A System-Level Cost Modeling Framework for Design for Remanufacturing: a Case Study of an Agricultural Machine Transmission

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Abstract Remanufacturing offers significant environmental and economic benefits by restoring end-of-life products to as-new conditions. Although extensive research has been conducted on the topic, adoption of practices remains limited across various industries. A primary barrier to broader implementation is the substantial upfront investment required, which necessitates reliable cost modeling to justify potential future savings. Existing cost models typically assess individual components independently, neglecting critical interdependencies within complex systems. To address this gap, we propose a generic, probability-based cost modeling framework that holistically considers component interdependencies, reusability, and reliability at the system level, enabling more accurate projections of cost savings over multiple life cycles. Our model identifies those critical components that maximize remanufacturing benefits across a product’s many lives. We demonstrate the utility of our framework through a case study involving a transmission front cover subassembly remanufactured by John Deere. The three alternative design scenarios explored in this study were developed using failure mode and effects analysis (FMEA), specifically targeting failures introduced or made worse by the original remanufacturing processes, such as disassembly. We leverage a Monte Carlo simulation (MCS) to perform life cycle cost analysis on the system with different design changes and present the normalized average cost savings after three remanufacturing cycles. The results of our study confirm that the consideration of component interdependencies is necessary for precise cost estimations that meet industry standards.

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