The Use of Single Moving Average and Linear Regression in Spare Part Sales Forecasting at PT. CNC

Given the highly competitive nature of Indonesia's automotive sector, accurate sales forecasting has become a crucial business strategy. This research investigates the application of Single Moving Average and Linear Regression methods for forecasting spare part sales at PT. CNC, an automotive spare parts manufacturer in Indonesia. The study analyzes monthly sales data from January 2019 to December 2022, employing both Single Moving Average and Linear Regression forecasting methods. Model performance was evaluated using multiple accuracy metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with data normalized using min-max normalization. The analysis yielded error metrics of MSE = 0.043, RMSE = 0.208, MAE = 0.005, and MAPE = 4.36%, demonstrating the effectiveness of these forecasting methods for spare part sales prediction.