Exploring smart quality predictive modelling approach: a case study of the injection-molding industry

Abstract

Technology-driven quality monitoring and control can effectively predict, prevent and reduce defects in the manufacturing industry and improve productivity. The study aims to explore real-time injection molding process monitoring and demonstrate intelligent quality control through a case study. The process environment is monitored to capture variabilities through which the relationship of variables with the quality characteristics of molded parts is derived. The defects are represented as a function of process variables using statistical analysis of the past process and product data employing appropriate machine learning methods. From the fitted models, decision rules are retrieved, and desirable process conditions required for making defect-free molded parts are recommended for quality control practice. Further, these models are deployed to predict the defects in the parts during production by observing the real-time process conditions in the manufacturing process environment. This study has drawn significant research and practical implications for the manufacturing industry as it can effectively control quality and automatically fine-tune the process for better quality and productivity.

Keywords:

• Decision tree
• internet of things
• machine learning
• process variability
• quality control

Additional information

Notes on contributors

Janak Suthar

Janak Suthar is an Assistant Professor at IRMA (Institute of Rural Management, Anand). He previously worked as a Data Analyst at Zensung Pvt Ltd in Mumbai. Janak holds a PhD in Operations Management from IIM Mumbai and a master's in engineering from Mumbai University. His research focus on quality management, manufacturing process optimization, and AI/ML applications in manufacturing, with publications in esteemed journals. As a Visiting Professor at DSIMS Mumbai, he adeptly taught operation analytics with Python and data visualization.

Jinil Persis

Jinil Persis is currently working as Assistant Professor at Indian Institute of Management (IIM), Kozhikode, India in quantitative methods and operations management area. Prior to this, she worked at National Institute of Industrial Engineering (NITIE-Mumbai, India) in Operations and supply chain management area. She obtained her Ph.D. and Masters in Engineering with Industrial Engineering specialisation in 2015 and 2011 respectively from College of Engineering, Guindy (CEG), India. Her research interests include Evolutionary computation, Artificial intelligence and Machine learning applications. She has published her research in journals of international repute such as Computers in Industry, IEEE Transactions on Engineering Management, Production Planning and Control, Annals of Operations Research, Journal of Cleaner Production, Journal of Environmental Management and Wireless Personal Communication.

V. G. Venkatesh

V. G. Venkatesh is an Associate Professor at EM Normandie Business School, France, and is a corporate trainer and academic with a Ph.D. in global sourcing from Waikato University (A triple-crowned School) in New Zealand and brings 20+ years of experience with industry stints from Bangladesh, Honduras(Central America), Hong Kong, and Sri Lanka, and academic experience in Europe & Asia-Pacific regions. He is a certified supply chain professional from APICS-USA, a qualified trainer, and a member of reputable operations societies worldwide, including LIFETIME Chartered member with the Institute of Logistics and Transport (CILT-UK) and International Purchasing Education and Research Association (IPSERA). He has authored/Co-authored papers in ABDC (A*/A), ABS, CNRS listed/top-ranked journals such as Transportation Research (Part E & D), Production Planning and Control, International Journal of Production Economics, Annals of Operations Research, International Journal of Production Research, Supply Chain Management- An International Journal and more. His research and teaching interests are supply networks, logistics infrastructure, digitalisation, and sustainability.

Yangyan Shi

Yangyan Shi is a Senior faculty member at Macquarie Business School, Macquarie University, and adjunct Professor at the Jiangsu University of Technology in China, a Chartered Member of CILT-A, and a member of the Centre for Supply Chain Management at the University of Auckland, New Zealand. He had several years of industrial experience in logistics and supply chain management in Australia, New Zealand, China, and the UK. He has been publishing his academic research in leading international journals, including International Journal of Operations and Production Management, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, International Journal of Physical Distribution and Logistics Management. His research interests include operations management, supply chain management, procurement, and third-party logistics.