Recent trade reports suggest that RFID implementation continues to lag lofty projections. A primary concern is that, despite the high cost of implementing RFID systems, realized read‐rates fall short of expectations. This results in the invisible inventory conundrum whereby tagged merchandise may still not be accurately represented in inventory records. Drawing from data science to address this issue, we ask: How can directed data mining models be used to identify laboratory test performance criteria for RFID tags that operate reliably across the idiosyncratic facilities (i.e., unique DCs, warehouses, and stores) that comprise apparel retailers’ supply chains? We investigate this question by advancing a methodology that integrates laboratory test performance data, field tests of RFID tags fixed to apparel items and scanned under normal operating conditions, and the application of five directed data mining models to the integrated data set of laboratory and field test results. Our analyses of 45,416 observations show that two directed data mining models may identify—with near‐100% accuracy—laboratory test criteria that discriminate tags having 99% or greater read‐rates in the field. Accordingly, our study validates a generalizable methodology for identifying technical performance standards for tags that operate reliably within apparel retailers’ supply chains.

中文翻译：

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RFID配备的供应链中的“无形库存难题”：一种评估标签性能的数据科学方法

最新的贸易报告表明，RFID的实施继续落后于高远的预测。一个主要的问题是，尽管实施RFID系统的成本很高，但实现的读取率仍未达到预期。这导致了看不见的库存难题，由此标记的商品可能仍无法在库存记录中准确显示。我们从数据科学的角度出发来解决这个问题，我们问：如何使用定向数据挖掘模型来确定RFID标签的实验室测试性能标准，这些RFID标签可以在构成服装的特殊设施（即独特的DC，仓库和商店）中可靠地运行零售商的供应链？我们通过提出一种方法来研究这个问题，该方法可以集成实验室测试性能数据，固定在服装物品上的RFID标签的现场测试并在正常操作条件下进行扫描，以及将五个定向数据挖掘模型应用于实验室和现场测试的集成数据集结果。我们对45,416个观测值的分析表明，两个直接的数据挖掘模型可以（接近100％的准确性）确定实验室测试标准，以区分在现场具有99％或更高读取率的标签。因此，我们的研究验证了一种通用方法，可用于确定在服装零售商的供应链中可靠运行的标签的技术性能标准。