individual cannot form a complex representation of the information domain” [21, p. 243]. Calori et al. [6, p. 440], who explore CEOs’ understanding of the environment and study their cognitive complexity in relation to the scope of their organization, consider that “interpretation of the relationships would be more instructive”—that is, convey more information about that issue when measures that are based on the content of the topic of their research are used. Following these authors, this paper argues that assessing managers’ cognitive complexity based on the content of international business issues would yield more information than would using general measures, so we need to use measures that allow for that. However, the measures of cognitive complexity that are used most often, content-free tests like Bieri’s repertory grid measure and Crockett’s Role Category Questionnaire, do not allow managers’ cognitive complexity as it relates to specific issues to be measured. Similarly, questionnaires like Fletcher et al.’s [8] Attributional Complexity Scale and Arora et al.’s [1] questionnaire items measure cognitive complexity generally and do not measure cognitive complexity as it relates to specific issues. Compared to these measures, cognitive mapping has the advantage of measuring cognitive complexity in relation to specific issues like international organizational behavior by asking respondents for their opinions on that matter and representing their responses as maps. Both differentiation and integration are important dimensions of cognitive complexity [3, 5]. Woznyj et al. [33, p. 7] state that one of the reasons that Kelly’s Role Construct Repertory Test is the most commonly used measure of cognitive complexity is that “it closely aligns with the definition of cognitive complexity.”

224

E. Cicekli

However, Kelly’s test measures only the differentiation dimension of cognitive complexity and ignores the integration dimension. When both dimensions are considered, the method that most closely aligns with the definition of cognitive complexity is cognitive mapping. Compared to Bieri’s repertory grid measure and Crockett’s Role Category Questionnaire measure, which measures only the differentiation dimension of cognitive complexity, cognitive mapping has the advantage of measuring both the differentiation and integration dimensions of cognitive complexity. Questionnaires like Fletcher et al.’s [8] Attributional Complexity Scale and Arora et al.’s [1] questionnaire measure may also be used to measure cognitive complexity. However, there may be information saliency effects at play when questionnaires are used in measuring individuals’ perceptions about themselves. Salancik and Pfeffer [28] argue that attitude measurement could itself create attitudes because of the effects of information saliency. Such may also be the case for thoughts. When we ask a respondent the degree to which they agree with an item like “I think a lot about the influence that society has on other people” [8, p. 879], or “I believe life is a balance of contradictory forces that are to be appreciated, pondered and managed” [1, p. 410], we may be making these notions salient for the respondents and creating thoughts they did not previously have. Thus, we may be making them more cognitively complex than they were before we posed the questions. Such a result may be beneficial for the respondent and the organization, but it does not serve us in terms of accuracy of measurement. Moreover, there may be a Dunning–Kruger effect when questionnaires are used to measure individuals’ perceptions about themselves. People may hold overly favorable views of their abilities in intellectual domains, and their incompetence in these domains may rob them of the metacognitive ability to recognize where they are incompetent [20]. Hence, in their minds, individuals may have a misrepresentation of the degree of complexity with which they think and answer the questions based on this misrepresentation. They may strongly agree with the statement, “I think a lot about the influence that society has on other people” [8, p. 879] when they may not think about the influence of society as much as they suppose compared to the general population. The reverse may also be true since highly competent people may also evaluate their abilities in intellectual domains lower than they are [20]. Cognitive mapping does not cause information saliency or Dunning–Kruger effects since it does not depend on individuals’ self-evaluation. Hence, compared to questionnaires such as Fletcher et al.’s [8] Attributional Complexity Scale and Arora et al.’s [1] questionnaire measure, cognitive mapping can be a more objective measure since it does not depend on self-reported levels of cognitive complexity. As discussed in the previous section, as part of measuring cognitive complexity, one must measure the degree to which an individual’s thinking is multilevel. We cannot measure the levels of analysis used in managerial thinking with Bieri’s repertory grid measure or Crockett’s Role Category Questionnaire, which asks respondents to rate or describe people based on their personal characteristics [26]. Arora et al.’s [1, p. 410] measure has some items that are related to levels of analysis, such as “We really live in a global village,” and Fletcher et al.’s [8, p. 879] Attributional Complexity Scale has some related items, such as “I think very little about the different ways that people influence each other,” and “I think a lot about the influence

17 Assessing Multilevel Thinking Using Cognitive Maps to Measure …

225

that society has on other people.” However, neither of these scales measures different levels of analysis per se. However, using cognitive maps makes an exploration of the level of analysis possible. Figure 17.1 shows that the manager mentions only the rewards and punishment he or she provides as an individual, so the level of analysis is at the micro-level (or at the meso level at best if he or she mentions topics like relationships). Figure 17.2, on the other hand, shows that the manager uses multilevel thinking by mentioning factors like employees’ individual characteristics (microlevel thinking), organizational culture (meso-level thinking), and national cultures (macro-level thinking). Hence, another reason to use cognitive maps when measuring cognitive complexity is that they allow us to examine the degree to which cognitive complexity is multilevel. The degree of multilevel thinking in cognitive complexity can also be measured by carrying out a content analysis of documents written by respondents on specific issues [32]. With this method, both dimensions of cognitive complexity can also be measured in relation to specific issues without the potential biases that may be present in measures that use questionnaires. However, in this method, the researchers cannot probe to encourage individuals to share or expand on their opinions, which is possible in the interview process of cognitive mapping. Neither does the contentanalysis method result in a graphical representation of the results, which is a clear and easy way to summarize research results and give feedback on what managers can improve. Although this paper emphasizes the advantages of cognitive mapping over other methods of measuring cognitive complexity, every method has its own advantages. For instance, questionnaires are more advantageous than cognitive mapping in terms of ease of use and wider application to many respondents in a short period of time. However, even when other measures are used for their respective advantages, cognitive mapping should be used in addition because of its own advantages and to achieve triangulation in research.

17.5 Discussion and Conclusion Global managers are required to be skilled in international business management matters like the management of organizational behavior, which includes complex and multifaceted issues that require the ability to consider many factors in making decisions. Cognitive complexity is positively associated with topics related to organizational behavior, such as effectiveness in communication [12], effective leadership, and organizational commitment [35]. However, no studies yet explore global managers’ cognitive complexity—the number of factors they consider and the number of links they make among the factors when they think about these issues— in addressing issues that pertain to the management of international organizational behavior. Another matter that is not yet studied is the degree to which thinking is multilevel in measuring cognitive complexity. Cognitive complexity involves seeing multiple

226

E. Cicekli

perspectives and considering ideas, people, and situations from various angles [4], which should include seeing things at different levels of analysis to perceive the world. We argue that the current conceptualization and operationalization of the cognitive complexity dimension of global mindset need a revision that includes exploring managers’ cognitive complexity in general terms and as it relates to specific issues like organizational behavior. We must also study the number of dimensions or constructs that managers use to describe an issue from the micro, meso, and macro levels of analysis (i.e., multilevel differentiation), and the number of links they make among the dimensions and at what levels (i.e., multilevel integration). Among the many methods with which to measure cognitive complexity, cognitive mapping has several advantages, including measuring both of the dimensions of differentiation and integration; measuring cognitive complexity in relation to specific issues, such as international organizational behavior; measuring the complexity of thinking at the micro, meso, and macro levels; and making the in-depth research results visible and easily comprehensible as graphics. Clearly, measurement of managers’ cognitive complexity of managers in issues pertaining to international business management, such as the management of organizational behavior, would benefit from using cognitive mapping. The paper proposes premises that are sufficiently significant to be explored empirically. Future research is needed to measure multilevel managerial cognitive complexity on various issues using cognitive maps. The paper is the first to highlight the need to measure cognitive complexity in relation to specific issues, focusing on the degree to which cognitive complexity is multilevel in terms of the dimensions of differentiation and integration, and the first to propose using cognitive maps (in addition to other methods commonly used in research) to take advantage of the method’s benefits and achieve triangulation in research.

