Designing SAIPA supply chain resilience scenarios to evaluate the production process

Subject Areas : Industrial Management

Somayeh Shafaghizadeh ¹ , Sadoullah Ebrahimnejad ² , Mehrzad Navabakhsh ³ , Seyed Mojtaba Sajadi ⁴

1 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
3 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
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.

References:

1. Aghajani, H, Ravansetan, K, Safaei, A, Yahyazadeh far, M(2017) Design of resilient Supply Chain Model in Iran Khodro with structural Equation Modeling and Qualitative Techniques, Journal of Industrial Management,12(40)


2. Aggarwal, S, Srivastava, M, Bharadwaj, S(2020) Towards a Definition and Concept of Collaborative Resilience in Supply Chain: A Study of 5 Indian Supply Chain Cases, International Journal of Information Systems and Supply Chain Management, 13(1)


3. Carvalho, H, A P Barroso, V H Machado, S Azevedo, V Cruz-Machado (2012) Supply Chain Redesign For Resilience Using Simulation Computers & Industrial Engineering, 62 (1), 329–341


4. Charnes, A, WW Cooper, and E Rhodes (1978) Measuring the efficiency of decision making units European journal of operational research 2(6),429-444


5. Hosseini S, Ivanov D, Dolgui A, (2019) Review of quantitative methods for supply chain resilience analysis Transportation Research Part E: Logistics and Transportation Review,125, 285-307


6. Kamble, S, Jabbour, C J C, Gunasekaran, A, Ndubisi, N O, Belhadi, A,Venkatesh, M (2021) Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries Technological Forecasting and Social Change, 163, 120447


7. Liang, L, WD Cook, and J Zhu, (2008) DEA models for two‐stage processes: Game approach and efficiency decomposition Naval Research Logistics, 55(7),643-653


8. 8 Pettit TJ, Croxton KL, Fiksel J, (2019) The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience Journal of Business Logistics40(1), 56-65


9. Pilevari, N, Golzar, AH (2021) Provide a Model in Determining the Impact of Block-Chain Technology on Supply Chain Performance Using Fuzzy Inference Systems in the Automobile IndustryJournal of Industrial Management,16 (55)


10. Ponomarov, Serhiy Y, and Mary C Holcomb (2009) Understanding the Concept of Supply Chain Resilience The International Journal of Logistics Management, 20(1)


11. Rajesh R (2019) Network design for resilience in supply chains using novel crazy elitist TLBO Neural Computing and Applications, 32, 7421–7437


12. Rajesh R (2020) A novel advanced grey incidence analysis for investigating the level of resilience in supply chains Annals of Operations Research, 1-50


13. Ruel, S, Baz, JE (2021) Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era International Journal of Production Economics, 233, 107972


14. Tan, Wen Jun, Allan N Zhang, and Wentong Cai (2019) A Graph-Based Model to Measure Structural Redundancy for Supply Chain Resilience International Journal of Production Research, 57 (20).