Prediksi Penjualan Kendaraan Niaga Berdasarkan Kinerja Purnajual dan Pertumbuhan Pasar - Novika Ginanto; Setia Wirawan (LPPM, 2021)
Indonesia is one of the largest automotive market in South East Asia with highly demand of passenger and commercial vehicle. Commercial vehicle is used to distribute product to customers, then commercial vehicle strongly related with business growth. Gaikindo said that automotive business growth went down as 10.6%, it would effect to automotive company performance, especially vehicle stock ratio. Vehicle stock ratio can affect to financial and resources planning. Therefore, the forecasting was to be important to predict the market demand in future. Basically, commercial vehicle would be used in along day due to business value, therefore aftersales services was critical point. In this case, sales forecasting of commercial vehicle (dependent variable) was approached by trend of aftersales performance and market growth (independent variable). Aftersales performance consist of aftersales revenue and unit served volume, then market growth using SAMSAT data. Prediction method used multiple linear regression due to forecasting capability with many variables. And the result using SPSS application was confirmed that independent variable affect to commercial vehicle sales volume and not multicollinearity. The result error of MAD was 3.80. So that, sales forecasting of commercial vehicle can be predicted based on aftersales performance and market growth using multiple linear regression. Indonesia is one of the largest automotive market in South East Asia with highly demand of passenger and commercial vehicle. Commercial vehicle is used to distribute product to customers, then commercial vehicle strongly related with business growth. Gaikindo said that automotive business growth went down as 10.6%, it would effect to automotive company performance, especially vehicle stock ratio. Vehicle stock ratio can affect to financial and resources planning. Therefore, the forecasting was to be important to predict the market demand in future. Basically, commercial vehicle would be used in along day due to business value, therefore aftersales services was critical point. In this case, sales forecasting of commercial vehicle (dependent variable) was approached by trend of aftersales performance and market growth (independent variable). Aftersales performance consist of aftersales revenue and unit served volume, then market growth using SAMSAT data. Prediction method used multiple linear regression due to forecasting capability with many variables. And the result using SPSS application was confirmed that independent variable affect to commercial vehicle sales volume and not multicollinearity. The result error of MAD was 3.80. So that, sales forecasting of commercial vehicle can be predicted based on aftersales performance and market growth using multiple linear regression.