Spare parts demand forecasting and inventory management using machine learning models: A comprehensive application

This thesis was conducted at Türkiye's first and largest integrated air conditioning factory, focusing on spare parts management. The study addresses key challenges in demand forecasting and inventory management of spare parts, with the goal of minimizing the risks of overstocking and stock shortages. A comprehensive framework from Bacchetti and Saccani (2012), merging spare parts classification, demand forecasting, inventory management, and performance evaluation, was applied. Aligning with the forecasting framework proposed by Boylan and Syntetos (2010), the study encompassed pre-processing, processing, and post-processing phases. In the preprocessing phase, demand was classified into categories such as Erratic, Intermittent, Lumpy, and Smooth, with Intermittent being the most prevalent. Demand forecasting methods, including Na¨ıve Forecast, Holt-Winters' Seasonal Method, Croston Modification (SBA), ARIMA, MLR, MNLR, SVR, ANN, Random Tree, REP Tree, Random Forest Regressor, XGBoost, and LightGBM, were evaluated for their suitability. In the post-processing phase, total inventory cost calculations and the optimal service level ratio were determined using the Newsvendor model. Additionally, a data-driven approach employing Sample Average Approximation was utilized for optimization inspiring from Huber et al. (2019). XGBoost outperformed all other models by achieving the minimum cost while meeting the target service level. The main contribution of this study lies in incorporating demand classification as a feature in forecasting models, emphasizing the balance between service levels and costs, and demonstrating its practical significance through real-world application in spare parts management.