Spare parts are one of the keys to the success of a power plant company to achieve a world-class company. The method of forecasting required for the control and cost of the gas turbine type power plant spare parts. The purpose of this study is to model demand using the ARIMA method, linear trend, and single exponential smoothing for short-term forecasting of gas turbine power plant spare parts. Forecasting the demand for spare parts in the power generation industry is carried out using real and accurate data on the level of demand for spare parts for 3 years. The results of the analysis show that the ARIMA model provides the most accurate level of forecasting with the right MAPE value and helps provide an overview of the optimal spare parts procurement process in the short term.
Comparison of ARIMA, Linear Trend and Single Exponential Smoothing for short-term forecasting of power plant spare parts inventory
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