References 1. Arora A, Jaju A, Kefalas AG, Perenich T (2004) An exploratory analysis of global managerial mindsets: a case of US textile and apparel industry. J Int Manag 10(3):393–411 2. Barr PS, Stimpert JL, Huff AS (1992) Cognitive change, strategic action, and organizational renewal. Strateg Manag J 13(S1):15–36 3. Bartunek JM, Gordon JR, Weathersby RP (1983) Developing “complicated” understanding in administrators. Acad Manag Rev 8(2):273–284 4. Bird A, Osland JS (2009) Global competencies: an introduction. In: Lane HW, Maznevski MI, Mendenhall ME, McNett J (eds) The Blackwell handbook of global management: a guide to managing complexity. Wiley, Oxford, UK, pp 57–80 5. Boyacigiller N, Beechler S, Taylor S, Levy O, Lane HW, Maznevski ML (2004) The crucial yet elusive global mindset. In: Lane HW, Maznevski MI, Mendenhall ME, McNett J (eds) The Blackwell handbook of global management: a guide to managing complexity. Wiley, Oxford, UK, pp 81–93 6. Calori R, Johnson G, Sarnin P (1994) CEO’s cognitive maps and the scope of the organization. Strateg Manag J 15(6):437–457

17 Assessing Multilevel Thinking Using Cognitive Maps to Measure …

227

7. Curseu PL, Janssen SE, Raab J (2012) Connecting the dots: social network structure, conflict, and group cognitive complexity. High Educ 63(5):621–629 8. Fletcher GJ, Danilovics P, Fernandez G, Peterson D, Reeder GD (1986) Attributional complexity: an individual differences measure. J Pers Soc Psychol 51(4):875–884 9. Gheorghe A, Fodor O, Pavelea A (2020) Ups and downs on the roller coaster of task conflict: the role of group cognitive complexity, collective emotional intelligence and team creativity. Psihol Resur Um 18(1):23–37 10. Green GC (2004) The impact of cognitive complexity on project leadership performance. Inf Softw Technol 46(3):165–172 11. Gröschl S, Gabaldón P, Hahn T (2019) The co-evolution of leaders’ cognitive complexity and corporate sustainability: the case of the CEO of Puma. J Bus Ethics 155(3):741–762 12. Hale CL (1980) Cognitive complexity-simplicity as a determinant of communication effectiveness. Commun Monogr 47(4):304–311 13. Hannerz U (1996) Cosmopolitans and locals in world culture. In: Hannerz U (ed) Transnational connections: culture, people, places. Routledge, London, UK, pp 102–111 14. He C, Baranchenko Y, Lin Z, Szarucki M, Yukhanaev A (2020) From global mindset to international opportunities: the internationalization of Chinese SMEs. J Bus Econ Manag 21(4):967–986 15. Hitt MA, Beamish PW, Jackson SE, Mathieu JE (2007) Building theoretical and empirical bridges across levels: multilevel research in management. Acad Manag J 50(6):1385–1399 16. Javidan M, Bowen D (2013) The ‘global mindset’ of managers: what it is, why it matters, and how to develop it. Organ Dyn 42(2):145–155 17. Kang I, Lee S, Choi J (2004) Using fuzzy cognitive map for the relationship management in airline service. Expert Syst Appl 26(4):545–555 18. Kefalas A (1998) Think globally, act locally. Thunderbird Int Bus Rev 40(6):547–562 19. Kovarova M, Filip M (2015) Integrating the differentiated: a review of the personal construct approach to cognitive complexity. J Constr Psychol 28(4):342–366 20. Kruger J, Dunning D (1999) Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J Pers Soc Psychol 77(6):1121–1134 21. Levy O, Beechler S, Taylor S, Boyacigiller NA (2007) What we talk about when we talk about ‘global mindset’: managerial cognition in multinational corporations. J Int Bus Stud 38(2):231–258 22. Mathieu JE, Taylor SR (2007) A framework for testing meso-mediational relationships in organizational behavior. J Organ Behav Int J Ind Occup Organ Psychol Behav 28(2):141–172 23. Mitchell TR (1970) Leader complexity and leadership style. J Pers Soc Psychol 16(1):166–174 24. Neves LVM, Tomei PA (2018) The effect of global mindset on leadership behavior: an analysis of a diversified sample of countries. Int J Knowl Cult Change Manag Annu Rev 17(1):19–37 25. Nummela N, Saarenketo S, Puumalainen K (2004) A global mindset: a prerequisite for successful internationalization? Can J Adm Sci 21(1):51–64 26. O’Keefe DJ, Sypher HE (1981) Cognitive complexity measures and the relationship of cognitive complexity to communication. Hum Commun Res 8(1):72–92 27. Rubin RB, Henzl SA (1984) Cognitive complexity, communication competence, and verbal ability. Commun Q 32(4):263–270 28. Salancik GR, Pfeffer J (1978) A social information processing approach to job attitudes and task design. Adm Sci Q 23(2):224–253 29. Santmire TE, Kraus S, Santmire TE, Wilkenfeld J, Holley KM, Gleditsch KS (1998) The impact of cognitive diversity on crisis negotiations. Polit Psychol 19(4):721–748 30. Simon HA (1955) A behavioral model of rational choice. Q J Econ 69(1):99–118 31. Tripodi T, Bieri J (1966) Cognitive complexity, perceived conflict, and certainty. J Pers 34(1):144–153 32. Van Hiel A, Mervielde I (2003) The measurement of cognitive complexity and its relationship with political extremism. Polit Psychol 24(4):781–801 33. Woznyj HM, Banks GC, Dunn AM, Berka G, Woehr D (2020) Re-introducing cognitive complexity: a meta-analysis and agenda for future research. Hum Perform 33(1):1–33

228

E. Cicekli

34. Yammarino FJ, Dionne SD, Chun JU, Dansereau F (2005) Leadership and levels of analysis: a state-of-the-science review. Leadersh Q 16(6):879–919 35. Yan-Hong Y, Jing Z (2010) The influence of cognitive complexity on leadership effectiveness: moderating effects of environmental complexity. In: 2010 international conference on management science and engineering 17th annual conference proceedings. IEEE, pp 1792–1797

