Probabilistic System-Level Cost Modeling for Design for Remanufacturing

Abstract Remanufacturing offers significant environmental and economic benefits by restoring end-of-life products to as-new condition. Although extensive research has been conducted on the topic, the adoption of remanufacturing 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. Most existing models treat components independently and ignore intercomponent dependencies. We develop a probabilistic, system-level cost modeling framework that integrates reliability, reusability, and a dependency matrix to capture cascading effects across components over multiple life cycles. Our model identifies those critical components that maximize remanufacturing benefits across a product’s many lives. A toy example and an industry case study (John Deere PowrQuad transmission subassembly) illustrate how design alternatives affect cumulative cost. Using a Monte Carlo simulation to perform life-cycle cost analysis on the system with different design changes, we show the normalized average cost savings after three remanufacturing cycles. Accounting for dependencies meaningfully alters cost projections, and ignoring them underestimates accumulated cost by up to 20% in our examples. The results of our study confirm that accounting for component interdependencies is necessary to produce cost estimates that meet industry standards.