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Integrated-decision support system (DSS) for risk identification and mitigation in manufacturing industry for zero-defect manufacturing (ZDM): a state-of-the-art review

Article dans une revue avec comité de lecture
Author
AKBAR, Muhammad Awais
107452 Laboratoire de Conception Fabrication Commande [LCFC]
527366 National University of Sciences and Technology [Islamabad] [NUST]
NASEEM, Afshan
527366 National University of Sciences and Technology [Islamabad] [NUST]
ZAMAN, Uzair Khaleeq Uz
107452 Laboratoire de Conception Fabrication Commande [LCFC]
527366 National University of Sciences and Technology [Islamabad] [NUST]
ccPETRONIJEVIC, Jelena
107452 Laboratoire de Conception Fabrication Commande [LCFC]

URI
http://hdl.handle.net/10985/25921
DOI
10.1007/s00170-024-14601-9
Date
2024-10
Journal
The International Journal of Advanced Manufacturing Technology

Abstract

Risk management has always been a trend in manufacturing related literature in the era of zero-defect manufacturing (ZDM). However, a gap still exists to present a holistic viewpoint of the integration for a product and its related processes involved during decision-making in manufacturing industry. The (knowledge-driven) integrated-decision support system indicates the opportunity by integrating the product design and manufacturing processes related risks in a manufacturing industry to make better decisions at the shop foor. It further proposes a direction towards development of a decision support system framework for their respective risks’ identifcation as well as mitigation to enhance the quality, while minimizing time and cost. Over the years, risk identifcation has been considered well but risk mitigation has mostly been overlooked in the published literature. This paper scanned over a thousand papers from renowned journals published between 2005 and 2024. Currently, the evolu tion involved in the advancement of decision support tools for risk management has been reviewed by utilizing systematic literature review methodology. The study also provides a design overview, highlighting its features, pros, and cons of the existing methods which can be used for risk identifcation, prioritization, and mitigation in the development of a dynamic decision support system to aim (data-driven) zero-defect manufacturing (ZDM). Lastly, the paper discusses the current challenges and opportunities to lessen the manufacturing recalls in the industry, followed by phases of the proposed model.

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