Chapter 18

Usability Evaluation of Ataturk University Web Site with Morae Program and the Effect of Pandemic During Testing Process: An Application for Undergraduate Students Elif Kilic Delice, Tuba Adar, and Merve Ceren Taskent Abstract Ataturk University has renewed the university website within the scope of studies conducted to become a new generation university. In this context, the Usability Testing (UT) with Morae V.3.3.0 program was applied for the first time to the user group consisting of undergraduate students in order to determine the usability level of the Ataturk University New Website (AUNWS). According to UT results, the usability problems, usage needs, expectations from the website, and satisfaction levels of the undergraduate students were determined. As a result of the study, the problems that need to be solved primarily in the AUNWS are the complex design of the home page and the insufficiency of the search button. Moreover, the AUNWS and Ataturk University Old Website (AUOWS) was compared in terms of usability levels in the study, and the participants found the new website more usable. The tests conducted under COVID-19 pandemic conditions have caused the distance education students to concentrate on the tests more willingly and more carefully during the pandemic process. The test results were positively affected since students do all their processes through the university website due to distance education. Keywords Usability testing · University website · Morae · Statistical analysis · Undergraduate students · Pandemic

18.1 Introduction University websites have different user groups of the society, especially students and academics. The purpose of these websites is to communicate with users and provide users with up-to-date information and services effectively. Since different E. K. Delice (B) · T. Adar · M. C. Taskent Department of Industrial Engineering, Faculty of Engineering, Ataturk University, Erzurum, Turkey e-mail: [email protected] T. Adar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_18

229

230

E. K. Delice et al.

user groups have different needs, university websites should be designed to meet all these needs. If a website raises difficulty while using, users get lost in the site, or basic questions of users are not answered, this means that such site is no longer used. This also applies to university websites. For this reason, usability is an important issue in website designs. The concept of usability is defined by the International Organization for Standardization (ISO) as a measure of the extent to which a system can be used to achieve the specified goals (effectiveness), resources such as time, money, mental effort, etc. required to be spent to achieve the specified goals (efficiency), and the degree to which the user finds the system acceptable (satisfaction) [4]. In this context, usability evaluation methods are a helpful tool in determining website problems and a way to overcome these problems. These methods lead to design changes and enable these changes to be evaluated [3]. Usability Testing (UT) has an important place among usability evaluation methods. UT is a method that provides feedback in interface designs and includes end-users. UT aims to examine the interaction between the website and the user and identify usability problems preventing website usage. The interaction between the end-user and the website occurs when users use the product. Meanwhile, comments, behaviors, and expectations of users about the website are observed and recorded. This data is then analyzed and used to improve the website [16]. In the literature, it is seen that special surveys developed for usability evaluation such as Web Site Analysis and Measurement Inventory (WAMMI) are generally used to evaluate the usability of university websites [2, 6, 10–13, 19]. The survey results were evaluated using descriptive statistics and hypothesis tests. Çınar [8], Ya˘gcı [21] and Alotaibi [1] used eye-tracking technique, WAMMI, verbal protocol, and heuristic evaluation in the usability evaluation of websites. UT was used for the evaluation of university websites in a few studies [5, 7, 18, 20]. While the UT was applied manually in the past, it may be applied using new generation programs such as Morae V.3.3.0. Jacob Nielsen, one of the leading researchers in usability [19], also recommends the Morae program. In this study, for the first time, UT was applied to undergraduate students using Morae V.3.3.0 program to evaluate the usability of the Ataturk University New Website (AUNWS) that was renewed on 2 September 2019. Moreover, the usability results of the AUNWS and Ataturk University Old Website (AUOWS) were compared with statistical analyzes. During the pandemic process, test sessions were held, and the effects of distance education on the test results during the pandemic were observed. Although UT was performed with a small number of people in the literature, in this study, one-to-one testing with a group of 40 users and determining the effects of a pandemic on test applications increase the originality of this study. This work was supported by the Research Fund of Ataturk University with FBA-2020-8189 project number.

18 Usability Evaluation of Ataturk University Web Site …

231

18.1.1 Data and Methodology UT focuses on many aspects of a person’s integration with a website (learning and ease of use, reduction of errors, personal experience, etc.) UT performed for websites helps in measuring the efficiency of site designs [16]. Nielsen and Landauer [14] suggested testing 5 people to reveal 85% of usability problems and a minimum of 15 people as a homogeneous group to discover all usability problems in the design. UTs are tests performed with volunteer users. During the testing, users are asked to think aloud while performing the tasks assigned to them. They are asked to explain why they are doing what they are doing while using the website by verbal protocol method. Thus, their thoughts about the website, the difficulties they experience, or the situations they are satisfied with are revealed [17]. Considering UT studies, many testing steps were determined, and similar steps were followed in this study [2, 15]. UTs may be performed easily, and all data may be easily analyzed with Morae V.3.3.0 program. More detailed data may be collected than manual tests, and graphs and tables may be created for data analysis with this program. This program records user actions (user face, voice, screen, mouse movements); automatically provides standard measurement data (such as mouse clicks, mouse movement distance, and task time). At the end of each testing session, a 10-question System Usability Scale (SUS) is applied to determine users’ satisfaction with using websites. After the tests are completed, the website may be evaluated comprehensively according to effectiveness, efficiency, and satisfaction, which are the main criteria of usability analysis. In this study, the test sessions recorded with Morae V.3.3.0 program were evaluated in terms of these three criteria. The following data were collected for each criterion: Effectiveness Task Success Rate: The success rates were obtained using a 0–3 success scale (0-Failed, 1-Completed with difficulty, 2-Completed, 3-Completed with ease). An average time to complete each task was determined with pre-tests conducted in this study. These times were used to assign success scores. Efficiency Task Completion Time: The time set between the start and end of a task. For each task, the lower and upper limits of the completion times in seconds were determined. According to this limit, the average task completion time of the participants was evaluated. Average maximum time between two data inputs: The average values of the maximum time between mouse or keyboard and data input are taken into account. Average number of mouse clicks: This shows the average value of the number of clicks with the mouse while performing a task.

232

E. K. Delice et al.

Average mouse movement: The distance between mouse movements on the screen was measured in pixels for each task. The average distance of mouse movement per task is taken into account. Satisfaction Average value of user satisfaction: The degree of satisfaction of the participants with the website is determined by the SUS survey. In this study, UT was applied to a group of undergraduate students using five testing materials described below. All materials were presented to the participant within Morae V.3.3.0 program. Information and Consent Document: Document informing the users participating in the test voluntarily about the test and obtaining their consent. Demographic and Information Survey: It is applied before UT. It has been prepared to collect information such as the personal characteristics of the users who will participate in the testing (age, gender, educational status, etc.) and the Internet and the usage habits of Ataturk University. Task Card: Tasks are required to test the usability of web pages. According to Nielsen [15], when deciding on testing tasks for usability evaluation, the basic rule is to represent as many user tasks as possible. A usage scenario of the website was created for undergraduate students, and tasks were prepared considering this scenario. During UT, the participants were asked to fulfill the following tasks. Task 1: Access the first page of the announcement lists. Task 2: Access the academic calendar information. Task 3: Get information about university accommodation opportunities. Task 4: Find the software provided by the university. Task 5: Find faculty-student affairs contact information. Task 6: Get information about University “Turkey Scholarships” opportunities. Task 7: Access the profile information of Associate Professor Elif Kılıç Delice. Task 8: Find the SCOPUS database to search for articles. SUS survey: It is applied after UT. In this study, SUS was used, which had been adapted to Turkish by Demirkol and Seneler ¸ [9], of which validity and reliability were proven. Post-testing interview document: It is used to collect the opinions, requests, and suggestions of the participants about the websites. UT was performed in the ergonomics laboratory of the Industrial Engineering Department using a desktop computer with an Intel i7 processor, 8 GB RAM, Windows 10 operating system. Trial tests were performed with four undergraduate students. In addition to the trial tests, the testing materials were made ready as a result of the project team’s examination of the materials many times. Due to the pandemic, it was difficult to reach undergraduate students. However, some of the participants did not want to be tested in the laboratory because they were afraid of being COVID-19.

