A Systematic Review of MCDM Techniques for Decision-Making in Smart Manufacturing Systems under Industry 4.0

Arkyadeep Sarkar; Shankha Shubhra Goswami

doi:10.31181/msa31202649

Authors

Arkyadeep Sarkar Department of Mechanical Engineering, Abacus Institute of Engineering and Management, India Author https://orcid.org/0000-0002-4597-4904
Shankha Shubhra Goswami Department of Mechanical Engineering, Abacus Institute of Engineering and Management, India Author https://orcid.org/0000-0002-0033-3089

DOI:

https://doi.org/10.31181/msa31202649

Keywords:

Industry 4.0, Smart Manufacturing Systems, Management Science, Multi-Criteria Decision-Making, Complex Systems

Abstract

The accelerated rate of Industry 4.0 development has turned traditional manufacturing systems into highly networked, smart, and data-driven settings, thus making decision-making processes exceptionally complicated. Smart manufacturing systems are characterized by a number of conflicting criteria, interdependencies, and uncertainty, and thus require powerful and systematic decision-support tools. This paper is a systematic review of the use of multi-criteria decision-making (MCDM) in smart manufacturing systems within the Industry 4.0 paradigm. A systematic literature review methodology is followed, which includes database selection, a keyword-based search, and inclusion and exclusion criteria based on the PRISMA framework. The analyzed literature is categorized into major areas of application, such as technology choice, supplier selection, production optimization, sustainability measurement, and risk management. Moreover, a comparative study of the popular application of MCDM techniques, including AHP, ANP, DEMATEL, TOPSIS, and hybrid methods, is conducted to outline their strengths and weaknesses and their applicability to various decision settings. The research points out key research gaps, such as the lack of full integration of artificial intelligence, inadequate treatment of uncertainty, and the absence of real-time decision frameworks. Lastly, possible future research directions are suggested, focusing on the creation of hybrid and artificial intelligence-enhanced MCDM models for smart manufacturing systems. This review presents important lessons for researchers and practitioners who are interested in adopting effective decision-making models in Industry 4.0 settings.

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