Simulation Applications to Structural Dynamics in Service and Manufacturing Supply Chain Risk Management
Abstract
Facility disruption impact on supply chain performance is studied using the example of outsourced academic journal publishing services affected by recent floods in Chennai. A discrete event simulation model is used to identify the performance impact of facility disruptions for the primary vendor. Eighteen scenarios are analyzed in terms of different disruption durations, sourcing strategies and demand patterns. Sensitivity analysis is performed for several input parameters to illustrate the model’s behavior. The analysis allows identification of the optimal sourcing strategy depending on a combination of factors: duration of disruptions, demand patterns and sourcing costs. The results indicate that higher performance can be observed by increasing the dual sourcing component as disruption durations increase. The results have some major implications. First, the analysis can be used to identify the patterns “disruption duration – sourcing strategy” with the lowest performance decrease in order to employ the most efficient reactive sourcing strategy. Second, it becomes possible to identify the most preferable (in terms of sales or efficiency) proactive and reactive sourcing strategies and compare the impacts of different patterns “demand – disruption duration – sourcing strategy” according to multiple performance dimensions.
Notes
Acknowledgement
The author acknowledges the contribution of Mr. Maxim Rozhkov to the development of the simulation model in AnyLogic. We also thank the entire team of AnyLogic Company for their great support regarding anyLogistix application.
References
- Ambulkar S, Blackhurst J, Grawe S (2015) Firm’s resilience to supply chain disruptions: scale development and empirical examination. J Oper Manag 33(34):111–122CrossRefGoogle Scholar
- Atan Z, Snyder LV (2012) Disruptions in one-warehouse multiple-retailer systems. Available at SSRN: http://ssrn.com/abstract=2171214
- Choi TM (2013) Local sourcing and fashion quick response system: the impacts of carbon footprint tax. Transport Res E-Log 55:43–54CrossRefGoogle Scholar
- Choi TM, Wang Y, Wallace SW (2016) Risk management and coordination in service supply chains: information, logistics and outsourcing. J Oper Res Soc 67(2):159–164CrossRefGoogle Scholar
- Cui T, Ouyang Y, Shen ZJM (2010) Reliable facility location design under the risk of disruptions. Oper Res 58:998–1011CrossRefGoogle Scholar
- Dolgui A, Ivanov D, Sokolov B (2018) Ripple effect in the supply chain: an analysis and recent literature. Int J Prod Res. Published onlineGoogle Scholar
- Deleris LA, Erhun F (2011) Quantitative risk assessment in supply chains: a case study based on engineering risk analysis concepts. In: Kempf KG, Keskinocak P, Uzsoy R (eds) Planning production and inventories in the extended enterprise, Int Ser Oper Res Man, vol 152. Springer, New York, pp 105–131CrossRefGoogle Scholar
- Gupta W, He B, Sethi SP (2015) Contingent sourcing under supply disruption and competition. Int J Prod Res 53(10):3006–3027CrossRefGoogle Scholar
- Ho W, Zheng T, Yildiz H, Talluri S (2015) Supply chain risk management: a literature review. Int J Prod Res 53(16):5031–5069CrossRefGoogle Scholar
- Iakovou E, Vlachos D, Xanthopoulos A (2010) A stochastic inventory management model for a dual sourcing supply chain with disruptions. Int J Syst Sci 41(3):315–324Google Scholar
- IADA (2016) http://www.iada-home.org/en/journal-of-paperconservation/current-issue.html. Accessed 5 Feb 2016
- Ivanov D, Sokolov B, Pavlov A, Dolgui A, Pavlov D (2016) Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transport Res E-Log 90:7–24CrossRefGoogle Scholar
- Ivanov D (2017a) Simulation-based ripple effect modelling in the supply chain. Int J Prod Res 55(7):2083–2101CrossRefGoogle Scholar
- Ivanov D (2017b) Supply chain simulation and optimization with anyLogistix: decision-oriented introductory notes for model-based management decision-making. E-textbook, Berlin School of Economics and Law (preprint). Available at https://blog.hwr-berlin.de/ivanov/teaching-readmore/
- Ivanov D (2017c) Simulation-based single vs dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. Int J Integrated Supply Manag 11(1):24–43CrossRefGoogle Scholar
- Ivanov D, Rozhkov M (2017) Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company. Ann Oper Res. Published onlineGoogle Scholar
- Ivanov D, Tsipoulanidis A, Schönberger J (2017) Global supply chain and operations management: a decision-oriented introduction into the creation of value. Springer, ChamCrossRefGoogle Scholar
