Classification Models for RFID-based Real-Time Detection of Process Events in the Supply Chain: An Empirical Study
Journal
ACM Transactions on Management Information Systems (TMIS)
ISSN
2158-656X
ISSN-Digital
2158-6578
Type
journal article
Date Issued
2015-01-01
Author(s)
Abstract (De)
RFID technology allows the collecting of fine-grained real-time information on physical processes in the supply chain that often cannot be monitored using conventional approaches. However, because of the phenomenon of false-positive reads, RFID data streams resemble noisy analog measurements rather than the desired recordings of activities within a business process. The present study investigates the use of data mining techniques for filtering and aggregating raw RFID data. We consider classifiers based on logistic regression, decision trees, and artificial neural networks using attributes derived from low-level reader data. In addition, we present a custom-made algorithm for generating decision rules using artificial attributes and an iterative training procedure. We evaluate the classifiers using a massive set of data on pallet movements collected under real-world conditions at one of the largest retailers worldwide. The results clearly indicate high classification performance of the classification models, with the rule-based classifier outperforming all others. Moreover, we show that utilizing the full spectrum of data generated by the reader hardware leads to superior performance compared with the approaches based on timestamp and antenna information proposed in prior research.
Language
German
HSG Classification
contribution to scientific community
Refereed
No
Publisher
Association for Computing Machinery
Publisher place
New York, NY
Volume
5
Number
4
Start page
1
End page
30
Pages
30
Subject(s)
Division(s)
Eprints ID
239213
File(s)![Thumbnail Image]()
Name
paper.pdf
Size
1.39 MB
Format
Adobe PDF
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