Forecasting Intermittent Demand For MRO Spare Parts - Darmawan, Rachmat; Ichtiarto, Bonivasius Prasetya (Universitas Mercu Buana, 2020)
In the face of current global economic challenges, maintaining efficiency is the core of inventory management and order fulfillment is crucial for any heavy equipment industry looking to change the supply chain. Along with the complexity of increasing costs, accelerating inventory fulfillment, delivery order commitment fulfills demand. It can be seen that some of the conditions caused by the recycling activity have caused a gap between the demand and the sales made. That the forecast is still far from expected to meet real sales demand, if it continues to allow for unrealized sales and market losses. The phenomena to be known in this study include the expectation of accurate predictions about the availability of spare parts so that it meets customer needs. Utilize this type of quantitative research, based on historical demand data that reflects the nature of demand patterns by improving the accuracy of stock stocks and the level of service related to operating activities against meeting target expectations and the reality of results obtained. Real demand data based on demand turnover ratio in 2017-2018, in selecting the best forecast model of trend analysis is done with the result of setting exponential growth giving the smallest value of MAPE 12,789 MAD 11,333 MSD 271,595. Trend analysis results show that data plots do not fluctuate normally, so the assumption test is performed to calculate the number of requests (demand size) and the time between arrival of requests (inter-demand interval). Testing the assumption of demand size (zt) following the ARIMA model (0,1,1), it is found that stationary data from outputs are produced for ACF plots and PACF replacement data have been considered. As an example of the safety stock calculation results with a 95% service level such as depleted, the spin-on oil filter reached 4,248 pcs from the previous forecast of 4,08 pcs. In the future, it will not only be forecasting of existing secondary data, but will be upgrading from business model to final delivery as the industry becomes more competitive.