Impact on teaching in higher education. Machine learning and systems dynamics to assess the domino effect in the supply chain.
Abstract
The objective of this study is to analyze the impact on higher education by evaluating the domino effect in the supply chain. Historical demand data for automotive components was collected from interviews with automotive companies. Machine learning was then integrated to forecast supply chain demand in the automotive industry, and systems dynamics was used to design a dynamic model to help evaluate the domino effect. The results reveal that the domino effect increases delays in customer orders and reduces finished goods inventory levels. The study also identifies the bullwhip effect, which exacerbates the domino effect and negatively impacts the customer.
References
Aslay, F., and Ting, N.S. (2022). Machine Learning-Based Estimation of Output Current Ripple in PFC-IBC Used in Battery Charger of Electrical Vehicles: A Comparison of LR, RF and ANN Techniques. IEEE Access, 10,50078–50086. https://doi.org/10.1109/ACCESS.2022.3174100
Badakhshan, E., Humphreys, P., Maguire, L., and Mcivor, R. (2020). Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain, 58(17), 5253– 5279 https://doi.org/10.1080/00207543.2020.1715505
Bevilacqua, M., Ciarapica, F. E., Marcucci, G., and Mazzuto, G. (2020). Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study. International Journal of Production Research,58(20), 6370–6398. https://doi.org/10.1080/00207543.2019.1680893
Bhuvaraghavan, S. T., and Matheen, U. (2024). Visualizing the ripple effect: COVID-19’S Impact and predictive insights. International Journal of Engineering Research and Sustainable Technologies (IJERST), 2(1), 1–8. https://doi.org/10.63458/ijerst.v2i1.70
Bueno-Solano, A., and Cedillo-Campos, M. G. (2014). Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts. Transportation Research Part E, 61, 1–12. https://doi.org/10.1016/j.tre.2013.09.005
Bussieweke, F., Mula, J., and Campuzano-Bolarin, F. (2024). Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach. International Journal of Production Research. 63(5), 1649–1673. https://doi.org/10.1080/00207543.2024.2383293
Cannella, S., Ponte, B., Dominguez, R., Framinan, J. M., Cannella, S., Ponte, B., Dominguez, R., Jose, M., y Cannella, S. (2021). Proportional order-up-to policies for closed-loop supply chains: the dynamic effects of inventory controllers effects of inventory controllers. International Journal of Production Research, 59(11), 3323–3337. https://doi.org/10.1080/00207543.2020.1867924
De la Cruz Madrigal, V. H., Avelar Sosa, L., Mejía-Muñoz, J. M., García Alcaraz, J. L., and Jiménez Macías, E. (2025). Dynamical System Modeling for Disruption in Supply Chain and Its Detection Using a Data-Driven Deep Learning-Based Architecture. Logistics, 9 (2), 51. https://doi.org/10.3390/logistics9020051
Disney, S. M., Ponte, B., and Wang, X. (2021). Exploring the nonlinear dynamics of the lost-sales order-up-to policy. International Journal of Production Research, 59(19), 5809–5830. https://doi.org/10.1080/00207543.2020.1790687
Duan, W., Ma, H., and Xu, D. S. (2021). Analysis of the impact of COVID-19 on the coupling of the material flow and capital flow in a closed-loop supply chain. Advances in Production Engineering and Management, 16(1), 5–22. https://doi.org/10.14743/apem2021.1.381
Garvey, M. D., and Carnovale, S. (2020). The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation. International Journal of Production Economics, 228, 107752. https://doi.org/10.1016/j.ijpe.2020.107752
Ghadge, A., Er, M., Ivanov, D., and Chaudhuri, A. (2022). Visualisation of ripple effect in supply chains under long-term, simultaneous disruptions: a system dynamics approach. International Journal of Production Research, 60(20), 6173–6186. https://doi.org/10.1080/00207543.2021.1987547
Hald, K. S., and Coslugeanu, P. (2021). The preliminary supply chain lessons of the COVID-19 disruption—What is the role of digital technologies? Operations Management Research. 282–297. https://doi.org/10.1007/s12063-021-00207-x
Hoque, M. E., Thavaneswaran, A., Appadoo, S. S., Thulasiram, R. K., and Banitalebi, B. (2021). A novel dynamic demand forecasting model for resilient supply chains using machine learning. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 218–227. 10.1109/COMPSAC51774.2021.00040
Hosseini, S., and Ivanov, D. (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert Systems with Applications, 161, 113649. https://doi.org/10.1016/j.eswa.2020.113649
Iftikhar, A., Ali, I., Arslan, A., and Tarba, S. (2024). Digital innovation, data analytics, and supply chain resiliency: A bibliometric-based systematic literature review. Annals of Operations Research, 333(2), 825–848. https://doi.org/10.1007/s10479-022-04765-6
Ivanov, D. (2019). Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Computers and Industrial Engineering, 127 (September 2018), 558–570. https://doi.org/10.1016/j.cie.2018.10.043
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 and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922
Ivanov, D., and Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning and Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
Ivanov, D., Dolgui, A., and Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. 57(3), 829–846 https://doi.org/10.1080/00207543.2018.1488086
