Searching for On Shelf Availability Solutions

Yesterday, I joined a class on On Shelf Availability run jointly by 8th and Walton, Crossmark, Rockfish and the University of Arkansas.  This was the pilot class and based on the feedback they solicited I'm sure it will be even better when they choose to run it again.

As a pilot, it was very well presented and covered a lot of ground.  I particularly liked some of the more introductory material:

  • Making it clear that an in-stock measure ("I believe I have product in the store") is not the same thing as on-shelf availability ("It's on the shelf where a customer can find it").  This is key because while we can easily get in-stock metrics they are in no way a substitute for on-shelf measurement.  On Shelf is much harder to measure well so most of us don't do it.  It can be done though and I'll post more on this later.
  • Highlighting the supply chain challenges of ensuring product is in the store at all
  • An excellent session on some of the issues with store operations and lack of process discipline that cause off-shelf problems

Frankly, it was a little lighter and less well organized on what to do about improving OSA, and that's a problem for most class attendees who, I think, wanted to come away with "here are the 3 things I need to do to fix OSA".

  1. We looked at SPARC (from Rockfish), the Walmart in-store mobile app that let's CPG reps (like Crossmark) query store data about specific products AND do something about the problems they find like requesting a shelf replenishment "pick" or printing replacement shelf tags.  
    This is a very useful app if you are an in-store rep and suspect an issue with a particular product, but not terribly useful if you are wondering which products or stores might have an issue.  (Bear in mind that many CPGs have millions of item-store combinations with Walmart and even relatively small suppliers can have tens of thousands - you can't check these one by one)
  2. We briefly reviewed an off-shelf detection solution from Atlas.  This tracks the daily point of sale data for all your products at all stores and spots periods of zero sales that are unusually long, indicating an off-shelf position.  This sounds simple enough but doing it accurately is an analytic challenge.  (More on this later).
    I know enough about this particular application to believe the math is sound and I'm hearing good things about it in the community.  (Full-discosure: I may well be working with them very soon).  
    We did not talk about how a good off-shelf detection system accurately generates alerts for just 1%-3% of all item-store combinations on any given day.  In contrast, physical audits generally find 8% are not on shelf.   Why the gap?  Well it takes some time for alert detection systems to spot an issue (very low-volume items take so long it's almost not worth checking them) and of course many off-shelf problems are short-lived and corrected by store operations long before it can be spotted by lack of sales.  The off-shelf detection system should be finding the medium to high velocity items that are not being routinely corrected by store operations, the ones that really matter to you, but it's not a complete solution.
  3. We had an introduction to Field Agent a fascinating application that crowd-sources the ability to check and record  store conditions.  You could use this to check whether your product is on -shelf: Field-Agent simply asks people in the right stores at the right time (via their mobile app) to check the shelf for your product and,if you wish, take a picture of it.
    This is an amazing idea for in-store data collection generally, but, for this application it only provides "eyes and ears" not a rep who can fix the problem.   Note also that  while Field-Agent could detect many more off-shelf conditions than a pos-based off-shelf detection system, it can only do it by checking every item in every store near continuously (which would be costly) and many of the additional problems found will be for lower volume products or temporary off-shelf conditions that are fixed by routine store operations.
  4. Finally, we looked at a tool from the University of Arkansas that (among other things) estimates the impact to on-shelf availability of changing case-pack quantity (the number of sellable units in each case).  I know this tool very well indeed having contributed heaviliy to it's development - right now, it's still under testing with a release planned in the early part of next year so the class did not get a very good look it.  That's a shame because it's the only tool in the set that attempts to associate On-Shelf Availability with a root-cause.  Not the only root-cause of course, just one we happen to know about at this point. 

Fixing OSA is a tough problem - perhaps that's why I'm so interested in it?  We don't have all the answers yet, but I do believe these are the right tools to be tacking the problem.  However, they need to work in tandem not in competition:

  • Off-Shelf detection systems, like the one from Atlas, scan the entirety of the system, all products in all stores every day to find the problems that are not being fixed by store operations and are worth fixing.
  • In-store reps (armed with SPARC for Walmart stores at least) help store associates fix these problems efficiently and provide feedback on what was wrong to further improve the Off-Shelf detection system.
  • Field Agent could be the basis of a superb OSA measurement system.  Measuring when shoppers are actually in store (evenings and weekends) rather than when reps are typically there (business hours) and using a well constructed sampling plan to avoid measuring everything.  Today, on-shelf availability is measured poorly if at all and that's going to make it very hard to improve.
  • Continuing research to identify additional root-causes of poor On-Shelf Availaibility so that we can work to reduce the size of the problem and fix fewer off-shelf events one by one.  What's preventing this now, I would argue, is the lack of a good measurement system.  Neither I (nor anyone else) can't build a predictive model on what drives good On-Shelf Availability with no reliable data for On-Shelf Availability :-)

This post is the third in a series on On-Shelf Availability.