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Finding the RFID Signal in the Noise
Use insights based on Nate Silver's The Signal and the Noise to make better use of RFID data analytics to improve processes.
Jan 10, 2018—
Realizing the full potential of RFID often requires going beyond using it simply as a means of fast data collection in otherwise unchanged processes (a "paving the cowpaths" approach). Higher return on investment is possible if RFID is used to facilitate enhanced or entirely new processes (see RFID Research Supports Real-World Experimentation).
In some cases, RFID directly integrates into how an activity is performed, but it's also possible to use data analytics with RFID to indirectly make operations and marketing improvements. Using insights gleaned from the data, predictions can be made about anything from customer and employee behavior to supply chain events—which, in turn, can lead to changed processes (see The Emerging Marketplace for RFID Data Analytics, or Finding a Needle in a Haystack and A Guide to RFID Analytics Software for Retail and CPG Companies).
As RFID infrastructure develops, information sharing across supply chains becomes more common, and data analytics software and data aggregators become more capable, companies will have many more variables and data points that can be incorporated into their analyses and subsequent predictions. While that sounds like a good thing, Silver observes that when people have more information, they are prone to cherry-pick data that fits their biases and desired outcomes. Arguments that sound convincing can be made for any side of most issues, especially to those predisposed to believing a particular point of view. This is not always done consciously, so organizations and analysts need to work hard to prevent and detect it.
In his article Free the People, RFID Journal editor Mark Roberti suggests letting workers who can benefit from RFID play significant roles in driving how it is used. This is compatible with lean principles that suggest that frontline workers often have the best vantage point from which to suggest operational improvements (see Lean and Six Sigma Create Valuable Synergies for RFID Adopters).
The lowest-level workers may not have the perspective to see insights from RFID data spanning outside their immediate purview (e.g., across departments and supply chains), but the point is that data collection and analytics should be organically driven from those who are in a position to know. If data analytics can help find needles in haystacks, but there are a multitude of haystacks in the barn of big data, companies should take a bottom-up approach to identify which haystacks are most likely to have the needles.
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