Royal Philips is a large scale producer of consumer electronics, personal appliances, lighting, and healthcare appliances. Demand data from 12 Business Units (BU) of Royal Philips was examined in the study; four business units from each of three divisions: DAP, PA, and CE. From the data supplied, different forecast techniques were evaluated to determine which procedure produces the highest quality forecasts. Three forecasting techniques were evaluated using the provided data. The three forecasting techniques evaluated are the exponential smoothing forecasting method, the exponential smoothing with a linear trend forecasting method, and the Winters forecasting method.
The Visual Basic for Applications (VBA) language was used to implement the functionality of the exponential smoothing, exponential smoothing with linear trend, and the winters forecasting methods forecasting models into Microsoft Excel for this study. Additionally, VBA was used to compute the Mean Absolute Error, which was used to compare each of the models. Overall, the exponential smoothing with a linear trend forecasting method is the best forecasting model for the examined business units. The exponential smoothing with a linear trend model should be used in most cases where the coefficient of variance of the demand data is small. The exponential smoothing model should be used in most cases where the coefficient of variance is of the demand data is large. The Winters method forecasting models had much higher variability in the resulting forecasts of the examined business units. This higher variability may have been due to the complexity in the estimation of the model parameters. Thus, the Winters method, while good in theory, isn’t necessarily the best choice for forecasting in practice with the examined business units and similar products.