Advances in Industrial and Production Engineering: Select Proceedings of FLAME 2022 9819913276, 9789819913275

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Lecture Notes in Mechanical Engineering

Rakesh Kumar Phanden Ravinder Kumar Pulak Mohan Pandey Ayon Chakraborty   Editors

Advances in Industrial and Production Engineering Select Proceedings of FLAME 2022

Lecture Notes in Mechanical Engineering Series Editors Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini , Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine Editorial Board Francisco Cavas-Martínez , Departamento de Estructuras, Construcción y Expresión Gráfica Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA Justyna Trojanowska, Poznan University of Technology, Poznan, Poland Jinyang Xu, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

Lecture Notes in Mechanical Engineering (LNME) publishes the latest developments in Mechanical Engineering—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNME. Volumes published in LNME embrace all aspects, subfields and new challenges of mechanical engineering. To submit a proposal or request further information, please contact the Springer Editor of your location: Europe, USA, Africa: Leontina Di Cecco at [email protected] China: Ella Zhang at [email protected] India: Priya Vyas at [email protected] Rest of Asia, Australia, New Zealand: Swati Meherishi at swati.meherishi@ springer.com Topics in the series include: • • • • • • • • • • • • • • • • •

Engineering Design Machinery and Machine Elements Mechanical Structures and Stress Analysis Automotive Engineering Engine Technology Aerospace Technology and Astronautics Nanotechnology and Microengineering Control, Robotics, Mechatronics MEMS Theoretical and Applied Mechanics Dynamical Systems, Control Fluid Mechanics Engineering Thermodynamics, Heat and Mass Transfer Manufacturing Precision Engineering, Instrumentation, Measurement Materials Engineering Tribology and Surface Technology

Indexed by SCOPUS and EI Compendex. All books published in the series are submitted for consideration in Web of Science. To submit a proposal for a monograph, please check our Springer Tracts in Mechanical Engineering at https://link.springer.com/bookseries/11693

Rakesh Kumar Phanden · Ravinder Kumar · Pulak Mohan Pandey · Ayon Chakraborty Editors

Advances in Industrial and Production Engineering Select Proceedings of FLAME 2022

Editors Rakesh Kumar Phanden Department of Mechanical Engineering Amity School of Engineering and Technology Amity University Noida, Uttar Pradesh, India Pulak Mohan Pandey Department of Mechanical Engineering Indian Institute of Technology Delhi New Delhi, India

Ravinder Kumar Department of Mechanical Engineering Amity School of Engineering and Technology Amity University Noida, Uttar Pradesh, India Ayon Chakraborty Engineering Project Management School of Engineering, IT and Physical Sciences Federation University Australia Ballarat, VIC, Australia

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-99-1327-5 ISBN 978-981-99-1328-2 (eBook) https://doi.org/10.1007/978-981-99-1328-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This book combines cutting-edge research articles on industrial and production engineering from the third biennial international conference on Future Learning Aspects for Mechanical Engineering (FLAME), organized by Amity University Uttar Pradesh, India, from 3 to 5 Aug 2022. The primary mission of FLAME 2022 was to lay a platform that brings together academicians, scientists, and researchers across the globe to share their scientific ideas and vision in thermal, design, industrial, production, and interdisciplinary areas of mechanical engineering. FLAME 2022 played a pivotal role in setting up a bridge between academia and industry. The conference hosted almost 600 participants to exchange scientific ideas. During three days of the meeting, researchers from academics and industries presented the most recent cutting-edge discoveries, conducted various scientific brainstorming sessions, and exchanged views on practical socioeconomic problems. This conference also provided a scope to establish a network for collaboration between academia and industry. The primary emphasis was on the recent developments and innovations in various fields of mechanical engineering through plenary and keynote lectures. The general chair of the conference FLAME 2022 was Prof. (Dr.) Basant Singh Sikarwar and convener was Dr. Rakesh Kumar Phanden. It was supported by the University of Leicester UK, Budapest University of Technology and Economics Hungary. In particular, this volume discusses different topics of industrial and production engineering in sixty-eight chapters, such as sustainable manufacturing processes, logistics, Industry 4.0 practices, circular economy, lean six sigma, agile manufacturing, additive manufacturing, IoT and Big Data in manufacturing, 3D printing, simulation, manufacturing management and automation, surface roughness, multiobjective optimization and modeling for production processes, developments in casting, welding, machining, and machine tools. The contents of this book will be helpful for researchers as well as industry professionals. We want to acknowledge all the participants who contributed to this volume. Thanks to the publishers and every staff of the department and institute who have directly or indirectly helped to accomplish this goal. We also profoundly express our gratitude for the generous support provided by Amity University Noida. We would also like to express our gratitude to the Founder and President of Amity University, v

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Dr. Ashok K. Chauhan, for providing all kinds of support. This book is not complete without his blessings. Noida, Uttar Pradesh, India Noida, Uttar Pradesh, India New Delhi, India Ballarat, Australia November 2022

Dr. Rakesh Kumar Phanden Dr. Ravinder Kumar Dr. Pulak Mohan Pandey Dr. Ayon Chakraborty

Contents

Agile Project Management: Evaluation of Implementation Barriers Using the BWM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arya Biswas, Marina Marinelli, Mukund Janardhanan, and Avinash Bhangaonkar Improvement of Service Flow and Cost Optimization for an Automobile Service Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aditya Vashishth, Sidharth Radhakrishnan, Yashaswin Tanwar, Shyamal Samant, and Rakesh Kumar Phanden

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A Study of Key Challenges in Implementation of Digital Supply Chain in the Context of Indian SMEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nitin Kumar Chauhan, Vikas Kumar, and Sandhya Dixit

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Forecasting Price of Small Cardamom in Southern India Using ARIMA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jagadeesh Babu Myneedi, Nitin Kumar Lautre, and Ravikumar Dumpala

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Impediments to Environmental Sustainability Adoption Within Supply Chain of an Indian Nickeling SMEs—An ISM and MICMAC Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Garima Dhankher, Sanatan Ratna, Devarapalli Akhil, Prem Narayan Vishwakarma, Rakesh Kumar Phanden, and Manander Singh

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Ranking and Prioritization of the Factors Impacting the Implementation of Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepanshu, Aman Deep Kachhap, and Abdul Gani

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Assessment of Environmental Sustainability of Manufacturing Practices of Indian SMEs in COVID Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. R. Ajay and Ravinder Kumar

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Identification and Analysis of Enablers of Social Sustainability in Indian SMEs: Fuzzy DEMATEL Approach . . . . . . . . . . . . . . . . . . . . . . . . Rhythm Joshi and Ravinder Kumar

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Strengthening the Social Sustainability of Indian SMEs in the Current Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ubaid Ur Rehman and Ravinder Kumar

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Identification of Critical Success Factors (CSFs) for Implementation of Industry 4.0 in MSME Sector . . . . . . . . . . . . . . . . . . 103 Ramandeep Singh, Manish Kumar Ojha, and Rahul Sindhwani Fortifying the Human Resources in Indian SMEs in COVID Era . . . . . . . 115 Rhythm Joshi and Ravinder Kumar ISM Model for Factors Affecting E-waste Remanufacturing in Indian Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Swatantra Kumar Jaiswal and Suraj Kumar Mukti Multi-agent-Based Ant Colony Approach for Supply Chain Delivery Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Itoua Wanck Eyika Gaida, Mandeep Mittal, and Ajay Singh Yadav Simulation of Minimum Energy Deep Drawing Operations Using DEFORM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Subhash Chandra and Belete Nega Analysis of Supervised Domain of Cybersecurity for Fraud Detection Through Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Neetu Mittal and Tejas Shankar Raheja To Study and Analyze the Factors of Economic Sustainability of Indian Manufacturing SMEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Lakavathh Manobiiram and Ravinder Kumar To Study Operational Educational Institution Building on Sustainability Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Ujjwal Bhardwaj, Ravinder Kumar, Pratham Goel, and Ram Arora Implementation of Improved Machine Learning Technique in Stock Analysis and Market Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Neetu Mittal, Ranbir Singh, and Kapil Sharma Analysing Relationship Among Lean Six Sigma Critical Success Factors: An Interpretive Structural Modeling Approach . . . . . . . . . . . . . . 201 Vishwas Yadav, Pardeep Gahlot, Raj Kumar Duhan, and Rakesh Kumar Phanden Medical Product Manufacturing Process Capability Improvement Using Six Sigma–DMAIC Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Milind Shrikant Kirkire and Gayatri Abhyankar

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Lean Execution Barriers in Indian Engineering Industries . . . . . . . . . . . . 231 Shyam Sunder Sharma, Aishwerya Johari, and Rahul Khatri Comparison of Cloudlet-Based Mobile Cloud Computing Models and the Rise of Cloud Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Devansh Chauhan, Nipun Rao, Alvin Masih, Dolly Sharma, and Shilpi Sharma A Decision Feedback Model for Big Data Analytics in Smart Grid . . . . . 253 Swagat Khatai, Swetaleena Sahoo, Siddharth Swarup Rautaray, and Sarita Nanda Learning of Embedded System for Incorporating: Organization . . . . . . . 265 Reeya Agrawal and Arti Badhoutiya Hard Turning Modeling Using Different ANN Architectures . . . . . . . . . . . 275 Rabinarayan Bag, Ramanuj Kumar, Ashok Kumar Sahoo, and Amlana Panda Identification, Ranking, and Prioritization of Factors Impacting Green Product Design Using the Fuzzy AHP Approach . . . . . . . . . . . . . . . 285 Aditya Vardhan, Haris Ehtesham, and Abdul Gani Application of Single Minute Exchange of Die to Reduce Changeover Time in a Winding Machine of a Capacitor Line . . . . . . . . . . 297 M. Shilpa, M. R. Shivakumar, S. Hamritha, Rakesh Kumar Phanden, Amrit Gupta, and Abhishek Pushpak The Electrical Discharge Machining Process Performance Analysis for Titanium Alloy Machining: Using TOPSIS and Taguchi Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Rohit Kumar Singh and Ravindra Pratap Singh Neural Network Based Classifier for Tool Wear Monitoring and Prediction During Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 P. J. Bagga, M. A. Makhesana, Premal Doshi, Krutik Jain, and K. M. Patel Experimental Investigations on Eco-Friendly Lubrication Techniques for Improving Machining Performance . . . . . . . . . . . . . . . . . . . 331 B. K. Mawandiya, M. A. Makhesana, V. J. Suthar, N. G. Mahida, and K. M. Patel Optimization of MRR and TWR in Electric Discharge Drilling of Ti-Alloy Using Hybrid Approach of Taguchi-Based GRA and PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Md. Tasnim Arif and Amit Sharma

