Forecast Model for Return Quality in Reverse Logistics Networks

Abstract

Giving rise to the field of reverse logistics are the governmental legislations mandating used electronics take-backs and sustainable recovery, which often burden manufacturers with the challenge of high implementation costs but no guaranteed profitability. One way to tackle this challenge is to demystify the multi-faceted uncertainties of product returns, namely timing, quantity and quality, that currently inhibit optimal design and operations of reverse logistics networks (RLN). In recognition of the limitations particularly caused by uncertainty of returns’ quality in the strategic, tactical and operational planning of the RLN, this research seeks to develop a forecast model for the prediction of the returns’ quality of future electronics returns. The proposed forecast model comprehensively incorporates three major factors that affect quality decisions which are usage, technological age and remaining economic value of expected product returns to predict its quality grade. While technological age and economic trends can readily be established, the main complexity lies in modeling of usage-dependent reliability distribution of returned electronics. The novelty of the proposed forecast model lies in deducing usage distributions through segmentation of the consumer base by socioeconomic factors such as age, income, educational status and location. These usage distributions are then used to estimate remaining useful life of returned products and their components, the associated repair costs and the subsequent profitability of reprocessing based on economic value in the market. This research develops analytical models of expected return quality based on empirical usage distributions and pricing trends. The analytical models are then applied in Monte Carlo simulations to forecast expected returns’ quality from different regions, including large and small population centers, in Canada