Deliver the efficiency of top-down planning while respecting individual store differences
Retail covers a broad range of products, go-to-market methods and consumer experiences. When it comes to effective store-level planning and allocation, experts have argued that it’s easier for some retail sectors than others – for example, grocery stores are able to make appropriate adjustments for demographic differences across geographic areas. Similarly, specialty stores have a distinct advantage because they focus on a narrow, niche demographic.
While this may be true, “mix” remains a thorny problem for most retailers. The root of the problem is the many assumptions that top-down planning requires in the name of efficiency. Each assumption obscures an attribute that makes a location unique. It’s been said that planning for the average store means planning for no store.
Retailers know this, and in an effort to maximize each store’s performance without breaking the bank, they use a variety of traditional planning techniques that attempt to account for the differences among stores. Again, it’s a balancing act. These techniques include:
- Clustering. Stores are assigned to clusters consisting of locations the merchandisers feel should share a common merchandising program. Stores are often clustered based on store format and volume or size of store
- Clustering + Other Attributes. Climate, for example, thus distinguishing between locations that experience true winter seasons and those that don’t
- Frequent Shopper. Some retailers use frequent shopper data as a factor in their merchandising decisions
- Market specialists. Hired to understand the peculiar circumstances faced by a specific store. The specialist tweaks the generic merchandise plan to address local needs.
Rather than deploying a variety of compensating programs, some retailers simply choose a different objective. Instead of striving for balance, they strategically choose a “winner.” The approach taken by Whole Foods is attractive, at least superficially. Its culture celebrates each store’s role as a local citizen, loyal to local tastes. The result is a “layered” offering of a core assortment of items followed by regional and district selections. What remains is left to the discretion of store management. This decentralized strategy deliberately tips the scales toward the customer. It’s a conscious decision to prioritize certain objectives over others.
Exceptions can make for interesting reading, but the fact remains that many retailers express concern about the ability of a top-down approach to meet individual store needs. Why? Some stores defy clustering. Therefore, the argument goes, fully optimized merchandise planning requires a bottom-up approach.
It’s time we asked the obvious question: does it seem like technology is missing from this story? Where are the modern tools that accommodate high-level merchandise decisions, store-level operational planning and customer-centric merchandise execution?
To paraphrase some retailers we spoke to, it’s not that technology is missing per se. Technology showed up and then ran home when things got tough. (Many retail planning software solutions support some store-level operations tasks but the scope tends to stop well short of driving allocations.)
There’s a sentiment that retailers are left stuck in an Excel swamp because supply chain technology vendors couldn’t solve the problem.
To be fair, the problem is complex, as noted above. A quick example from the fashion world illustrates this. Imagine your apparel buyers are convinced yellow maxi coats will be hot next fall. The company makes financial commitments. Planners get busy creating a high-level plan, probably at the “channel” level. But the Distribution Analyst has to consider the proper allocations for the mall in Memphis versus the pop-up in Pasadena. Those two locations may differ across many important metrics: footprint, demographics, seasonality, logistical support, sell-through history, competitive landscape, sales trends, to name a few. Even for regional players the task is difficult, and the speed of fashion doesn’t help.
The high-level plan lacks local flexibility; the store plan lacks corporate context. More specifically, a planner lays out the merchandise plan by week, by category, by defined business metrics. This plan is aligned with strategic company targets. In contrast, store plans are created based on various factors noted above. Often these plans are based on the previous year’s performance. They may not review until the next planning cycle, perhaps six months away. Store plans are not used for allocation purposes.
There are three parts to the solution for this problem.
- Update plans more often based on demand signals
- Synchronize the store plan and the high-level merchandise plan
- Use the right tools
Merchandise financial plans and store plans must be re-forecasted weekly. The plans are tethered to one another and both are used to allocate merchandise to stores. The store plan covers current sales, forecasted sales and forecasted inventory, which in turn drives product distribution. This yields more accurate distribution of products.
Store plans also accommodate current trends, store openings, store re-models, disruptions, etc. Without a store plan, distribution to stores with declining sales will create inventory risk. Soon, inventory positions are off across multiple locations and the effect grows with time.
The critical path forward requires automation to synchronize processes and free planners’ time. This helps improve full-price sell through at each individual store location and ensure each location receives only what it can sell.
Other benefits include:
- Allocate to store’s sales volume and sales potential
- Plan for sales trend rather than trying to chase trends
- Understand effects of promotions
- Increase inventory turns
- Reduce markdowns
- If fulfilling from store for e-commerce, better inventory position to fulfill orders, reduction in shipping cost and time to fulfill orders
How has digital planning helped you or do you rely on spreadsheets and gut feel to tackle retail’s growing complexity? Share your stories with us.
- White Paper: Navigating the Next Steps in AI-Powered Retail Planning
- Analyst Insights: How Merchandise Planning Drives Enterprise Success