Design for End of Life (DfEOL) recovery is a complex process that requires consideration of various design aspects including design for product life extension, design for reliability, design for disassembly, design for components reuse, and design for recyclability. There is a need for an analytical tool that helps designers integrate all these design aspects together and moreover investigate the impact of design features on the recovery network. The designer needs to predict the variability that design features bring into the reverse logistics network, including the variability in the amount, quality and timing of return flows and uncertainty in the remanufacturing operations such as disassembly time. In addition to the product design, the EOL recovery system performance is also affected by human decision making. The willingness of customers to keep used products in storage, the qualitative criteria used by remanufacturing companies to sort and categorize the returned used products and the manual disassembly operations influenced by the operator’s cognitive biases are examples of human decision making processes that impact product recovery. The nonlinear character of reverse logistics system along with the dynamic complexity as a result of uncertainties and cognitive biases are particularly troublesome. This paper establishes a simulation-based System Dynamics (SD) model of product life cycle to check interrelationship among product design features and their impacts on the amount, quality and timing of the return flows to the waste stream. The complex product take back process and recovery operations are modeled. Designers could use the results of the model to compare different design scenarios and to receive information about what design features bring problems or create opportunities for EOL recovery.
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