Kim, H., Kim, J., and Clipperton, A. (2025). WIP: Developing Hands-On Semiconductor Fabrication and Testing Courses Through Industry-University Partnerships in a Teaching-Focused Undergraduate School. 2025 ASEE-GSW Annual Conference. https://peer.asee.org/55090
Kosasih, E., and Brintrup, A. (2021). A machine learning approach for predicting hidden links in supply chain with graph neural networks. International Journal of Production Research 60 (17), 5380-5393. https://doi.org/10.1080/00207543.2021.1956697
Laguir, I., Modgil, S., Bose, I., Gupta, S., and Stekelorum, R. (2023). Performance effects of analytics capability, disruption orientation, and resilience in the supply chain under environmental uncertainty. Annals of Operations Research, 324 (1), 1269–1293. https://doi.org/10.1007/s10479-021-04484-4
Lai, X., Chen, Z., Wang, X., and Chiu, C. H. (2023). Risk propagation and mitigation mechanisms of disruption and trade risks for a global production network. Transportation Research Part E: Logistics and Transportation Review, 170(December 2022), 103013. https://doi.org/10.1016/j.tre.2022.103013
Lei, Z., Cui, L., Tang, J., Chen, L., and Liu, B. (2024). Supply chain resilience in the context of I4.0 and I5.0 from a multilayer network ripple effect perspective. Annals of Operations Research, 342(2), 1149–1192. https://doi.org/10.1007/s10479-023-05618-6
Li, L. (2020). Education supply chain in the era of Industry 4.0. Systems Research and Behavioral Science, 37(4), 579–592. https://doi.org/10.1002/sres.2702
Liu, M., Tang, H., Chu, F., Zheng, F., and Chu, C. (2022). A reinforcement learning variable neighborhood search for the robust dynamic Bayesian network optimization problem under the supply chain ripple effect. IFAC-PapersOnLine, 55(10), 1459–1464. https://doi.org/10.1016/j.ifacol.2022.09.596
Llaguno, A., Mula, J., and Campuzano-Bolarin, F. (2021). State of the art, conceptual framework and simulation analysis of the ripple effect on supply chains. International Journal of Production Research, 60 (6). 2044–2066. https://doi.org/10.1080/00207543.2021.1877842
Mitra, A., Jain, A., Kishore, A., and Kumar, P. (2022). A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach. Operations Research Forum, 3 (58). https://doi.org/10.1007/s43069-022-00166-4
Modgil, S., Singh, R. K., and Hannibal, C. (2022). Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, 33(4), 1246–1268. https://doi.org/10.1108/IJLM-02-2021-0094
Ntakolia, C., Kokkotis, C., Karlsson, P., and Moustakidis, S. (2021). An explainable machine learning model for material backorder prediction in inventory management. Sensors, 21(23), 7926. https://doi.org/10.3390/s21237926
Oetomo, D. S., Fajar Ramdhani, R., and Hermawan, A. (2025). A Dynamic System Model for Multi-Echelon Inventory Policy Optimization Considering Bullwhip Effect and Supply chain Disruptions in the Sport Footwear Industry. Jurnal Teknologika, 15(1), 629–642. https://doi.org/10.51132/teknologika.v15i1.447
Olivares-Aguila, J., and ElMaraghy, W. (2021). System dynamics modelling for supply chain disruptions. International Journal of Production Research, 59(6), 1757–1775. https://doi.org/10.1080/00207543.2020.1725171
Ramakrishna, M. T., Venkatesan, V. K., Izonin, I., Havryliuk, M., and Bhat, C. R. (2023). Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data. Entropy, 25(2), 245. https://doi.org/10.3390/e25020245
Rathore, R., Thakkar, J. J., and Jha, J. K. (2021). Impact of risks in foodgrains transportation system: a system dynamics approach. International Journal of Production Research, 59(6), 1814–1833. https://doi.org/10.1080/00207543.2020.1725683
Riad, M., Naimi, M., and Okar, C. (2024). Enhancing Supply Chain Resilience Through Artificial Intelligence: Developing a Comprehensive Conceptual Framework for AI Implementation and Supply Chain Optimization. Logistics, 8(4), 111. https://doi.org/10.3390/logistics8040111
Sánchez-Ramírez, C., Ramos-Hernández, R., Fong, J. R. M., Alor-Hernández, G., and García-Alcaraz, J. L. (2020). A system dynamics model to evaluate the impact of production process disruption on order shipping. Applied Sciences (Switzerland), 10(1), 208. https://doi.org/10.3390/app10010208
Sodiya, E. O., Jacks, B. S., Ugwuanyi, E. D., Adeyinka, M. A., Umoga, U. J., Daraojimba, A. I., and Lottu, O. A. (2024). Reviewing the role of AI and machine learning in supply chain analytics. GSC Advanced Research and Reviews, 18(2), 312–320. https://doi.org/10.30574/gscarr.2024.18.2.0069
Truong, T. H., Lin, B.-W., and Tung, C.-P. (2025). Strategic legislation for the promotion of university–industry collaborations: a case study of Taiwan. The Journal of Technology Transfer, 50(1), 304–344. https://doi.org/10.1007/s10961-024-10110-9
Wilson, M. C. (2007). The impact of transportation disruptions on supply chain performance. Transportation Research Part E: Logistics and Transportation Review, 43(4), 295–320. https://doi.org/10.1016/j.tre.2005.09.008
Xue, J., y Li, G. (2023). Balancing resilience and efficiency in supply chains: Roles of disruptive technologies under Industry 4.0. Frontiers of Engineering Management. 10 ,171– 176. https://doi.org/10.1007/s42524-022-0247-8
Yu, K. D. S., and Aviso, K. B. (2020). Modelling the Economic Impact and Ripple Effects of Disease Outbreaks. Process Integration and Optimization for Sustainability. 4, 183– 186. https://doi.org/10.1007/s41660-020-00113-y
