Resilient Supplier Selection and Order Allocation with Analysis of Interacting Risks in Bayesian Networks

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Disruptions can impact not only suppliers and manufacturers but also influence each other at the beginning of the chain or customer demand at the end of the chain. In this paper, the extent of this impact is modeled and solved using a Bayesian network. Inflation rate is used to predict and reduce demand uncertainties in a linear programming model with two objective functions of increasing geographic dispersion and reducing total cost (transportation, purchasing, ordering, etc.). In this model, suppliers and manufacturers collaborate to increase supply chain resilience. For the first time, the concept of supplier resilience level is proposed. The proposed model for order allocation, in addition to price and other ordering costs, also considers the cost of improving the resilience level of suppliers. Also, customer satisfaction level is implicitly increased by increasing the cost of unmet demand. To this end, a case study was conducted in one of Iran’s automotive companies. To validate the proposed model, a numerical example was solved and sensitivity analysis was performed. To reduce the number of scenarios, fuzzy c-means clustering and balanced interaction analysis were used. The proposed model can prepare manufacturers for better decision-making and planning in the face of future risks and uncertainties.

Language:
Persian
Published:
IRANIAN JOURNAL OF TRADE STUDIES (IJTS), Volume:28 Issue: 111, 2024
Pages:
211 to 254
https://www.magiran.com/p2780762  
سامانه نویسندگان
  • Corresponding Author (2)
    Jafar Gheidar Kheljani
    Associate Professor Management and Industrial Engineering Department, Malek-Ashtar University Of Technology, Tehran, Iran
    Gheidar Kheljani، Jafar
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