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Adoption of Digitization Practices in SMEs in the Era of Covid 19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Vineet Pandey, Pravendra Tyagi, and Sumit Gupta Economic Ordering Policies for Growing Items with Linear Growth Function Under Trade-Credit Financing . . . . . . . . . . . . . . . . . . . . . 361 Mehak Sharma and Mandeep Mittal Impact of Carbon Emission on the Seller Buyer Model: A Stackelberg Game Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Tisha Raghav, Mandeep Mittal, and Rita Yadav Improvement in Productivity with Swing Grinding and Gate Cutting Fixture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Pravin Jadhav and Pramod Salunkhe Analysis of the Factors Affecting MRR in AFM and Centrifugal Process Using Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Rishabh Chaturvedi and Pankaj Kumar Singh Optimization and Weight Reduction of Injection Moulding Machine Components for Beverage Filtering . . . . . . . . . . . . . . . . . . . . . . . . . 401 Jayesh Anavkar, Milind Kirkire, Arman Fanaskar, Obieddullah Firfire, Aman Bawani, Faizan Dhamaskar, and Anand Bhise Design and Development of Arduino Based CNC Laser Engraver . . . . . . 417 Utprabh Mishra, Taresh Gupta, Madhukar Chhimwal, and Ramakant Rana

About the Editors

Assoc. Prof. Dr. Rakesh Kumar Phanden is B.Tech. Mechanical Engineering, M.Tech. Integrated Product Design and Manufacturing and Ph.D. in Integration of Process Planning and Production Scheduling from the National Institute of Technology (NIT) Kurukshetra, India in 2012. He is currently working as Associate Professor in the Department of Mechanical Engineering at Amity University, India. He has chaired several technical sessions at conferences and reviewed articles for many SCIs and Scopus-indexed journals of national and international repute. He has published 5 books and edited 4 special issues for a Scopus-indexed journal. He is serving as an editorial board member for several journals. He has 15 years of teaching experience at private and government institutes and universities in India and abroad. He has contributed more than 50 papers at the national/international levels including SCI journals. His current areas of interest include nature-inspired optimization algorithms for various mechanical engineering problems, cyber-physical systems, energy-aware modeling of systems, manufacturing systems, production scheduling, integration of process planning and scheduling and product design and manufacturing, industrial engineering and production, and welding techniques. Dr. Ravinder Kumar is a Professor of the Mechanical Engineering Department at Amity University, India. He holds a bachelor’s degree in Mechanical Engineering with Honors. He did his master’s and Ph.D. from Delhi College of Engineering, University of Delhi, India. He has over 20 years of teaching experience in undergraduate, postgraduate, and doctoral programs. He has published over 50 research papers in reputed international journals and conferences. His areas of interest include sustainable supply chain management, industrial digitalization, industry 4.0, sustainable manufacturing, sustainability assessments, and SCM issues in SMEs. He has published papers in journals of national and international repute. Prof. Pulak Mohan Pandey is working as a Professor in the Department of Mechanical Engineering, Indian Institute of Technology (IIT) Delhi. He is presently on deputation as Director, BIET Jhansi. His area of research includes rapid prototyping, unconventional machining, CAD/CAM, biomedical applications, micromachining, xi

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nano finishing, and finite elements applications to manufacturing. He has guided 40 Ph.D. and 35 master’s dissertations at IIT Delhi. He has published more than 200 hundred research papers in the journals of high repute and also published books and magazines with reputed international publishers. He has also published more than 20 patents in the field of the manufacturing process. Dr. Ayon Chakraborty is an Associate Professor in Engineering Project Management within the School of Engineering, IT, and Physical Sciences at Federation University, Australia. Prior to joining the university, he was working as an Associate Professor in the Operations Management and Decision Sciences area at the Indian Institute of Management (IIM) Tiruchirappalli, India. He also worked as a Post-Doctoral Research Fellow, at Services Science Discipline, Information Systems School at the Queensland University of Technology, Australia. He has more than 11 years of experience in teaching and consulting business students and executives at various institutes such as the Queensland University of Technology, and James Cook University Australia (Singapore Campus). His research interests are in the area of quality management such as Lean, Six Sigma, and other quality tools and techniques. In recent years, he also worked on practices related to sustainable development and circular economy, specifically in Micro, Small, and Medium Enterprises (MSMEs). He is also involved in international collaborative research projects such as Erasmus Plus and Global Challenges Research Funding (GCRF) with Aston University in UK. Both these projects focus on the sustainability and well-being of MSMEs and are sponsored and funded by UK Government.

Agile Project Management: Evaluation of Implementation Barriers Using the BWM Arya Biswas, Marina Marinelli, Mukund Janardhanan, and Avinash Bhangaonkar

Abstract As today’s business environment is highly competitive and dynamic, APM is seen as a winning project management methodology to be adopted, especially in the IT business. However, with the popularization of APM, new challenges emerge. This study identifies the barriers in APM implementation and obtains relative rankings for their importance using the input of industry experts and Multi-Criteria DecisionMaking (MCDM) techniques. Following the thorough review of the literature and five individual interview sessions, a total of 13 barriers of APM were identified and categorized under 4 main themes (High-level Organizational, project planning, team operation, and Quality Management). The final weights of the 13 APM barriers were obtained with the application of a recently introduced MCDM technique called Best–Worst Method (BWM). The findings from this study reveal that the top five subbarriers are in order of priority: the lack of adequate and accurate description of tasks, the unclear time requirements and project schedule, the lack of effective communication/knowledge sharing, the lack of clarity in team roles and task ownership and the lack of understanding of agile project management principles. This reflects the fact that barriers are spread out into a wide range of factors, highly dependent on the inherent features of APM as well as on human communication and social skills. Keywords Agile · Project management · APM · Barriers · BWM · MCDM · Organization · Planning · Team management

A. Biswas · M. Marinelli · M. Janardhanan (B) · A. Bhangaonkar School of Engineering, University of Leicester, Leicester LE1 7RH, UK e-mail: [email protected] M. Marinelli School of Civil Engineering, National Technical University of Athens, 15780 Athens, Greece © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Phanden et al. (eds.), Advances in Industrial and Production Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-1328-2_1

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1 Introduction When it comes to managing projects, there is a plethora of project management methodologies available to choose from. Agile Project Management (APM) is one such project management framework which has become quite popular, especially in delivering software projects in the information technology (IT) industry because of its flexible nature. APM is an iterative approach to delivering a project throughout its life cycle and the project is delivered incrementally in progressive cycles rather than delivering the whole project at once [1]. One of the main accentuations of APM is to develop a software at the earliest possible time without adhering to any strict or rigid processes [2]. Unlike traditional project management methods, agile considers processes like planning, designing, and documentation to be redundant and inconsequential [3]. Though APM has a lot of benefits and advantages over the traditional project management methods like waterfall, implementing agile principles for project management itself can be challenging [4]. Some of the areas where people have encountered challenges while using APM are difficulty in scheduling project tasks, managing the project scope, knowledge management, etc. [2, 5]. Mentioned in their study that companies whose culture and philosophy are different from the principles and values of agile, encounter difficulty in transitioning to agile. For example APM methods will be better implemented in companies with low management control as opposed to a company which has a more rigid and controlling management environment [6]. The aim of this study is to employ the Best–Worst Method (BMW), which is a novel Multi-Criteria Decision-Making (MCDM) method, to rank the various challenges in terms of importance and shed light to the actions required for their alleviation.

2 Literature Review 2.1 Agile Project Management Agile is based on 4 main values or principles laid out in the agile manifesto [7]. They are as follows: . . . .

“Individuals and interfaces over process and tools, Operational software over complete documentation, Collaboration with customers over contract negotiation, Able to respond to change over following a plan.”

According to [8], these agile principles laid out by the agile manifesto, are centered on the value of the customer, iterative and incremental delivery (short sprint cycles),

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intense collaboration, small integrated teams, self-organization, and continuously providing small improvements to a product or a project with each sprint cycle. There are different methods in which agile principles can be applied to manage projects, but all the methodologies are based on agile values and principles [2]. One of the most prevalent project management frameworks based on agile principles is the Scrum framework which consists of mainly four distinct events or processes: sprint planning, sprint, sprint review, and sprint retrospective [9]. Unlike the traditional methods, sudden changes in product specification are not as costly in APM [5]. The overall project cost while performing APM is much less than other methods, which can be attributed to the flexible nature of agile. The APM’s iterative methodology allows the project team to effortlessly respond and adapt to any changes in the product specification without requiring redesigning of the entire project. According to [10] APM aids in better managing user requirements, improving the quality of the code development process, and plays a crucial role in delivering a project in the quickest timeframe possible. Another benefit of APM is that it saves resources in a project by negating the need for time-consuming operations during the project planning phase [5]. One of the unique features of APM, which is the continuous involvement and feedback of clients and stakeholders, ensures that the clients’ interests are always taken into account, consequently leading to a higher customer satisfaction [11]. As APM works in short sprints (short iterative cycles), it allows clients and stakeholders to gain a feel for the product and provide their valuable feedback which consequentially helps the agile team to continuously improve the product in succeeding sprint cycles. As agile principles emphasize in providing autonomy to the members of an agile project team, it can lead to an increased morale and work engagement, thereby promoting innovation and creativity [12].

