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Retail demand planning: Top down – simplicity or just hidden complexity?

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article image Diagram 2: Retail demand planning

In this series on demand planning, forecasting approaches for retail businesses are being examined. Two approaches were introduced and defined in the first article as Top Down (aggregate level forecasting) and Bottom Up (store level forecasting) demand planning.

In the second article I discussed why the Top Down approach is currently more prevalent, and looked at some of the significant challenges inherent in retail demand planning.

In this third article, I examine the Top Down approach and discuss why one of its principal allures - its simplistic approach to demand planning – creates complexity, inefficiencies, and additional workload throughout the distribution network. Specifically, I will look at how Top Down forecasting influences a business’ ability to:

(i) purchase the right stock into the business, and

(ii) replenishing the right stock to stores.

Purchasing

Diagram 1 below provides an example of how an aggregate level forecast is often matched to a distribution network.

In this example, stores in both states are supplied by a central Distribution Centre located in Victoria. The business aggregates the state sales forecasts and applies it to the central Distribution Centre to drive purchasing. While using aggregate forecasts to assist purchase order creation is useful, it does not paint a complete picture. What the business is going to sell, and what and when it needs to buy is not a one-for-one relationship.

At a fundamental level, purchase requirements are influenced by three factors:

• future sales,

• current stock position of the stores versus the desired stock position, and

• the store lead-times.

The Top Down approach of applying an aggregate forecast at a DC level misses the last two of these factors, which can lead to too much or too little stock arriving too early or too late. Excess inventory holding or missed sales are the natural consequence.

Businesses need to have future visibility of store replenishment requirements to accurately manage inventory holdings and purchasing. This is only possible using store level, or Bottom Up, forecasting techniques (see Diagram 2 below) and teaming it with Distribution Replenishment Planning logic. This will be discussed in more detail in the next article.

Store Replenishment

As discussed, aggregate level forecasting cannot be used to drive store replenishment; another method is required. Most often the “Min / Max” replenishment approach is relied upon - the store requests a fixed quantity once a certain onhand trigger point is reached. With constant sales this results in a neat ‘saw tooth’ stock movement at the store .

However, when faced with volatile sales patterns (such as seasonal or promotional activity) the result is much different.

The example on the left provides a ‘Best Case’ scenario, where sales are consistent over time. The example on the right shows the impact of a sales peak on your stock position – a promotional or seasonal peak drives the store to a stockout position and likely missed sales.

In a seasonal or promotionally intensive environment either the Min is set high enough to meet peak demand, and the business carries unnecessary stock throughout the year, or it is set lower than peak demand (as in the case above) and faces missed sales. Either outcome reduces the profitability of the business.

The Top Down approach provides up front simplicity for demand planning - only an aggregate level forecast is required. But this simplicity creates avoidable costs throughout the rest of the supply chain. Purchasing and store replenishment are not integrated, capacity management is hindered by lack of future visibility, significant inefficiencies exist in meeting seasonal / promotional demand, and stocking requirements at stores do not automatically adjust to changes in sales patters.

Next month’s article explores how a Bottom Up demand planning approach can surmount the difficulties discussed here, and support a cost optimised supply chain.

* Luke Tomkin is a senior manager with GRA

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