18 Usability Evaluation of Ataturk University Web Site …

233

As a result, the number of users increased as much as we could, and 40 users were tested to perform statistical analysis. For UT, users were admitted to the Ergonomics laboratory in accordance with their appointment times, and the laboratory environment and computer hardware were disinfected before each testing. Attention was paid to mask usage and social distance. Participant names are kept confidential while evaluating the survey and test results. After the recording starts on the user’s computer, an Information and Consent Document about this test study first appears on the screen. After the participant reads this information and accepts to participate in the testing, he/she presses the “start” button, and Demographics and Information Survey is displayed. After completing this survey, the testing begins. The participant has started to do the task after first clicking on the link of the university home page that appeared on the screen and reaching the home page. Meanwhile, the testing is followed by the observer from the observer’s computer, and the participant’s degree of success in the tasks is determined. These ratings are assigned by the observer using a 0–3 scale during the testing. The observer also learns about the participant’s search behavior. After the tasks are completed, SUS appears on the user’s screen, and the user’s satisfaction level is measured with this survey. In the post-testing interviews, questions were asked about the expectations of the users from the website and the aspects of the website they liked or disliked. While the participants were using the AUNWS, it was noticed that a learning effect occurred because they had used the old website in the previous section. In other words, trying the test questions on the AUOWS for the first time caused the questions to be learned and positively affected the test on the AUNWS. In order to eliminate this learning effect, half of the participants started testing with the AUOWS first, while the other half started testing with the AUNWS first. It was statistically analyzed whether there is a significant difference between the AUOWS and AUNWS in terms of usability criteria for each task. The hypothesis for Task 1 is shown below as an example. Similar hypotheses have been established for other tasks and SUS questions. Ho : There is no difference between the success rates of undergraduate students while performing Task 1 at the AUNWS and the AUOWS. H1 : There is a difference between the success rates of undergraduate students while performing Task 1 at AUNWS and AUOWS. For this purpose, Paired Samples Test and Wilcoxon Signed Rank Test were used. The test results were analyzed manually with SPSS V.23.0.

18.1.2 Results According to the demographic and information survey results of 40 participants who were first administered UT, 26% of the participants were male, and 92.5% of the participants were between the ages of 18–25. The Internet is generally used by

234

E. K. Delice et al.

47% of participants for socializing, shopping, or entertainment purposes. While 35% of the participants used the Ataturk University website for 2–3 years, 52.5% of the participants logged in to the website several times a week. Ataturk University website is used by 43% of undergraduate students to benefit from e-services. Paired Sample Test, and Wilcoxon Signed Rank Test results are shown in Tables 18.1 and 18.2. In Table 18.1, it is seen that Ho hypotheses were rejected in some tasks in terms of usability data since p values were less than 0.05. In other words, there is a significant difference between these tasks in terms of these data. Table 18.1 Paired sample test results of usability criteria Task

Task success rate Z

P

Task completion time H0

t

P

H0

1

−0.037

0.970

A

−1.735

0.091

A

2

−2.518

0.012

R

4.046

0.000

R

3

−3.051

0.002

R

2.217

0.033

R

4

−2.277

0.023

R

−2.550

0.015

R

5

−2.251

0.024

R

−4.588

0.000

R

6

−1.097

0.273

A

−1.249

0.219

A

7

−0.370

0.711

A

−0.333

0.741

A

8

−4.443

0.000

R

5.952

0.000

R

Task

Maximum time between two data inputs

Number of mouse clicks

t

H0

t

P

P

H0

1

1.166

0.251

A

−4.978

0.000

R

2

5.179

0.000

R

1.397

0.170

A

3

2.765

0.009

R

−1.311

0.197

A

4

2.859

0.007

R

−1.820

0.076

A

5

−0.783

0.438

A

−4.525

0.000

R

6

1.889

0.066

A

−2.927

0.006

R

7

1.541

0.131

A

−2.442

0.019

R

8

6.024

0.000

R

0.949

0.349

A

Task

Distance of mouse movement

1

t

P

H0

2

0.880

0.384

A

3

4.523

0.000

R

4

2.512

0.016

R

5

1.131

0.265

A

6

−2.527

0.016

R

7

−1.119

0.270

A

8

−0.277

0.783

A

A accepted, R rejected

18 Usability Evaluation of Ataturk University Web Site …

235

Table 18.2 Wilcoxon signed-rank test results for SUS Q

Satisfaction level T

P

Q H0

Satisfaction level t

P

H0

1

−3.818

0.000

R

6

4.349

0.000

R

2

4.407

0.000

R

7

−5.619

0.000

R

3

−5.066

0.000

R

8

6.818

0.000

R

4

5.341

0.000

R

9

−4.113

0.000

R

5

−5.237

0.000

R

10

3.407

0.002

R

A accepted, R rejected

Table 18.2 shows that the Ho hypotheses were rejected for all questions. In other words, there are significant differences in satisfaction levels between the AUNWS and AUOWS. The success rate and distribution by tasks are shown in Figs. 18.1 and 18.2 for the AUNWS, using a 0–3 success scale. On the AUNWS, the average success rate for all tasks is higher than 1, and the tasks have been completed. Moreover, Task 8, one of the most failed tasks on the AUOWS, is among the most successful tasks on the AUNWS. Task 3, on the other hand, is the second most successful task on the AUNWS, while it is among the top

Fig. 18.1 Task success rate for AUNWS

236

E. K. Delice et al.

Fig. 18.2 Task success rate distribution for AUNWS (%)

3 most unsuccessful tasks on the AUOWS. The main reason for the high average success rates of Task 8 and Task 3 on the AUNWS is that this information is more easily accessible in the new site design. Despite the high failure rate in Task 8 on the AUOWS, half of the participants on the AUNWS completed this task very easily, considering Fig. 18.2. It is seen that they complete Task 2, the task of accessing academic calendar information, very easily. This information is highly visible under the menu called “Students” on the home page of the new site. Other tasks are generally seen to be done very easily. The analysis results regarding the average task completion times of the undergraduate student group are shown in Fig. 18.3. In Fig. 18.3, it is seen that the tasks 2, 8 and 6 on the AUNWS were completed in a shorter time than the other tasks. Figure 18.1 shows that the success rates of Tasks 2 and 8 were high. Also, the completion time of Task 3 is very close to the completion time of Task 6. Success rate and task completion times are in harmony.

