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A two-stage fuzzy inference model to determine raw materials criticality in life sciences industries

Author

Listed:
  • Levi R. Abreu

    (Federal University of Ceara)

  • Marcelo S. Nagano

    (University of Sao Paulo)

Abstract

Life sciences industries, such as pharmaceutical and biochemical factories, have many limitations regarding raw materials management and procurement, including economic and regulatory decisions. Determining the criticality of these raw materials involves several risks in uncertain environments. However, with the raw materials criticality, the decision makers can help rank and prioritize actions for managing raw materials and their suppliers. Unfortunately, fuzzy system applications for raw materials criticality in life science industries were not found in the literature. Therefore, this article proposes a decision-making model using a two-stage fuzzy inference model to determine the criticality of the raw material in life science industries, using quality, regulatory, economic, and supply chain criteria. A practical application is carried out using data from a real pharmaceutical industry, and the main aspects of the new approach are discussed. Finally, an in-depth sensitivity analysis is performed by performing complete factorial experiments, demonstrating that the decision method does not have a trade-off among the criteria. The proposed fuzzy model can be adapted to various industrial sectors. In addition, the proposed decision model offers the possibility to work on raw materials criticality as a driver for continuous improvement initiatives in supply chain management.

Suggested Citation

  • Levi R. Abreu & Marcelo S. Nagano, 2023. "A two-stage fuzzy inference model to determine raw materials criticality in life sciences industries," Operations Management Research, Springer, vol. 16(4), pages 2048-2063, December.
    • Handle: RePEc:spr:opmare:v:16:y:2023:i:4:d:10.1007_s12063-023-00392-x
    DOI: 10.1007/s12063-023-00392-x
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