Modelling and analysis of artificial intelligence for commercial vehicle assembly process in VUCA world: a case study

Abstract

Real-time monitoring, is  now the integral component in smart manufacturing with the rapid application of Artificial Intelligence (AI) in manufacturing. Machine Learning (ML) algorithms and  Internet of things (IoT) make the volatility, uncertainty, complexity, and ambiguity world (VUCA) more reliable and resilient with the stable industrial environment. In this study, two machine learning algorithms such as K-mean clustering and support vector, are used in combination with IoT-enabled embedded devices to design, deploy and test the effectiveness of the vehicle assembly process in the VUCA context. To accomplish this, the design includes both real-time data and training vector data, which were collected from IoT-enabled devices and evaluated using ML algorithms leading to the novel element called Smart Safe Factor (SSF), a critical threshold indicator that helps in limiting different units in assembly line-ups from excess wastages and energy losses in real-time. Test results highlight the impact of AI in enhancing the productivity and efficiency. Using SSF, 21.84% of energy is saved during the entire assembly process and 8% of excess stocks in storage have been curtailed for monetary benefits. This study deliberates the applications of AI and ML algorithms in a Vehicle Assembly (VA) model, connecting critical parameters such as cost, performance, energy, and productivity.

KEYWORDS:

• Original equipment manufacturer
• artificial intelligence
• vehicle assembly
• uncertainity
• VUCA

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Arunmozhi Manimuthu

A. Manimuthu is currently working as a Research Fellow, Energy Research Institute (ERI@N), Nanyang Technological University (NTU), Singapore. Before joining NTU, he was invited to work as Visiting Research Fellow at Robotics and Automation Research Lab (ROAR), Singapore University of Technology and Design (SUTD), Singapore. He teaches courses like Artificial Intelligence, Machine learning, Operation management, Data Analytics, Business Research Methods, and Embedded Process Automation. He has been a keynote speaker for 15+ events in various reputed research institutions globally. His current research interest focuses more on AI, Autonomous Vehicles, Cybersecurity, Machine learning, and Big Data Analytics.

V. G. Venkatesh

V. G. Venkatesh is an Associate Professor at EM Normandie Business School, France. He had years of industrial and teaching experience in logistics and supply chain management from Honduras, Sri Lanka, New Zealand, Colombia, the USA, Australia, China, France, and Bangladesh. He has been actively published in the reputable journals focusing on social sustainability, supplier networks, transportation infrastructure, and strategic procurement.

V. Raja Sreedharan

V. Raja Sreedharan is an Assistant Professor in the Supply Chain Management, BEAR Lab (Business, Economie et Actuariat), Rabat Business School, Universite Internationale de Rabat, Morocco. He Graduated from the College of Engineering Guindy with a PhD in Lean Six Sigma and has published works in peer reviewed journals. His current research interests focus on managing the VUCA in the business operations, and process improvement for services industries. He also consults in the field of process optimisation with industries in private sectors.

Venkatesh Mani

Mani Venkatesh, Associate Professor, in the Department of Strategy and Entrepreneurship, Montpellier Business School (MBS), France. He holds his PhD from Indian Institute of Technology (IIT), Roorkee and Post Doctorate from Faculty of Economics (FEP), University of Porto, through prestigious Erasmus Fellowship (European Union). He possesses over 21 years of academic and industrial experience, of which over a decade he had served in fortune 500 companies in various senior management roles. His research entails the most pressing strategic issues in the global supply chains including supply chain social sustainability, circular economy, interplay between industry 4.0 and sustainability, and digital transformation from the perspective of emerging economies. He has contributed many research articles in referred journals: International Journal of Production Economics, International Journal of Production Research, Transportation Research Part A, Supply Chain Management: An International Journal, Annals of Operations Research, Technological Forecasting and Social Change, Production Planning and Control, Business Strategy and the Environment, International Journal of Information Management, and Journal of Cleaner Production. His book titled ‘supply chain social sustainability for manufacturing: measurement and performance outcomes from India’ published by Springer Nature is among the top used publications that concern one or more of the United Nations Sustainable Development Goals (SDGs). He also serves as editorial advisory board member of Management Decision (Emerald publications).