A Systems-Theoretic Framework for Sustainable Production Optimization and Resilience

Thesis Abstract

This study addresses the pressing need to integrate sustainability and resilience into production systems through a systems-theoretic framework that links strategic objectives with operational capabilities. The problem centers on the fragmentation of optimization efforts that favor short-term efficiency at the expense of long-term sustainability and systemic robustness in manufacturing networks. The aim is to develop and validate a holistic framework that (i) couples production optimization with resilience metrics, (ii) models interdependencies across supply, process, and product domains, and (iii) supports decision-making under uncertainty and disruption. Specific objectives include (1) articulating a systems-theoretic model of sustainable production that integrates environmental, economic, and social dimensions; (2) identifying pivotal leverage points for resilience enhancement within multi-stage production networks; (3) deriving a composite objective function that harmonizes throughput, energy intensity, waste reduction, and recovery from disturbances; (4) proposing a simulation-based optimization method and a decision-support toolkit for managers; and (5) validating the framework via empirical data from a mid-size electronics manufacturing supply chain. The methodology adopts a mixed-methods design grounded in systems theory and complex adaptive systems principles. The population comprises five electronics manufacturing firms operating dispersed suppliers and contract manufacturers. A purposive sample of 20 production lines across these firms is selected to capture diversity in process technology, scale, and disruption histories. Data collection entails (i) archival data on production throughput, energy consumption, scrap rates, and downtime covering a 24-month window, (ii) structured surveys capturing managerial perceptions of resilience and sustainability practices (n = 200 respondents with a 78% effective response rate), and (iii) semi-structured interviews with 30 senior operations and supply chain executives. Instruments include validated metrics for sustainability (e.g., material efficiency, waste-to-landfill, CO2 intensity), resilience (e.g., recovery time, redundancy, flexibility), and production performance (e.g., OEE, cycle time). Validity and reliability are established through pilot testing, Cronbach’s alpha above 0.80 for multi-item scales, and confirmatory factor analysis with acceptable fit indices (CFA ?2/df < 3, RMSEA < 0.08). Analytical methods combine system dynamics and stochastic optimization. Model specification draws on the Viable System Model as the theoretical backbone, supplemented by the Resilience Engineering perspective and the Triple Bottom Line to capture environmental and social performance. A system dynamics model encodes stock-and-flow processes for throughput, energy use, material waste, and recovery capacity, while incorporating disruption scenarios (supplier failure, equipment downtime, demand volatility) with probabilistic parameters. A multi-objective stochastic optimization approach (weighted sum and Pareto-based methods) seeks to optimize a composite objective balancing production efficiency, environmental impact, and resilience. Simulation experiments explore scenario-based scenarios (low/high disruption frequency, varying energy prices) to assess robustness. Data analysis includes regression analysis to link resilience investments to performance gains, structural equation modeling to test the theoretically proposed interdependencies, and sensitivity analysis to identify leverage points. Thematic analysis of interview transcripts complements quantitative findings, revealing managerial rationales for trade-offs and governance mechanisms. Expected findings indicate that integrative systems-level optimization yields measurable improvements in key performance indicators OEE increased by 6–12%, energy intensity reduced by 8–14%, and waste-to-landfill decreased by 10–16%, alongside shorter recovery times (15–30%) under simulated disruptions. The framework is anticipated to reveal critical levers, such as modular process design, supplier diversification, real-time data analytics, and cross-functional governance, that synergistically enhance resilience without sacrificing efficiency. The study contributes to knowledge by operationalizing a comprehensive systems-theoretic framework that unifies sustainable production optimization and resilience in manufacturing, bridging a gap between theory and practice for multi-criteria decision-making under uncertainty. It advances methodological approaches by integrating system dynamics with stochastic multi-objective optimization and mixed-methods validation. Practical implications include a decision-support toolkit comprising a modular model library, scenario templates, and dashboard visualizations to aid managers in balancing throughput, sustainability, and disruption readiness. The principal conclusion is that sustaining competitive advantage in modern production requires explicit, systems-level integration of sustainability and resilience, supported by governance structures and data-driven analytics. Recommendations emphasize investment in modular, data-enabled architectures; proactive risk sensing and redundancy; supplier collaboration; and continuous learning cycles to embed the framework into daily operations. Further research is suggested to extend the framework to service-oriented manufacturing and to examine long-term behavioral adaptations in supply networks.