Value-driven uncertainty-aware data processing for an RFID-enabled mixed-model assembly line

Abstract

The use of radiofrequency identification (RFID) technology generates a high-volume, simple and unreliable data stream due to the technology’s inherent unreliability. Such a data stream cannot be directly used for applications, as doing so would lead to inaccurate and unreliable result. In this paper, we propose a value-driven uncertainty-aware data-processing method that considers RFID detection reliability, timeliness and the throughput of an assembly line to characterize the potential benefits of RFID implementation in a mixed-model assembly system. The proposed method includes three components: a complex event processing system, a Bayesian inference model and a value-driven optimization model. We then demonstrate the use of the method by analyzing an automotive mixed-model assembly line. Some insights into management techniques are offered based on a comparison with several existing barcode implementations. The results of the method application also demonstrate that data uncertainty cannot be ignored in RFID cost–benefit analysis. Besides decreasing the cost of RFID technology, improving reading reliability and designing a sophisticated network can also offer significant benefits.