RFID based Data Mining for E-logistics

Yi Wang, Quan Yu, Kesheng Wang

2013

Abstract

Radio Frequency Identification (RFID) is a useful ICT technology for E-logistics Enterprises. One of the standards used for RFID is Electronic Product Code Information Services (EPCIS). However, it is non-trivial to get effective knowledge from massive data to improve the existed production or logistic system comparing with convenient data collection. In this paper, we develop an intelligent platform which combines RFID for data acquisition, Data Mining for knowledge discovery and enterprise applications in the field of E-logistics. Especially association rule is applied to mine the associations between the distribution nodes and product quality within a product distribution logistic network on the basis of RFID datasets. The analysis result is the same as in the problem hypothesis, which concludes that it will be applicable for such kind of product distribution network analysis.

References

  1. Agrawal, R.N. Sengupta, K. Shanker, 2009. Impact of information sharing and lead time on bullwhip effect and on-hand inventory, European Journal of Operational Research, 192, pp. 576-593
  2. Abad, E., Palacio, F., Nuin, M., Zárate, A. González de, Juarros, A., Gómez, J.M and Marco, S., 2009. RFID smart tag for traceability and cold chain monitoring of foods: demonstration in an intercontinental fresh fish logistic chain, Journal of Food Engineering, 93 (4) , pp. 394-399.
  3. Amador, C., Emond, J.P. and Nunes M.C.N., 2009. Application of RFID technologies in the temperature mapping of the pineapple supply chain, Sensing and Instrumentation for Food Quality and Safety, 2009 (3), pp. 26-33.
  4. Baragoin, C., Andersen, C.M., Bent, G., Lee, J. and Schommer, C., 2001. Mining your own business in telecoms using DBM intelligent miner for data, IBM Redbooks, Corinne Baragoin and International Business Machine Corporation.
  5. Boztug, Y. and Silberhorn, N., 2006. Modellierungsansätze in der Warenkorbanalyse im Überblick, Journal für Betriebswirtschaft, Vol. 56, No. 2, pp.105-128.
  6. Boztug, Y. and Reutterer, T., 2008. A Combined Approach for Segment-Specific Analysis of Market Basket Data. European Journal of Operational Research, Vol. 187, pp. 294-312.
  7. Buechter, O. and Wirth, R., 1998. Discovery of association rules over ordinal data: A new and faster algorithm and its application to basket analysis. Research and Development in Knowledge Discovery and Data Mining, pp. 36-47.
  8. Cabanes, G., Bennani, Y., Chartagnat, C. and Fresneau, D., 2008. Topographic connectionist unsupervised learning for RFID behavior data mining, Secondary Topographic connectionist unsupervised learning for RFID behavior data mining, pp. 63-72.
  9. Dutta, A., Lee, H., Whang, S., 2007. RFID and operations management: technology, value, and incentives, Production and Operations Management, 16 (5), pp. 646-655.
  10. Dickinson, R., Harris, F. and Sircar, S., 1992. Merchandise compatibility: An exploratory study of its measurement and effect on department store performance. International Review of Retail, Distribution and Consumer Research, 2 (4) (1992), pp. 351-379
  11. Emond, J. P. and Nicometo, M., 2006. Shelf-life prediction and FEFO inventory management with RFID. In: Cool Chain Association Workshop. Temperature Measurements-When, Where and How? Knivsta, Sweden.
  12. Elisabeth, I., Zsolt, K., Péter, E. and László, M., 2006. The RFID Technology and Its Current Applications. Proceeding of The Modern Information Technology in the Innovation Process of the Industrial EnterpriseMITIP, pp.29-36.
  13. El-Sobky, H. and AbdelAzeim, M., 2011. A novel model for capturing and analyzing customers' preferences for ceramic tiles, Secondary A novel model for capturing and analyzing customers' preferences for ceramic tiles, pp. 460-465.
  14. EPCglobal IncTM, 2007. EPC Information Services (EPCIS) Version 1.0.1 Specification.
  15. Han, J., Kamber, M. and Pei, J., 2012. Data mining: concepts and techniques, Amsterdam: Elsevier.
  16. Hilderman, R., Hamilton, H. and B. Barber, 1999. Ranking the interestingness of summaries from data mining systems. In Proc. of the 12th International Florida Artificial Intelligence Research Symposium (FLAIRS'99), pp.100-106, Orlando, FL, May 1999.
