Methodology aids forecasting with limited amounts of data
Industrial Engineering
The value of simple models in new product forecasting and customer-base analysis: Research Articles
Applied Stochastic Models in Business and Industry - Bridging the Gap between Academic Research in Marketing and Practitioners' Concerns
Design science in information systems research
MIS Quarterly
An intelligent fast sales forecasting model for fashion products
Expert Systems with Applications: An International Journal
Production risk management system with demand probability distribution
Advanced Engineering Informatics
Forecasting model selection through out-of-sample rolling horizon weighted errors
Expert Systems with Applications: An International Journal
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
Supporting product design by anticipating the success chances of new value profiles
Computers in Industry
A multivariate intelligent decision-making model for retail sales forecasting
Decision Support Systems
| Hi-index | 12.06 |
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.