18 Usability Evaluation of Ataturk University Web Site …

237

Fig. 18.3 Average task completion time for AUNWS

The analysis results regarding the maximum time between two data inputs of the undergraduate student group are shown in Fig. 18.4. In Fig. 18.4, the time between two data inputs for Tasks 2, 8, and 7 is shorter for the AUNWS than for other tasks. These tasks are at the top in terms of success rate and completion time. Although Task 7 is not in the top 3 in terms of success rate and completion time, it is in the top 3 in terms of time between two data entries. The analysis results regarding the average mouse clicks of the undergraduate student group are shown in Fig. 18.5. In Fig. 18.5, the highest number of mouse clicks was seen in Task 5. This is because both websites contain this information in nested pages. However, considering the number of mouse clicks, the clicks in Task 5 decreased by 75.10% on the AUNWS compared to the AUOWS. From the user feedback, it was understood that this information was not noticed much in the AUOWS. It is seen that the number of mouse clicks on the AUNWS is lower on the basis of tasks compared to the AUOWS. This is because the transitions between the pages are fewer, and the design elements such as font size and color are used more accurately than on the AUOWS. The analysis results regarding the average mouse movement of the undergraduate student group are shown in Fig. 18.6. In Fig. 18.6, it can be seen that the task with the greatest distance of mouse movement is task 1. When the AUNWS is examined, it is seen that the number of

238

E. K. Delice et al.

Fig. 18.4 The average maximum time between two data inputs for AUNWS

pixels in the new website is quite low compared to the AUOWS. From this point of view, it is seen that the information on the AUNWS is easily accessible. It is known that excessive navigating to search for information on the page in long-term use of the site causes negative physiological effects such as eyestrain, headache, visual impairment, and dry eyes. As a result of the SUS application, mean values were found for each question, and these values are shown in Fig. 18.7 for AUNWS. In Fig. 18.7, it is seen that the participants want to use the AUNWS at a high rate (question 1), and they find the website less complex (question 2). The majority of the participants find the website easy to use (question 3) and they think that they can learn how to use the website easily (question 7). In general, the AUNWS was found to be more usable than the AUOWS by undergraduate students. Students were more satisfied with the use of the AUNWS than the AUOWS.

18 Usability Evaluation of Ataturk University Web Site …

239

Fig. 18.5 The average number of mouse clicks for AUNWS

18.2 Discussion and Conclusions In this study, the usability problems, expectations, how effectively and efficiently they use the website, and satisfaction levels of undergraduate students while using the renewed AUNWS were determined by using UT. Moreover, the AUNWS was compared with the AUOWS, and it was found that the AUNWS was more usable. Test participants were more satisfied with the AUNWS than with the AUOWS. However, in the post-testing interviews, the usability problems that should be solved first on the AUNWS were found to be complex in the design and insufficient of the search button. The testing implementation process takes a lot of time as each user will be tested one-to-one. This process needs to be well planned. Due to COVID-19, there were problems in this process. Especially during the pandemic period, there were problems in the preparation of the test environment, materials, and trial tests because of distance education. Moreover, it was very difficult to find test participants among undergraduate students. Some participants hesitated to be in the testing environment due to COVID-19. However, the distance education of the test participants during the pandemic process and the continuous use of AUNWS in this process were effective in getting better results compared to the AUOWS. In addition, constant contact with

240

E. K. Delice et al.

Fig. 18.6 Distance of mouse movement for AUNWS

the computer and web environment in this process caused the students to participate in the tests more willingly and more carefully. In future studies, UT may be performed on users consisting of postgraduate students and academicians. Moreover, the usability levels of websites related to other e-services of the university may be evaluated. New models may be developed by using fuzzy theory to model uncertain or missing data available in usability evaluations.

18 Usability Evaluation of Ataturk University Web Site …

241

Fig. 18.7 Average value of user satisfaction for AUNWS

References 1. Alotaibi MB (2013) Assessing the usability of university websites in Saudi Arabia: a heuristic evaluation approach. In: 10th international conference on information technology: new generations, pp 138–142 2. Ate¸s V, Karacan H (2001) Abant ˙Izzet Baysal Üniversitesi web sitesi kullanılabilirlik analizi. J Inf Tech 2(2):33–38 3. Battleson B, Booth A, Weintrop J (2001) Usability testing of an academic library web site: a case study. J Acad Librariansh 27(3):188–198 4. Bevan N (1995) Human-computer interaction standards. In: Proceedings of the 6th international conference on human-computer interaction, pp 885–890 5. Budak VÖ, Erol ÇS, Gezer M (2017) Kurumsal bir mobil web sitesinin kullanılabilirli˘ginin geli¸stirilmesi. Electron J Vocat Coll 15–26 6. Ça˘glar E, Mentes SA (2012) The usability of university websites—a study on European University of Lefke. Int J Bus Inf Syst 11(1):22–40 7. Çetin NM, Alemda˘g E, Tüzün H, Yıldız M (2017) Evaluation of a university website’s usability for visually impaired students. Univ Access Inf Soc 16:151–160 8. Çınar NÖ (2015) Usability evaluation of mobile and desktop websites: a study of comparing usability evaluation methodologies. Master thesis. Information Systems Department, and Middle East Technical University, Ankara 9. Demirkol D, Seneler ¸ Ç (2018) A Turkish translation of the system usability scale: the SUS-TR. Usak Univ J Soc Sci 11(3):237–253 10. Jabar MA, Usman UA, Sidi F (2014) Usability evaluation of universities’ website. Int J Inf Process Manag 5(1):10–17

242

E. K. Delice et al.

11. Jabar MA, Usman UA, Awal A (2013) Assessing the usability of university websites from users’ perspective. Aisha J Basic Appl Sci 7(10):98–111 12. Jayathunga DP, Jayawardana JMDR, Wimaladharma STCI, Herath HMUM (2017) Usability recommendations for an academic website: a case study. Int J Sci Res Publ 7(4):145–152 13. Mentes SA, Turan AH (2012) Assessing the usability of university websites: an empirical study on Namık Kemal University. Turk Online J Educ Technol TOJET 11(3):61–69 14. Nielsen J, Landauer T (1993) A mathematical model of the finding of usability problems. In: Proceedings of ACM INTERCHI’93 conference, Amsterdam, pp 206–213 15. Nielsen J (1993) Usability engineering. Academic Press, Boston 16. Norlin E, Winters CM (2002) Usability testing for library web. American Library Association, Chicago 17. Rubin J (1994) The handbook of usability testing:how to plan, design and conduct effective tests. Wiley, New York 18. Sengel ¸ E (2013) Usability level of university web site. Proc Soc Behav Sci 106:3246–3252 19. Sengel ¸ E, Öncü S (2010) Conducting preliminary steps to usability testing: investigating. Proc Soc Behav Sci 2(2):890–894 20. Undu A, Akuma S (2018) Investigating the usability of a university website from the users’ perspective: an empirical study of Benue State University website. Int J Comput Inform Eng 12(10):922–929 21. Ya˘gcı S (2016) Üniversite web sitelerinin kullanılabilirlik sorunları üzerine bir çalı¸sma: Ba¸skent Üniversitesi web sitesinin incelenmesi. Master thesis. Social Sciences Institute, Baskent University, Ankara

