Integrated Supply Chain–Finance Optimization Using Mixed Integer Programming: A Comprehensive Analysis

Samuel Oladapo Taiwo

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 6, 784-804

Abstract: This study develops an integrated Mixed Integer Programming (MIP) framework for simultaneous optimization of supply chain design and financial performance. Unlike traditional models that decouple operational and financial decision-making, the proposed Integrated Supply Chain–Finance Optimization (ISFO) framework embeds Net Present Value (NPV), working capital constraints, and financial risk measures directly into strategic and tactical supply chain optimization. A multi-objective formulation enables structured analysis of profitability–risk trade-offs, while scenario-based stochastic programming captures demand, supply, and financial uncertainty. The results demonstrate that operational design decisions significantly alter liquidity exposure, capital structure, and long-term firm value. The study contributes a unified modeling architecture that enhances cross-functional integration between operations and finance, offering both theoretical advancement and practical decision-support relevance for industrial-scale supply chains.

Keywords: Integrated supply chain–finance optimization; Mixed Integer Programming (MIP); Multi-objective optimization; Supply chain finance (SCF); Conditional Value-at-Risk (CVaR); Stochastic programming under uncertainty (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i6:id:1398

DOI: 10.32628/IJSRST25126503

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