DC Velocity - IdeaBook 2014

The foundation of segmentation is data-driven analysis of demand dynamics and the profitability of customers and products. This analysis provides the information needed to tailor service agreements and supply chain policies in order to raise the overall profitability of the portfolio while providing reliable and suitable service. Because the dynamics of demand and profitability change frequently, particularly in today’s rapidly changing business landscape, this analysis must be institutionalized and performed on a standard cadence.

There are a number of ways to perform demand and cost-to-serve analyses. Financial systems typically do not provide an accurate view of profitability by customer and product, so other tools may be needed. It’s important, however, to avoid complex costing models for the purpose of setting appropriate supply chain policies. Leading companies have started with a simple model that assigns transportation, inventory, and ordering costs to products based on their volume and other ordering dynamics. This type of analysis typically produces data that can be plugged into a decision-mak-ing framework such as the one shown in Figure 4.

From this framework, you can see that the A and B customers are profitable and the C and D customers are unprofitable. When you look more closely at a profitable customer like A2, you can see that even among profitable customers there are “winners” and “losers.” This is shown on the right side of Figure 4, where customer A2 is further analyzed using a product-profit-ability matrix, which shows that products P1 and P2 are profitable, while P3 and P4 are not.

The objective here is to understand which customer/ product combinations are winners and which are losers, and then to structure supply chain policies such that some or all of the losers are turned into winners. This may require changing the replenishment model and service-level agreements for a specific customer/product combination. For example, a tire manufacturer that provides the same one-day lead time for both A customers and D customers may want to change the policy to three days for the D customers. This would move the inventory buffer point upstream in the supply chain, reducing overall inventory. The upstream buffer would hold a larger pool of inventory, thus increasing the odds that downstream demand will be satisfied with the exact product required. This change may have the effect of turning D customers into B customers.

2. Implement differentiated demand policies in core
supply chain functions

It was not too long ago that demand was thought of as a single requirement to which the supply chain reacted. Today, we know that demand signals can come in the form of orders, forecasts, and safety stock, and that they can come from different channels (retail, Web, distributors, and enterprise) and from different sources (original equipment manufacturers [OEMs], aftermarket/spares). Furthermore, demand signals can come from different customer types, as discussed in the previous section (large, highly profitably customers versus small, unprofitable customers).

In order for the supply chain to align with segmentation strategies, the demand signals within core supply chain management functions—such as master planning, transportation planning, distribution planning, and factory planning—must be prioritized in a way that aligns with those strategies. The demand priorities must be driven by the overall segmentation strategy that is tied to the service/

profitability framework discussed in the previous section.

Supply chain management systems for these core functions must be intelligent enough to incorporate and make decisions using these priorities. The systems must also be easy to configure and be able to adapt to changing priorities.