Search Results for "shelfsnap"
Frito-Lay Drives Shelf-Level Demand
· CommentsI believe that one of the emerging trends that will differentiate leaders from followers in the consumer goods industries is that leaders will excel at integrating their demand management and replenishment processes with category management. Consequently, it benefits supply chain executives to better understand leading practices in category management.
Supply Chain Digest recently aired a videocast featuring Dave Boissevain, Director of Retail Strategy and Planning at Frito-Lay, which laid out at least one leading practice: the use of store-level planograms.
A planogram (POG) is a diagram of fixtures (e.g., shelves) and products that illustrates how and where retail products should be placed in a store. Leading retailers have moved to having category captains produce POGs that are communicated and explained to the retailer for all the goods in a category (like salty snacks in the case of Frito-Lay).
It is the category captain’s job to maximize the profitability of the retailer’s shelf. Think of this as the velocity at which store profits are created based on product margin, supply chain costs, and how goods flow off the shelf. This is sometimes called “return-on-shelf.”
In putting together a planogram, the category captain needs to use syndicated data and point of sale (POS) data to understand trends.
They also need to understand product affinities to better allocate shelf real estate to profitable stock keeping units (SKUs). Frito-Lay conducted a study where it went into certain stores and eliminated 10 percent of the salty snack SKUs. The company then eliminated 20%, 30%, and 40% of the SKUs in that category. It did this to better understand the incrementality of the item. If an item was removed, how would it affect the sales of other SKUs in the store? The interesting thing that Frito-Lay discovered is that for large format stores customers actually liked the layout better when the SKUs were reduced by 20 or 30 percent.
Historically, planograms were “one-size-fits-all” that applied to every store in a chain of a similar size. So, all stores with 20-ft aisles used one POG, all stores with 100-ft aisles used another, and so on.
Eight to ten years ago the state of the art improved. Category captains began to do demand-based clustering. So, if a chain had 100 stores, they might have 7 different demand clusters. Planograms would then be created based on the demographics of that cluster and the size of the aisle. Thus, a category captain could end up creating 49 different POGs if the chain had different size stores in all clusters. Because a category captain was more than likely to be a category captain at other (often larger) chains, the creation of these POGs was labor intensive.
Now, the next leap has been made: the ability to dynamically create store-specific POGs based on an optimization engine. This engine produces POGs in 3 seconds versus the 30 minutes it used to take. Consequently, not only can you do store-specific POGs, you can now do them more frequently, maybe every month instead of annually. Frito-Lay is using a new product from JDA Software (an ARC client) for this purpose.
When Frito-Lay moved from POGs developed for store clusters to store-specific POGs, it improved growth by 2 to 4 percentage points! What Frito-Lay found was that the assortment, based on the POG, might be fine Monday through Thursday, but a store-level POG really drove growth in peak selling times, Friday through Sunday. Thus, moving to store-level planograms creates more demand. This forward plan for increased demand needs to be integrated into the supply chain planning and replenishment processes.
To get retailers to agree to this, Dave said that they need to be educated on how the black box optimization engine works. Retailers, after all, ultimately make the final decision on assortment. Moving to monthly POGs also creates more work for retailers. Finally, better planning has limited value unless the plans are fully executed. Frito-Lay is experimenting with a solution from ShelfSnap to help improve execution.
However, Dave said that once retailers try this, they don’t go back!
Filling a Void in Shelf-level Collaboration
· CommentsI recently spoke with Mike Spindler, the CEO of ShelfSnap, about the company’s entry into the “shelf-level collaboration” arena.
(Quick aside: As I mentioned in a previous posting, I prefer the term “Shelf-level Collaboration Solutions” over Demand Signal Repository (DSR) products because I believe it’s both more accurate and evocative.)
ShelfSnap is in start-up mode, and although the company has conducted some interesting pilots for large consumer goods companies, it has not yet been selected as a long-term partner by these companies. Nevertheless, I think ShelfSnap offers an interesting solution that fills a void when it comes to shelf-level collaboration.
Right now, DSRs are populated mainly with POS, syndicated data, store-level inventory data, and shipments from the retail DC. One form of shelf-level collaboration occurs when a DSR merchandising application sends out an alert saying that an SKU at a particular store is out of stock at the shelf, or that it is selling at a much slower rate than expected. These alerts are based on inference. The application does not know that a store has not restocked the shelf; it makes that prediction based on math. The math can be very simple: this product is jumping off the shelves at other stores but it’s not moving at this store; we know that inventory was shipped to the store, so this must mean that stockers have not moved the product out of the backroom yet. The math gets a lot more complex when it comes to predicting things like inaccurate perpetual inventory levels.
Mike’s point is that these are inferences. If you tell a Walmart store manager that a shelf needs to be restocked in the hope that he will assign someone to do the job, you better be right. You don’t want to generate false positives and send labor to the shelf to fix a problem that does not exist. Even if only 10 percent of your alerts are false positives, you could become “the boy who cried wolf” too often. So, some consumer goods companies use the merchandising alerts to reroute their third party merchandisers to visit a particular store to make sure the alert is correct.
But Mike points out that this does not fully solve the problem. Mike began his career at Nielsen, where he used to ask a group of his managers to separately go to a store and check to see if the planogram was correct for a particular category. In this store, Product A should have three facings and be in the middle of the top shelf, Product B should have one facing and be on the far right hand side of the bottom shelf, and so on. Mike said the managers never came back with the same data.
The ShelfSnap solution is based on workers using a digital camera to take a picture of the store shelf, and then the solution’s patent-pending Image Recognition Technology analyzes color, package size, and label design to identify the correct product. These pictures are fed into the ShelfSnap Analytical Engine that then generates quantitative analytical results: What shelf was the product on? How many linear feet did the facing have? Was the product out of stock? This data can then be turned into prioritized merchandising exceptions. They also provide analysis of new product cut-ins, displays, and point-of-purchase display reporting.
ShelfSnap Analytics (Source: ShelfSnap; click to enlarge)
I basically see the ShelfSnap solution as providing a practical way for manufacturers and retailers to use planogram data for shelf-level collaboration, or for retailers themselves to have more effective task management at the store. I also see it as improving the alerts generated by existing DSR solutions. Say a DSR is predicting that the perpetual inventory level at a particular store is incorrect (the store says it has 12 of the item, but the DSR application thinks it only has 9). This math is based on lower sales than expected for that item at that store. But if the product is supposed to have three facings on the prime shelf but it only has one facing on the bottom, the product location could explain the variance in sales rather than an incorrect inventory level.
In a recent posting, Adrian Gonzalez quoted Jim Stengel, the former global marketing officer at Procter & Gamble, on the greatest challenges facing marketers in the consumer goods industry. To paraphrase slightly, Jim asked how consumer goods manufacturers could add “digital” to “their branding model?” How can they “work with retailers to have maximum impact on value creation?” This is an application that contributes in both of those areas.
