Demand forecasting of short life cycle products using data mining techniques

Afifi, Ashraf

Abstract

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.

Citation

Afifi, A. (2020). Demand forecasting of short life cycle products using data mining techniques. In Artificial Intelligence Applications and Innovations. AIAI 2020

• Conference Name: 16th Int. Conf. on Artificial Intelligence Applications and Innovations (AIAI-2020)
• Conference Location: Greece
• Start Date: Jun 5, 2020
• End Date: Jun 7, 2020
• Acceptance Date: Apr 11, 2020
• Online Publication Date: May 29, 2020
• Publication Date: May 29, 2020
• Deposit Date: Apr 15, 2020
• Publicly Available Date: Apr 24, 2020
• Publisher: Springer
• Series Title: IFIP Advances in Information and Communication Technology
• Series Number: 583
• Series ISSN: 1868-4238
• Book Title: Artificial Intelligence Applications and Innovations. AIAI 2020
• ISBN: 9783030491604
• Keywords: Demand forecasting; Short life cycle products; Data mining; Clus- tering; Rule induction
• Public URL: https://uwe-repository.worktribe.com/output/5872770