Still Don't Believe In RFID?
I've said it before. I'll say it again. Radio frequency identification (RFID) technology is going to slam the practice of business intelligence in the supply chain.I've said it before. I'll say it again. Radio frequency identification (RFID) technology is going to slam the practice of business intelligence in the supply chain.Here's the reason: data. Lots of it. Analyzing supply chain performance is already hard enough, largely because it's difficult to design metrics. It's possible that RFID will actually make it easier to generate KPIs. It will certainly make them measurable. The problem is that RFID will generate a full-on deluge of data in total -- a flood that BI pros will find themselves desperately wading through for meaningful information.
All is not lost, however. This RFID battle can be won on the BI side -- indeed, it will have to be. Data left unanalyzed is, by definition, useless data. To that end, there are vendors (click here and here for two examples) already creating tools that could someday help funnel all that RFID data through the analytical wringer. These software packages are in the very early going, at least as seen from the perspective of what will be needed to make RFID data truly measurable in the end. But they're the start of what could become a data goldmine.
Remember people who used to say that Wal-Mart's championing of RFID could never fly because nobody on the product supply side could find a way to make radio frequency tags economically feasible? Well now there are even vendors making products specifically designed to meet those very Wal-Mart suppliers' needs.
I'm not saying any of these software products are necessarily going to work as advertised, of course. What percentage of IT tools ever do? But, again, it's a start.
In September 2004, we polled readers to see how many of them thought all this talk about RFID technology was just so much baloney. A combined fifth of respondents said RFID was either "mostly" or "totally" a bunch of hype.
Those people were wrong.
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