Part II

Engineering and Technology Management

Chapter 19

Automated Anomaly Detection in Real-Time Data Streams: An Application at Token Financial Technologies Company Dicle Aslan Abstract Abnormalities are samples in data that do not fit the normal patterns. Various reasons such as malware, fraud, cyber-attack, terrorist activities, faults, system behavior changes, instruments, and human error might generate abnormalities. Anomaly detection is a technique that provides unexpected situations or patterns to be found in the data. These unexpected situations or patterns are called anomalies, outliers, and unexpected cases in the literature that do not fit the expected behavior of the data. Diverse research and applications have been carried out for anomaly detection, which is critical for the industries. To predict the anomaly, there are numerous learning methods as supervised, semi-supervised and unsupervised. In this scope, this paper proposes a novel concept as building an automated anomaly detector system for a business operation platform at Token Financial Technologies company, a leader in the payment systems industry in Turkey, by using the Isolation Forest algorithm developed in Python. Thanks to this system, as Token Financial Technologies company, abnormal data in the system might be detected in real-time. In this study, to integrate the business operation platform, we have first examined the data of deleting banking applications on the EFT-POS devices and detected the anomaly. The detection helped Token Financial Technologies to save more than 55% of the banking applications on the devices from deletion by contacting banks and customers instantly in the last quarter of 2020. Keywords Anomaly detection · Automated anomaly detector · Isolation forest · Outlier detection · Payment systems · Unsupervised learning

19.1 Introduction Anomaly might be defined as states or patterns that do not match the expected behavior of data [2]. These are rare observations that differ significantly from other observations. Figure 19.1 shows that there are two observation areas, N1 and N2. D. Aslan (B) Token Financial Technologies, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_19

245

246

D. Aslan

Fig. 19.1 Anomalies in a 2-dimensional dataset [2]

The points in the areas are normal; however, the points in these areas, o1, o2, o3, are anomalies. For Fig. 19.1, it might easily be the detection of normal and abnormal points. However, generally, the border of normal and abnormal behavior cases is often uncertain. Therefore, an abnormal investigation in a close area might appear normal, which is difficult to identify. There are certain difficulties in anomaly detection. Normal and anomaly data for anomaly detection might not often generate the obvious clusters in most data sets. A normal data might be close to the cluster with anomaly data, and an anomaly data might be close to the cluster with normal data. In this case, anomaly detection becomes quite difficult. The behaviors or data that we call normal might change over time. It is therefore not always possible to define normal behaviors. It might not be possible to apply a certain anomaly detection technique to all areas. For instance, a small fluctuation in body temperature in the medical field might indicate anomaly behavior, while a small fluctuation in stocks might indicate normal behavior. Therefore, it is not possible to apply an anomaly detection method to all areas. In order to detect noise anomalies in the datasets, noise removal is necessary. However, distinguishing noise is a very difficult process [2]. The occurrences anomaly types are point anomaly, contextual anomaly, and collective anomaly. If an individual case is distant from other normal data, anomaly data is a point since anomaly detection depends on a feature. For instance, the amount spent on our credit cards (amount spent) can be used to detect anomalies. Figure 19.1 shows the point anomaly. If a data sample is abnormal in each text, it is called a contextual abnormality and a conditional anomaly. The concept of a context is induced by the structure in the dataset and should be specified as part of the problem formulation. This context is an example of an anomaly if some of our data points to the anomaly in some cases and points to normal data in other cases, that is, if it exhibits anomaly behavior in a context. Figure 19.2 presents the time series that shows temperature changes at certain times of the year. In this example, low temperature at time t1 is normal behavior in winter, while the low temperature at time t2 is anomalous in summer.

19 Automated Anomaly Detection in Real-Time Data …

247

Fig. 19.2 Contextual anomaly in a temperature time series [2]

This is an example of a collective anomaly if the data associated with each other create anomaly behavior in the entire dataset. While some of the data associated with it might form anomalies together, these data might not show any anomaly behavior in the individual dataset. We can illustrate this type of anomaly when some computer-generated actions show anomaly when they occur together. Figure 19.3 shows an example illustrating the output of a human electrocardiogram. The red line region shows abnormality since the stabile low value is abnormally present during the long-term period. Token Financial Technology company is a Koç Holding company. Token provides retail and payment services and solutions having more than three decades of experience. Token, which is the leader in the payment industry in Turkey, operates more than 600,000 active payment terminals and 600,000 applications, including banking, meal card, and other retail applications. The company creates value-added services for the Fintech ecosystem.

Fig. 19.3 Collective anomaly in a human electrocardiogram output [2]

248

D. Aslan

In the literature, the automated anomaly detection system for real-time data streams is the first experiment for each EFT-POS (Electronic Funds Transfer at Point of Sale) device applied by Token Financial Technologies company. Anomaly detection and outlier detection topics are most widely used in the literature. Moreover, these studies are carried out by the industries with the effects of developing technology. The study presents an exhaustive survey of the techniques of outlier detection developed in statistical and machine learning algorithms. This study uses classification, clustering, and nearest neighbor statistically based techniques [6]. Agyemang et al. [1] provide a comprehensive review of outlier detection mining techniques for both numeric and symbolic data. The studies provide novelty detection in statistical and neural network-based domains [9]. A systematic review of anomaly detection systems and cyber-intrusion detection systems is discussed in [10]. Recent studies are often related to anomalies in the Internet of Things (IoT) topic. With the expanded utilization of the IoT framework, the study uses several machine learning techniques to accurately estimate the attacks and anomalies on the IoT systems. In this study, machine learning (ML), logistic regression (LR), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) algorithms are used and compared these methods according to performance measures. The system obtained 99.4% accuracy by using DT, RF, and ANN techniques [5]. Huch et al. [7] propose machine learning classifiers to predict the system’s health status applied in a real-world dataset. The results showed that the recurrent neural networks technique is more efficient in order to predict anomalies in health issues. The study develops an intrusion detection system based on anomaly behavior. The experimental results of the study provide accurate detection for known and unknown sensor attacks [11]. Stojanovic et al. [13] apply machine learning techniques to identify the Fintech sector anomalies. They provide a contribution to this crucial subject by evaluating anomaly detection approaches in this area. The findings show that machine learning technologies can help detect fraud with variable degrees of success. The study provides a mathematical method for semi-supervised anomaly detection derived from an unsupervised learning paradigm. It is based on support vector data description (SVDD) [4]. Hybrid semi-supervised anomaly detection for the high-dimensional dataset is proposed by Song et al. [12]. In this study, the data consists of a deep autoencoder (DAE) and anomaly detector based on the k-nearest neighbor graph ensemble. Khan et al. [8] present a real-time anomaly detection on an unmanned aerial vehicle by using an isolation forest algorithm. Goldstein and Uchida [3] suggest using 19 different algorithms used in 10 different datasets to identify unsupervised anomalies. This paper is intended to be a new survey for unsupervised learning that is well-funded. In recent years, according to research, semi-supervised and unsupervised learning anomaly detection and IoT topics are mostly studied in the literature. The conventional method for detecting anomalies was as follows: Describe the appearance of “normal situations” (this usually involves cluster analysis). Outliers are any events that don’t fit within those profiles. Isolation Forest is unique in that it starts with outliers rather than regular observations.

