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Introducing Total Retail Loss: Can It Offer a More Meaningful ROI for RFID?

New research explores what shrinkage and other known losses mean for radio frequency identification—and for measuring the technology's impact on the industry.
By Adrian Beck
Dec 04, 2016

Back in the early days of the development of radio frequency identification and the Internet of Things, when both developers and potential users were trying to formulate return-on-investment (ROI) cases, one of the recurring themes was that RFID could make a significant difference in the realm of shrinkage management. In particular, it was frequently thought that it would help to significantly limit the ability of thieves to take products out of stores without being identified, as well as curtail problems of returns fraud, as the status of the product (bought or not bought) would be clearly identifiable. Fast-forward to today, and it is rare to see any of the recent successful trials and rollouts of RFID making much of an impact upon shrinkage rates—some do, but few claim it to be the primary reason why they embarked on introducing the technology.

Given the apparent cost of shrinkage to retail businesses, with some estimates suggesting it to be a $123 billion global problem, why does it not appear to feature highly, if at all, in many of the RFID business cases made public to date?

It is likely that among the main reasons are the ongoing ambiguity concerning the actual definition of shrinkage and the significant difficulties in providing any reliable measures of its true causes. Recent research has shown that there is no clear industry standard regarding what should be included or excluded when the term "shrinkage" is used—it is a catch-all term, varying hugely from one retailer to the next. The reality is that most retailers' shrinkage numbers are primarily based upon a calculation of the difference between expected and actual inventory values, generated by relatively infrequent physical stock counts. What then typically happens is that this "unknown cause" loss number is subsequently apportioned to one of four buckets of loss: external theft, internal theft, inter-company fraud and administrative error or process failures, with the proportions based more upon personal prejudice and guess work than actual reality.

It is also the case that the location of this data-collection process is primarily in retail stores, with few companies offering much detail about the losses they incur in other parts of their business. This, in some circumstances, creates the problem of stores inheriting the losses that occur elsewhere. For instance, if a distribution center mistakenly under-delivers to a given shop, then this loss, if not realized, will eventually appear in that store's shrinkage number.

What this has meant is that using the measure of shrinkage to understand the impact of any store intervention is riven with difficulty: Did the supposed loss happen in the store at all? If it did, how do we know what the cause might have been? Was it stolen by a staff member or a customer? Was it simply damaged, had it gone out of date and not been recorded by employees when it was thrown away, or was it simply due to the inability of workers to count stock properly at audit time? The possible causes are many and varied, and unpicking how the use of RFID might impact upon this number is extremely difficult—its level of imprecision and lack of transparency seriously undermine its ability to be used as an effective evaluation measure.

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