Stochastics and Engineering Research Group
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STOCHASTICS AND ENGINEERING RESEARCH GROUP
Stochastics and Engineering Research Group
Stochastics and Engineering Research Group
European Journal of Operational Research
Spare parts supply chains are highly dependent on the dynamics of their installed bases. A decreasing number of capital products in use increases the nonstationary supply-side risk especially towards the end-of-life of capital products. This supply-side risk appears to present itself through varying lead times coupled with supply disruptions. To model the nonstationary supply-side risk, we consider an exoge- nous Markov chain that modulates random lead times and disruption probabilities. Assuming that order crossovers do not occur, we prove the optimality of a state-dependent base stock policy. Later, we conduct an impact study to understand the value of considering stochastic lead times and supply disruption risk in spare parts inventory control. Our results indicate that the coupled effect of random lead times and disruptions can be larger than the summation of individual effects even for moderate lead time variances. Also, the effect of nonstationarity on total cost can be as large as the summation of all risk factors com- bined. In addition to this managerial insight we present a procedure for supply risk mitigation based on an empirical model and our mathematical model. Experiments on a real business case indicate that the procedure is capable of reducing costs while making the inventory system more prepared for disruptions.
Preprint
In this paper, we provide an analytic characterization of time-dependent distribution of random spare parts demand distribution. This finding is useful for OEMs who seek to plan their operations and spare parts inventory for planning of their medium and long-term operations. These theoretical findings are applied to the following practical problems:
1. Spare parts inventory control in case of growing and declining installed bases: Our method creates savings up-to 1%.
2. Lifetime extension investment of manufacturers for their existing products: Optimum time of investment and installed base size is derived for a given investment cost.
3. Remanufacturers’ availability of used products.
Stochastics and Engineering Research Group
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Stochastics and Engineering Research Group
Mustafa HEKİMOĞLU, Ph.D
Deniz KARLI, Ph.D.