Implementasi Metode Arima Data Warehouse Untuk Prediksi Permintaan Suku Cadang - Hendrik Hidayatullah; Fitri Sukaesih; Yanuar Arif Hizbulloh; Tb Ai Munandar (Jejaring Penelitian dan Pengabdian Masyarakat (JPPM), 2023)
Food production company is a company that focuses on the production of instant noodles, and machine reliability is crucial in the production process. To maintain machine reliability, regular maintenance is necessary, and the availability of spare parts is also a key factor in reliability planning. Therefore, spare part management is crucial in the company as it can affect the spare part control system and vice versa. Poor spare part management planning can result in fluctuations in demand for goods. Uncertainty forces the company to determine the minimum and maximum spare parts inventory to be managed. Lack of standards during spare part deliveries leads to excess spare parts. Excess spare parts cause inventory to accumulate in the workshop. However, if there is a shortage of spare parts, it makes maintenance difficult in the production department. Based on the data used, this research is classified as quantitative research that produces numbers. The aim of this research is to predict the demand for spare parts for maintenance processes using the ARIMA (Autoregressive Integrated Moving Average) method. This research is carried out because the proper and effective use of spare parts is essential in maintaining machine and industrial equipment reliability. The ARIMA method is used to identify patterns in spare part demand data and make accurate predictions for future demand. Spare part demand data for a certain period of time is collected and analyzed using statistical software. The results of the research show that the ARIMA method can be used to predict spare part demand with a high level of accuracy. With this prediction, the company can better plan to meet demand and optimize spare part inventory management. The results of this research can provide benefits to the company in improving their operational efficiency and effectiveness while reducing costs related to spare part inventory shortages.