Each store will make alterations to accommodate the mismatch between the planogram and the actual space, which means planned facings may not be implemented, leading to stock-outs while inventory sits in the back room rather than on the shelf. When a planogram is based on an average, implementation compliance is poor.
Thus, using averages to plan means that you are never truly meeting the needs of a particular store and its customer base because every planogram is sub-optimal, leading to missed sales through assortment gaps and stock-outs that can have much wider ramifications. stores with similar demand patterns) can vary dramatically in sales density. Why Do Averages-Based Planograms Fail?įrom large chains to small, retailers know that stores of the same size and in the same cluster (i.e. In this whitepaper, we’ll dive further into automated store-specific planogram optimization and the benefits this type of localized planning strategy can facilitate in all areas of a retail business.
These localized planograms have multiple benefits, including decreasing out-of-stocks and overstocks, reducing inventory costs, and improving operational efficiency in stores-replenishment, for example. With automation, central planning teams can create a high volume of accurate planograms that are adapted to store needs. This is true, and it is the reason that automation is the other necessary component in this process. “The creation of hundreds or even thousands of store-specific planograms is an impossible task!”
The answer is store-specific planograms, which can enable variations in assortment, inventory, and space allocation to meet each store’s needs.
When managing a category across hundreds or thousands of store locations and a diverse set of customers, how can you get the benefits of central planning while also adapting to store-specific needs-without defaulting to planning based on averages?
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