If you\u2019re a supply chain professional\u00a0like me, then your answer is probably \u201cnothing.\u201d<\/p>\n
You know that the data produced by supply chain transactions can be manipulated and mined to populate everything, including reports, graphs, and dashboards, and also provide a sense of\u00a0the operational tempo of an organization\u2014the rate at which the company is making and spending money. For this reason alone, supply chain analytics should be the central analytic activity.<\/p>\n
However, and more importantly, by co-mingling supply chain data with data from marketing, the value of the analytics can increase by an order of magnitude because it can highlight opportunities for revenue and profit growth.<\/p>\n
Enriching Supply Chain Analytics\u00a0for Deeper Insight<\/h3>\n
Typically, when people think of supply chain analytics, they think of facts and figures about orders, purchases, inventories, trucks, and warehouses. While this is all admittedly very valuable, it is one-sided and \u201csiloed\u201d because it is inwardly and operationally focused. But some of these transactions really represent something more: they represent a customer behavior<\/i> and the task \u2013 the value-add \u2013 is to incorporate this behavior in a way that provides transferable insights among the customers. Fortunately, this value can be captured with just a bit of the right kind of data.<\/p>\n
This new data describes particular attributes of the customer (for example, large vs. small, working within a particular market segment or across markets, serving a particular geography or national, intermediary, etc.). By connecting this information to operational data, a powerful linkage between what a customer is<\/i> with how he behaves<\/i> (order size, order frequency, market basket, rate of payment, etc.) can be\u00a0created. In this way, marketing and operations can come together to better understand and group<\/i> customers based on a set of similar attributes.\u00a0These actionable insights can be leveraged to grow revenues and profits by identifying underserved customers and markets, as well as product and service gaps.<\/p>\nDeeper Insights from Segmentation<\/h3>\n
Developing these insights requires multiple steps. First, there is a need to group customers according to a logical set of criteria to facilitate the identification of group behaviors. This can be difficult as it often requires the development of subjective and relative definitions. For example, what is a large customer? What is a large order? What is a long lead time? A large customer in one company may be a small one in another \u2013 likewise for orders and lead times.<\/p>\n
Further, what should the threshold be that separates a large customer, a large order, a long lead time from a medium one? Developing these definitions and then classifying the data is a combination of science and craft. The result enables a new perspective where patterns and behaviors can be compared within and across groups. The result also facilitates the development of generalizations and rules, identification of trends and outliers, and the formulation of plans, actions, and policies. The diagram below details elements of the process at a high level.<\/p>\n
![](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2016\/01\/supply-chain-lg.jpg\")
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