Demand planning directly and significantly impacts retail business profitability. Just consider how often poor forecasting leads to missed sales or excess inventory holdings, resulting in lost revenue and wasted investment.
There are two main choices in approach to retail demand planning: top down and bottom up. Top down retail demand planning cedes control over demand forecasting to a centralised head office, while bottom up gathers individual forecasts from each store and uses these to drive restocking decisions.
There are legitimate uses for a top down retail demand planning strategy. It is often the choice for retailers with short life cycle products and in industries where sales history has little relationship to the future sales.
In the majority of situations, however, a bottom up retail demand planning strategy offers the potential for far superior business outcomes.
Despite this, a hugely disproportionate number of retailers currently use the top down approach to retail demand planning. In part this is due to historical limitations in both the associated technologies and the
sophistication of previous processes that did not support the more demanding bottom up strategy.
Today, with recent advancements in these areas, the bottom up retail demand planning method is gathering momentum with progressive and savvy businesses looking to reduce their cost to serve, improve service levels, and reduce inventories.
In this article, GRA looks at the consequences of both top down and bottom up retail demand planning methodologies.
Top down retail demand forecasting specifically influences a business’ ability to purchase the right stock into the business, and replenish the right stock to the store.
It is best to examine these retail demand planning methodologies by using an example. Take a typical small-to-medium automotive parts and accessories retailer based in the states of Victoria and South Australia, with four stores in the first state, and three in the second. All seven stores are directly supplied with their stock by one central distribution centre in Victoria.
To arrive at a forecast, the states generate sales forecasts, which are aggregated by the business and applied to the distribution centre. This appears to be a simple, adequate solution that assists with purchase order creation. However, this form of retail demand forecasting does not paint a complete picture.
In a top down retail demand planning approach, purchase requirements are influenced by three factors:
current stock position of the stores versus the desired stock position, and
The top down retail demand planning approach of applying an aggregate forecast at a distribution centre level misses the last two of these factors, which can lead to too much or too little stock arriving too early or too late. The result being excess inventory holding or missed sales.
When stocking with a top down retail demand planning strategy, what the business is going to sell, and what and when it needs to buy is not a one for one relationship. To handle replenishment under top down retail demand planning methodology, stores rely on additional methods.
Most often the “Min/Max” replenishment approach is relied upon. This is where the store requests a fixed quantity once a certain stock on-hand trigger point is reached. With constant sales this results in steady inventory decreases until the “Min” measure is reached, and it is then topped up to the “Max” measure with the replenishing stock.
However, when faced with sales patterns, such as seasonal or promotional activity, the result is quite different. A quick increase in sales on the store stock position can lead to “stock outs”, while a quick decrease will lead to overstocks, either of which will reduces the profitability of the business.
In summary, the top down approach to retail demand planning provides up front simplicity for demand planning, as only an aggregate level forecast is required. However, this simplicity carries the consequence of avoidable costs throughout the rest of the supply chain.
Using bottom up demand forecasting creates different outcomes. To return the example of a small-to-medium automotive parts and accessories retailer, rather than aggregated forecasts at a central distribution centre driving allocation of stock, each individual store prepares its own forecast.
By forecasting at a store level, both stock position and future customer demand can be used to determine replenishment requirements. Having future visibility of demand and replenishment requirements by week or day is essential for maximising sales potential and avoiding lost sales, especially for promotional or seasonal lines where sales from one week to the next can vary dramatically.
In terms of store inventory holdings, the “one size fits all” or store grading approach common to store stocking policies in a retail network can be eliminated with bottom up retail demand planning strategies.
Inventory held at each location becomes specific to that store’s customer demand requirements, down to a product level. Without this level of precision, delivering consistent service levels across the network, optimally balancing inventories, and minimising supply chain operating costs is not possible.
By planning demand at a store level, there is no need to forecast at distribution centres, nor estimate
purchase order requirements. Distribution Replenishment Planning (DRP) can be used to roll up store
level replenishment requirements to the distribution centre or warehouse level, thereby removing assumptions and aligning stocking, replenishment and purchasing through an integrated planning methodology.
Error associated with translating a sales forecast at an aggregate level to store replenishment requirements is eliminated with a bottom up retail demand planning approach. Importantly, alignment of supply chain processes delivers a “single set of numbers” for sales, finance and supply chain functions.
To summarise, a bottom up retail demand planning strategy creates opportunities to:
- employ true DRP
- optimise service levels and costs
- adapt operational plans to different future scenarios
- manage seasonal, erratic and promotional demand patterns,; and
- provide more flexibility in managing to each stores’ own sales patterns.