Risk Assessment in the Global Supplier Selection Considering Supply Disruption: A Simulation Optimization Approach

Document Type : Research Paper

Author

Faculty of Management and Accounting, Allameh Tabataba'i University

Abstract

Increasing uncertainties in the supply chains have caused more attentions to the supply chain risk management approaches. Because of the inherent turbulences in the international transactions, these uncertainties in the global context are more important. On the other hand, due to competitive pressures, businesses has been prepared themselves to operate in a global context to take advantage of the international markets. In addition, supplier selection is a challenge for purchasing managers by having more uncertainties in supply from the foreign supplier (exchange rate risk, extended lead times, regional risks). On the other hand, lower price procurement and having more diversified suppliers are the benefits that a company could obtain from global supply chains. In this paper a scenario based supply chain model for global purchasing of substitutable products is introduced and as a solution method a simulation-optimization approach is proposed. The model is applied on the modified data adopted from a case study and sensitivity analyzes (on the risk attitude of retailers, product substitutability and exchange rate) are presented for different amounts of parameters.

Keywords

References

Amin S.H., Baki F. (2017). A facility location model for global closed-loop supply chain network design, Applied Mathematical Modelling, 41, pp. 316–330.
 
Aqlan F., Lam S.S. (2015). A fuzzy-based integrated framework for supply chain risk assessment, International Journal of Production Economics, 161, pp. 54–63.
 
Aqlan F., Lam S.S. (2016). Supply chain optimization under risk and uncertainty: A case study for high-end server manufacturing, Computers & Industrial Engineering, 93, pp. 78–87.
 
Choi T.M., Wen X., Sun X., Chung S.H. (2019). The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era, Transportation Research Part E: Logistics and Transportation Review Volume 127, pp. 178-191.
 
Chopra S., Meindl P. (2018). Supply chain management: Strategy, planning and operation, 7th edition, Pearson.
 
Cigolini R., Pero M., Rossi T., Sianesi A. (2014) Linking supply chain configuration to supply chain performance: A discrete event simulation model, Simulation Modelling Practice and Theory, 40, pp. 1-11.
 
Cohen M.A, Lee H.L. (2020) Designing the Right Global Supply Chain Network, Manufacturing & Service Operations Management, Vol. 22, No. 1, pp. 1-10.
 
David L. Olson Desheng Wu (2011). Risk management models for supply chain: a scenario analysis of outsourcing to China, Supply Chain Management: An International Journal, Vol. 16(6), pp. 401-408.
 
Deleris L.A., Erhun F. (2005). Risk management in supply networks using Monte-Carlo simulation, Proceedings of the 2005 Winter Simulation Conference, Orlando, FL, USA.
 
Fan Y., Stevenson M. (2018). A review of supply chain risk management: definition, theory, and research agenda, International Journal of Physical Distribution & Logistics Management, 48, 3, pp. 205-230.
 
Farahani, M., Shavandi, H. & Rahmani, D. (2017). A location-inventory model considering a strategy to mitigate disruption risk in supply chain by substitutable products. Computers & Industrial Engineering, 108, pp. 213-224.
 
Ge H., Nolan J., Gray R., Goetz S., Han Y. (2016). Supply chain complexity and risk mitigation–A hybrid optimization-simulation model, Intern. Journal of Production Economics, 179, pp. 228-238.
 
Hajian Heidary M., Aghaie A. (2019). Risk averse sourcing in a stochastic supply chain: A simulation-optimization approach, Computers & Industrial Engineering, 130, pp. 62-74.
 
Hajian Heidary M., Aghaie A. (2015) Risk measurement in the global supply chain using monte-carlo simulation, Journal of Industrial Engineering and Management Studies, 2, 2, pp. 1-15.
 
Hammami R., Temponi C., Frein Y. (2014) A scenario-based stochastic model for supplier selection in global context with multiple buyers, currency fluctuation uncertainties, and price discounts, European Journal of Operational Research, 233(1), pp. 159-170. 
Hammami R., Y. Frein (2014) Integration of the profit-split transfer pricing method in the design of global supply chains with a focus on offshoring context, Computers & Industrial Engineering, 76, pp. 243–252.
 
Hou J., Zhao X. (2020) Toward a supply chain risk identification and filtering framework using systems theory, Asia Pacific Journal of Marketing and Logistics, https://doi.org/10.1108/APJML-05-2020-0342.
 
