Mathematical Modeling of Supply Chain Optimization in the Automotive Manufacturing Industry

Thesis Abstract

The automotive manufacturing industry faces increasing pressures to optimize supply chain operations amid rising global competition, fluctuating demand, and complex logistical networks. Effective supply chain management is critical for reducing operational costs, enhancing responsiveness, and maintaining competitive advantage. However, many organizations grapple with inefficiencies stemming from suboptimal inventory levels, disrupted supplier relationships, and inadequate coordination across supply chain stages. This study aims to develop a comprehensive mathematical model to optimize supply chain processes within a leading automotive manufacturing firm, focusing on integrating procurement, production, inventory management, and distribution networks to achieve cost minimization and service level enhancement. The specific objectives are to (i) identify key decision variables influencing supply chain efficiency, (ii) formulate an integrated optimization model based on linear programming and network flow theories, (iii) validate the model using empirical data, and (iv) evaluate the impact of different supply chain scenarios on overall performance. The study employs a mixed-methods research design, combining qualitative analysis of supply chain practices with quantitative modeling techniques. The population comprises key supply chain stakeholders, including procurement managers, logistics coordinators, and production planners, with a sample size of 50 respondents selected through stratified random sampling to ensure representativeness. Primary data collection involves structured questionnaires and semi-structured interviews designed to capture operational metrics, decision-making criteria, and logistical constraints. Secondary data includes historical supply chain performance records and transaction data obtained from the organization's enterprise resource planning system, spanning a three-year period. To ensure data validity and reliability, instrument validation involved expert review, and a pilot study was conducted with 10 supply chain professionals, achieving reliability coefficients above 0.8. The data analysis encompasses descriptive statistics, correlation analysis, and sensitivity analysis under varying scenarios. The core analytical framework employs linear programming models facilitated via the Solver add-in within Microsoft Excel and advanced optimization algorithms implemented in Python's PuLP library. Expected findings suggest that the proposed integrated model will significantly enhance decision-making efficiency, leading to reductions in total supply chain costs by approximately 12% and improvements in service level fulfillment by 8%, compared to baseline measurements. Additionally, the model is anticipated to reveal critical bottlenecks and potential inventory reduction strategies, facilitating proactive risk mitigation. The study’s contribution to knowledge lies in extending existing supply chain optimization frameworks to the automotive sector, incorporating real-world constraints and industry-specific variables, thereby bridging the gap between theoretical models and practical applications. The main conclusion indicates that a strategically formulated mathematical model can serve as a powerful decision-support tool for automotive manufacturers seeking supply chain resilience and cost competitiveness. Recommendations include adopting the integrated model within organizational planning processes, investing in data-driven decision systems, and conducting periodic scenario analyses. The study also advocates for further research exploring the integration of stochastic elements to address supply uncertainties and real-time data analytics for dynamic optimization. Overall, the research underscores the importance of tailored quantitative models in elevating the efficiency and robustness of automotive supply chains in an increasingly volatile industry environment.