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Which Node of Supply Chain Suffers Mostly to Disruption in the Pandemic?

The Journal of Distribution Science / The Journal of Distribution Science, (P)1738-3110; (E)2093-7717
2021, v.19 no.11, pp.59-68
https://doi.org/https://doi.org/10.15722/jds.19.11.202111.59
NGUYEN, Tram Thi Bich

Abstract

Purpose: The Covid-19 pandemic has had excessively severe impacts on all the nodes and edges of any supply chain due to changes in consumer behaviours and lockdown restrictions from governments among countries. This article aims to provide a simulating experiment on how a supply chain deals with supply disruption risks by flexibility in the inventory level of each sector as a buffer considering the overall cost to fulfil demand in the market. Research design, data and methodology: Agent-based simulation techniques are used to determine the cost-efficiency and customer waiting time related to varying inventory levels of each member in the supply chain when using inventory buffers. Findings: This study has shown that any sudden changes in the inventory level of each sector are likely to impact the rest of the supply chain. Among all sectors, the wholesaler will be impacted more severely than others. Also, the manufacturing sector is the most suitable node to adjust inventory depending on its manufacturing ability. Conclusion: The findings of the study provide insightful implications for decision-makers to adjust inventory levels and policymakers to maintain manufacturing activities in the context of the pandemic restrictions to deal with the excessive demand and potential supply disruption risks.

keywords
Agent-based Modelling, Supply Disruption, Inventory Management, Simulation Experiment

Reference

1.

Bonadio, B., Huo, Z., Levchenko, A. A., & Pandalai-Nayar, N.(2021). Global supply chains in the pandemic. Journal of International Economics, 133, 103534. https://doi.org/10.1016/j.jinteco.2021.103534

2.

Borshchev, A. (2013). The Big Book of Simulation Modeling —AnyLogic Simulation Software. Anylogic North America, 1–614. Retrieved from http://www.anylogic.com/big-book-ofsimulation-modeling

3.

Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. International Conference of the System Dynamics Society, 22, 25-29. Oxford. Retrieved from http://www.econ.iastate.edu/tesfatsi/systemdyndiscreteeventa bmcompared.borshchevfilippov04.pdf

4.

Cárdenas-Barrón, L. E., Shaikh, A. A., Tiwari, S., & Treviño-Garza, G. (2020). An EOQ inventory model with nonlinear stock dependent holding cost, nonlinear stock dependent demand and trade credit. Computers & Industrial Engineering, 139, 105557. https://doi.org/10.1016/j.cie.2018.12.004

5.

Chowdhury, P., Paul, S. K., Kaisar, S., & Moktadir, M. A. (2021). COVID-19 pandemic related supply chain studies: A systematic review. Transportation Research Part E: Logistics and Transportation Review, 148, 102271. https://doi.org/10.1016/j.tre.2021.102271

6.

Dente, S. M. R., & Hashimoto, S. (2020). COVID-19: A pandemic with positive and negative outcomes on resource and waste flows and stocks. Resources, Conservation and Recycling, 161, 104979. https://doi.org/10.1016/j.resconrec.2020.104979

7.

Guan, D., Wang, D., Hallegatte, S., Davis, S. J., Huo, J., Li, S., Bai, Y., Lei, T., Xue, Q., Coffman, D., Cheng, D., Chen, P, Liang, X., Xu, B., Lu, X., Wang, S., Hubacek, K., & Gong, P. (2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour, 4(6), 577-587. https://doi.org/10.1038/s41562-020-0896-8

8.

Harrison, J. R., Lin, Z., Carroll, G. R., & Carley, K. M. (2007). Simulation modeling in organizational and management research. Academy of Management Review, 32(4), 1229-1245. https://doi.org/10.5465/amr.2007.26586485

9.

Hendricks, K. B., & Singhal, V. R. (2005). An Empirical Analysis of the Effect of Supply Chain Disruptions on Long-Run Stock Price Performance and Equity Risk of the Firm. Production and Operations Management, 14(1), 35-52. https://doi.org/10.1111/j.1937-5956.2005.tb00008.x

10.

Islam, M. T., Azeem, A., Jabir, M., Paul, A., & Paul, S. K. (2020). An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03639-z

11.

Jacobs, R., & Chase, R. B. (2018). Operations and supply chain management (15th ed.). McGraw Hill.

12.

Kim, S. H. (2021). A Study on the Distribution Platform Business based on Shinsegae Group. Journal of Distribution Science, 19(4), 15-24. https://doi.org/10.15722/jds.19.4.202104.15

13.

Law, A. M. (2014). Simulation Modeling and Analysis (5th ed.). McGraw Hill Education.

14.

Lücker, F., Seifert, R. W., & Biçer, I. (2019). Roles of inventory and reserve capacity in mitigating supply chain disruption risk. International Journal of Production Research, 57(4), 1238-1249. https://doi.org/10.1080/00207543.2018.1504173

15.

Mollenkopf, D. A., Ozanne, L. K., & Stolze, H. J. (2021). A transformative supply chain response to COVID-19. Journal of Service Management, 32(2), 190-202. https://doi.org/10.1108/JOSM-05-2020-0143

16.

Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99-115. https://doi.org/10.1016/j.ejor.2020.08.001

17.

Schmitt, A. J., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1), 22-32. https://doi.org/10.1016/j.ijpe.2012.01.004

18.

Shahi, S., & Pulkki, R. (2015). A simulation-based optimization approach to integrated inventory management of a sawlog supply chain with demand uncertainty. Canadian Journal of Forest Research, 45(10), 1313-1326. https://doi.org/10.1139/cjfr-2014-0373

19.

Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2020). Developing a framework for enhancing survivability of sustainable supply chains during and post-COVID-19 pandemic. International Journal of Logistics Research and Applications, 1-21. https://doi.org/10.1080/13675567.2020.1810213

20.

Singh, S., Kumar, R., Panchal, R., Manoj, &, Tiwari, K., & Tiwari, M. K. (2021). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 59(7). https://doi.org/10.1080/00207543.2020.1792000

21.

Sundarakani, B., Pereira, V., & Ishizaka, A. (2021). Robust facility location decisions for resilient sustainable supply chain performance in the face of disruptions. The International Journal of Logistics Management, 32(2), 357-385. https://doi.org/10.1108/IJLM-12-2019-0333

22.

Yan, S., & Ji, X. (2020). Supply chain network design under the risk of uncertain disruptions. International Journal of Production Research, 58(6), 1724-1740. https://doi.org/10.1080/00207543.2019.1696999

23.

Zhao, T., Xu, X., Chen, Y., Liang, L., Yu, Y., & Wang, K. (2020). Coordination of a fashion supply chain with demand disruptions. Transportation Research Part E: Logistics and Transportation Review, 134(April 2019), 101838. https://doi.org/10.1016/j.tre.2020.101838

The Journal of Distribution Science