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首页RFID技术驱动的服装销售预测方法验证
RFID技术驱动的服装销售预测方法验证
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更新于2024-09-09
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本文是一篇关于服装销售预测的外文研究论文,主要探讨了如何有效地利用无线射频识别(RFID)数据来提升零售业的预测准确性。作者Shigeaki Sakurai、Masanori Sanbe和Katsutoshi Watanabe来自东芝公司的不同部门,他们在文中提出了一种新颖的方法,通过数据挖掘技术从服装店收集的RFID数据中构建预测模型。 该方法的核心是将RFID数据与销售行为紧密关联起来,如顾客的试穿次数(fitting)、取货(pickup)数量,以及进店的客户总数等。这些数据被用于训练支持向量机(SVM)等机器学习模型,以建立未来销售量与顾客活动之间的关系。通过这种模型,能够实时或预估未来的销售量,从而帮助零售商做出更精准的库存管理和营销决策。 论文强调了这种方法在实际应用中的效率验证,作者们通过对某家服装公司两家分店的RFID数据进行数值实验,展示了其预测效果的可靠性。这种方法的优势在于能够自动化处理大量数据,并在快速变化的零售环境中提供及时的销售趋势预测,这对于提高服装行业的运营效率和竞争力具有重要意义。 关键词包括:无线射频识别(RFID)、服装店、支持向量机(SVM)、销售量预测。这篇论文不仅提供了理论基础,也为业界实践者提供了一个可操作的工具,以便于他们利用现代信息技术优化服装销售策略。对于那些关注零售数据分析和智能化运营的读者来说,这是一篇值得深入研究的有价值参考资料。
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Application of the RFID Data Mining to an Apparel Field
Shigeaki Sakurai
Corporate Research & Development Center
Toshiba Corporation
Kawasaki, Japan
Email: shigeaki.sakurai@toshiba.co.jp
Masanori Sanbe
Distribution System Laboratory
Toshiba TEC Corporation
Izunokuni, Japan
Email: Masanori Sanbe@toshibatec.co.jp
Katsutoshi Watanabe
Tokyo Main Branch
Toshiba TEC Corporation
Tokyo, Japan
Email: Katsutoshi
Watanabe@toshibatec.co.jp
Abstract—This paper proposes a new method that efficiently
uses the RFID data collected from apparel shops. This method
learns prediction models from the data by using data mining
techniques. The models represent relationships between the
number of sales in the next term and the actions of customers,
such as the number of pick-up, the number of fitting, the
number of customers, and so on. It is possible to predict sales
volume by applying the present RFID data to the models. This
paper verifies the efficiency of the method through numerical
experiments based on the RFID data collected from two
branches of an apparel company.
Keywords-RFID; Apparel Shop; SVM; Predication of Sales
Volume;
I. INTRODUCTION
Owing to the progress of RFID (Radio Frequency IDen-
tification) readers and RFID tags, it is possible to easily
bury them in various objects and places. Large amount of
RFID data will be collected from sensor networks composed
of them in near future. We anticipate that the data can
be used to help our decision making in various situations.
We have such high needs that speedily access the data
and appropriately analyze it. Various methods activating the
RFID data have been studied aggressively.
For example, Akahoshi et al. [1] propose a method that ac-
cesses the data collected from sensor networks. The method
uses the framework of traditional relational database. The
sensor networks are composed of apparatuses dynamically
set in the networks. Dass and Mahanti [5] propose a method
that discovers frequent patterns from data collected in real
time. The method combines two strategies: the strategy of
wide priority and the one of depth priority. Ihler et al.
[7] propose a method that generates an explanatory model
of abnormal events. The model is based on the Pearson
distribution. Also, they propose a method that statistically
infers the parameters of the model from sequential data.
Kuramitsu [8] proposes a method that discovers repeatedly
observed patterns collected from various sensors. Also, he
proposes a method that extracts abnormal patterns. Teng
and Lin [12] propose a method that efficiently discovers
patterns by expressing the data attached the time with the
tree structure. Here, the patterns are related to the time
interval between actions and events representing the actions.
In addition, Sakurai et al. [10] [11] propose methods that
efficiently discover patterns from sequential data, even if the
methods are not always limited in the analysis of the data
collected from sensor networks. The pattern is a sequential
row of events and reflects the interests of users. Here, the
interests are described based on time constraints and events
related to the interests.
Next, we introduce some application examples of the
RFID readers and the RFID tags in real world environments.
Wal-Mart [9], Metro group [13], and Marks & Spencer [4]
introduce them to efficiently manage items processed in the
supply chain. Here, Wal-Mart is the biggest supermarket
company in the world and the headquarter is located in the
USA. Metro group is one of famous companies in the retail
field and mainly performs the business in the Europe. Marks
& Spencer is the biggest retailer in the UK. In addition, some
experiments in the publisher field and the apparel field are
performed in Japan. Mitsukoshi, one of famous department
stores in Japan, introduces the RFID readers and the RFID
tags at the apparel shop dealing with imported casual items.
It operates them in daily business. The application examples
show that the RFID readers and the RFID tags can decrease
the management cost of the stock.
The examples show the limited success brought by the
RFID readers and the RFID tags. We believe that the RFID
data collected by the RFID readers and the RFID tags has
various possibilities. Also, we believe that the detail analysis
of the RFID data can built various services improving our
life. On the other hand, it is not possible to establish the only
one analysis method dealing with various services. This is
because the analysis method depends on target fields and
each field has respective needs. It is necessary to focus on a
2010 13th International Conference on Network-Based Information Systems
978-0-7695-4167-9/10 $26.00 © 2010 IEEE
DOI 10.1109/NBiS.2010.17
28
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