The decision has been made.

In my previous blog I talked about a large retailer with excess inventory and its intention to hold a sale to move more than $100m worth of stock. I observed this would cause other retailers to follow and it has happened. A large variety store chain has announced  that $130m of stock must go in their ‘stock-take sale’ and another is advertising its ‘up to 50% off sale’.

I hope these store chains did not have more than $200m of excess inventory in their warehouses, so suppliers must respond to this artificial demand, which has occurred outside the normal seasons for retail sales. As major retailers will exercise their power against suppliers who do not comply with demands, how can suppliers respond?

This situation is an example that illustrates no matter how good your forecast accuracy or your planning, an unexpected demand can make it meaningless. This is not to dismiss forecasts and plans, because you must have structure to the business. However, it shows there is a choice between investing large sums on state of the art planning software applications, or designing your supply chains and internal processes to better respond to changes in demand patterns.

Understand demand patterns

The first step in responding to change in demand is to better understand the demand pattern of your inventory and stock keeping units (SKU). To do this  requires segmentation of inventory. The most common technique in consumer packaged goods (CPG) and fast moving consumer goods (FMCG) businesses is the ABC analysis. This uses annual sales volume by each SKU at cost of goods sold (COGS) value, to segment into three demand patterns.

An ABC analysis  for finished goods inventory items enables decisions about the amount of inventory per SKU, the location for inventory items and the control system required. While this is a good first step, it is too coarse as a response tool; why? Because a category can contain SKUs with similar annual sales, but very different patterns of sales.

For this reason, Tom Rafferty my colleague at Supply Chain STO,  developed the coefficient of variation for management (CoVM). This technique enables the variability of sales by each SKU to be identified and the demand pattern categories to be extended from three to six. In my next blog, I will discuss how CoVM provides better management responses to changes in demand patterns.