Mathematical Modeling of Supply Chain Resilience in Global Electronics Manufacturing

Thesis Abstract

The increasing complexity and global dispersion of electronics manufacturing supply chains have amplified vulnerabilities to disruptions, prompting a critical need for robust models that can predict and enhance supply chain resilience. This study aims to develop a comprehensive mathematical framework capable of accurately modeling resilient behaviors within the global electronics manufacturing sector, with specific emphasis on identifying critical vulnerabilities and potential recovery mechanisms. The research pursues three specific objectives first, to construct a dynamic stochastic model that encapsulates the key variables influencing supply chain resilience; second, to quantify the impact of various risk mitigation strategies using the proposed model; and third, to evaluate the robustness of existing resilience measures through simulation under disruptive scenarios. Employing a mixed-methods research design, this study synthesizes quantitative modeling with qualitative insights. The quantitative component utilizes a survey method targeted at supply chain managers within the electronics manufacturing industry across Asia, North America, and Europe, with a sample size of 150 respondents selected through stratified random sampling to ensure representativeness across regions and firm sizes. The qualitative component involves semi-structured interviews with 20 industry experts, analyzed through thematic analysis to supplement and contextualize the quantitative findings. Data collection instruments include structured questionnaires validated through pilot testing, along with interview protocols designed to elicit insights into resilience strategies and perceived vulnerabilities. The quantitative data will be analyzed using advanced statistical techniques such as multiple regression analysis to identify key determinants of resilience, factor analysis to reduce dimensionality of risk factors, and Monte Carlo simulations to model uncertainty within the proposed resilience framework. The core analytical technique involves formulating a system of differential equations representing supply chain flows, incorporating stochastic parameters reflective of disruptions and recovery efforts, and solving these equations via numerical methods such as Runge-Kutta algorithms. The model will be calibrated using collected data, with sensitivity analyses conducted to assess the impact of various risk mitigation strategies and supply chain configurations. Ethical considerations include obtaining informed consent from all participants and ensuring the confidentiality and anonymity of data. Expected findings include the identification of critical supply chain segments most susceptible to disruption, quantification of the effectiveness of different resilience strategies, and validation of the proposed mathematical model against real-world disruption scenarios. It is anticipated that the model will demonstrate high predictive accuracy for supply chain recovery times and costs under varying risk conditions. The results are expected to reveal that flexibility in supplier relationships, inventory diversification, and digital integration significantly enhance resilience, while particular vulnerabilities stem from overreliance on single suppliers and extended lead times. This research contributes novel insights into the application of complex systems modeling and stochastic processes within the context of electronics manufacturing supply chains, bridging a significant gap in existing literature that predominantly relies on qualitative assessments. It advances current understanding by integrating mathematical modeling with empirical data to offer a predictive tool for supply chain resilience optimization. Consequently, the study provides practical implications for industry practitioners seeking to enhance resilience through data-driven strategies and supports policymakers in designing resilient supply chain infrastructures. The main conclusion underscores the importance of an integrated resilience framework rooted in rigorous mathematical modeling that incorporates industry-specific variables. Recommendations include adopting the proposed model for strategic planning, reinforcing supply chain diversification, investing in real-time data analytics, and fostering collaborative risk management practices across industry stakeholders. Future research avenues include extending the model to incorporate emerging technologies such as blockchain and artificial intelligence, as well as applying it to other manufacturing sectors with complex, globalized supply chains.