Sustainability is one of the top priorities for the United States and the United Nations. Environmental, economic and social indicators alarm us that our current model of progress is unsustainable. Increasing social concerns over the environmental externalities of business or daily activities urge us to seek better strategies to mitigate the negative environmental impact. Sustainable issues in today's long and complex value/supply chain should be immediately addressed. The current practice on sustainable supply chain lacks a holistic evaluation of the price of sustainability from a systemic point of view. Instead of measuring sustainability in terms of the entire product life-cycle, companies generally stay within their traditional approaches, which focus mainly on internal processes such as manufacturing and logistics. This EArly-Grant for Exploratory Research (EAGER) award provides funding for the development of a new framework for analyzing the price sustainability from a systemic view point. The outcome of this research, if successful, can fundamentally impact government's and companies' perception on sustainability issues in supply chain and also contribute to the field of Operations Research.
This project's product life-cycle approach to sustainability considers a wider range of activities and makes cross-functional cooperation compulsory. Each stage of a product life-cycle will be analyzed: raw material supply, product design, manufacturing, transportation, retailer distribution, consumer usage and disposal. Once the trade-off of each stage is fully understood, we can design an index for the price of sustainability. Upon computing these indices over the entire value/supply chain, given the limited amount of budget and resources, managers can identify bottlenecks and optimize on the total reduction of negative impacts on the environment. Mathematical programming techniques (e.g., solving a stochastic knapsack problem, or a dynamic resource allocation problem) can be readily applied to solve these potentially multi-objective or multi-stage optimization problems. In this research, innovative methodologies to analyze such complex systems and devise feasible solutions in a most cost-effective way will be developed.
