Demand Forecasting Using Data Mining Aided Product Classification
Date Issued
2009
Date
2009
Author(s)
Huang, Sheng-Yu
Abstract
Product classification is the core of every information activity related to product management. Almost all companies classify their products according to some attributes for different management purposes such as sales, procurement, and inventory control. Within these business functions, demand management is the leading pulling force while demand forecasting is the most critical function of demand management. Previous studies have suggested many ways to improve the accuracy of prediction using traditional time-series analysis with trend, and one of notable techniques is aggregating sales records of individual product. The purpose of sales aggregation is to reduce the data variation, which can then result in a better sales forecast. Product classification can be used as the scheme for deciding which items should be combined into one product class.ost companies cluster or group their products based on qualitative features such as brand, color, package, etc, even though for different purposes. The sales trends might be distorted or become unremarkable if the products are carelessly clustered together. This study aims to cluster products by analyzing their quantitative characteristics, namely sales pattern, and make it more suitable for demand forecasting. This study defines the similarity of sales pattern among various products by adopting distance-based method in data mining, and furthermore develops a two-phase optimization model: starting with a given number of groups, minimizing the average distance within groups, then looking for maximization of average distance among separated groups through incrementing number of groups assigned.ecause of the non-linear nature of objective function, integer programming is a popular way to solve the problem. However, when the number of items to be classified increases, the size of feasible solution set grows exponentially as well and makes the problem insolvable due to the time and computing resource it requires. To conquer the difficulty, this study proposes a heuristic algorithm, called Data-Mining Aided Product Classification (DMAPC).MAPC first analyzes sales records using time-series analysis and transfers them into a number of indexes which can best describe their patterns. Then, DMAPC searches the optimal product grouping result using GA-based heuristic and the extracted indexes from first stage. A demand forecasting learning platform is used in the final stage. In order to show the effectiveness and efficiency, a prototype was constructed and tested to demonstrate the power of DMAPC using complexity and computational analysis.
Subjects
Data Mining
Demand Forecasting
Generic Algorithm
Product Classification
Supply Chain Management
Time Series Analysis
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