Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak
DOI:
https://doi.org/10.37266/ISER.2021v9i1.pp15-31Keywords:
Supply chain, COVID-19 Pandemic, Digital twin, Simulation, System dynamics, Recovery, Artificial neural networksAbstract
As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.References
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Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2019). Artificial Neural Networks for Engineering Applications: Academic Press.
Araz, O. M., Choi, T. M., Olson, D. L., & Salman, F. S. (2020). Role of Analytics for Operational Risk Management in the Era of Big Data. Decision Sciences.
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202.
Centers for Disease Control and Prevention, & Centers for Disease Control and Prevention. (2020). Clinical questions about COVID-19: questions and answers. Centers for Disease Control and Prevention.
Choi, T.-M., Chan, H. K., & Yue, X. (2016). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.
Choi, T. M., & Lambert, J. H. (2017). Advances in risk analysis with big data. Risk Analysis, 37(8), 1435-1442.
Currie, C. S., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020). How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation, 1-15.
De Oliveira, M. P. V., McCormack, K., & Trkman, P. (2012). Business analytics in supply chains–The contingent effect of business process maturity. Expert systems with applications, 39(5), 5488-5498.
Dieckmann, P., Torgeirsen, K., Qvindesland, S. A., Thomas, L., Bushell, V., & Langli Ersdal, H. (2020). The use of simulation to prepare and improve responses to infectious disease outbreaks like COVID-19: practical tips and resources from Norway, Denmark, and the UK. Advances in Simulation, 5, 1-10.
Dryhurst, S., Schneider, C. R., Kerr, J., Freeman, A. L., Recchia, G., Van Der Bles, A. M., ... & van der Linden, S. (2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 23(7-8), 994-1006.
Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment. International Journal of Operations & Production Management.
Fang, Y., Nie, Y., & Penny, M. (2020). Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis. Journal of medical virology, 92(6), 645-659.
Forrester, J. W. (1997). Industrial dynamics. Journal of the Operational Research Society, 48(10), 1037-1041.
Gao, Y., & Er, M. J. (2005). NARMAX time series model prediction: feed-forward and recurrent fuzzy neural network approaches. Fuzzy sets systems, 150(2), 331-350.
Geissbauer, R., Vedsø, J., & Schrauf, S. (2016). A strategist’s guide to industry 4.0. Strategy and business, 83, 148-163.
Giannakis, M., & Louis, M. (2011). A multi-agent based framework for supply chain risk management. Journal of Purchasing Supply Management, 17(1), 23-31.
He, S., Peng, Y., & Sun, K. (2020). SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dynamics, 101(3), 1667-1680.
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.
Huang, M., Yang, M., Zhang, Y., & Liu, B. (2012). System dynamics modeling-based study of contingent sourcing under supply disruptions. Systems Engineering Procedia, 4, 290-297.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics Transportation Review, 136, 101922.
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 1-14.
vanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846.
Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083-2101.
Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business analytics. Communications of the ACM, 45(8), 45-48.
Kumar, S., & Eickhoff, J. H. (2005). Outsourcing: When and how should it be done? Information knowledge systems management, 5(4), 245-259.
Langroodi, R. R. P., & Amiri, M. (2016). A system dynamics modeling approach for a multi-level, multi-product, multi-region supply chain under demand uncertainty. Expert Systems with Applications, 51, 231-244.
Li, J., & Chan, F. (2013). An agent-based model of supply chains with dynamic structures. Applied Mathematical Modelling, 37(7), 5403-5413.
Li, Q., & Liu, A. (2019). Big data driven supply chain management. Procedia CIRP, 81, 1089-1094.
Lin, T., Horne, B. G., Tino, P., & Giles, C. L. (1996). Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks, 7(6), 1329-1338.
Linton, T., & Vakil, B. (2020). Coronavirus is proving we need more resilient supply chains. Harvard business review, 5.
Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: theory building through structured experiments and simulation. International Journal of Production Research, 56(12), 4337-4355.
Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics: Research Applications, 13(1), 13-39.
Olivares-Aguila, J., & ElMaraghy, W. (2020). System dynamics modelling for supply chain disruptions. International Journal of Production Research, 1-19.
Oliveira, J. B., Jin, M., Lima, R. S., Kobza, J. E., & Montevechi, J. A. B. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice
Theory, 92, 17-44.
Petrovic, D. (2001). Simulation of supply chain behaviour and performance in an uncertain environment. International Journal of Production Economics, 71(1-3), 429-438.
Ritchie, R. Mortality risk of covid-19 - statistics and research. Retrieved September 16, 2020, from https://ourworldindata.org/mortality-risk-covid
Schlüter, F., Hetterscheid, E., & Henke, M. (2019). A simulation-based evaluation approach for digitalization scenarios in smart supply chain risk management. Journal of Industrial Engineering Management Science, 2019(1), 179-206.
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.
Sterman, J. (2010). Business dynamics: Irwin/McGraw-Hill c2000..
Tian, L., & Noore, A. (2004). Software reliability prediction using recurrent neural network with Bayesian regularization. International Journal of Neural Systems, 14(03), 165-174.
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big data, 2(1), 1-32.
Tsai, J.-M., & Hung, S.-W. (2016). Supply chain relationship quality and performance in technological turbulence: an artificial neural network approach. International Journal of Production Research, 54(9), 2757-2770.
Wang, H., Wang, Z., Dong, Y., Chang, R., Xu, C., Yu, X., ... & Cai, Y. (2020). Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China. Cell discovery, 6(1), 1-8.
Published
2021-04-09
How to Cite
Arishi, A., Krishnan, K. K., & Maru, V. (2021). Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak . Industrial and Systems Engineering Review, 9(1), 15-31. https://doi.org/10.37266/ISER.2021v9i1.pp15-31
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