Designing SAIPA supply chain resilience scenarios to evaluate the production process

Subject Areas : Industrial Management

Somayeh  Shafaghizadeh ¹ (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Sadoullah  Ebrahimnejad ² (Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran)
Mehrzad  Navabakhsh ³ (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Seyed Mojtaba  Sajadi ⁴ (New Business Department, Faculty of Entrepreneurship, University of Tehran)

Received: 2021-01-23 Accepted : 2021-08-31 Published : 2021-11-17

Keywords: Simulation, Network data envelopment analysis, Resilience Factors, Resilient Supply Chain,

Abstract :

Contemporary supply chains are complex networks of processes that are subject to many disruptions; a resilient supply chain will be able to respond more quickly to changes by creating capabilities. The effect of supply chain network components on each other under conditions of uncertainty contributes to complexity and disruption. The supply chain must be pushed towards a resiliency strategy in order to reduce disruptions and deal with issues that arise from the supply chain. The purpose of this paper is to analyze network processes from supplier to distributor, in proportion to the convergence of processes by a combination of resilience factors in the automotive industry. The design of the proposed scenarios with the combination of effective resilience factors is presented, which is based on the opinion of industry experts and also takes the vulnerable factors and disorders of each level into account. First, the sources of supply chain risks such as disruptions, delays and vulnerabilities are identified and then twenty-four scenarios are designed with a combination of resilience factors of flexibility, visibility, velocity, and visibility. The company''s complex supply chain is simulated based on the system''s past rate and statistical distribution functions, and then the network DEA is used to select the superior scenario. The indicators of each scenario or simulation output are selected based on the DEA, ranking the most efficient scenario. Finally, the relationships between them have been explored using mathematical analysis and the creation of a regression model between the simulation indices and the output of scenarios.

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