Inventory management in sustainable closed-loop supply chains with continuous improvement

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The proposed research program will benefit from the knowledge created in previous research projects and build on that acquired knowledge to develop mathematical models that are more representative of environmental issues in closed-loop supply chains (CLSC). It will also investigate how learning, captured by learning curves,  can improve the sustainability performance of such chains. Two approaches, classical and non-classical, will be used to study CLSCs. The classical approach focuses on minimizing the sum of (classical) costs of a CLSC with no consideration of environmental factors, while the non-classical approach draws a parallel between an inventory and logistics system and a thermodynamic system by using its laws to reduce the system's entropy (disorder) and improve its sustainable performance. This research program will be divided into phases. Phases 1 and 2 will focus on extending the classical and non-classical models from previous projects by including environmental factors in an attempt to have models that resemble real scenarios more faithfully. Phase 1 will integrate the classical CLSC models developed before into a model that captures the following features: returned items are disassembled prior to recovery, the number of recovery times is limited, the return rate is variable, production and recovery processes are imperfect, etc. Gradually, the mathematical models will be built upon the introduction of environmental factors such as greenhouse gas emissions, energy usage, solid waste, biodegradability, landfill, fuel consumption, etc. Phase 2 will build on the relationship of entropy, which wastes useful work (exergy), and use Extended Exergy Accounting as a tool to attain environmental, social and economic sustainability in CLSC. It will also develop an exergetic sustainability index that may better reflect the true amount of consumed resources. Later, Phases 3 of the project will investigate how learning in some CLSC functions affects the performance of the models developed in Phases 1 and 2. This is an important step since the only sustainable advantage an organization will have in the future is its ability to learn faster than its competitors. Phase 4 will analyse and compare the results of the previous phases to identify similarities and differences, and whether a CLSC system can benefit from integrating the two indicated approaches. The proposed research will build on Ryerson's expertise as a centre of professional education, training and contemporary research. The results from this research will provide managers with recommendations on how to improve the environmental, economic and social sustainability of their firms.