2.2 Challenges Encountered with Practicing APM Project Scope Management. APM is meant to be flexible and adopt changes in the project requirement as and when required. However, constantly accommodating new requests can encourage irrational and unsystematic modifications, putting the development team in a bind. It can also lead to scope creep, lack of project control as well as cost and time overruns [5, 12, 13]. Along the same lines, the agile principles do not encourage comprehensive project planning during the initial stages of a project [14]. This lack of detailed project planning might lead to improper project resource planning and therefore, expose the project to risks like going over the estimated budget [5]. Requirements and schedule management. Agile principles do not encourage a structured form of communication, and most of the knowledge while practicing APM is tacit [5]. Due to the lack of extensive documentation and the software requirements being explained only in time for development, a lot of issues and misconceptions can emerge. As a result, team members may not fully comprehend the requirements complexity or several aspects/characteristics of a feature [2, 15]. Furthermore, [13]

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note that additional challenges in requirements emerge from the fact that APM traditionally requires the team to reach consensus. This may not be easy to achieve with multiple competing requirements. In this context, it becomes difficult for a team to determine the order of development for various product features and requests, ultimately threatening the value of a business [16]. Team Operation. Collaboration between team members is considered one of the most critical factors while implementing APM [5, 13]. Team members are required to be highly motivated in order to facilitate APM which could alienate individuals who are unable to meet the necessary levels of motivation [4]. A successful implementation of APM requires proper trust, cooperation, collaboration, and cohesion between the agile team members and also between the team and various stakeholders [17], but this will be increasingly difficult to achieve especially when the team is larger and the team members belong to different cultures, time zones, and speak different languages [5, 18] Good communication between the team members is a critical requirement for the efficient sharing of knowledge within the team, given also that the lack of documentation leaves a substantial amount of the knowledge for an agile project undocumented [13]. Furthermore, Agile teams are encouraged to be self-organizing, i.e. the task allocation is performed by the team members themselves and they are collectively responsible for selecting tasks that they would be working on [19]. However, the process of self-assignment of tasks in an agile team often led the members of the team to select tasks which they were experienced in, causing an increase in specialization but a loss in cross-functionality. Also, the expectation for collaborative estimation of the tasks, the process can become lengthy and ineffective [2]. Mentioned in their study that the developers in an agile team were not able to accurately quantify their efforts required on tasks, and this is especially true for larger projects. Quality Management. Attempting to detect errors in the software codes during the later stages of an agile software development workflow is much more damaging and expensive than being able to identify these bugs/defects early on. According to [20] there is a deficit of sufficient time for testing software when delivering projects utilizing the agile principles. This results from the fact that the scope of the agile project is always changing due to the frequent changes in customer requirements. Furthermore, [4] also report that the performance metrics used by organizations are not always appropriate for agile projects which inevitably creates uncertainty over the achievement of the required value. Organizational factors. Organizational challenges related to culture, organizational learning, and business transformations are also encountered while practicing APM. The lack of knowledge, training, understanding, and experience in agile methodologies and principles is highlighted in numerous previous researches including [2, 4, 13]. also highlight the difficulties in introducing the agile ways of working to a particular team or the wider organization as they might be more accustomed to traditional methods which are vastly different from APM. Furthermore, being agile in a non-agile environment is another challenge which is faced when a team is able to successfully adopt agile within their team but face barriers when trying to operate within the wider traditionally structured organization. Challenges

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related to management buy in and potential misconception of agile was another barrier identified [4, 13].

3 Methodology 3.1 Exploration of Literature This stage is commenced by surveying literature sources to understand the work performed by other people that is relevant to the topic of this study. Then further exploration of the literature was undertaken to comprehend the research gap in this area of study. Following this, all the relevant literature were comprehensively analyzed to identify the barriers and challenges encountered while practicing APM. The challenges identified from the various literature sources were carefully examined to exclude any duplication of barriers.

3.2 Interview with Industry Experts and BWM Implementation For the 2nd stage of the methodology, experts from the suitable industrial background were shortlisted. An interview panel consisting of five UK-based professionals with experience in APM was formed for this study as shown in Table 1. The industry experts were interviewed in August 2021 through google meets, as with the current risks of COVID-19 it was deemed best to avoid face-to-face Table 1 Profile of industry experts involved in the research

Expert

Position

Years of total Years of experience experience in agile

Expert 1 Technical Delivery Manager

20

5

Expert 2 Integration Project Manager

18

5

Expert 3 Senior Project 24 Manager

7

Expert 4 Business Intelligence Developer

20

10

Expert 5 Director and QA consultant

16

10

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meetings. The interview duration ranged from around 40 to 85 min, with most interviews averaging to a duration of about 55 min. With the experts’ permission, audio recordings of their comments/responses were made, and relevant notes were taken. During the interviews, there was discussion around the challenges associated with APM as per the literature review. After the discussion, the barriers for APM were finalized, categorized under suitable main themes, and ranked by each expert using the BWM. BWM is a Multi-Criteria Decision-Making (MCDM) technique which was developed by [21] with the aim to enable the determination of the weights of various criteria using two vectors of pairwise comparisons. BWM was chosen over other MCDM techniques because, it requires less pairwise comparisons to get consistent results in contrast to other MCDM techniques. Rezaei [21] compared BWM to Analytic Hierarchy Process (AHP) and concluded that BWM outperforms AHP; not only did BWM need fewer number of pairwise comparisons, but it also helped achieve more reliable results. The first step of BWM commences with the decision-maker (in this case the industry experts chosen for the study) determining the best (or the most important) criteria and the worst (or the least important) criteria among the main barriers. Following this, the best criterion is compared to the other criteria based on their importance, and similarly the other criteria are compared to the worst criterion. The calculations of the weights for the major barriers are obtained individually for each of the experts by following the step-by-step procedure of BWM described in Rezaei [21]. Following this, the average weight for each of these main groups of barriers can be found. Then, following the same BWM steps, the weights of all the sub-barriers under each group of barriers are calculated and global weights emerge by multiplying each sub-barrier’s weight by the previously calculated group’s weight.

4 Results 4.1 Interview Results Overview The interview process established that among the main barriers of APM, project planning, and team and knowledge management challenges are most frequently encountered. Among the APM sub-barriers, challenges related to agile principles understanding, project scope management, and knowledge sharing were mentioned the most by the industry experts. Furthermore, 2 interviewees stated that people who were experienced in working with more traditional methods often displayed resistance with adopting agile and were hesitant to work in short iterative sprints. It was also stressed that an organization needs to spend a lot of time and effort in training people and getting the culture to change in order to implement the agile work practices, and this can be quite challenging especially if the demographic of a company consists mostly of people from the older generation. Furthermore, it was stated that agile has a steep learning curve and it can be hard to explain APM to the

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management and the executives of an organization. The experts agreed that a project success or failure in an agile environment depends on the interactions between the team members and also with the stakeholders and there can be issues if some of the team members are not cooperative or prefer to work alone. It was also mentioned that the teams working on the integration aspects of software development are frequently required to work cross-functionally and interactively with many other teams and this could pose a challenge if the necessary levels of collaboration are not achieved. The interviewees also mentioned that a lot of agile teams do not create any kind of documentation or reporting which could have helped in indicating the actual reflection of their current situation in a project and their plans for the future sprints. In this context, new team members don’t have the necessary resource to refer or understand about how a particular feature works. Regarding project planning, it was stressed that gathering the correct requirements. can be particularly challenging and time-consuming and so can be the task of prioritizing the deliverables and managing the expectation for the final product. It was mentioned that in a traditional project management method it is relatively easier to manage the project scope/deliverables and provide a definitive timeline (or schedule) for the completion of project. In contrast, APM with its flexibility is likely to cause scope creep. Things can also get worse when many middlemen in between the client and developers make the actual description of a task to be lost in communication. Furthermore, when persons of differing level of experience are involved in the same project, this makes consensus with regard to the task time and effort required more difficult, time-consuming, and less accurate at the same time. Task allocation to the person best suited was also mentioned as a separate issue.

4.2 BWM Results BWM results for the main barriers. Table 2 presents the final weights of all the main barriers (Organizational, Project Planning, Team operation and Quality Management). The final weights were derived by taking the average of the weights provided by the experts for each of the main barrier factors. The final KSI value is found to be 0.072 which is close to zero, this suggests that the values provided by the experts are consistent and can be considered reliable. In a similar fashion by applying the steps of BWM, the final weights for each of the sub-barriers categorized under the respective main barriers have been identified. In the first round of BWM implementation (ranking of the main categories of barriers—Table 2), the barriers found to be “most important” (“highest”) were the project planning ones (3 votes). One vote was also given to each of the high-level organizational barriers and the team operation barriers. Similarly, the experts found almost unanimously that the “least important” barriers (“Worst”) were those related to quality management (4 out of 5 votes). Following this, the experts implemented the steps of the method to indicate the relative importance of the different groups of barriers and the average of allocated weights was also calculated (Table 2) to reveal

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Table 2 Experts weightage for the main groups of barriers Group of Barriers

Exp1

Exp2

Exp3

Exp4

Exp5

Average

High-level organizational

0.485 (B)

0.172

0.187

0.136

0.179

0.232

Project planning

0.291

0.466 (B)

0.460 (B)

0.273

0.472 (B)

0.392

Team operation 0.078 (W)

0.258

0.281

0.500 (B)

0.268

0.277

Quality management

0.146

0.103 (W)

0.072 (W)

0.091 (W)

0.081 (W)

0.099

ξ L* consistency check)

0.097

0.052

0.100

0.045

0.065

0.072

that barriers in planning take the first place (39.2%), team operation factors come second (27.7%), and high-level organizational barriers come third (23.2%). Quality Management difficulties (9.9%) complete the list of barriers. The calculated values of ξL* which is very close to 0 confirm the reliability of the rankings provided. Experts have implemented BWM procedures to demonstrate the relative importance of sub-barriers within each group of barriers, depending on their perception. Global weights and rankings were also obtained using aggregated (mean) values for both hierarchy levels (Table 3). The top five sub-barriers accounting for an aggregated weight of almost 60%, are in order of priority: the lack of adequate and accurate description of tasks (21.2%), the unclear time requirements and project schedule (11.4%), the lack of effective communication/knowledge sharing between team members and other stakeholders (10.1%), the unclear roles in the team and members struggling to take ownership of tasks (8.3%), and the lack of insight/ experience/understanding of agile project management principles (7.1%).

