A while back we did a project for a large produce company (let’s call them Produce) digging into their RFID data. Not surprisingly, the team at Produce we worked with was based in a smallish, but growing town in Arkansas that is home to a certain Retailer. They had been struggling for a while (as were many in this situation) to show any value from all the $$$ they had spent on implementing RFID for this Retailer. They came to us with an Interesting Problem – can you help us figure out if the RFID reads can improve our in-stock percentage throughout the day?
Yes, they were concerned that they were missing the peak selling times during the day when consumers came looking for their produce. They had enough signals to optimize their supply chain on a daily basis, but they didn’t know if their sections were being stocked during the day at the right intervals. Just because their fresh produce was in the backroom, didn’t mean it was going to sell!
All that wonderful RFID data (about 12M reads for 6 months) added to the hourly POS data from the Retailer could give us a clue as to what was happening. Now dealing with RFID data is messy, to say the least. There is sometimes more noise than music, as I like to say. But we cleaned it up (threw out the reads that had a case of the Produce’s product going in and out of the stockroom 13000 times in one week), and compared the intra-day RFID movements from the stockroom to the floor against the hourly POS sales.
And indeed, most stocking of their product – based on the first read of an RFID tag moving to the stockroom – happened between 7 and 9am. And their sales peak – well it started around 11a and increased until 6 p or so. There was definitely a risk that around 3 or 4, the product on the shelf wasn’t the best or freshest the store had to offer.
There was also some evidence that the days that suffered the most from out-of-stocks (defined by a day when the stock fell below a goal) had the worst re-stocking patterns. Produce didn’t have access to intra-day inventory levels, but we calculated as estimate based on the end of day inventory and the RFID stocking movements. Looking at the peak selling times and putting OOO into quartile buckets, it became clear that the when re-stocking happens during the peak selling time, there are fewer OOOs.
Now, this may all sound really obvious. But here’s the kicker. Produce had held this opinion (hypothesis) for a long time and had argued with Retailer for months about the restocking practice. But Retailer refused to change their personnel practice…until they saw the data. That opened the door for a lot of productive conversations between Produce and Retailer.