19 Automated Anomaly Detection in Real-Time Data …

249

This paper provides the unsupervised anomaly detection of the deletion of banking applications on devices as a first real use case at Token Financial Technologies company. Banking application deletion is crucial for the company since most of the revenue comes from the applications.

19.2 Methodology This part describes the approach, data collection, model, and labeling of the data steps. In this study, the data collecting part and building model are the most critical and hard from the other ones. After the preparation of the data and attributes, we used unsupervised learning by using Python to detect the anomaly.

19.2.1 Approach In this study, we first gather the related data in order to build an anomaly detection model. We collect the data of the deletion of banking applications on EFT-POS devices, which belongs to Token Financial Technology company. Since this is a critical issue for the company to detect anomalies in real-time data streams. The data do not include any missing values and invalid measurements. This consists of weekly application deletion data for the last 3 years. In this study, anomaly detection is defined as a binary classification. We label the data as “normal” and “anomaly” class. Then we build and develop the Isolation Forest model on Python to find the best solution for our case. We use an unsupervised learning technique that is fit and suitable for anomaly detection to get a better solution. In the following parts, we will explain these steps in detail.

19.2.2 Collecting Data We have considered integrating the automated anomaly detection tool into the business operation platform. To detect the operational anomalies, we firstly initiate taking the banking applications deletion data, which is a crucial issue for the company. There are weekly banking application deletion data of 20 partners in our data set, including the last 3 years. There are 150 application deletion records for each partner. The data of the deletion of banking applications has contextual anomalies. This context is an example of an anomaly if our data points to normal and anomaly in some cases, while in a specific context such as different partners. Other operational data such as production, quality, sales, installation applications, and so on linked to each other is integrated into the next stage.

250

D. Aslan

19.2.3 Model To predict the anomalies in the dataset on our model, we have used the Isolation Forest algorithm as an unsupervised learning technique complied in Python. We have applied this Python code for univariate data (application deletion), which belongs to partners, to find the anomalies and anomaly score in the dataset. The critical point is the identifying of the parameters of the Isolation Forest algorithm. The classical “parameter” code in the Isolation Forest model is as follows: class sklearn.ensemble.IsolationForest(n_estimators=100, max_samples=’auto’, contamination=’legacy’, max_features=1.0, bootstrap=False, n_jobs=None, behaviour=’old’, random_state=None, verbose=0, warm_start=False) In our model, contamination is 12% based on our intuition from the visualization. The value is 22% in the literature generally. Contamination means the proportion of the outliers in the dataset. The number of features is 1. Each point is randomly separated from other points by the Isolation Forest method. The model generates a tree based on its number of divisions, with each point representing a node in the tree. Because data point anomalies often have far shorter tree paths than usual data points, trees do not need to have a wide depth in the isolation forest, so it is possible to use a smaller maximum depth, resulting in a low memory requirement. In the forest isolation method, based on n_estimators and max_sample parameters, a forest of trees is generated, and the score is extracted from it.

19.2.4 Labeling the Data There are two possible label results of the anomaly detection algorithm, such as anomaly or normal. We assign the label for each bank data. An anomaly score or confidence value shows the degree of abnormality. Considering a huge amount of data and handling datasets manually to detect anomalies sounds like hard, timeconsuming, error-prone activity. Moreover, as a traditional way to determine the anomaly in the dataset, the fixed value is generally considered. However, this way does not provide an accurate and efficient outcome to find the anomalies. Since the fixed value does not fit all data and features, to make this task continuously more accurate, easier, and faster, we have developed an automated anomaly detection system that will be integrated into the business operation platform. Thanks to this system, anomalies might be detected automatically for each item in the dataset with univariate and multivariate cases.

19 Automated Anomaly Detection in Real-Time Data …

251

19.3 Results In this part, we indicate the results of our model. Figures 19.4, 19.5, and 19.6 show the trend of banking application deletion weekly and detected anomalies in the realtime dataset as an example. We have also found the anomaly score for each partner to make an alert in the system. Figures show the weekly banking application deletion trend with anomalies and anomaly scores for each partner. An anomaly score is the alarm level used instead of a fixed threshold level. For instance, for partner A, as the application deletion reaches out 490, which is the critical value, the system automatically warns the system users.

Fig. 19.4 Weekly application deletion trend—Partner A, score: 490

Fig. 19.5 Weekly application deletion trend—Partner B, score: 45

252

D. Aslan

Fig. 19.6 Weekly application deletion trend—Partner C, score: 6

19.4 Discussion and Conclusion In recent years, anomaly detection has become a crucial issue for industries to find abnormal cases in the systems. Thanks to anomaly detection systems, unexpected situations and exceptions might be detected, and necessary actions might be taken to solve the problem. One contribution of this study to the literature is that it is the first attempt to apply an automated anomaly detection system integrated into the business operation platform at Token Financial Technology company. Another contribution is that the study covers the banking application deletion data for 20 partners of Token Financial Technology company and detecting the anomalies in data and finding the anomaly score for each partner. To build an automated anomaly detector in Python, we have used the Isolation Forest algorithm. Thanks to this study, in the last quarter of 2020, Token Financial Technology saved more than 55% of the banking applications on the devices from deletion by contacting banks and customers instantly. For further research, we will consider the other relevant data such as production, quality, sales, complaint to develop an automated multivariate anomaly detector in the business operation platform. Moreover, semi-supervised and other unsupervised techniques might be used and compared the results with the unsupervised learning technique, Isolation Forest. Acknowledgements This work was supported by Token Financial Technologies company.

