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

Strategy #5: Overcome limitations to forecasting by using better data

I know many of you believe, like I do, that there should be no reason for separate systems supporting allocation and store replenishment. Philosophically the objectives of these two systems are exactly the same: Get the product you have available to the stores where customers are looking to purchase it when they expect it.

So why do there continue to be two separate solutions for these very similar processes?

Answer: Forecasting limitations

In over 25 years in retail, with most of my exposure being centered on planning and inventory management processes and systems, I’ve seen numerous philosophies and initiatives come and go. One of the most intriguing has always been attempts to apply the automation existing in many forecasting, replenishment and other supply chain systems to fashion allocation. My memory is littered with examples of attempts and failures in doing this – from both colleagues and personal experience. The few who have claimed success in the past usually measure success as “ability to execute” rather than “ability to achieve better allocation results”.

Why is it so difficult to forecast fashion? There are a number of reasons, but the primary issue is short life. Traditional forecasting systems need long periods of historical activity to identify selling trends and begin producing results they have confidence in. Add to this the complexity of sized merchandise and the data is much too granular to draw SKU / store level conclusions from. Many have come up with complex algorithms, constraints and rules that attempt to address this issue. My experience has been that while these can do a better job than a traditional forecast, that’s really not saying much and the effort isn’t justified by the result.

So, as retailers, we have adopted an alternative approach, allocation. If we look at allocation conceptually it’s mainly a surrogate to address the limitations of forecasting and store replenishment. Since the products don’t live long, we supplement our need for more historical selling time by applying our knowledge of similar products or product groups and use those to give us more data. This allows us to begin seeing selling patterns. We then apply calculations that interpret the relationships in this base of data to derive a calculated recommendation.

These calculations are simpler than forecasting routines, but together with the additional merchandise that makes up the base of data they are much less volatile and therefore return reasonably stable results. We review this result and change it based on other dimensions of data we analyze – and based on assumptions and intuition.

Most retailers have long felt intuitively that we can do better, but how?

What you can do now

Since allocation is generally a mechanism to more simply forecast sales and inventory need, short of implementing a new system we must improve the allocation data and calculations. As discussed in previous posts in this series, spending more time selecting the products we use as the base of data can have profound impact on the quality of allocation results. If we spend more time finding the data that more closely reflects the trending, lifecycle, seasonality and historical demand of the item we’re allocating, results ultimately improve.

Often there is also opportunity to improve our allocation calculations. Many existing solutions have multiple calculation choices, and some even allow us to define new calculations. Most retailers fall into a pattern of using just a small number of these (often just one). This is frequently a symptom of a difficult implementation which resulted in too much change to adopt all at once so the simplest options get used. If you have a system that has been in place for months or even years, you’re past the learning curve of changed process associated with your system. Challenge yourself to understand the objective of each available calculation and experiment with them to see if those you haven’t been using can be made to return better results. Analyze the weaknesses of each and if you have the ability to modify or add to them – try it!

What you should consider when looking for new capabilities

Recently a few companies have had success applying forecasting to fashion allocation. They have done this by combining advancements in technology with innovation in retail science to understand the relationships of behavior across many different product and store types and levels. The resulting understanding of behavior across multiple dimensions is used to derive the likely behavior of the product you need to allocate.

With the best of these systems, even though the underlying logic is much more complex execution has thankfully been simplified. Since these systems also understand what you as an allocator are trying to achieve, they can execute to that automatically. Only when they cannot do what you’ve asked of them does the allocator need to intervene. Even then, issues are addressed using business logic rather than trying to manage complicated calculations, statistics or controls.

Footnote

Replenishment users have long been chasing the elusive “perfect demand forecast”. Interestingly, it turns out that a better forecast is only a small part of getting a better allocation result. In fact taken alone an improved forecast will often have no impact on an allocation result at all. More important than the perfect forecast is how you support it with inventory.

An imperfect forecast can drive a superior result if the decision about how to place inventory in support of that forecast is aware of:

1) The weaknesses that exist in the forecast
2) The objective you are trying to achieve with this product

This will be the subject of an upcoming post to the Profit Lab series on Allocation.

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Follow this series to learn all 10 strategies for improving allocation. We will be deconstructing the allocation process and exploring opportunities to improve within your current allocation processes and technology limitations. We will also review key areas to think about if you are considering investing in improved allocation capabilities.

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For resources on allocation, visit: http://quantumretail.com/solutions/allocation-replenishment/resources

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