Proposing a Supervised Machine Learning Approach for Data-driven Simulation in Supplier Selection Problem

Document Type : Article

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Abstract

Supplier selection is a crucial aspect of supply chain management. Traditionally, multi-criteria decision-making methods and experts' experience have been the go-to approaches for this process. However, in today's highly competitive business environment, making decisions quickly and accurately has become paramount. Consequently, innovative data-driven technologies and machine learning methods have gained significant importance. Surprisingly, the combination of simulation and machine learning has received limited attention in research endeavors. This study evaluates supplier performance based on specific characteristics utilizing a combination of simulation and machine learning techniques. The research investigates its applications in data-driven decision support for supplier selection. We tackled the supplier selection challenge by simulating the problem using Arena software. The dataset generated from the simulation served as input for our machine learning model. We employed different algorithms, namely Decision Tree (DT), K-Nearest Neighbor (KNN), and Logistic Regression (LR), to analyze the data. Our research demonstrates the remarkable effectiveness of machine learning algorithms in supplier selection. Based on the results, the DT algorithm with 99% accuracy, the LR algorithm with 98% accuracy, and the KNN algorithm with 96% accuracy assign orders to suppliers with the highest probability of delivering them on time. Our approach proves invaluable in analyzing the supplier base and identifying critical suppliers or combinations thereof, minimizing disruptions caused by adverse supplier performance. These findings underscore the potential of integrating advanced computational methods to significantly enhance decision-making processes within supplier selection in supply chain management. Our analysis highlights the pivotal role of combining simulation and machine learning techniques, offering a robust framework for improving supplier selection methods in the fast-paced and competitive landscape of modern industries. This approach not only improves existing methods but also promises a new era in supply chain management. The synergy of simulation and machine learning can revolutionize how businesses choose strategic suppliers and ensure speed and accuracy in decision-making processes.

