Last Time Buy and Control Policies with Phase-Out Returns : A Case Study in Plant Control Systems

This research involves the combination of spare parts management and reverse logistics. At the end of the product life cycle, products in the field (so called installed base) can usually be serviced by either new parts, obtained from a Last Time Buy, or by repaired failed parts. This paper, however, introduces a third source: the phase-out returns obtained from customers that replace systems. These returned parts may serve other customers that do not replace the systems yet. Phase-out return flows represent higher volumes and higher repair yields than failed parts and are cheaper to get than new ones. This new phenomenon has been ignored in the literature thus far, but due to increased product replacements rates its relevance will grow. We present a generic model, applied in a case study with real-life data from ConRepair, a third-party service provider in plant control systems (mainframes). Volumes of demand for spares, defects returns and phase-out returns are interrelated, because the same installed base is involved. In contrast with the existing literature, this paper explicitly models the operational control of both failed- and phase-out returns, which proves far from trivial given the non-stationary nature of the problem. We have to consider subintervals within the total planning interval to optimize both Last Time Buy and control policies well. Given the novelty of the problem, we limit ourselves to a single customer, single-item approach. Our heuristic solution methods prove efficient and close to optimal when validated. The resulting control policies in the case-study are also counter-intuitive. Contrary to (management) expectations, exogenous variables prove to be more important to the repair firm (which we show by sensitivity analysis) and optimizing the endogenous control policy benefits the customers. Last Time Buy volume does not make the decisive difference; far more important is the disposal versus repair policy. PUSH control policy is outperformed by PULL, which exploits demand information and waits longer to decide between repair and disposal. The paper concludes by mapping a number of extensions for future research, as it represents a larger class of problems