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Demand forecasting of short life cycle products using data mining techniques

Afifi, Ashraf

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Authors

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Dr Ashraf Afifi Ashraf.Afifi@uwe.ac.uk
Senior Lecturer in Engineering Management



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)
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

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Copyright Statement
This is the accepted verison of a conference paper published online at https://doi.org/10.1007/978-3-030-49161-1_14







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