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

Ajit Sharma, Zhibo Zhang and Rahul Rai

International Journal of Production Research, 2021, vol. 59, issue 16, 4960-4994

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.

Date: 2021
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DOI: 10.1080/00207543.2021.1930234

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