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RFID Tag Performance: Linking the Laboratory to the Field through Unsupervised Learning

Author

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  • Scott C. Ellis
  • Shashank Rao
  • Dheeraj Raju
  • Thomas J. Goldsby

Abstract

Despite the promise that the technology offers, RFID adoption continues to lag behind initial projections. Industry studies intimate that, in part, this lag is attributable to the RFID performance gap – the difference between desired RFID tag read‐rates and those experienced in the field. To explore this phenomena, we employ a mixed research methodology involving intensive case studies of three major retailers as well as large‐scale laboratory and field data collection and analysis. Our case studies indicate that major retailers are incurring several operational inefficiencies, including slowed processing times, manual counts to verify inventory levels, and use of “safety factors,” to overcome RFID tag read failures. The practical significance of these findings motivates our subsequent investigation of laboratory test criteria that, when passed, result in RFID tags that perform reliably in the field. To facilitate quantitative analyses, we apply unsupervised learning techniques – i.e., the sequential application of cluster analysis and association rules – to a dataset of 45,416 observations that merge RFID tag laboratory test performance data with read‐rate performance data collected from retail supply chains. Our findings identify a pool of RFID tags in which over 99% of the tags have at least a 99% read‐rate. Thus, for academics, our study advances a novel unsupervised learning protocol that can be applied to “big data” to gain insights into meaningful supply chain issues, such as RFID tag performance. For practitioners, we establish laboratory test criteria that can be used to identify RFID tags that operate reliably in real‐world applications.

Suggested Citation

  • Scott C. Ellis & Shashank Rao & Dheeraj Raju & Thomas J. Goldsby, 2018. "RFID Tag Performance: Linking the Laboratory to the Field through Unsupervised Learning," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1834-1848, October.
  • Handle: RePEc:bla:popmgt:v:27:y:2018:i:10:p:1834-1848
    DOI: 10.1111/poms.12785
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    Cited by:

    1. Li-Hao Zhang & Shan-Shan Wang, 2022. "Strategic analysis of RFID adoption sequences in a supply chain with Cournot competition: effects of ordering-timing strategies," Annals of Operations Research, Springer, vol. 315(2), pages 2169-2208, August.
    2. Yang, Huixiao & Chen, Wenbo, 2020. "Game modes and investment cost locations in radio-frequency identification (RFID) adoption," European Journal of Operational Research, Elsevier, vol. 286(3), pages 883-896.
    3. Sunil Mithas & Zhi‐Long Chen & Terence J.V. Saldanha & Alysson De Oliveira Silveira, 2022. "How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4475-4487, December.
    4. Alireza Farnoush & Ashish Gupta & Hamidreza Ahady Dolarsara & David Paradice & Shashank Rao, 2022. "Going beyond intent to adopt Blockchain: an analytics approach to understand board member and financial health characteristics," Annals of Operations Research, Springer, vol. 308(1), pages 93-123, January.
    5. Robert P. Rooderkerk & Nicole DeHoratius & Andrés Musalem, 2022. "The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3727-3748, October.

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