Demand Forecasting of Short Life Cycle Products Using Data Mining Techniques

被引:4
作者
Afifi, Ashraf A. [1 ,2 ]
机构
[1] Univ West England, Fac Environm & Technol, Dept Engn Design & Math, Bristol, Avon, England
[2] Zagazig Univ, Fac Engn, Ind Engn Dept, Zagazig, Egypt
来源
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2020, PT I | 2020年 / 583卷
关键词
Demand forecasting; Short life cycle products; Data mining; Clustering; Rule induction; ALGORITHM; SYSTEM;
D O I
10.1007/978-3-030-49161-1_14
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Products with short life cycles are becoming increasingly common in many industries due to higher levels of competition, shorter product development time and increased product diversity. Accurate demand forecasting of such products is crucial as it plays an important role in driving efficient business operations and achieving a sustainable competitive advantage. Traditional forecasting methods are inappropriate for this type of products due to the highly uncertain and volatile demand and the lack of historical sales data. It is therefore critical to develop different forecasting methods to analyse the demand trend of these products. This paper proposes a new data mining approach based on the incremental k-means clustering algorithm and the RULES-6 rule induction classifier for forecasting the demand of short life cycle products. The performance of the proposed approach is evaluated using real data from one of the leading Egyptian companies in IT ecommerce and retail business, and results show that it has the capability to accurately forecast demand trends of new products with no historical sales data.
引用
收藏
页码:151 / 162
页数:12
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