5 Conclusions With the emerging concerns regarding the challenges of APM, organizations are trying to figure out solutions to enhance the implementation and practice of APM. Following a review of the literature, a total of 13 barriers were identified and categorized under 4 main groups (High-level Organizational, project planning, team operation, and Quality Management). The relative weights of the various APM barriers were estimated through a MCDM model (Best–Worst Method) based on the input of 5 APM practitioners. The results reveal that barriers in project planning are ranked first in importance, followed by team operation challenges and high-level organizational barriers. The top five sub-barriers accounting for an aggregated weight of almost 60%, are in order of priority: the lack of adequate and accurate description of tasks, the unclear time requirements and project schedule, the lack of effective communication/

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Table 3 BWM weights given by experts for the sub-barriers and respective rankings Barrier

Local weight

Global Weight

Lack of insight/ experience/understanding of agile project management principles

0.307

0.071

5

Lack of independence and autonomy in the organization’s culture

0.224

0.052

9

Difficulties emerging from the co-operation with 0.2 other non-agile business departments

0.046

11

Lack of management support and understanding of agile

0.269

0.062

8

Unclear time requirements and project schedule

0.292

0.114

2

Lack of adequate and accurate description of tasks, insufficient and ambiguous requirements, changing requirements, scope creep

0.542

0.212

1

Inefficient project task prioritisation, unclear task 0.167 dependencies

0.065

6

Team Lack of effective communication / knowledge 0.366 organisation sharing between members and other stakeholders and cooperation Lack of motivation and low trust level in the team 0.146

0.101

3

0.041

12

High- level Organisational

Planning

Quality

Rank

Unclear roles in the team, members struggle to take ownership of tasks

0.299

0.083

4

Team members struggle to efficiently make collective decisions and task estimations

0.189

0.052

9

Lack of sufficient time for testing and quality assurance

0.36

0.036

13

Unclear performance metrics / value criteria

0.64

0.063

7

knowledge sharing between team members and other stakeholders, the lack of clarity in team roles and task ownership, and the lack of insight/experience/understanding of agile project management principles. This reflects the fact that barriers are spread out into a wide range of factors, highly dependent on the inherent features of APM as well as on human communication and social skills. This makes the alleviation of barriers more complex and calls for a careful investigation of the causal links between the barriers so that the ones with higher driving power are identified and tackled in priority.

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References 1. Hoda R, Noble J, Marshall S (2008) Agile project management. in New Zealand Computer Science Research Student Conference 2. Koi-Akrofi GY, Koi-Akrofi J, Matey H (2019) Understanding the characteristics, benefits and challenges of agile it project management: A literature based perspective. Int J Softw Eng & Appl (IJSEA) 10(5):25–44 3. Karlesky M, Vander Voord M (2008) Agile project management. ESC, 247(267): p 4 4. Gregory P, et al. (2015) Agile challenges in practice: a thematic analysis. in International Conference on Agile Software Development. Springer 5. Masood Z, Farooq S (2017) The benefits and key challenges of agile project management under recent research opportunities. Int Res J Manag Sci 5(1):20–28 6. Miller GJ (2013) Agile problems, challenges, & failures. Proj Manag Inst 7. Cunningham W (2001) Manifesto for agile software development. [cited 2022 January 14]; Available from: https://agilemanifesto.org 8. Lalsing V, Kishnah S, Pudaruth S (2012) People factors in agile software development and project management. Int J Softw Eng & Appl 3(1):117 9. Ng GC (2019) A study of an agile methodology with scrum approach to the filipino companysponsored it capstone program. arXiv preprint arXiv:1902.01821 10. Santos MdA, et al. (2013) Improving the management of cost and scope in software projects using agile practices. arXiv preprint arXiv:1303.1971 11. Lozo G, Jovanovic S (2012) A flexible hybrid method for IT project management. J Emerg Trends Comput Inf Sci 3(7):1027–1036 12. Leybourne SA (2009) Improvisation and agile project management: a comparative consideration. Int J Manag Proj Bus 13. Fitriani WR, Rahayu P, Sensuse DI (2016) Challenges in agile software development: A systematic literature review. in 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE 14. Sidhardhan SK (2015) Benefits and challenges of agile project management 15. Moe NB, Aurum A, Dybå T (2012) Challenges of shared decision-making: A multiple case study of agile software development. Inf Softw Technol 54(8):853–865 16. Van Waardenburg G, Van Vliet H (2013) When agile meets the enterprise. Inf Softw Technol 55(12):2154–2171 17. Sharma S, Sarkar D, Gupta D (2012) Agile processes and methodologies: A conceptual study. Int J Comput Sci Eng 4(5):892 18. Ismail MFb, Mansor Z (2018) Agile project management: review, challenges and open issues. Adv Sci Lett, 24(7): p 5216–5219 19. Hoda R, Murugesan LK (2016) Multi-level agile project management challenges: A selforganizing team perspective. J Syst Softw 117:245–257 20. Rehman AU, et al. (2020) A comparative study of agile methods, testing challenges, solutions & tool support. In: 2020 14th International Conference on Open Source Systems and Technologies (ICOSST). IEEE 21. Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57

Improvement of Service Flow and Cost Optimization for an Automobile Service Center Aditya Vashishth, Sidharth Radhakrishnan, Yashaswin Tanwar, Shyamal Samant, and Rakesh Kumar Phanden

Abstract The automotive service sector is one of the most crucial parts of the Indian automotive landscape. Besides creating jobs for the economy, it contributes to the overall success of the automotive industry. The field of increasing the operational efficiency of the automotive service center remains relatively unexplored, as there are not many studies in this area. Many Kaizen improvement initiatives have been proposed. This work involves modeling of automotive service center using IBM ILOG CPLEX mixed integer optimization. This includes layout optimization, increased use of On-board diagnostics (OBD) reader, resource optimization, etc. This research draws parallels between automotive service centers and job shop production. The optimization of service center has been carried out like a job shop production. Keywords Automotive service center · Job shop production · CPLEX optimization

1 Introduction The automotive sector is one of the key drivers of the Indian economy and it plays an important role in the industrial development of India. The Indian automotive sector is one of the large sectors of the economy. It contributes to around 7% of the nation’s GDP [1]. The Indian automobile industry is also one of the fastestgrowing industries. It is the sixth-largest automotive industry in the world. A total of 1.1 million dollars is driven into the economy by this industry. Indian automotive industry is growing and is likely to reach US $250–280 billion by 2026 [2] The fast-growing automotive sector has given rise to an extensive automotive repair and maintenance industry. The automotive repair and maintenance industry of India was worth around Rs 34,000 crores [1]. It provides a wide range of mechanical, electrical A. Vashishth · S. Radhakrishnan · Y. Tanwar · S. Samant (B) · R. K. Phanden Department of Mechanical Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Sec 125, Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Phanden et al. (eds.), Advances in Industrial and Production Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-1328-2_2

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and engine-related maintenance services for automobiles. Indian automotive service industry faces significant challenges in the improvement of its operational efficiency [3]. It is evident that there is a scope of improvement in operational efficiency in the automotive service sector, especially in the Indian context. In this research, parallels have been made between an automotive service center and a job shop type production. In a job shop production small batches of various custom products are made. The automotive service industry, likewise, also carries out a variety of repair jobs. They both get uneven jobs with different processing requirements to be met for each job; also, it may be difficult to create a weekly fixed schedule because of the unevenness of the arrival of jobs daily. There is literature available on using optimization tools for the analysis of a job shop. Optimization of a job shop is carried out using mixed integer programming solvers, e.g., CPLEX, Gurobi, etc., [4] solved the job shop scheduling problems using both the Mixed Integer Programming model and the Constraint Programming model and taking machine operator constraint, [5] created a new objective as the sum of maximum earliness and tardiness and used mixed integer linear programming to solve the problem, [6] solved flexible job shop scheduling problem with parallel batch processing machines using MIP and CP approaches. This research follows a case study approach. The case of an automotive service center located in north India has been considered. The garage services around 15 cars in a week. The services performed are typically cooling system service, engine service, AC service, suspension service, wheel service, brake service, transmission service, chassis and body service, battery service, emission testing, etc. This research explores opportunities for the improvement of the operational efficiency of an automobile garage. The study explores options for layout improvement, service flow improvement, customer waiting for time reduction, throughput improvement, etc.

2 Literature Review This research paper focuses on increasing the operational efficiency of an automotive service center. The research in the operational efficiency improvement of automotive service centers is relatively unexplored. Grieger and Ludwig [7] proposed a conceptual reference framework (CRF) to systemize the automotive service systems. It provides the necessary infrastructure for digital service conceptualization [8]. Discuss methods to use knowledge-based database systems to create a tool to automatize decision-making in the automotive service center. Mohamed, Noorashid and Zolkepli [9] utilized Service Quality Model (SERVQUAL model) to examine the effect of service quality on customer satisfaction [10]. Used proximal policy optimization (PPO) to find the optimal solution for scheduling an intelligent manufacturing facility [11]. Proposed a model to improve the logistic system of auto service network by optimizing spare part deliveries, number of workers and stations in each service center [12]. Have increased the effectiveness of solving a single task

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and also improved the efficiency of transferring tasks in multitasking by applying a new surrogated-assisted evolutionary multitask approach for genetic programming hyper-heuristics. Literature review reveals that there is a limited literature on improvement in automotive service centers. As discussed earlier, there are similarities between an automotive service center and a job shop. Here, some literature related to tools used for enhancing operational efficiency, scheduling, etc., in a job shop are presented. Kress, Müller and Nossack [4] solved the job shop scheduling problems using Mixed Integer Programming and Constraint Programming models and taking machine operator constraints [5]. Created a new objective as the sum of maximum earliness and tardiness and used mixed integer linear programming for solving the problem. Ham and Cakici [6]. solved flexible job shop scheduling problems with parallel batch processing machines using both MIP and CP approaches. There appears to be limited research in the area of increasing the operational efficiency of an automotive service center. However, mixed integer programming tools (e.g., CPLEX optimization tool) have been widely applied for job shop scenarios. This research uses the CPLEX optimization tool for the optimization of some select use cases for an automotive service center. This will lead to improvement in operational efficiency.

