A Computational Optimization Framework for the Integration of FinTech into Supply Chain Management
DOI:
https://doi.org/10.65405/sjh.2.4.186Keywords:
Supply Chain Management, Supply Chain Finance, FinTech Optimization, Working Capital ManagementAbstract
This study develops an integrated optimization framework that links supply chain operations with FinTech-based financing mechanisms in order to jointly manage physical and financial flows. A linear programming model is proposed to combine inventory management decisions with two major supply chain finance instruments: dynamic discounting and reverse factoring. The objective is to minimize the total system cost, including purchasing, holding, shortage, and financing costs, under deterministic demand and liquidity constraints.
The model is implemented and solved using IBM ILOG CPLEX Optimization Studio. Computational experiments conducted on a set of representative benchmark instances demonstrate that the proposed framework ensures full demand satisfaction while maintaining zero inventory and backorders in the absence of fixed ordering costs. The results further highlight an optimal allocation between early payment and bank financing, where dynamic discounting is utilized up to the available liquidity budget and reverse factoring covers the remaining financing needs.
The findings confirm that liquidity constraints significantly influence financing strategies and working capital allocation. By integrating FinTech mechanisms into operational decision-making, the model improves financial efficiency without compromising operational performance. This research contributes to the literature at the intersection of Operations Research, supply chain management, and digital finance by providing a tractable and practically applicable decision-support framework.
References
[1] Rahal, I. (2024). The Supply Chain Management for Perishables Products: A Literature Review.
[2] Rahal, I., & Elloumi, A. (2024). A Multi-Objective Model for Perishable Products Supply Chain Optimization. Iranian Economic Review.
[3] Imen, R., & Abdelkarim, E. (2024). Supply Chain Management for Perishable Products: A Literature Review. IUP Journal of Supply Chain Management, 21(1).
[4] Rahal, I., & Elloumi, A. (2022). The analysis of the Bullwhip effect on the supply chain management of perishable products.
[5] Rahal, I., & Elloumi, A. (2021). Inventory management of perishable products: a case of melon in Tunisia.
[6] Rahal, I. (2023). Analyse de la gestion des stocks de produits périssables en utilisant le modèle EOQ en Tunisie.
[7] Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business logistics, 22(2), 1-25.
[8] Christopher, M. (2022). Logistics and supply chain management. Pearson Uk.
[9] Elliot, V. H., De Goeij, C., Gelsomino, L. M., & Woxenius, J. (2020). Supply chain finance is not for everyone. International Journal of Physical Distribution & Logistics Management, 50(9-10), 775-807.
[10] Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788.
[11] Williamson, O. E. (1985). The economic institutions of capitalism. Firms, markets, relational contracting. In Das Summa Summarum des Management: Die 25 wichtigsten Werke für Strategie, Führung und Veränderung (pp. 61-75). Wiesbaden: Gabler.
[12] Rahal, I., & Khalifa, Z. (2024). Optimization of Educational Supply Chain Management with Blockchain Technology. University of Zawia Journal of Engineering Sciences and Technology, 2(2), 97-107.
[13] Khalifa, Z., & Rahal, I. (2024). Integration of blockchain technology in the sustainable supply chain management. International Science and Technology Journal, 34(1), 1-23.
[14] Catalini, C., & Gans, J. S. (2020). Some simple economics of the blockchain. Communications of the ACM, 63(7), 80-90.
[15] Zheng, J., Luo, X., & Zhu, F. (2025). Exploring the impact of blockchain technology on supply chain transparency and corporate financing costs. International Review of Financial Analysis, 104895.
[16] Wang, W., Chen, Y., Wang, Y., Deveci, M., Al-Hinai, A., & Kadry, S. (2025). Analyzing the barriers to blockchain adoption in supply chain finance using an integrated interval-valued Fermatean fuzzy RAFSI model. Financial Innovation, 11(1), 9.
[17] Akerlof, G. A. (1978). The market for “lemons”: Quality uncertainty and the market mechanism. In Uncertainty in economics (pp. 235-251). Academic Press.
[18] Hofmann, E., & Kotzab, H. (2010). A supply chain‐oriented approach of working capital management. Journal of business Logistics, 31(2), 305-330.
[19] Kou, G., & Lu, Y. (2025). FinTech: a literature review of emerging financial technologies and applications. Financial Innovation, 11(1), 1.
[20] Lee, H., Tang, C., Yang, S. A., & Zhang, Y. (2023). Dynamic trade finance in the presence of information frictions and fintech. Manufacturing & Service Operations Management, 25(6), 2038-2055.
[21] Gomm, M. L. (2010). Supply chain finance: applying finance theory to supply chain management to enhance finance in supply chains. International journal of logistics: research and applications, 13(2), 133-142.
[22] Kshetri, N. (2017). Blockchain's roles in strengthening cybersecurity and protecting privacy. Telecommunications policy, 41(10), 1027-1038.
[23] Bazarbash, M. (2019). Fintech in financial inclusion: machine learning applications in assessing credit risk. International Monetary Fund.
[24] Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: a literature review. International journal of production research, 57(15-16), 4719-4742.
[25] Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business horizons, 58(4), 431-440.
[26] Dantzig, G. B. (2016). Linear programming and extensions.
[27] Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2011). Linear programming and network flows. John Wiley & Sons.
[28] Bertsimas, D., & Tsitsiklis, J. (1998). Introduction to linear optimization. IIE Transactions, 30, 855-863.
[29] Wagner, H. M., & Whitin, T. M. (1958). Dynamic version of the economic lot size model. Management science, 5(1), 89-96.
[30] Silver, E. A., Pyke, D. F., & Thomas, D. J. (2016). Inventory and production management in supply chains. CRC press.
[31] Wuttke, D. A., Blome, C., Heese, H. S., & Protopappa-Sieke, M. (2016). Supply chain finance: Optimal introduction and adoption decisions. International journal of production economics, 178, 72-81.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Shihab Journal of Humanities
This work is licensed under a Creative Commons Attribution 4.0 International License.
License
Copyright (c) 2026 Shihab Journal of Humanities
This work is licensed under a Creative Commons Attribution 4.0 International License.