Hsieh C-C, Wu C-H. (2009) Coordinated decisions for substitutable products in a common retailer supply chain. European Journal of Operational Research, 196 (1):pp. 273-88.
Kleijnen J.P.C. (2005) Supply chain simulation tools and techniques: A survey. International Journal of Simulation Process Modelling, 1, pp. 82–89.
 
Mangla S.K., Kumar P., Barua M.K., (2014) Monte Carlo Simulation Based Approach to Manage Risks in Operational Networks in Green Supply Chain, Procedia  Engineering  97 , pp. 2186 - 2194.
 
Manuj I., Mentzer J.T. (2008) Global supply chain risk management strategies, International Journal of Physical Distribution & Logistics Management, 38: 3, pp. 192-223.
 
Mari S.I., Lee Y.H. (2015) A Literature Review On Emerging Issues In Global Supply Chain Management, Korean Supply Chain Management Conference, South Korea.
 
Meixell M.J., Gargeya V.B. (2005) Global supply chain design: A literature review and critique, Transportation Research Part E, 41, pp. 531–550.
 
Mizgier K.J., Wagner S.M., Jüttner M.P. (2015). Disentangling diversification in supply chain networks, International Journal of Production Economics, 162, pp. 115–124.
 
Miller H.E., Engemann K.J. (2008). A Monte Carlo simulation model of supply chain risk due to natural disasters, Int. J. Technology, Policy and Management, 8, 4, pp. 460 – 480.
 
Mohit Srivastava & Helen Rogers (2021). Managing global supply chain risks: effects of the industry sector, International Journal of Logistics Research and Applications, DOI: 10.1080/13675567.2021.1873925.
 
Munir M., Jajja M.S.S., Chatha K.A., Farooq S., (2020). Supply chain risk management and operational performance: The enabling role of supply chain integration, International Journal of Production Economics, 227, pp. 107-667.
 
Oliveira J.B., M. Jin, R.S. Lima, J.E. Kobza, J.A.B. Montevechi. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints, Simulation Modelling Practice and Theory, 92, pp. 17-44.
 
Park Y.B., Kim H.S. (2016). Simulation-based evolutionary algorithm approach for deriving the operational planning of global supply chains from the systematic risk management, Computers in Industry, 83, pp. 68–77.
 
Pravin K., Shankar R., Yadav S. S. (2007). Flexibility in global supply chain: a review of perspectives, Seventh Global Conference on Flexible Systems Management, India.
 
Ramanathan U. (2014). Performance of supply chain collaboration – A simulation study, Expert Systems with Applications, 41(1), pp. 210–220.
 
Ray P., Jenamani M. (2016). Sourcing decision under disruption risk with supply and demand uncertainty: A newsvendor approach, Annals of Operations Research, 237:1, pp. 237–262.
 
Rockafellar R.T., and UryasevS. (2000). Optimization of conditional value at risk, journal of risk, 2: 3, pp. 21-41.
 
Sari K. (2017). A novel multi-criteria decision framework for evaluating green supply chain management practices, Computers & Industrial Engineering, 105, pp. 338-347.
 
Silva L.M.F., Ana Camila Rodrigues de Oliveira, Maria Silene Alexandre Leite & Fernando A. S. Marins (2020): Risk assessment model using conditional probability and simulation: case study in a piped gas supply chain in Brazil, International Journal of Production Research, DOI: 10.1080/00207543.2020.1744764
 
Schmitt A.J., Singh M. (2009). Quantifying supply chain disruption risk, using Monte Carlo and discrete event simulation, Winter Simulation Conference, USA.
 
Tang O., Musa S. N. (2011). Identifying Risk Issues and Research Advancements in Supply Chain Risk Management. International Journal of Production Economics, 133: pp. 25–34.
 
Uryasev, S. (2002). Conditional value-at-risk: optimization algorithms and applications. Finance Engineering News, 14(2), pp. 1-6.
 
Vidal C.J., Goetschalcks M. (1997). Strategic production-distribution models: A critical review with emphasis on global supply chain models, European Journal of operational research, 98, pp. 1-18.
 
Xinbo. Zh., Shuai. H. and Zhong. W. (2016). Stochastic programming approach to global supply chain management under random additive demand, Operational Research, 18, pp. 389–420.
 
Zhu, Q., Krikke, H. and Caniëls, M.C.J. (2017). Integrated supply chain risk management: a systematic review, The International Journal of Logistics Management, 28(4), pp. 1123-1141.
International Journal of Supply and Operations Management
Volume 10, Issue 4
November 2023
Pages 501-522

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