Optimal procurement strategy for multi-echelon remanufacturing systems under quality uncertainty

Abstract

Uncertainties in the quality and price of returned items add to the management complexity of remanufacturing system. To achieve the optimal procurement strategy for improved economic performance, this paper develops a mathematical model to quantify the impact of quality-based categorization of returned products under different return scenarios. Through the stochastic analysis of quality uncertainty with returns, it derives the optimal procurement strategy for cost minimization. The results show that an optimal procurement decision leads to over 80% cost savings in multi-echelon remanufacturing systems. Further, to bridge the gap between optimality theory and practical applications, this paper employs maximum likelihood estimation to speculate the key parameters of quality distribution in an empirical case whereby the optimal procurement strategies are formulated. Thus, the study provides a mathematical foundation for remanufacturers to make optimal decisions for the minimization of total cost. Importantly, the paper innovatively employs a quality coefficient to signal the overall condition in a batch of returned cores before accepting or dismantling, and then establishes the explicit relationship among the quality coefficient and production indexes in remanufacturing.