New approaches to modeling resilient decisions under deep uncertainty to manage the downstream supply chain

Bais, Abdellah; Amechnoue, Khalid

doi:10.17270/J.LOG.001030

Artykuł - szczegóły

Tytuł artykułu

New approaches to modeling resilient decisions under deep uncertainty to manage the downstream supply chain

Autorzy

Bais Abdellah , Amechnoue Khalid

Treść / Zawartość

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DOI

10.17270/J.LOG.001030

Języki publikacji

Abstrakty

Background: The supply chain process has been widely modeled, especially with respect to the optimization of system performance. Recent years have highlighted the outbreak of many crises, such as the financial crisis of 2008, COVID-19, and the semiconductor shortage. The impact of such crises has become more challenging for manufacturers, particularly in the automotive industry. In this context, the present study was undertaken with the specific aim of providing an integrated approach for resilient decision-making under deep uncertainty (DMDU), especially in the downstream supply chain. Methods: The research is based on design science research (DSR) and case study methodologies. A design methodology was used to develop the framework. A case study is included to prove the pertinence of the framework. Results: The findings of this study demonstrate that the suggested comprehensive approach is helpful for companies and could help top management with strategic decision-making when customers decide to increase or decrease demand. This paper develops and models two approaches that help firms to manage and make resilient decisions when the supply chain is facing deep uncertainty, such as the bullwhip effect. Finally, the proposed models are implemented in a real-life case study within an automotive company to illustrate the applicability and efficacy of the proposed approach. Conclusions: To the best of our knowledge, this is the first study suggesting this approach. The paper is original and contributes towards sharing a new approach to understanding the supply chain within an uncertain context. As part of its contribution, the study draws attention to how managers and executors should integrate this approach into the global strategy of a company. Moreover, it explores some of the most complex variables that affect supply chain performance under high demand variation so it is possible to clearly show the risks associated with obsolete materials and products.

Słowa kluczowe

decision-making strategic planning resilience supply chain management uncertainty

podejmowanie decyzji planowanie strategiczne odporność zarządzanie łańcuchem dostaw niepewność

Wydawca

Wyższa Szkoła Logistyki

Czasopismo

LogForum

Rocznik

2024

Tom

Vol. 20, no. 2

Strony

227--242

Opis fizyczny

Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Bais Abdellah

abdellah.bais@etu.uae.ac.ma

National School of Applied Sciences, Abdelmalek Essaadi University, Department of Mathematics, Informatic and Applications, Morocco

https://orcid.org/0000-0003-0575-2108

autor

Amechnoue Khalid

kamechnoue@uae.ac.ma

National School of Applied Sciences, Abdelmalek Essaadi University, Department of Mathematics, Informatic and Applications, Morocco