3 Methodology This research aims to come up with suggestions for improving the operational efficiency of an automobile service center through a case study.

3.1 Profile of a Case Organization The automotive center is located in north India. This automotive service center offers a complete range of automotive repair and maintenance services. Facilities/resources available at the center are shown in Fig. 1. Some of the services offered by the organization are given in Table 1.

3.2 Proposed Research Methodology The proposed methodology is presented in Fig. 2 The steps mentioned in the proposed methodology (Fig. 2) have been described in Sects. 3.2.1 to 3.2.6.

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Fig. 1 Facilities available at the center Table 1 Service time and major resources employed for each process step Service name

Time taken

Major resources employed

# Resources (A)

Weekly resources cost (B)

Inspection

30 min

None

0

Job order

10–15 min

None

0

0

0

OBD

10–20 min

OBD Reader

1

55

55

Engine service and oil change

25–120 min

Injection testing device

1

156

156

AC servicing 40–60 min

Coolant Exchanger

1

1000

1000

Suspension

30 min

General Equipment

1

20

20

Brakes

25–30 min

General Equipment

1

20

20

Wheel servicing

80–120 min

Wheel balancing machine and wheel alignment machine

2

882

882

Chassis and body work

140–240 min

General Equipment

1

30

30

Battery

25–30 min

Hygrometer, battery charge tester

1

20

20

Emission

15 min

Emission testing machine

1

980

980

0

Machine cost (weekly) C =AxB 0

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Fig. 2 Flow chart of the proposed methodology

3.2.1

Setting Business Priorities

The researchers held discussions with the business owners regarding the business priorities. It emerged that cost reduction of the services offered and increase in throughput (number of vehicles serviced in a week) are the main priorities of the business.

3.2.2

Gathering Data on the Process

In order to gain data about the process, all the steps in the process sequence must be known. In Fig. 4, gives the process sequence of the service center. Inspection is done to analyze the vehicle’s problem, then in job order, approval of the customer is taken so that further work can be done on the vehicle. After the step, OBD is used to identify further issues in the vehicles. Engine servicing & oil change are done in which the engine and the injector system are checked and serviced while the lubricating oils and engine oil are replaced. The air conditioning system is checked to see whether it provides cooling correctly. Here, components like the blower, compressor, air filter and coolant are checked. If no problem is found, coolant replacement occurs and suspension service is carried out where suspension oil and greasing replace. After the suspension part, wheel and brake servicing is done in which the wheel is countered for balancing and alignment. An overall chassis and vehicle body inspection is conducted where minor defects are eliminated (Fig. 3). Next, the service time and the major resources employed for each of the process steps must be noted. These are given in Table 1.

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Fig. 3 Process sequence of the automotive service center

Fig. 4 Original layout of the automotive service center

3.2.3

Establishing the Performance Metrics

Establishing the Performance Metrics is a crucial part of this methodology. This will help in setting up improvement goals. This will also help in identifying the suggested improvement measures (Kaizen initiatives). Improvement measures (Kaizens) should lead to improvement in the performance metrics.

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The following two performance metrics have been proposed: • Average number of cars serviced in a week • Average service cost 3.2.4

Developing the Optimization Model

This research work develops an optimization model. The optimization model is given in Table 3 (Table 2). Table 2 Optimization model Objective Function Minimize completion time

min Cmax; Where, Cmax is time taken for completing all the services

Constraints Start time of xsw ≥ 0; where, xsw is the start time of services on workshop w services, i.e., workshops must begin services at time = 0; ≥ xσsh−1 ,s + pσsh−1,s (1) Equation (1) is the precedence constraint. Where x is the start time of the process and p is the processing time of the respective process. Xσsh,s shows the starting time of hth operation of service s, while xσsh−1 , shows the start time of (h-1)th operation and pσsh−1,s shows the processing time of (h-1)th operation xsw ≥ xwk + pwk – V ・ zswk (2) xwk ≥ xwj + pwj – V ・ (1 – zswk )(3) Equation (2) and Eq. (3) state that no two or more service can occur at a workshop together. V is a large number to ensure correctness of both the constraints. Here x represents the starting time of the process while p represents the processing time of the process. Xwk is the start time of wth operation on machine k and p is the processing time of wth operation on machine k Cmax ≥ xσsw, s + pσsw, s (4) Equation (4), Makespan should always be the largest completion time of the last operation of all jobs. Xσsw, s is the start time of service w of job s and pσsw, s is the processing time of service w of job s zswk ∈ {0, 1}(5) zswk is 1 if service s precedes service k at workshop w Eq. (5) keeps the value of zswk as 0 or 1

Other constraints

Xσsh,s

Decision variables

xsw It is the start time of services on workshop w

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Table 3 Performance Metrics before and after the implementation of proposed Performance Baseline Kaizen metrics value improvement initiative 1 (using resource optimization)

Average number of cars serviced in a week Average service cost (in Rupees)

3.2.5

Kaizen improvement initiative 2 (using a machine with both wheel balancing and wheel alignment)

Kaizen improvement initiative 3 (using an OBD reader)

Kaizen improvement initiative 4 (layout optimization)

Kaizen improvement initiative 5 (combining all kaizen initiatives from 1 to 4)

15

20

21

16

15

24

4500

3605

3507

4224

4450

3232

Identifying Improvement Opportunities (Kaizen Improvements)

After performing the above steps, Various measures are listed below for the improvement of the automotive service center: a. Layout Optimization (Kaizen improvement initiative 4)—By doing layout optimization the time taken in transition from one service to another will be reduced and by doing this the overall completion time is to be reduced. In it the vehicle ramp and other features were placed farther than the other working area of the automotive service center due to which there was the loss of time in commuting from one workstation to another which has been eliminated in the optimized layout shown in Fig. 5. In it we moved the ramp closer to the engine and radiator service area so that the time taken to take the vehicle from the engine service area to the washing ramp for suspension service can be reduced. It reduced the total servicing time of the vehicles and increased the number of vehicles which can be serviced per week in the automotive service center. The effect of this initiative on performance metrics is shown in Table 3. b. OBD reader (Kaizen improvement initiative 3)—With the help of OBD reader the time taken for inspecting various parts of the vehicle for trouble can be drastically reduced. OBD is connected to various sensors placed all over the body of the vehicle and OBD reader is used to read the error codes generated by such sensors which help to single out the parts having a problem. OBD reader has been shown in Fig. 6. Using the OBD reader has led to reduction in the inspection time of faults in the vehicle. It led to reduction in total service time of vehicle and increased the number of vehicle services in a week in the automotive service center. The effect of using OBD reader on performance metrics is shown in Table 3.

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Fig. 5 Optimized layout for the automotive service center Fig. 6 OBD reader [13]

c. Resource optimization (Kaizen improvement initiative 1)—By equipping the automotive service center with more machines the service time taken for any particular service can be reduced and the manpower saved there can be redirected to other services. For example, by utilizing two wheels balancing machines two of the wheels of the vehicle can be balanced together which in turn reduces the time consumed in wheel servicing. The variation of average cost per vehicle to the number of wheel balancing machines used is shown in Fig. 7. From the graph, it is clear that while using four wheel balancing machines the average service cost per the vehicle is least and also the number of vehicles being serviced in a week is maximum.

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Fig. 7 Representation of Average cost per vehicle with respect to resources

d. Using a single machine for both wheel alignment and wheel balancing (Kaizen improvement initiative 2)—By using this machine the time taken for overall wheel servicing process can be reduced. It will help in reducing the time consumed for wheel alignment which is being done separately which will lead to reduction in overall time taken in wheel servicing. The variation of average cost per vehicle to the number of machines doing wheel balancing and wheel alignment used is shown in Fig. 8. From the graph, it is clear that while using four combined wheel balancing and alignment machines the average service cost per vehicle is least and the number of vehicles serviced in a week is maximum.

4 Result Kaizen improvement initiatives (1) include using multiple wheel balancing machine to reduce the time taken in wheel servicing, (2) include using a machine with both wheel balancing and wheel alignment in wheel servicing, (3) include using an OBD reader, (4) include layout optimization. The changes in the performance metrics. From the table, we can see that by utilizing the various kaizen initiatives the average service cost per vehicle has decreased and the number of vehicles that can be serviced in a week in the automotive service center has increased.

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Fig. 8 Representation of Avg cost per vehicle with respect to resouces (combined wheel balancing and alignment machines)

5 Conclusion Methods to optimize the automotive service center are proposed with the changes to reduce total completion time and increase the capacity of the vehicles that could be serviced in a particular time. All kaizen initiatives decreased the average service cost per vehicle and increased the number of vehicles that can be serviced in a week in the automotive service center. All suggestions and improvements were made to optimize the automotive service center and increase its profit and service quality.

