Abstract
A malfunctioning or failed part in a machine must be replaced as soon as possible with the same or a similar functioning part. On the one hand, it is required to have enough parts in stock to address customer requirements and avoid expensive downtimes and on the other hand, capital gets tied up in the form of non-revenue generating inventories. Finding the balance between servicing customers and reducing inventory costs is an imminent struggle for organisations large and small. This study aims at categorising spare parts into groups based on their characteristics like replenishment lead time, functional importance, unit costs etc. so as to determine the ‘optimal’ inventory policy for each group. Simulation optimisation methodology is developed for this problem of the two conflicting interests of service level and total costs and is implemented using MATLAB software. The program proposes the best inventory policy for all spare parts within a group and at the same time suggests the respective inventory policy parameters for each part within the group. The methodology is illustrated using an example with self-generated spare parts data. The thesis thus develops a structured approach to managing spare parts and helps to move from intuition-based to data-based inventory management and ordering behaviour for spare parts, that in some cases may cost millions of Euros.
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