Spare Parts Inventory Management : A Classification and A Forecast Model In Fertilizer Industry

Abstract

The agricultural sector is crucial for the growth of Indonesia’s economy. The fertilizer company, in particular, is an essential part of the agricultural sector, and needs to manage its spare parts inventory to prevent production failures and minimize the company’s plant maintenance expenses. This paper presents a fertilizer company’s classification and demand forecasting of spare parts. The classification is conducted using a multi-criteria ABC method with Exponential smoothing weighting, which analyzes the trade-offs between the criticality of spare parts and the total inventory value. On the other hand, demand forecasting is carried out using the Croston forecasting model, Syntetos-Boylan Approximation, and Single exponential smoothing. From the 2024 data analysis, the results of the classification of spare part items show significant changes in categories A, B, and C before and after applying the Multi-criteria ABC method. The results also show that the forecasting using the Single Exponential Smoothing model gives lower error than the other two models. The data used are 10 samples of spare part items with indicator A for the period 2016 to 2021.

Keywords: Spare parts inventory management; Croston model; multi-criteria ABC; Single Exponential Smoothing; Spare parts forecasting; Syntetos-Boylan Approximation