Demand Forecasting of Short Life Cycle Products using Data Mining Techniques

EasyChair Preprint 986

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 short life cycle products. This paper proposes a new approach based on data mining techniques for forecasting the demand of short life cycle products. The performance of the proposed approach is evaluated using real retail data and results show that it has the capability to accurately forecast the demand.

Keyphrases: Clustering, Data Mining, Demand Forecasting, Rule in-duction, Short life cycle products

@booklet{EasyChair:986,
  author    = {Ashraf Afifi},
  title     = {Demand Forecasting of Short Life Cycle Products using Data Mining Techniques},
  howpublished = {EasyChair Preprint 986},
  year      = {EasyChair, 2019}}