APPROACH TO OPTIMISING AND MANAGING WAREHOUSE STOCKS IN A CAR SERVICE

Abstract

The automotive service industry is characterised by a high degree of complexity and dynamism in its logistics processes. A pivotal challenge confronting the organisation is the effective management of inventory. The maintenance of optimal inventory levels is of paramount importance in ensuring uninterrupted service and minimising the costs associated with storage and delivery. Inventory management is an intricate process that extends beyond the mere maintenance of availability. It necessitates the strategic decision-making process concerning supplier selection, order planning, and resource allocation over time. The present study proposes a model for inventory management in automotive service stations that reflects the complexity of real-world operating conditions. The model incorporates a multitude of suppliers, each exhibiting distinct pricing structures and delivery terms. Constraints are imposed on the selection of suppliers, thereby influencing the overall dynamics of the supply chain. It is important to note that a particular auto part may be available from more than one supplier, with unit prices varying across suppliers and over time. The model operates under the assumption that the demand for each part for each period is known in advance, as are the storage costs, the lower and upper limits on inventory levels, the supplier-specific order quantity constraints, and the initial inventory level at the beginning of the planning horizon. The objective of the proposed model is to minimise the total inventory costs, including purchasing, holding, and potential shortage costs, by applying integer linear programming techniques. The problem is formulated with decision variables representing quantities to be ordered and stocked, and includes a set of linear constraints reflecting all operational requirements. In order to enhance computational efficiency, the model employs heuristic methods to identify high-quality approximate solutions within acceptable time frames. This approach is of particular value in applications where the dimensionality of the problem and the number of suppliers can significantly increase the computational complexity. Empirical evidence has demonstrated the potential of the model to enhance inventory management in practical scenarios. The primary benefits observed include a reduction in overall costs, enhanced delivery coordination, and increased flexibility in inventory planning. These improvements contribute to enhanced operational efficiency and increased competitiveness for automotive service providers. It is recommended that future research concentrate on extending the model to incorporate further real-world factors, including lead times, demand variability, and the capacity to return excess components to suppliers. Moreover, the examination and comparison of disparate inventory management strategies under varying conditions has the potential to yield valuable insights for the optimisation of service operations. In conclusion, the proposed mathematical framework provides an effective tool for the optimisation of inventory procurement and storage processes in automotive service providers. The integration of the precision afforded by linear optimisation with the adaptability of heuristic methodologies signifies the model's practical relevance within contemporary business contexts, wherein enhancing resource efficiency is a pivotal factor in achieving success.