References 1. Banerjee A (2017) Auto Servicing Market In India To Be Worth Rs 34 000 Cr By 2020 CarXpert—BW Businessworld, Jun. 10, 2017. https://www.businessworld.in/article/Auto-Ser vicing-Market-In-India-To-Be-Worth-Rs-34-000-Cr-By-2020-CarXpert/10-05-2017-117902/ (accessed May 13, 2022) 2. Automobile Industry, Indian Automobile Companies—IBEF, May 06, 2022. https://www.ibef. org/industry/india-automobiles (accessed May 13, 2022)

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3. James AT, Kumar G, Bhalla M, Amar M, Jain P (2021) Analysis of challenges for automobile service garages in India: a structural modeling approach. J Adv Manag Res 18(3):392–413. https://doi.org/10.1108/JAMR-04-2020-0059 4. Kress D, Müller D, Nossack J (2019) A worker constrained flexible job shop scheduling problem with sequence-dependent setup times. J Adv Manag Res 41(1):179–217. https://doi. org/10.1007/s00291-018-0537-z 5. Yazdani M, Aleti A, Khalili SM, Jolai F (2017) Optimizing the sum of maximum earliness and tardiness of the job shop scheduling problem. Comput Ind Eng 107:12–24. https://doi.org/10. 1016/j.cie.2017.02.019 6. Ham AM, Cakici E (2016) Flexible job shop scheduling problem with parallel batch processing machines: MIP and CP approaches. Comput Ind Eng 102:160–165. https://doi.org/10.1016/j. cie.2016.11.001 7. Grieger M, Ludwig A (2019) On the move towards customer-centric business models in the automotive industry—a conceptual reference framework of shared automotive service systems. Electron Mark 29(3):473–500. https://doi.org/10.1007/s12525-018-0321-6 8. Imeri B, Lajci S, Lajci N (Oct. 2020) Utilizing knowledge management for improving the operational performance of an automotive service center, https://doi.org/10.33107/ubt-ic.202 0.289 9. Bahari Mohamed, Noor Azizah Noorashid, Ariff Firdaus Zolkepli, (Jan. 2022) The effect of service quality on customer statisfaction as mesured by servqual: A case study of automotive maintenance and repair service center,” Jurnal Al-Sirat, 1(20), pp 79–90, Available: https://ejo urnal.kuipsas.edu.my/index.php/qwefqwefq/article/view/227 10. Wang L et al. (May 2021) Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning, Comput Netw, 190, https://doi.org/10.1016/j.comnet.2021.107969 11. Makarova I, Shubenkova K, Pashkevich A, Buyvol P, Mavrin V, Abeshev K (2020) Improvement of automotive service management by means of computer simulation. Transp Res Procedia 44:160–167. https://doi.org/10.1016/J.TRPRO.2020.02.023 12. Zhang F, Mei Y, Nguyen S, Zhang M, Tan KC (2021) Surrogate-Assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Trans Evol Comput 25(4):651–665. https://doi.org/10.1109/TEVC.2021.3065707 13. Florian Schäffer, File:Autoboss v-30.png. https://commons.wikimedia.org/wiki/File:Aut oboss_v-30.png (accessed Jun. 15, 2022)

A Study of Key Challenges in Implementation of Digital Supply Chain in the Context of Indian SMEs Nitin Kumar Chauhan , Vikas Kumar, and Sandhya Dixit

Abstract In spite of the potentially enormous benefits, small and medium Enterprises (SMEs) are falling behind in digital transformation. Emerging technologies provide a variety of applications that can help these companies to enhance their performance and get around the size-related obstacles that stand in the way of conducting business. SMEs play a major role in the economy as they generate jobs on a larger scale. In the era of industry 4.0, SMEs are also required to upgrade their supply chain with the integration of digital technologies, e.g., Cloud Computing, IoT, and Artificial Intelligence to meet global competition. During the pandemic COVID19 suddenly a gap is created to keep business running smoothly due to supply chain disruption and the SMEs are most affected due to pandemic. A need for the integration of technology with the supply chain is created to make the supply chain agile and sustainable. This paper studies the challenges in order to adopt the digital transformation in Indian SMEs. Keywords Digital supply chain · DSC · Digital transformation · Small and medium enterprises · SME

1 Introduction Over the previous five decades, the SME sector has developed rapidly. There are more than 50 million SMEs, which are generating 40% of the country’s exports and producing millions of jobs each year. The SME sector has significant development potential in the next years in terms of employment generation, entrepreneurial culture, and innovations [1]. But the sector has long grappled with challenges. Despite the fact that technology has evolved in recent years due to the development of mobile N. K. Chauhan (B) · V. Kumar · S. Dixit JC Bose University of Science and Technology, Faridabad 121006, India e-mail: [email protected] N. K. Chauhan JSS Academy of Technical Education, Noida 201301, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Phanden et al. (eds.), Advances in Industrial and Production Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-1328-2_3

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phones and the internet, the continued low level of technology adoption by SMEs has always resulted in poor levels of productivity. These factors have made them uncompetitive in a market that is constantly expanding. Despite the fact that SMEs in urban areas have demonstrated a favorable attitude toward technology uptake, rural areas are still lagging behind. This will continue to be an issue in the coming years as well, as previously stated. The majority of SMEs participate as stakeholders in the supply chains of large corporations, who are constantly updating their supply chains through the incorporation of digital technologies. In a quickly changing world, SMEs must modernize its supply chains through digital transformation in order to provide their consumers with faster, more affordable, and higher-quality products. Now the competition is between supply chains rather than between companies [2]. The conventional systems for the supply chain have not been able to cope pace with the increasing expectations of customers and the emergence of new products. The future lies in the development of a network that is not just malleable but also creative, and which makes use of ecosystem partners and digital technologies in order to allow unprecedented levels of agility and flexibility. The purpose of this study is to highlight the various critical challenges that must be addressed in order to deploy a digital supply chain successfully in SMEs.

2 Literature Review To keep up with today’s fast-moving and unpredictable markets, supply chains have to be flexible enough to shorten the cycle of product development, bring new goods to market quickly, and adapt to a wide range of consumer demands quickly [3]. Despite the potential benefits, the digitalization of the supply chain has slowed for small and medium-sized businesses (SMEs). SME digital gaps have been shown to reduce productivity and raise disparities across individuals, businesses, and geographies [4]. This paper reviews the literature on trends and patterns in SME digital uptake to find the various challenges to support SMEs in adapting digital technology in business practices.

2.1 Digital Supply Chain In this new era of complexity and competition, digitization has evolved as a new phenomenon affecting many facets of life globally. More than 90% of user using the internet have completed e-transactions, and 40% of businesses have employed advanced big data analytics technologies [5]. The processes involved in supply chains have also been significantly impacted by digitalization, and this is abundantly clear that the transition from a conventional to a digital supply chain (DSC) presents

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companies with a competitive edge that may generate long-term value for their businesses. A digital supply chain is described as the creation of new information systems and the use of innovative technologies for improving supply chain integration and agility in order to deliver a superior service to the customer and ensure the long-term success of the company. The digital supply chain is the outcome of the utilization of new-age digital technology to all nodes of the supply chain from beginning to finish [6]. With the integration of these innovative technologies, the firms make their supply chain more efficient and responsive as well by focusing on customers, reducing intra- and inter-organizational costs and this generates greater value for company. The Internet of Things (IoT), Cloud Computing (CC), Big Data Analytics (BDA), Block Chain Technology (BCT), and Artificial Intelligence are the major essential enablers for the digitalization of supply chain (SC). These digital enablers assist to the formation of new interactions among the stakeholders of the supply chain and finally lead to the creation of partnerships with added value (Table 1). The rise of Industry 4.0 as a result of improved Information and Digital Technologies entails the creation of new possibilities, concepts, and difficulties in supply chain management. The DSC may be maintained integrally and smartly with optimal access to data on performance, demand, and overall dynamics. In a digital environment, real-time, worldwide, and holistic information may be used to manage processes, materials, supply–demand planning, asset utilization, inventories, financial position, and strategies. DSC provides significant technical improvements that enable the automatic collecting and analysis of massive amounts of data, as well as Table 1 Five facets of digital supply chains S. N

Domain

Reference

Explanation

1

IoT

[7–9]

It is a revolutionary shift in technology that creates an ecosystem in which it enables communication among all nodes of supply chain and can make possible anticipatory diagnosis and the evaluation of performance

2

Cloud Computing

[10, 11]

Cloud Computing may promote cooperation and productivity by enabling service through networking, database storage, and retrieving data when needed

3

Artificial Intelligence

[12–14]

AI is the technology most often mentioned in practitioner research as the enabler of the autonomous, predictive supply chain

4

Big Data Analysis

[8, 10, 15]

It is a new business intelligence practice that uses data mining, statistical analysis, and predictive analytics on large databases

5

Block Chain Technology

[16–18]

A distributed digital ledger that assures visibility, traceability, and privacy

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supply chain partner integration in real time [19]. Adoption of new cutting-edge technologies can enable to achieve more accurate judgments and enhance supply chain integration. It would primarily help to improve SC agility by allowing the real-time information gathering, facilitating information exchange, and coordinating reactions across supply chain members. The researchers have studied the application of emerging technologies such as Big Data Analytics (BDA), Internet of Things (IoT), Cloud Computing (CC), and Artificial Intelligence (AI) and their effects on supply chain management through an exhaustive literature review [20]. The opportunities created with the 4th industrial revolution for boosting and quality have been studied by researchers [21]. Despite the fact that technological growth has affected nearly every element of life, technological breakthroughs have always given a starting point for change. The key to designing an Intelligent Digital Supply Chain is to use technology that changes things [22].

