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
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.
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-profitability
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.
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