A Solution for New Product Sales Forecasts
Date Issued
2007
Date
2007
Author(s)
Ieong, Ka Ieng Ao
DOI
en-US
Abstract
Developing new Product is not only a competitive strategy for a business to enlarge its market share but also a survival strategy required to respond quickly to the market. Inaccurate forecasts for new products may lead to a false business decision that results in a large inventory or a significant shortage, which then costs a corporation to lose its competitive ability in the market. Therefore, an accurate sales forecast for new products is very important for managers to deal with uncertainty and make the right decision.
Two issues need to be addressed in the problem of new product sales forecast: limited data and selection of forecasting model. In the past, new product sales forecast usually relies on the experience and the expertise of a manager to select the forecasting model with only limited data available. Instead of human judgments, this study proposes a New Product Forecast Procedure (NPFP) and a New Product Forecast Solution (NPFS) to solve the new product sales forecast problem. NPFP and NPFS together provide an auto-learning platform to select the best parameter for each of the six forecast methods and finally the best forecast technique using rule-based weighted MAPE standard. The future forecasts will then be made according to the selected method.
NPFS includes four modules: Data Handling, Forecast Model, Learning, and Forecast Calculation. A five-step Heuristic Data Handling Algorithm (HDHA) is developed to clean and generate data for the new product using classification and pretest data. The Forecasting Model module includes three classical and three modified heuristic forecast methods. Each method has its own applicable scope, so that the solution can be applied to different kinds of sales pattern. The Learning module chooses the best forecast method according to the Heuristic Rule-based Learning Algorithm (HRLA) that identifies applicable methods and considers the number of actual data points. Finally, Forecast Calculation Module computes forecasts for new product using the selected best method.
Among most of the twenty seven designed scenarios, the NPFS has better performance than the methods that widely adopted in practice, such as moving average. The NPFS is also applied to three real-world cases for products of tea, cosmetics, and soft drinks. In each case, several successful new products in different classifications are identified. As expected, the NPFS has better performance than commonly used methods. In conclusion, the NPFS can improve the accuracy of new product sales forecast and can be easier adopted that the commonly-used methods without relying too much on human judgments.
Subjects
需求管理
需求預測
新商品銷售預測
新商品銷售預測流程
新商品銷售預測解決方案
啓
發式演算法
Demand Management
Demand Forecasting
New Product Sales Forecasts
New Product Forecast Procedure
New Product Forecast Solution
Heuristic Algorithm
Type
other