纺织学报 ›› 2014, Vol. 35 ›› Issue (5): 142-0.

• 管理与信息化 • 上一篇    下一篇

采用基因库构建的季节性服装需求预测

张细香1,2,马卫民2,刘建勋3   

    1. 嘉兴学院商学院
    2. 同济大学经济与管理学院
    3. 嘉兴学院图书馆
  • 收稿日期:2013-03-25 修回日期:2014-01-08 出版日期:2014-05-15 发布日期:2014-05-09
  • 基金资助:

    国家自然科学基金资助项目;教育部人文社会科学研究青年基金项目

Demand forecasting method for seasonal clothing based on construction gene database

  • Received:2013-03-25 Revised:2014-01-08 Online:2014-05-15 Published:2014-05-09

摘要: 季节性新款服装的历史销售数据少、生命周期短,利用数据序列的趋势信息进行预测方法不合适。鉴于服装企业的POS数据中包含许多特征信息,本文利用层次聚类与动态时间弯曲距离从POS数据中进行特征信息抽取,以构建服装销售基因库。并提出基于基因库进行季节性服装需求预测的方法,根据新款服装信息、销售信息计算其与基因库中基因的相似度,根据相似度查找相似基因,并利用相似基因的信息来预测新款服装的需求。该方法为服装企业如何利用POS数据进行定量预测以构建快速响应系统提供解决方案。最后,以某知名服装2011年POS数据抽取了基因库,并对2012年3月1日到2012年7月31日的新款SKU进行预测,结果表明该预测方法的有效性。

关键词: 需求预测, 特征库抽取, 相似度, 层次聚类, 动态时间弯曲距离

Abstract: New seasonal clothing has these properties: few history sale data and short life cycle, it’s not appropriate to forecast the demand of new seasonal clothing using traditional forecast method based on data sequence serial. However, there exists lots of characteristic information in apparel enterprise’s POS data. A method to abstract characteristic information from POS data was provided to build the sale gene database, which classified the apparel sale data by combining hierarchical clustering and dynamic time wrapping (DTW). And a forecasting method was put forward to calculate the demand of new seasonal clothing based on the constructed gene database, which computed the similarity between a new clothing and a gene of the characteristic database, searched the nearest gene, and compute the demand of the new seasonal clothing based on the information from the nearest gene. The proposed method provides a solution for apparel companies to forecast its demand quantificationally based on its POS data, which can be used to build a quick response system. Experiment was carried out, which abstracted the gene database based on 2011 POS data of a famous apparel enterprise, and the new SKUs sold between 2012-03-01 and 2012-07-31 were used to forecast its demand based on the constructed gene database. And the result proved the effectiveness of the proposed forecasting method.

Key words: demand forecast, gene database, similarity, hierarchical clustering, dynamic time wrapping

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