Spare Parts Inventory Management System in a Service Sector Company

Abstract

Spare parts inventory management is crucial in the success of a service providing company. In this study, the spare parts of a service provider in the automotive sector are classified according to their characteristics in groups and different inventory control policies are applied to the categorized groups. The Analytical Hierarchy Process (AHP), one of the Multi-Criteria Decision Making (MCDM) methods, is used to classify the spare parts into groups. As a result of the application of AHP, classes of spare parts are determined according to the VED analysis, classifying the spare parts according to their criticality. Furthermore, the ABC analysis performed by the company was improved by using cost and demand criteria. After performing both analysis, three new classes of spare parts are determined with the combination of ABC and VED classification techniques. For each class, an appropriate inventory control policy is decided according to the spare parts importance and criticality. Based on the literature review, the (R, S, s) inventory control policy is chosen to be applied in each class, taking into consideration the review period, order up-to-level and reorder point of items. In the inventory control model, the review period for the same class items is assumed to be constant based on the information provided by the company. For verification purposes, necessary cost calculations including total ordering and holding costs are performed by means of Microsoft Excel. In order to be able to vastly observe the system behavior, different cost scenarios are generated by increasing and decreasing the service level and review period of the system. Using, OptQuest, an optimization tool, embedded into ARENA simulation software, the different scenarios were analyzed and the total minimum cost is reached. For supporting the daily operations of the company, a user-friendly decision support system is built, where the end-user can easily add/remove spare parts to/from the system, classify them and compare the results of inventory control policies with the current system. The DSS will also assist the company to manage and control their real-time inventory and perform spare parts stock level tracking and decide when to place orders.

Minimizing OHS Risks with Spherical Fuzzy Sets as a Verdict to Inventory Management: A Case Regarding Energy Companies

“…e proposed approach is also suitable to be used with different MCDM methods than SAW and easily be adapted to differentiated working environments when required [23,40,[42][43][44].…”