The Profit Lab: Determining need… what’s your strategy?

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THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #8: Create product strategies to understand each store’s true need

OK, you’ve spent the time and effort to select the perfect historical activity criteria. You now have the best possible representation of future activity you can get, now what? How will you support that with inventory?

Let’s start by taking a look at traditional approaches. Once you have an idea of how an item will sell, what do you do next? The common assumption is that if all stores have the same time supply (i.e. weeks of supply) of inventory all will be well. Alternatively, many systems use the premise that a store’s inventory need is equal to its contribution percent of the forecast or historical selling. Unfortunately these assumptions fall short in a few ways.

First, we never have the perfect forecast or criteria for all stores. As such even if we give the same time supply of merchandise they won’t sell through equally. In addition to understanding the accuracy of the projection or forecast, it’s also valuable to understand the inaccuracy. A store with a need of six units because it sells one every day is different than a store that needs six because every once in a while it sells four or five. Understanding this may cause you to make a different decision regarding how (and when) to support the need with inventory.

Second, most of us are constrained to some extent based on packs. So if a store needs 9 units and we have a pack of 6 we send either 6 or 12. We’re now under or over stocked. Which is the right decision? What if I have most of my stores on the cusp of this rounding point? I can’t treat them all the same because I don’t’ have enough inventory. Now what?

Third, we haven’t considered the true economic impact of the decision. If I send three percent of my inventory to a store that generated three percent of historical sales what is the likelihood and cost of some of those units going to markdown? How does that compare to the likelihood of missing a sale? What’s the cost of that? The answer will be different for each location.

Fourth, what is the relationship of the time supply to the presentation? What if presentation represents six weeks of supply in half your stores, but you only have four weeks of supply at the DC? If we constrain to presentation some stores will get less than three or even two weeks of supply.

Finally, we haven’t considered the role of the item in the assortment. Chances are you’re treating all items the same. An item that is in the assortment to drive traffic has different inventory requirements than an item whose role is to round out an assortment. These are different from the profit generators, which are different from your core assortment and key items etc. These roles vary by product but can also vary by location for a given product. Considering this “role” of the merchandise will lead to different inventory needs.

What you can do now

Starting with the assumption that you’ve chosen a good base of data, most conventional allocation systems are then limited to the calculations and constraints to determine the inventory need by store.  We need to manage these based on what we’re trying to achieve with the merchandise. Here are some things to consider:

  • If it’s a slow mover, ratchet down the presentation requirements and let your allocation system drive who gets the inventory.
  • If it’s a traffic driver, make sure you don’t short-change small stores with too conservative a minimum. If you do the larger locations will take everything.
  • If it’s a high margin, profit item, don’t be as concerned about chasing opportunities that may look like over stocks. Select more aggressive pack rounding options (round up) if you have the choice. The larger profit margin can quickly cover the impact of markdowns if you sell a few more units.
  • If it’s a low margin item, DO be conservative about chasing opportunity because sending markdowns may be devastating to profit. Select more conservative pack rounding options (round down) given the choice.

Ideally you’re already looking at opportunities to improve your presentation requirements and pack sizes. I’ve always felt that presentation should never be more than 1/3 the demand for any location over the lifecycle of short life merchandise. Pack sizes should be reflective of the smallest multiple you’ll need to ship. This is especially true if you’re constrained to 1 pack configuration. Consider setting a minimum of zero on fringe sizes outside of very core assortment apparel. Let demand drive that activity. If you include the core in your historical base of data you’ll capture changes in demand for fringe sizes.

One more note: If you’re spending a lot of time manipulating the recommendations your allocation system is providing you probably need to spend more time on fixing that upstream. Multiple examples have shown that effort spent in good criteria and constraints then left alone produce better results than intuition and manual overrides. In fact, based on personal experiences I’ve taken to referring to such manual intervention as “de-optimizing”. Challenge yourself and your team to see how much final intervention they can avoid by spending more time in the criteria up front.

What you should consider when looking for new capabilities

Advancements in technology and in science have enabled the most modern of systems to consider all of these things simultaneously when recommending allocations. The best systems generate regularly updated forecasts which can be used for new and existing items. The forecast shares not only the end unit need, but also the learning that went into deriving that need so all of that understanding can be used in solving the inventory side of the problem as well.

This understanding together with defining the role of the product can give these sophisticated systems the information they need to focus on how much inventory is required to meet your financial and strategic objectives with the product. The role reflects most of the complicated data metrics and parameters.  Traditional systems used to require merchants to understand, interpret, define and manage these settings manually.

This process actually simplifies merchant interaction with the system despite advancing sophistication and management of the more complex problem solving necessary to get incremental improvement in results.

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