Last time buy and repair decisions for spare parts

Abstract

a b s t r a c tOriginal Equipment Manufacturers (OEM's) of advanced capital goods often offer service contracts for system support to their customers, for which spare parts are needed. Due to technological changes, suppliers of spare parts may stop production at some point in time. As a reaction to that decision, an OEM may place a so-called Last Time Buy (LTB) order to cover demand for spare parts during the remaining service period, which may last for many years. The fact that there might be other alternative sources of supply in the next periods complicates the decision on the LTB. In this paper, we develop a heuristic method to find the near-optimal LTB quantity in presence of an imperfect repair option of the failed parts that can be returned from the field. Comparison of our method to simulation shows high approximation accuracy. Numerical experiments reveal that repair is an excellent option as alternative sourcing, even if it is more expensive than buying a new part, because of the option to postpone the repair until the parts are needed. In addition, we show the impact of other key parameters on costs and LTB quantity.

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

“…For example, the criticality of a machine and hence its related spare parts can be based on different criteria, such as the capital cost of the machine, its rarity (i.e. absence of redundancy), the degree of deterioration (measured by assessing its conditions [85]), the difficulty of repair in case of downtime (measured by the mean time to repair [74]), its availability (measured by the mean time between failures [6]), the throughput of the machine (whether it is a bottleneck), whether the outputs of the machine are intended for important customers, or whether the machine has already produced its intended schedule of production (i.e. the current required demand).…”

A framework for investigating optimization of service parts performance with big data

“…A review of literature supports these findings. Extensive research has been conducted in this area to include studies on machine availability (Ghodrati et al 2013), preventative maintenance (Jiang et al 2015), and level of repair Chang et al (2005) across industry segments such as Original Equipment Manufacturers (Behfard et al 2015), third-party providers (Kazemi Zanjani and Nourelfath 2014), and electronics repair (Schroter and Spengler 2004). Despite research demonstrating the impact of service parts availability on overall cost (Louit et al 2011) and customer service (Dekker et al 1998), the data from the current research suggests a gap between extant research and managerial application in that most research examines only direct relationships between service parts management and one performance goal.…”

“…Ranging from warehousing and distribution center (Diabat et al 2015;Thonemann et al 2002;Topan and Bayindir 2012) to labor (Papazov and Tashev 1988) and forecasting (Tibben-Lembke and Amato 2001), cost research in spare parts is far reaching. Several research efforts also incorporate cost saving measures such as pooling (Evers 1999;Guajardo and Rönnqvist 2015;Wong et al 2005) and postponement (Alptekinoglu et al 2013;Behfard et al 2015).…”

A Dynamic Failure Rate Forecasting Model for Service Parts Inventory

“…Due to changes in technology, companies may terminate service parts supply at some point of time. The so-called last time buy (LTB) [28] order is not considered in this model. Furthermore, the BTC and AFR may not be accurate in early usage because they are provided by OEMs, which do not reflect the customer demand.…”