19 Automated Anomaly Detection in Real-Time Data …

253

References 1. Agyemang M, Barker K, Alhajj R (2006) A comprehensive survey of numeric and symbolic outlier mining techniques. Intell Data Anal 10:521–538 2. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 15:1–72 3. Goldstein M, Uchida S (2016) A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLOS One, 1–31 4. Görnitz N, Kloft M (2013) Toward supervised anomaly detection. J Artif Intell Res 46:235–262 5. Hasan M, Islam M, Zarif II, Hashem MMA (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things 7:1–14 6. Hodge VJ, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev, 13–18 7. Huch F, Golagha M, Petrovska A, Krauss A (2018) Machine learning-based run-time anomaly detection in software systems: an industrial evaluation. In: IEEE workshop on machine learning techniques for software quality evaluation (MaLTeSQuE), pp 13–18 8. Khan S, Liew CF, Yairi T, McWilliam R (2019) Unsupervised anomaly detection in unmanned aerial vehicles. Appl Soft Comput J 83:1–15 9. Markou M, Singh S (2003) Novelty detection: a review—Part 1: statistical approaches. Sig Process 83:2481–2497 10. Patcha A, Park J-M (2007) An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput Netw 51:3448–3470 11. Pacheco J, Hariri S (2016) Anomaly behavior analysis for IoT sensors 12. Song H, Jiang Z, Men A, Yang B (2017) A hybrid semi-supervised anomaly detection model for high dimensional data. Comput Intell Neurosci, 1–9 13. Stojanovic B, Bozic J, Hofer-Schmitz K, Nahrang K (2021) Follow the trail: machine learning for fraud detection in fintech applications. Sensors 21:1594

Chapter 20

The Digital Future of the Construction Project Management Levent Sumer

Abstract Digital revolution is generally evaluated as the most important phenomenon that shapes the future in all the industries, including the construction and the real estate sectors. Each industry has different nature in adapting digitalization and implementing the digital transformation. While some of them, such as the manufacturing or automotive industries, has been transforming too fast, others, like the construction sector, may need a longer time to adopt the new technological and digital developments. Moreover, because of the industries’ different structures and traditions, the use of the digital transformation pillars and their impacts are also different. Adaptation of the digital tools and trends on project management systems are also essential to get an insight into the future management perspectives of the industries both at project and organization levels. Thus, this study investigates Turkish construction and real estate professionals’ perception of the future of construction project management from a digital transformation perspective. According to the survey results, the use of building information modeling (BIM), artificial intelligence (AI), and big data are found the most important factors that may have an impact on the digital future of construction project management. Based on the results of the research, some digital transformation-focused organizational change management steps are recommended for real estate and construction companies. Keywords Digital transformation · Building information modeling · Construction project management · Artificial intelligence · Big data · Change management

20.1 Introduction Adapting technology in business operations has been a must for many industries, especially in the last decades. From digitization, which focuses on converting the data from analog to digital format, to digitalization that aims to automate the business processes [12], digital transformation may be defined as a mindset shift that changes L. Sumer (B) Executive MBA Program, Bogazici University, Istanbul, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 F. Calisir (ed.), Industrial Engineering in the Age of Business Intelligence, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-08782-0_20

255

256

L. Sumer

the corporate culture and the way of traditional business approaches. The digital transformation is considered as more than a technology but an enabler for organizations to get prepared against future challenges and changes [5]. The construction industry is one of the earliest sectors that started using technological tools in the early 1970s by using software programs for making structural analyses [7]. Although it is still a highly labor-dependent sector and is considered as the least digitized sector [1, 10], it has started using advanced technologies both in design and construction phases, and the industry has been being transformed as a more digital-oriented sector. Digital transformation can increase productivity by 15% and decrease costs by around 5% [8]. The building information modeling (BIM) increases the efficiency of the project team from design to construction and operation phases and improve coordination and communication among parties, the use of artificial intelligence, construction with robots, the use of drones, big data, connectivity, energy efficiency, mobility are considered as the main digital approaches and tools of the construction industry. Design management, scheduling, materials management, crew tracking, quality control, contracts management, performance management, and document management are considered the main areas in digital solutions that may be implemented [1]. Many studies in the past focused on different aspects of the digitalization of the construction industry. Industrial production, which covers 3D printing, robotics where the use of drones is included, digitally controlled building sites where BIM is implemented [7], enterprise resource planning (ERP), cloud solutions, analytics [9], internet of things (IoT) and artificial intelligence (AI) [14] were determined as the main digital tools and the aspects of the digitalization of the construction industry. While some scholars focused more on BIM use in the construction sector [2, 5, 6], others searched the interactions of the IoT and BIM [4]. On the other hand, the use of blockchain technology in the construction industry [11], logistics quality, safety, and efficiency contributions of digital transformation [13, 15], digital transformation effects on organizations and barriers for digital transformation from an industry aspect [3, 10] were also the others important topics examined. Since the past evaluated studies that focused specifically on the impact of the digitalization of the construction project management is mainly limited with the use of BIM technologies. Thus, this study investigates the Turkish construction and real estate professionals’ general perception of the big picture and potential shapers of the future of the construction project management from a digital transformation perspective and aims to be one of the pioneer studies in this area.

20 The Digital Future of the Construction Project Management

257

20.2 Data and Methodology 20.2.1 Data Sources Two-phased research was designed to determine the perception of the construction and the real estate professionals regarding the use of digital transformation tools in construction project management. In the first phase, a roundtable discussion was made with senior construction professionals and academicians to evaluate the digital transformation tools studied in past research and determine and select the most relevant ones to the construction project management. In the second phase, a survey was conducted, and the respondents were asked to rank the impacts of the 6 selected digital transformation tools on the future of construction project management.

20.2.2 Methodology One hundred two professionals who work for different construction companies participated in the questionnaire. Among 102 participants, the rate of the professionals who work for the Project Management (PM) Company was 28.43%. That rate was followed by those who work for the Employers and the General Contractors with 23.53% and 19.61%, respectively. Civil engineering and architecture built up 72.55% of the participants’ professions, whereas electrical and mechanical engineers had a total rate of 12.75% among the respondents. Except for one respondent, all the participants had a university or a higher degree, where 50.98% of them had a master’s degree, and 11.76% of them held a Ph.D. degree. More than half of the respondents were senior managers, and 37.25% of the respondents held mid-level management positions. While 39.22% of the participants had more than 20 years of experience, the rate of the respondents with more than15 years of experience was 74.51%. The participants were also asked to answer the current technological tools they are using for construction project management. 84 of 102 respondents use computeraided programs (AutoCAD), while scheduling programs (MS Project with 67.65%) (Primavera 55.88%) had the second most frequently used software programs. Enterprise Resource Planning (ERP) and BIM were the least used tools among participants with 44.12% and 31.37% rates, respectively. In addition to the tools asked, some participants mentioned additional tools they use such as cloud technologies, power business intelligence, prolog, procore, digital dashboard tools, share point, netcad, lotus, and onedrive. The categorical questions and the frequencies of the responses are shown in Tables 20.1 and 20.2, and the use of current technological tools is shown in Fig. 20.1. Based on the past literature, as shown in Table 20.3, a list of the digital tools used in the construction industry was prepared. Before the survey was conducted, that list

258

L. Sumer

Table 20.1 Organization type and occupation and experience Organization type

Frequency (%) Occupation

Frequency (%) Experience

Frequency (%)

Employer

25.49

Civil Engineer

60.78

>20 Years

39.22

General Contractor

19.61

Architect

11.76

15–20 Years 35.29

Sub-contractor 9.80

Mechanical 6.86 Engineer

10–15 Years 17.65

Project Management Com

30.39

Electrical Engineer

5.88

5–10 Years

5.88

Others

14.71

Others

14.71

20

26

14

>20

24

16

15–20

26

10

15–20

19

17

20

10

30

15–20

15

21

15–20

20

13

27

15–20

8

28

15–20

13

23

20

22

18

15–20

24

12

15–20

24

12

20

15

25

15–20

19

17

15–20

13

23