Optimizing Machine Spare Parts Inventory Using Condition Monitoring Data

Sonja Dreyer (), Jens Passlick (), Daniel Olivotti (), Benedikt Lebek () and Michael Breitner ()
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Sonja Dreyer: Leibniz Universität Hannover
Jens Passlick: Leibniz Universität Hannover
Daniel Olivotti: Leibniz Universität Hannover
Benedikt Lebek: bhn Dienstleistungs GmbH & Co. KG

A chapter in Operations Research Proceedings 2016, 2018, pp 459-465 from Springer

Abstract: Abstract In the manufacturing industry, storing spare parts means capital commitment. The optimization of spare parts inventory is a real issue in the field and a precise forecast of the necessary spare parts is a major challenge. The complexity of determining the optimal number of spare parts increases when using the same type of component in different machines. To find the optimal number of spare parts, the right balance between provision costs and risk of machine downtimes has to be found. Several factors are influencing the optimum quantity of stored spare parts including the failure probability, provision costs and the number of installed components. Therefore, an optimization model addressing these requirements is developed. Determining the failure probability of a component or an entire machine is a key aspect when optimizing the spare parts inventory. Condition monitoring leads to a better assessment of the components failure probability. This results in a more precise forecast of the optimum spare parts inventory according to the actual condition of the respective component. Therefore, data from condition monitoring processes are considered when determining the optimal number of spare parts.

Date: 2018
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DOI: 10.1007/978-3-319-55702-1_61

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