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Cloud Manufacturing with Fuzzy Inference System: A Supply Chain Approach to Post COVID-19 Economy

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  • Sam Kolahgar
  • Mohammad Nateghi
  • Azadeh Babaghaderi

Abstract

The COVID-19 pandemic shocked the managerial team with unprecedented fluctuations in supply, demand, and transportation of goods and services. The lessons learned from the COVID-19 pandemic proved the urgent need for agility and flexibility in response to similar future crises. This paper proposes a cloud manufacturing model as a clustered supply chain approach that incorporates fuzzy inference systems to provide a platform for the post-COVID-19-economy. Cloud manufacturing is a way to standardize and increase the system’s reliability, and a fuzzy inference system is suited to deal with highly uncertain circumstances. A fuzzy inference system is integrated into a cloud manufacturing model to incorporate uncertainties related to Time, Quality, Cost, Reliability, and Availability in finding the optimum supply chain of manufacturers and service centers. The model is illustrated via a simulation in the manufacturing context. The proposed approach provides a tool to address the uncertainties and disruptions resulting from wide-scale crises such as the COVID-19 pandemic.

Suggested Citation

  • Sam Kolahgar & Mohammad Nateghi & Azadeh Babaghaderi, 2022. "Cloud Manufacturing with Fuzzy Inference System: A Supply Chain Approach to Post COVID-19 Economy," Business and Economic Research, Macrothink Institute, vol. 12(4), pages 1-32, December.
    • Handle: RePEc:mth:ber888:v:12:y:2022:i:4:p:1-32
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    References listed on IDEAS

    as
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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