https://orcid.org/0000-0002-8285-1989

Bibliografia

1. Altabsh, M., & Almaktoom, A. T. (2023). Integrating Healthcare Processes Through Supply Chain Principles. https://doi.org/10.46254/AN13.20230347
2. Bais, A. & Amechnoue, K. (2023). An Ex Post Facto Study of Global Crisis Impact on Supply Chain Performance: Case of the Automotive Sector. In 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (pp. 1-8). IEEE. https://www.doi.org/10.1109/IRASET57153.2023.10153004
3. Cabral, I., Espadinha-Cruz, P., Grilo, A., Puga-Leal, R., & Cruz-Machado, V. (2011, June). Decision-making models for interoperable lean, agile, resilient and green supply chains. In Proceedings of the International Symposium on the Analytic Hierarchy Process (pp. 1-6), https://www.doi.org//10.13033/isahp.y2011.124
4. Chen, C.L., Yuan, T.Y., Chang, C.Y., Lee, W.C. & Ciou, Y.C. (2006). A multi-criteria optimization model for planning of a supply chain network under demand uncertainty. In Computer Aided Chemical Engineering 21, 2075-2080. Elsevier. https://www.doi.org/10.1016/S1570-7946(06)80354-8
5. Christopher, M. (2016). Logistics & supply chain management. Pearson Uk.
6. Dai, J., Li, S., & Peng, S. (2017). Analysis on causes and countermeasures of bullwhip effect. In MATEC web of conferences (Vol. 100, p. 05018). EDP Sciences. https://www.doi.org/10.1051/matecconf/201710005018
7. De Lucio, J., Díaz-Mora, C., Mínguez, R., Minondo, A., & Requena, F. (2023). Do firms react to supply chain disruptions?. Economic Analysis and Policy, 79, 902-916. https://www.doi.org//10.1016/j.eap.2023.07.004
8. Fritz, M. M. (2022). A supply chain view of sustainability management. Cleaner Production Letters, 3, 100023. https://www.doi.org//10.1016/j.clpl.2022.100023
9. Hill, C. A., Zhang, G. P., & Miller, K. E. (2018). Collaborative planning, forecasting, and replenishment & firm performance: An empirical evaluation. International journal of production economics, 196, 12-23. https://doi.org/10.1016/j.ijpe.2017.11.012
10. Ireland, R. K., & Crum, C. (2006). Supply chain collaboration. USA: J.Ross Publishing
11. Kovárník, R., & Staňková, M. (2023). Efficiency of the Automotive Industry in the Visegrad Group. LOGI–Scientific Journal on Transport and Logistics 14(1), 12-23. https://www.doi.org/10.2478/logi-2023-0002
12. Kreye, M.E. (2019), "Does a more complex service offering increase uncertainty in operations?", International Journal of Operations & Production Management, Vol. 39 No. 1, pp. 75-93. https://doi.org/10.1108/IJOPM-01-2018-0009
13. Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: anatomy of a research project. European Journal of Information Systems, 17(5), 489-504.
14. Larson, P. D., & Rogers, D. S. (1998). Supply chain management: definition, growth and approaches. Journal of Marketing Theory and Practice, 6(4), 1-5. https://www.doi.org/10.1080/10696679.1998.11501805
15. Miclo, R., Fontanili, F., Lauras, M., Lamothe, J., Milian, B., 2015. Mrp vs. demand-driven mrp: Towards an objective comparison, in: 2015 International Conference on Industrial Engineering and Systems Management (IESM), IEEE. pp. 1072–1080.
16. Min, H. & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & industrial engineering 43(1-2), 231-249. https://www.doi.org/10.1016/S0360-8352(02)00066-9
17. Mosca, A., Vidyarthi, N., & Satir, A. (2019). Integrated transportation–inventory models: A review. Operations Research Perspectives, 6, 100101. https://www.doi.org//10.1016/j.orp.2019.100101
18. Nabil, L., El Barkany, A., & El Khalfi, A. (2018). Sales and operations planning (S&OP) concepts and models under constraints: Literature review. International Journal of Engineering Research in Africa, 34, 171-188. https://www.doi.org/10.4028/www.scientific.net/JERA.34.171
19. Ni, N., Howell, B.J. & Sharkey, T.C. (2018). Modeling the impact of unmet demand in supply chain resiliency planning. Omega 81, 1–16. https://www.doi.org/10.1016/j.omega.2017.08.019
20. Pan, A., Leung, S.Y.S., Moon, K.L. & Yeung, K.W. (2009). Optimal reorder decision-making in the agent-based apparel supply chain. Expert Systems with Applications 36(4), 8571-8581. https://www.doi.org/10.1016/j.eswa.2008.10.081
21. Paul, S. K., Sarker, R., & Essam, D. (2017). A quantitative model for disruption mitigation in a supply chain. European Journal of Operational Research, 257(3), 881-895. https://www.doi.org//10.1016/j.ejor.2016.08.035
22. Rahmanzadeh, S., Pishvaee, M. S., & Rasouli, M. R. (2023). A robust fuzzy-stochastic optimization model for managing open innovation uncertainty in the ambidextrous supply chain planning problem. Soft Computing, 27(10), 6345-6365. https://www.doi.org//10.21203/rs.3.rs-395983/v1
23. Sari, K. (2008). On the benefits of CPFR and VMI: A comparative simulation study. International journal of production economics, 113(2), 575-586. https://doi.org/10.1016/j.ijpe.2007.10.021
24. Shahed, K. S., Azeem, A., Ali, S. M., & Moktadir, M. A. (2021). A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk. Environmental Science and Pollution Research, 1-16. https://doi.org/10.1007/s11356-020-12289-4
25. Shekarian, E. (2020). A review of factors affecting closed-loop supply chain models. Journal of Cleaner Production, 253, 119823. https://www.doi.org//10.1016/j.jclepro.2019.119823
26. Taha, R. B., El-Kharbotly, A. K., & Sadek, Y. M. (2021). Comparing Mitigation Strategies for Supply Chain under Operational Disruptions Using Monte Carlo Simulation. Port-Said Engineering Research Journal, 25(2), 170-186.
27. https://DOI: 10.21608/pserj.2021.65383.1096
28. Tchokogué, A. (2021). Production and inventory planning and control: techniques and practices (in French). Canada:Editions JFD.
29. Velasco Acosta, A.P., Mascle, C., Baptiste, P., 2020. Applicability of demand-driven mrp in a complex manufacturing environment. International Journal of Production Research 58, 4233–4245.
30. Wajdi Tounsi. Comparaison des Approches DDMRP et EOQ: Modélisation et Simulation d’un Cas d’Étude. Ecole Polytechnique, Montreal (Canada), 2018.
31. Wang, M. (2018). Impacts of supply chain uncertainty and risk on the logistics performance. Asia Pacific Journal of Marketing and Logistics, 30(3), 689-704.
32. Wieteska, G. (2018). The domino effect-disruptions in supply chains. LogForum, 14(4). https://www.doi.org//10.17270/J.LOG.2018.302
33. Yadav, A.K. & Samuel, C. (2022). Modeling resilient factors of the supply chain. Journal of Modelling in Management 17(2),456–485. https://www.doi.org/10.1108/JM2-07-2020-0196
34. Ye, X. (1994). Modeling and simulation of production systems: an object-oriented approach (in Frensh) (Doctoral dissertation, INSA Lyon).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).

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