- Kim SH, Tomlin B (2013) Guilt by association: strategic failure prevention and recovery capacity investments. Manag Sci 59(7):1631–1649CrossRefGoogle Scholar
- Kleindorfer PR, Saad GH (2005) Managing disruption risks in supply chains. Prod Oper Manag 14(1):53–68CrossRefGoogle Scholar
- Kouvelis P, Li J (2012) Contingency strategies in managing supply systems with uncertain lead-times. Prod Oper Manag 21(1):16–176CrossRefGoogle Scholar
- Lee AJL, Zhang AN, Goh M, Tan PS (2014) Disruption recovery modeling in supply chain risk management. In: Proceedings of the 2014 IEEE international conference on management of innovation and technology (ICMIT), Singapore, pp 279–283Google Scholar
- Li Q, Zeng B, Savachkin A (2013) Reliable facility location design under disruptions. Comput Oper Res 40(4):901–909CrossRefGoogle Scholar
- Liberatore F, Scaparra MP, Daskin MS (2012) Hedging against disruptions with ripple effects in location analysis. Omega 40(2012):21–30Google Scholar
- Losada C, Scaparra MP, O’Hanley JR (2012) Optimizing system resilience: a facility protection model with recovery time. Eur J Oper Res 217:519–530Google Scholar
- Lu M, Huang S, Shen ZM (2011) Product substitution and dual sourcing under random supply failures. Transp Res B 45:1251–1265Google Scholar
- Nair A, Jayaram J, Das A (2015) Strategic purchasing participation, supplier selection, supplier evaluation and purchasing performance. Int J Prod Res 53(20):6263–6278CrossRefGoogle Scholar
- Pezzotta G, Rondini A, Pirola F, Pinto R (2016) Evaluation of discrete event simulation software to design and assess service delivery processes. In: Choi TM (ed) Service supply chain systems. CRC Press, London, pp 83–100CrossRefGoogle Scholar
- Rice JB, Caniato F (2003) Building a secure and resilient supply network. Supply Chain Manag Rev 7(5):22–30Google Scholar
- Sawik T (2016) On the risk-averse optimization of service level in a supply chain under disruption risks. Int J Prod Res 54(1):98–113CrossRefGoogle Scholar
- Schmitt AJ, Singh M (2012) A quantitative analysis of disruption risk in a multi-echelon supply chain. Int J Prod Econ 139(1):22–32CrossRefGoogle Scholar
- Schmitt TG, Kumar S, Stecke KE, Glover FW, Ehlen MA (2017) Mitigating disruptions in a multi-echelon supply chain using adaptive ordering. Omega 68:185–198Google Scholar
- Sethi SP, Yan H, Zhang H, Zhou J (2007) A supply chain with a service requirement for each market signal. Prod Oper Manag 16(3):322–342CrossRefGoogle Scholar
- Sheffi Y, Rice JB (2005) A supply chain view of the resilient enterprise. MIT Sloan Manag Rev 47(1):41–48Google Scholar
- Simchi-Levi D, Schmidt W, Wei Y, Zhang PY, Combs K, Ge Y, Gusikhin O, Sander M, Zhang D (2015) Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces 45(5):375–390CrossRefGoogle Scholar
- Snyder LV, Zümbül A, Peng P, Ying R, Schmitt AJ, Sinsoysal B (2016) OR/MS models for supply chain disruptions: a review. IIE Trans 48(2):89–109CrossRefGoogle Scholar
- Sokolov B, Ivanov D, Dolgui A, Pavlov A (2016) Structural analysis of the ripple effect in the supply chain. Int Prod Res 54(1):152–169CrossRefGoogle Scholar
- Song JS, Zipkin P (2009) Inventories with multiple supply sources and networks of queues with overflow bypasses. Manag Sci 55(3):362–372CrossRefGoogle Scholar
- Stavrulaki E, Davis MM (2014) A typology for service supply chains and its implications for strategic decisions. Serv Sci 6:34–46CrossRefGoogle Scholar
- Tomlin B (2006) On the value of mitigation and contingency strategies for managing supply chain disruption risks. Manag Sci 52(5):639–657CrossRefGoogle Scholar
- Trucco P, Petrenj B, Birkie SE (2017) Assessing supply chain resilience upon critical infrastructure disruptions: a multilevel simulation modelling approach. In: Khojasteh Y (ed) Supply chain risk management. Springer, Singapore, pp 311–334Google Scholar
- Tsai WC (2016) A dynamic sourcing strategy considering supply disruption risks. Int J Prod Res 54(7):2170–2184CrossRefGoogle Scholar
- Wang Y, Wallace SW, Shen B, Choi TM (2015) Service supply chain management: a review on operational models. Eur J Oper Res 247:685–698CrossRefGoogle Scholar
- Yang Z, Aydin G, Babich V, Beil DR (2012) Using a dual-sourcing option in the presence of asymmetric information about supplier reliability: competition vs. diversification. Manuf Serv Oper Manag 14(2):202–217Google Scholar
- Yu H, Zeng AZ, Zhao L (2009) Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega 37(4):788–800CrossRefGoogle Scholar