Designing a decision-support system for new product sales forecasting
This article proposes a new procedure, called the New Product Sales Forecasting Procedure (NPSFP), and a decision-support system, called the New Product Forecasting System (NPFS), for solving the new product sales forecasting problem. The NPSFP procedure standardizes the steps involved in sales forecasting, guiding the data acquisition and analysis, the choice of the forecasting model, the calculation of the actual forecasts, and the subjective manual adjustment of the forecasting results. The NPFS decision-support system includes four modules: one that guides data acquisition and analysis, one that contains forecasting model templates, one that helps the users choose the best model for the available data, and one that calculates and adjusts the actual forecasts. We constructed a simulation model and tested it to demonstrate the power of NPFS for solving the new product sales forecasting problem using scenario and computational analyses. For most of the 27 scenarios tested, NPFS performed better than the most commonly used method, the Moving Average. We also applied NPFS to solve three real-world sales forecasting problems for new tea, cosmetic, and soft drink products. As expected, NPFS performed better than the Moving Average method. In conclusion, using the NPFS decision-support system to solve new product sales forecasting problems can improve the accuracy of new product sales forecasts. This decision-support system is easier to use than the most widely used method and does not rely too much on human judgment.
new product sales forecasts new product forecast system demand management demand forecasting new product forecast procedure heuristic algorithm