The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing

Abstract

Manufacturing is undergoing a paradigmatic shift as it assimilates and is transformed by machine learning and other cognitive technologies. A new paradigm usually necessitates a new framework to comprehend it fully, organise extant knowledge, identify gaps in knowledge, guide future research and practice, and synthesise new knowledge. Paradoxically, such a framework to guide the research and practice of ML in manufacturing remains absent. This paper attempts to fill this gap by presenting the interpretive model of manufacturing as an integrative framework for ML in manufacturing. A systematic hybrid literature review approach has been adopted to conduct both thematic and conceptual synthesis of the literature. The descriptive literature review method has been used to conduct a thematic synthesis of the literature. The framework synthesis method has been used to complete a conceptual synthesis of the literature. The resultant framework, the interpretive model of manufacturing, is articulated as consisting of scan, store, interpret, execute, and learn as its purposive components. Research questions have been identified for each of these components, as well as at their interfaces, to develop a comprehensive and systematic research agenda. Additional areas for extending research have also been identified. Implications for manufacturing operations, manufacturing strategy, and manufacturing policy have been drawn out for practitioners and policy makers.

Keywords:

• Machine learning
• artificial intelligence
• manufacturing
• interpretive model of manufacturing
• interpretive model of technology
• decision making
• analytics

Disclosure statement

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

Additional information

Notes on contributors

Ajit Sharma

Ajit Sharma received the Ph.D. degree in Technology and Operations from the University of Michigan, Ann Arbor, MI, USA. He also holds a Bachelor of Technology in Manufacturing Engineering and a Masters in Industrial Management. Before entering academia, he worked in industry for 12 years, primarily in the robotics industry. He has worked for firms, such as FANUC Robotics America, Xerox, and General Motors. He has also done startups in the areas of robotics and technology consulting.He started his academic career at Carnegie Mellon University as a professor of business technologies, teaching courses in business technology consulting and information systems, for which he received the Dean's Teaching Award. He is currently an Assistant Professor of technology, innovation, and entrepreneurship at Wayne State University, Detroit, MI, USA. His research interests are in the applications of AI and robotics for amplifying the potential of individuals and organisations.Dr. Sharma is passionate about the societal implications of technology. Since 2015, he has helped found and run LIME Lab L3C, a low profit organisation that offers pro-bono robotics and technology training to K12 kids in Detroit.

Zhibo Zhang

Zhibo Zhang is a Ph.D. candidate and research assistant at the Department of Mechanical and Aerospace Engineering, University at Buffalo, SUNY. He received the M.S. degree in Mechanical Engineering from State University of New York at Buffalo in 2017. Prior to coming to USA, he worked as a mechanical engineer in China. His research interests span areas of machine learning, hybrid modelling, intelligent manufacturing, and image processing. He is currently working on the problem of physics-infused machine learning.

Rahul Rai

Rahul Rai joined the Department of Automotive Engineering in 2020 as Dean's Distinguished Professor in the Clemson University International Center for Automotive Research (CU-ICAR). He directs the Geometric Reasoning and Artificial Intelligence Lab (GRAIL, which is located at both CU-ICAR and Center for Manufacturing Innovation (CMI). Previously, he served on the Mechanical and Aerospace Engineering faculty at the University at Buffalo-SUNY (2012–2020). Dr. Rai also has industrial research centre experiences at United Technology Research Center (UTRC) and Palo Alto Research Center (PARC).Dr. Rai received his B.Tech. degree in 2000 and M.S. degree in 2002 in Manufacturing Engineering from the National Institute of Foundry and Forge Technology (NIFFT), Ranchi, India, and Missouri University of Science and Technology (Missouri S&T) USA, respectively. He earned his doctoral degree in Mechanical Engineering from The University of Texas at Austin USA in 2006.Dr. Rai's research is focused on developing computational tools for Manufacturing, Cyber-Physical System (CPS) Design, Autonomy, Collaborative Human-Technology Systems, Diagnostics and Prognostics, and Extended Reality (XR) domains. By combining engineering innovations with methods from machine learning, AI, statistics and optimisation, and geometric reasoning, his research strives to solve important problems in the above-mentioned domains. His research has been supported by NSF, DARPA, ONR, ARL, NSWC, DMDII, CESMII, HP, NYSERDA, and NYSPII (funding totalling more than $20M as PI/Co-PI). He has authored over 100 papers to date in peer-reviewed conferences and journals covering a wide array of problems.Dr. Rai is the recipient of numerous awards, including the 2009 HP Design Innovation, 2017 ASME IDETC/CIE Young Engineer Award, and 2019 PHM society conference best paper award. Additionally, Dr. Rai is Associate Editor of the International Journal of Production Research and ASME Journal of Computing and Information Science in Engineering (JCISE) journals and has taken significant leadership roles within the ASME Computers and Information in Engineering professional society.