2.2 Need of Digital Transformation of SC in SMEs In 2021, Govt revised the definition of Micro Small, and Medium Enterprises (MSME) and included the turnover of the company in definition of MSME. Now the MSMEs are categorized with a turnover maximum of 5 Cr., 50 Cr., and 100 cr., respectively. Earlier it was based on only investment, i.e., maximum of 25 Lac., 5 Cr., and 10 Cr. For MSMEs, respectively, in manufacturing sector and a maximum of 10 Lac., 2 Cr., and 5 Cr. For MSMEs, respectively, in service sector [23]. Now the line between manufacturing and service MSMEs has been erased. The SMEs will benefit from the new definition. Many successful MSMEs worry that if they grow larger than what is considered an MSME, they would lose their rights to certain benefits. The reason for this is that MSMEs want to stay small rather than grow. Redefining MSMEs so they don’t have to worry about expanding and yet reaping the rewards is a positive idea. In addition to the revised definition, the Indian government has also put a restriction on participation in tenders up to Rs 200 crore to Indian enterprises alone. Also “Make in India” program is launched to boost the Indian industrial economy. These initiatives taken by the government provide an opportunity to grow their business and provide a thrust to adapt digitalization of supply chain. All company and industry settings are rapidly changing. Today’s marketplace is characterized by severe rivalry, cost constraints, short-term market demand, and erratic demand patterns. Therefore, it is vital to envision a supply chain that can be quickly adapted to changing circumstances. As a result, smart supply chains are required to meet the rising challenges. Supply networks may be improved, waste reduced, and profitability increased by converting conventional supply chains to digital supply chains in India. DSC acts as an intelligent value-driven network using data analytics to exploit new techniques and methodologies for profit and revenue generation [19]. Industry 4.0 and disruptive technologies must also be examined for their capacity to bring considerable rewards like increased order fill rates and

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availability of materials, improved accuracy in master data, and reduced inventory, as well as new inventions [24, 25]. Consumer preferences and requirements are constantly changing due to factors such as globalization, shorter product development cycles, product complexity, mass customization, limited natural resources, and fast economic change. As a result, the supply chain evolves from a traditional linear system to one that is visible, flexible, and linked. This shift in supply chain activities might provide a competitive edge to a company. As a result of globalization, product complexity, shorter product development cycles, mass customization, and depleting natural resources, the system has been strained in recent years. Moreover, given the pandemic’s impact on global supply chains, local players have also been affected. Also, during the pandemic COVID-19 suddenly a gap is created to keep business running smoothly due to supply chain disruption and the SMEs are most affected due to pandemic. A need for integration of technology with the supply chain is created to make the supply chain agile and sustainable. As a direct consequence of the COVID-19 crisis, SMEs have been forced to migrate their operations online so that they endure lockdowns and disruptions to supply chain, as well as to establish a work setup to accommodate the limits of physical and social distancing at workplace. The implementation of digital technology has resulted in the creation of novel opportunities as well as new obstacles for the management of agile supply chains.

2.3 Challenges in Implementation of DSC The need for technology integration is very quick due to pandemic to make the supply chain agile and sustainable. The small industry faces various challenges to adopt this quick technological change. Along the supply chain, there can be a number of problems. The primary challenges that need to be overcome in order to build DSC are collecting all of the necessary data from a diverse range of sources, verifying data accuracy, and continuing to develop a software system and framework which may utilize the data to monitor and carry out the supply chain. It will be sluggish and prone to mistakes due to the length of the chain, which involves partners both inside and outside the organization. Many SMEs have issues with the availability and quality of the older data that they use for their supply chain operations. Hence the quality of data may a challenge for DSC. Data security is also a major concern in SMEs due to unavailability of a strong IT infrastructure. Mostly SMEs are owned by a single owner and all decisions are around the owner of the company. A proper agile organization structure is not present in SMEs and it can produce a hindrance toward digital transformation. The commitment of top management toward digital transformation plays an important role as it reflects the vision and mission of the company. As an investment is required to build the IT infrastructure for implementation of DSC, a financial support is required. Lack of financial resources can be a major challenge in order to implementation of DSC. The most crucial thing to remember is that the digital supply chain revolution goes much beyond merely technology advances. The

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DSC will need the development of a new skill set for Supply Chain experts in the future. Many SMEs focus on only short-term planning, but a long-term planning is a necessary part of DSC as customer expectations are constantly changing, demand is volatile, and regulatory compliance is more complex than ever. Effective information sharing is a crucial factor in DSC performance and SMEs are reluctant to share information sharing due to lack of trust among the stakeholders of SC. A collaboration and coordination among all stakeholders are required for a successful implementation of the DSC in the organization. The following are some of the challenges listed in Table 2 that have been identified from the extensive literature review in order to successful implementation of DSC in the context of Indian SMEs. It is necessary to address concerned challenges associated to DSC installation, optimization, and development and new-related managerial approaches are implemented for digital transformation. These issues discussed are taken into account widely by supply chain professionals and need to be considered by more and more firms in order to design an efficient and responsive supply chain.

3 Conclusion The supply chain’s digitization is a strategic topic for the researchers’ communities of supply chain management. There’re several technical, organizational, and strategic hurdles to address before the DSC can be successfully implemented. Developing the emerging research on technology adoption and its effect on the supply chain is vital. The aim of this article is to identify and discuss the implications of digital supply chain in SMEs. This paper also focuses on the potentials, opportunities, and implementation barriers for digital transformation in SMEs. This paper offers SMEs management an overview of DSC applications and their effect on the performance of organization. The SMEs’ management must be aware of the risks, and challenges in implementation of DSC to make their supply chain agile. In the context of the identified barriers in this article the managers of companies may need to develop a strategy to adopt certain technologies that may help to achieve their organizational goals. SME’s lack of information sharing, working capital crunch, lack of complementary resources, facing problems in adapting to changing regulatory frameworks, addressing challenges of digital safety and privacy are all barriers that governments are actively addressing in order to encourage SMEs to adopt a more digital approach. With “Make in India” initiatives the Indian SMEs look an opportunity to grow and participate in the growth of Indian economy by adapting new-age digital technologies for transforming their supply chain. As per McKinsey research using new-age technologies in the supply chain may save operating expenses by 30%, minimize lost sales and inventory significantly and concurrently improve supply chain agility. As a result of digitalization, SMEs have a wide range of chances to improve their performance and encourage innovation, boost efficiency, and compete on a more level playing field with bigger corporations. SMEs, on the other hand, may be reluctant to

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Table 2 List of challenges in implementation of DSC in Indian SME S.N

Factors

References

Explanation

1

Lack of long-term planning

[26–29]

Deficiency of proper demand plan and guidelines and tools

2

Lack of [10, 30–32] involvement of top management

The vision or perception of the top management toward IT adoption in SC for the growth of the organization

3

Lack of agile organization structure

[28, 30, 33]

Agile structure shows the preparedness and responsiveness toward the transformation

4

Lack of financial recourses

[34–36]

Scarcity of financial resources in developing countries like India

5

Lack of IT capabilities and infrastructure

[28, 29]

Necessary for the reliable, safe, and rapid dissemination of information relevant to supply chains both inside and outside of its boundaries

6

Lack of building [37–40] human capital

Reluctance to invest in training of employees

7

Lack of digital dexterity

[37, 41–46]

Insufficient ability at the primary level due to a lack of curiosity, a fear of computers, and the unattractiveness of the new technology

8

Lack of integration

[47, 48]

Inadequate awareness of integration of digital and non-digital supply chains

9

Poor data security

[49–52]

Hackers aim the small enterprises easily due to their lower level of security

10

Resistance to change

[53–56]

Status quo bias, Reluctant behavior toward Industry 4.0

11

Trust between partners

[47, 57]

Lacking trust among SC partners due to opportunistic or dishonest behavior

12

Lack of collaboration

[26, 47]

Companies’ reluctance on information sharing

13

Over confidence [26, 48] on suppliers

Having dependency on certain vendors in specific locations throughout the world

14

Legal uncertainty

[58–61]

Data protection, liability personal data-based business models come into question because of legal uncertainty regarding data theft and data property

15

Intellectual property risk

[62–65]

To protect some IP capital as property through formal legal rights such as patents or contractual agreements

16

Lack of information sharing

[26, 66, 67]

Reluctance to share information sharing due to lack of trust among the stakeholders

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make the financial commitment necessary to implement DSC because of the absence of a realistic estimate of the costs. Therefore, the researcher can do a deeper study into how financials affect organizational, operational, and legal structures by implementing new-age technology. For future research, a conceptual framework or model can be developed for addressing the challenges identified in this study. A cluster of challenges with same nature can be developed and an interrelationship among the challenges can be established. Researchers should also examine extensively the key issues in digitalization process of SME’s supply chain and other supply chains like agricultural, healthcare, etc., which are under-studied.

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Forecasting Price of Small Cardamom in Southern India Using ARIMA Model Jagadeesh Babu Myneedi, Nitin Kumar Lautre, and Ravikumar Dumpala

Abstract Small cardamom is one of the most popular and expensive spices in India. Two top constraints as judged in the year 2019 were labour shortage during production and price fluctuations during the marketing of this crop. This work is an attempt to forecast the price of small cardamom by using its price data from May 2015 to December 2019. It is evident from the data that there is no seasonality in the crop price data during that period. So, Sen’s slope estimator and Mann–Kendall tests are employed to estimate the price trend, and it is found that there is an increasing trend with a magnitude of 0.429. Thus, ARIMA (Autoregressive Integrated Moving Average) model is used to predict the price of the crop for the 2020 period, where it is applied different combinations of (p, d, q) values based on ACF (Auto-Correlation Function) and PACF (Partial Autocorrelation Function) plots. By using standard criteria such as RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error), and MAD (Mean Absolute Deviation), the accuracy of the selected models was assessed. The ARIMA (3,1,3) model performed better in forecasting the prices for small cardamom in southern India. COVID-19 (2019–2020) had a significant impact on the price of small cardamom in southern India, where the price has more fluctuations with a variance of 639,147.93 compared to the forecasted price variance of 65,199.97. Keywords Small Cardamom · ARIMA · Mann–Kendall · Sen’s slope estimator · Forecast · Variance

J. B. Myneedi (B) · N. K. Lautre · R. Dumpala Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. K. Phanden et al. (eds.), Advances in Industrial and Production Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-1328-2_4