  17. Ho, G. T. S., Choy, K. L. and Poon, T. C., 2010. Providing decision support functionality in warehouse management using the RFID-based fuzzy association rule mining approach, Secondary Providing decision support functionality in warehouse management using the RFID-based fuzzy association rule mining approach, pp. 1-7.
  18. Hoch, S. J. and Kim, B. D., Montgomery, A. L. and Rossi, P. E. 1995. Determinants of Store-Level Price Elasticity. Journal of Marketing Research, Vol. 32, No. 1 (Feb., 1995), pp. 17-29.
  19. Julander, C., 1992. Basket Analysis: A New Way of Analysing Scanner Data. International Journal of Retail & Distribution Management 20 (7), pp. 10-18.
  20. Koutsoumanis, K., Taoukis, P. S. and Nychas, G. J. E., 2005. Development of a safety monitoring and assurance system for chilled food products, International Journal of Food microbiology, 100 (1-3), pp. 253-260.
  21. Kärkkäinen, M., 2003. Increasing efficiency in the supply chain for short shelf life goods using RFID tagging International Journal of Retail and Distribution Management, 31 (10) pp. 526-536.
  22. Laniel, M., Emond, J. P., Altunbas, A. E., 2008. RFID behavior study in enclosed trailer/container for real time temperature tracking. In: Food Processing Automation Conference. Providence, Rhode Island, USA.
  23. Lewis, S., 2004. A Basic Introduction to RFID technology and Its Use in the Supply Chain. Laran RFID, White Paper.
  24. Masciari, E., 2011. Trajectory Outlier Detection Using an Analytical Approach, Secondary Trajectory Outlier Detection Using an Analytical Approach, pp. 377-384.
  25. Mild, A., and Reutterer, T., 2003. An improved collaborative filtering approach for predicting crosscategory purchase based on binary market basket data. Journal of Retailing and Consumer Services, 10, pp. 123-133.
  26. Patterson K. A, Grimm, C. M. and Corsi T. M. (2003). Adopting new technologies for supply chain management Transportation Research Part E: Logistics and Transportation Review, 39 (2), pp. 95- 121
  27. Payaro, A., 2004. The role of ict in reverse logistics: A hypothesis of rfid implementation to manage the recovery process. In Proceedings of the 2004 eChallenges conference, Vienna, Austria, 27-29th October.
  28. Saygin, C., Sarangapani, J., Grasman, S. E., 2007. A Systems Approach to Viable RFID Implementation in the Supply Chain. Springer Series in Advanced Manufacturing.
  29. Wang, K., 2007. Applying data mining to manufacturing: the nature and implications, Journal of Intelligent Manufacturing, Vol. 18, No. 4, pp.487-495.
  30. Wang, K, and Wang, Y., 2012. Data Mining for ZeroDefect Manufacturing, Tapir Academic Press, 2012.
  31. Wasserman, E., 2007. Airbus Grand Plans for RFID. RFID Journal, (<http://www.rfidjournal.com/ article/articleview/3661/1/80/>. Last viewed 2013,01,25)
  32. Zaharudin, A. A., Wong, C. Y., Agarwal, V., McFarlane, D., Koh, R., Kang, Y. Y., 2006. The intelligent product driven supply chain, Tech. Rep. 05, AUTO-ID LABS.
Download


Paper Citation


in Harvard Style

Wang Y., Yu Q. and Wang K. (2013). RFID based Data Mining for E-logistics . In Proceedings of the 4th International Conference on Data Communication Networking, 10th International Conference on e-Business and 4th International Conference on Optical Communication Systems - Volume 1: ICE-B, (ICETE 2013) ISBN 978-989-8565-72-3, pages 371-378. DOI: 10.5220/0004508303710378


in Bibtex Style

@conference{ice-b13,
author={Yi Wang and Quan Yu and Kesheng Wang},
title={RFID based Data Mining for E-logistics},
booktitle={Proceedings of the 4th International Conference on Data Communication Networking, 10th International Conference on e-Business and 4th International Conference on Optical Communication Systems - Volume 1: ICE-B, (ICETE 2013)},
year={2013},
pages={371-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004508303710378},
isbn={978-989-8565-72-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Data Communication Networking, 10th International Conference on e-Business and 4th International Conference on Optical Communication Systems - Volume 1: ICE-B, (ICETE 2013)
TI - RFID based Data Mining for E-logistics
SN - 978-989-8565-72-3
AU - Wang Y.
AU - Yu Q.
AU - Wang K.
PY - 2013
SP - 371
EP - 378
DO - 10.5220/0004508303710378