A Decision-Support Model for Filtering RFID Read Data in Supply Chains

The introduction of radio-frequency identification (RFID) tags in supply chains engenders the need for incorporating and utilizing the additional generated data. It is generally assumed that these data, once generated, are complete and rife with necessary information for making decisions. The reality is, however, that these data are not error free. Common errors observed in these data include false positives and false negatives. Given that these data are among the set of primary inputs for decision-making purposes, the read-rate accuracy is of paramount importance for effectively managing supply chains incorporating such data. Although there are means by which the RFID tag read rate could be improved to a certain extent, the errors in read rate cannot be completely eliminated, and decision makers are left to deal with such data while managing the supply chain. We present and illustrate few algorithms that can be used to reduce false read rates. We consider models for filtering data that are already being gathered in RFID systems and utilize it to improve read-rate accuracy. We implement the proposed models and illustrate their performance.