Keywords

Main Subjects

References

Araz, C. and Ozkarahan, I., 2007. Supplier evaluation and management system for strategic sourcing based
on a new multicriteria sorting procedure. International Journal of Production Economics, 106, pp. 585–606.
https://doi.org/10.1016/j.ijpe.2006.08.008.
2. Chen, C.T., Lin, C.T. and Huang, S.F., 2006. A fuzzy approach for supplier evaluation and selection in
supply chain management. International Journal of Production Economics. 102, pp. 289–301.
 https://doi.org/10.1016/j.ijpe.2005.03.009.
3. Ghadimi, P., Wang, C., Lim, M.K. and Heavey, C.,2019. Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains. Computers & Industrial Engineering. 127, pp. 588-600.https://doi.org/10.1016/j.cie.2018.10.050 .
4. Ahmad, I., Liu, Y., Javeed, D., Shamshad, N., Sarwr, D. and Ahmad, S., 2020. A review of artificial intelligence techniques for selection & evaluation. IOP Conf. Ser. Mater. Sci. Eng. 853. https://doi.org/10.1088/1757-899X/853/1/012055.
5. Liou, J.J.H., Chang, M.H., Lo, H.W. and Hsu, M.H., 2021. Application of an MCDM model with data mining techniques for green supplier evaluation and selection. Applied Soft Computing, 109, 107534.  https://doi.org/10.1016/j.asoc.2021.107534.
6. Giri, B.C., Molla, M.U. and Biswas, P., 2022. Pythagorean fuzzy DEMATEL method for supplier selection in sustainable supply chain management. Expert Systems with Applications, 193, 116396.
 https://doi.org/10.1016/j.eswa.2021.116396.
7. Ahmad, M.T., Firouz, M. and Mondal, S., 2022. Robust supplier-selection and order-allocation in two-echelon
supply networks: A parametric tolerance design approach. Computers & Industrial Engineering, 171, 108394. https://doi.org/10.1016/j.cie.2022.108394.
8. Shang, Z., Yang, X., Barnes, D. and Wu, C., 2022. Supplier selection in sustainable supply chains: Using the integrated BWM, fuzzy Shannon entropy, and fuzzy MULTIMOORA methods. Expert Systems with Applications 195, 116567.
 https://doi.org/10.1016/j.eswa.2022.116567.
9. Ishizaka, A., Khan, S.A., Kheybari, S. and Zaman, S.I., 2023. Supplier selection in closed loop pharma supply chain: a novel BWM–GAIA framework. Annals of Operations Research, 324, pp. 13–36. https://doi.org/10.1007/s10479-022-04710-7.
10. Khazaei, M., Hajiaghaei-Keshteli, M., Rajabzadeh Ghatari, A., Ramezani, M., Fooladvand, A. and Azar, A., 2023. A multi-criteria supplier evaluation and selection model without reducing the level of optimality. Soft Computing, 27, pp. 17175–17188. https://doi.org/10.1007/s00500-023-08954-8.
11. Kabadayi, N. and Dehghani mohammadabadi, M., 2022. Multi-objective supplier selection process: a simulation–optimization framework integrated with MCDM. Annals of Operations Research, 319, pp.1607–1629.
 https://doi.org/10.1007/s10479-021-04424-2.
12. Saputro, T.E., Figueira, G. and Almada-Lobo, B., 2023. Hybrid MCDM and simulation-optimization for strategic supplier selection. Expert Systems with Applications, 219, 119624. https://doi.org/10.1016/j.eswa.2023.119624.
13. Hajian Heidary, M., 2023. Risk Assessment in the Global Supplier Selection Considering Supply Disruption: A Simulation Optimization Approach. International Journal of Supply and Operations Management, 10, pp. 501–522.
https://doi.org/10.22034/IJSOM.2023.108989.2110.
14. Abdulla, A., Baryannis, G. and Badi, I., 2023. An integrated machine learning and MARCOS method for supplier evaluation and selection. Decision Analytics Journal, 9, 100342. https://doi.org/10.1016/j.dajour.2023.100342.
15. Abouloifa, H. and Bahaj, M., 2023. Using Machine Learning Algorithms to Increase the Supplier Selection Process Efficiency in Supply Chain 4.0. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. Springer, Cham. https://doi.org/10.1007/978-3-031-26384-2_19.
16. Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A. and Ivanov, D., 2019. A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management,49, pp. 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004.
17. Kumar, S., Dixit, A.K. and Akarte, M., 2023. In: Misra, R., et al. Advances in Data Science and Artificial Intelligence. ICDSAI 2022. Springer Proceedings in Mathematics & Statistics, 403. Springer, Cham. 403. https://doi.org/10.1007/978-3-031-16178-0_4.
18. Bodaghi, G., Jolai, F. and Rabbani, M., 2018. An integrated weighted fuzzy multi-objective model for supplier selection and order scheduling in a supply chain. International Journal of Production Research, 56, pp. 3590–3614. https://doi.org/10.1080/00207543.2017.1400706.
19. Sharma, R., Shishodia, A., Gunasekaran, A., Min, H. and Munim, Z.H., 2022. The role of artificial intelligence in supply chain management: mapping the territory. International Journal of Production Research, 60, pp. 7527–7550.
 https://doi.org/10.1080/00207543.2022.2029611.
20. Riahi, Y., Saikouk, T., Gunasekaran, A. and Badraoui, I., 2021. Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications,173, 114702.
 https://doi.org/10.1016/j.eswa.2021.114702.
21. Ha, C., Jun, H.B. and Ok, C., 2018. A mathematical definition and basic structures for supply chain reliability: A procurement capability perspective. Computers & Industrial Engineering. 120, 334–345.
https://doi.org/10.1016/j.cie.2018.04.036 
Sharif Journal of Industrial Engineering & Management
Volume 40, Issue 2
March 2025
Pages 86-98

Files

Share

How to cite

Statistics