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首页> 外文会议>IFIP WG 12.5 International workshops on artificial intelligence applications and innovations;Mining Humanistic Data Workshop;Workshop on 5G-Putting Intelligence to the Network Edge >Demand Forecasting of Short Life Cycle Products Using Data Mining Techniques
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Demand Forecasting of Short Life Cycle Products Using Data Mining Techniques

机译：使用数据挖掘技术的短生命周期产品需求预测

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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 A-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.
机译：由于竞争加剧，产品开发时间缩短和产品多样性增加，具有短生命周期的产品在许多行业中变得越来越普遍。准确预测此类产品的需求至关重要，因为它在推动有效的业务运营和实现可持续的竞争优势中发挥着重要作用。由于需求的高度不确定性和波动性以及缺乏历史销售数据，传统的预测方法不适用于此类产品。因此，开发不同的预测方法以分析这些产品的需求趋势至关重要。本文提出了一种基于增量A均值聚类算法和RULES-6规则归纳分类器的数据挖掘新方法，用于预测短生命周期产品的需求。使用来自IT电子商务和零售业务中领先的埃及公司之一的真实数据评估了该方法的性能，结果表明该方法可以准确地预测没有历史销售数据的新产品的需求趋势。

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