Spare parts inventory classification using Neutrosophic Fuzzy EDAS method in the aviation industry

Abstract

Every physical asset and product that contributes to production or service is called inventory in the production or service industry. Inventories counted among companies’ assets are important since they protect companies from various undesirable situations. Inventories often include large numbers, although they vary from company to company. Changes in the market affect the customer's procurement behavior. Inventories shaped by customers’ demands are therefore seen as a source of uncertainty and cost for companies. The aviation industry is one of the most important transportation modes in the transportation industry and has the ability to transport faster than other industries. One of the vital issues in aviation businesses is the uninterrupted continuation of services. Accordingly, spare parts management is one of the issues that aviation companies attach importance to. Generally, spare parts management in most aviation companies aims to achieve a high customer service level with minimum inventory and minimum inventory investment. Aviation companies can achieve this goal through inventory classification. By using well-prepared inventory classification, these companies can reduce their inventory costs and classify their inventory to increase customer satisfaction and efficient production. This study aims to classify spare parts inventories by using the Neutrosophic Fuzzy EDAS method to achieve high inventory management efficiency in cases of inconsistent and uncertain information in the aviation industry. By adopting this recent multi-criteria decision-making method (MCDM), this study not only provides high inventory management efficiency but also determines required spare parts classification criteria for the aviation industry.