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1 Introduction Agriculture and farming have long been a part of human culture, and technology is allowing smart farms to emerge. The agriculture industry is the backbone of a nation’s economy and prosperity. India is the world’s 2nd largest grower of agricultural products, and the agricultural industry accounts for 17.32% (May 2019) of the nation’s GDP [1], emphasizing the significance of the agricultural market. Since ancient times, spices have been an important element of India’s foreign trade and the core of global food culture. Although cardamom is the queen of spices, the green or small variety has a smaller size with a stronger aroma and flavour. It is among the most expensive exotic spices on the market. Until the year 2000, India was the major producer of cardamom; after that, Guatemala took over first place, and India was pushed to second place. The Western Ghats, situated in southern India (Kerala), is known for cardamom production and are commonly referred to as the country’s cardamom hills [2]. The Kerala state of India is the country’s main source of cardamom, accounting for 76 per cent of the overall harvest and 56 per cent of the country’s cardamom land. Karnataka (15%) and Tamil Nadu (9%), respectively, come in second and third (Spices Board of India- Cochin, 2016). Kerala has a significant part of cardamom cultivation, with around 39,080 hectares of land under cardamom growing, producing an aggregate of 10,222 tonnes of cardamom per year (Kerala Agricultural Statistics 2016–17). Idukki, Wayanad, Palakkad, and other districts of Kerala produce the majority of the cardamom produced in the state [3]. The Spices Board, established in 1987 and directed by the Union Commerce and Industry Ministry, identifies traders who exclusively participate in several auction venues. Cardamom farmers bring their products to auction places to sell their crops [2]. In 2007, this process was conducted online (E-Auction) because collection and transportation of the crop is a problem for farmers to take the crop from fields (hilly regions) to auction centres. Also, it helps to check the prices on the spices board website [4]. As the price of small cardamom depends on different factors like freshness, colour, aroma, and size, as well as seasonal changes and crop arrival in the market, Indian weather conditions, and domestic festival needs. Small cardamom crop has very high fluctuations in price for a few years, affecting the formers when the price is low. So, the price forecast of small cardamom is necessary, which will help the farmers to manage the crop cultivation based on the price. Also, the government can assist the farmers financially by granting low-interest loans. A variety of Deep Learning, Time-Series, and Machine Learning models are used to forecast agricultural product prices. An evaluation is conducted on the Production and Price Behaviour of Small Cardamom in India [5]. ARIMA and SARIMA models are used to forecast the price of large cardamom in the Indian market. SARIMA model was performed based on statistics of fit [2]. Holt-Seasonal Winter’s approach, LSTM neural network, and Seasonal ARIMA models were employed to predict monthly prices of areca nuts in Kerala marketplaces, and the LSTM model outperformed the others [1]. Paddy prices are forecasted in 5 major states, namely West Bengal, Delhi, Punjab, Tamil Nadu,

Forecasting Price of Small Cardamom in Southern India Using ARIMA …

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and Uttar Pradesh. ARIMA model was applied and compared the forecasted prices in those areas [6], and the same model was applied to forecast prices for the Kharif harvesting season, 2017–18 [7]. Described the prediction of future potato price trends in the Bangalore district (urban) and univariate seasonal ARIMA methods were used to predict the prices of potatoes [8]. To analyse the rainfall trend in West Kalimantan Sens’s slope estimator, Mann–Kendall tests are applied [9]. Sen’s slope estimator, Mann–Kendall test is employed for trend analysis of pollutants, and the ARIMA model is used to forecast the annual pollutants value for the following year [10]. The use of ARIMA, composite forecasting, expert judgement, econometric model, and exponential smoothing for forecasting the pricing of hogs in the United States was described with predictions using ARIMA models responding immediately to price volatility [11]. Using SVM (Support Vector Machine), stock prices in the Indian markets are predicted [12]. MVRVM (Multivariate Relevance Vector Machines), a derivative of RVM, is used to forecast cattle, hogs, and corn prices [13]. In Bijapur, North Karnataka [14], and Kolhapur, West Maharashtra [15], ARIMA models were employed to forecast onion prices. The prices were predicted to rise, according to the forecast. In this study, the monthly prices of small cardamom in southern India are forecasted by using various ARIMA models after performing Sen’s slope estimator and Mann–Kendall tests for seasonality. The best-fit model was selected based on various forecasting error parameters and the influence of COVID-19 pandemic months (2019–2020) in it.

2 Materials and Methodology The secondary data on daily wholesale small cardamom prices were gathered from the Spices Board of India website. For model fitting, data for the period from May 2015 to December 2018 was used, and for validation, data for the period, i.e., from January 2019 to December 2019, was utilized. The ARIMA (Autoregressive Integrated Moving Average) model was applied to predict the future prices of small cardamom. The Sen’s slope estimator and M–K test are performed using Excel-XLSAT version 2022.5. Moreover, Python3 software is utilized for ARIMA modelling.

2.1 Mann–Kendall Test As the data does not contain any seasonal effects thus, the M–K test is applied to obtain an increasing or decreasing trend of small cardamom price data. The null hypothesis (H0 ) of the M–K test states there is no monotonic trend in the data, whereas the alternate hypothesis (Ha ) states there is a trend (positive or negative). The primary test statistic (S) equation of this non-parametric test is Eqs. 1 and 2.

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S=

P−1  P 

sgm(xn − xm )

(1)

m=1 n=m+1

⎧ ⎨ 1i f xn − xm is more than 0 sgn(xn − xm ) = 0i f xn − xm is equal to 0 ⎩ −1i f xn − xm is below 0

(2)

where, the time-series of data points (x1 , x2 , x3 , x4 , . . . ..x z ) size is denoted by P. xm and xn are separate price data points with n is more than m. For the primary test statistic S, Mean Z[S] and Variance V[S] are givens as Eqs. 3 and 4. Z[S] = 0

(3)

  b  1 ta (ta − 1)(2ta + 5) Vari(S) = P(P − 1)(2P + 5) − 18 a−1

(4)

where b denotes the tied classes count and ta denotes the count of data values in the a th group. The normal standard statistics T[S] is computed by Eq. 5 in order to determine whether a statistically relevant trend is present or not. ⎧ S−1 ⎪ ⎨ √Vari(S) i f S is mor e than 0 T[S] = 0 if s = 0 ⎪ ⎩ √ S−1 i f S is below 0

(5)

Vari(S)

The negative (positive) value of T[S] defines that there is a downward (upward) trend. At the significance level, α test (two-tailed test) is conducted to estimate whether the data has a downward or upward trend. If the trend has existed in the data, then Ho (null hypothesis) is rejected when T[S] is more than T1 − α2 . The T1 − α2 values at various significance levels α can be acquired based on the typical normal distribution table.

2.2 Sen’s Slope Estimator Test This test is utilized to estimate the potential of the trend, which is calculated by using the M–K test. It utilizes a linear model to determine the slope of data pairs, and then it takes a median of those slope estimates (Ni ) which is calculated by Eq. 6. Ni =

Xn − Xm f or i = 1, 2, 3 . . . z n−m

(6)

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where, (X1 , X2 , X3 . . . ..Xz ) are z number of price data points in a time-series and Xm andXn are price values at time point m and n (m < n). The median of slope estimates (Ni ), indicated as (Nmed ), is Sen’s slope estimator, which is computed by Eq. 7. The numerical value of (Nmed ) gives the steepness of the trend. (Nmed ) =

N z+1 i f Z is odd 2

N z/2 +N (z+2)/2 2

2

2

(7)

i f z is even

2.3 ARIMA (Autoregressive Integrated Moving Average) The ARIMA (Box & Jenkins) model is applied to the time-series price data for predicting future prices. It is an integration of various time-series methods to provide a better interpretation and time-series data analysis. ARIMA model is made with the combination of autoregression (AR), order of differencing (I), and moving average (MA), where p represents the order of the AR model, d represents the order of lag differencing, and q represents the MA model. The starting step of fitting the model is that the time-series data should be confirmed with a stationarity test to know whether the data is non-stationary or stationary. Where Dickey–Fuller test (D–F) is utilized in this work for stationary check. The model proceeds to the next stage when the data is stationary; otherwise, differencing is used to make it stationary. Further, values of p, d, and q used for model fitting are identified through Autocorrelation (ACF) and Partial autocorrelation function (PACF) plots. Then, standard forecasting errors, i.e., Mean absolute deviation (MAD), Mean absolute percentage error (MAPE), and Root mean square error (RSME), are used to examine the accuracy of the model. Equation 8 is the simplified formulation of ARIMA (p, d, q). φ(A)∇ d qt = θ(A)dt

(8)

where, φ(A) denotes the polynomial of degree p and θ(A)dt denotes the polynomial of degree q, backward shift operator is A, and the differencing operator is ∇, qt represents the price parameter at time t, and dt is the error term at time instant t (Fig. 1).

3 Results and Discussion The daily data of small cardamom from May 2015 to December 2019 is utilized to predict monthly prices for the year 2020. Due to the pandemic (from January 2020 to December 2020), prices are very uncertain, so in this work, up to 2019, data was used to forecast prices. Data from May 2015 to December 2018 is used to fit the

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Fig. 1 Methodology steps

J. B. Myneedi et al.

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model, and from January 2019 to December 2019, data is used to validate the model. It is examined how the COVID-19 affected the price. The distribution of prices over the period shown in (Fig. 2), from 01–05-2015 to 01–03-2016, the price data is stationary as the data is almost constant throughout the period with slight fluctuations from the mean (615.705 Rs/Kg), from 01–05-2016 to 01–03-2017 price is gradually increased and hits high price (1400 Rs/Kg), from 01–07-2017 to 01–07-2018 there is a cyclic trend in the data. It is observed that the drastic decrease in the ACF plot (Fig. 4) confirms no seasonality in the overall price data. Trend analysis of data is carried out using Sen’s slope estimator test, Mann–Kendall test and ARIMA modelling is conducted to forecast the prices. The results of the M–K test are displayed in Table 1, which shows that the value of P is lower than significance level α (