Supply chain segmentation refers to a process where suppliers and customers are segmented into groups depending upon certain factors. Such segmentation allows supply chain management to allocate resources appropriately so the needs of different supply chain segments are met.
Supply chain segmentation is not new. As early as 1983, Peter Kraljic developed a two-by-two matrix that segmented suppliers by risk and profitability. While effective at the time, this form of static analysis is difficult to apply with today's highly complex and dynamic supply chains. In its place, Gartner recommends users adopt dynamic segmentation. This uses advanced analytics to determine appropriate supply chain responses to the needs of different customer channels such as is the case with omnichannel marketing. Here are three steps toward effective supply chain segmentation.
The fundamental weakness of static supply chain modeling is its inflexibility and inability to reflect changing realities. Added to that is the practical impossibility of visualizing supply chain performance without a mathematical model that reflects the supply chain in every material respect. Known as a digital twin, such a model is calibrated to respond exactly as the real supply chain does to inputs and disturbances. Taking into account constraints and limitations, and working with real data, a digital twin allows supply chain managers to better understand supply chain behavior and to evaluate different supply chain scenarios.
Because supply chain networks are complex, it's practically impossible to analyze them effectively without some form of machine learning. Machine learning uses algorithms (computer scripts) to analyze data and find hidden patterns in structured and unstructured data from the supply chain. By applying machine learning, it's possible to determine logical groupings and break the supply chain into segments with similar characteristics. Examples of supply chain segmentation could include:
The third step toward supply chain segmentation is determining the most effective strategies for each segment and, at the same time, maximizing overall business performance. Using prescriptive analytics, supply chain managers can determine scenarios that maximize overall revenue for each segment. At the same time, they can determine how these scenarios affect other supply chain segments and determine the right balance.
Using the digital supply chain model and corporate data, prescriptive analytics allows supply chain planners to determine the bottom-line impact of decisions to maximize segmented supply chain performance. Not only that, but prescriptive analytics can evaluate thousands of scenarios to determine which maximize profitability and how they impact each supply chain segment.
A dynamic, data-driven approach to supply chain segmentation using prescriptive analytics means supply chain managers make better supply chain decisions. Because they're able to assess the full impact of these decisions on the entire organization, managers avoid the nightmare scenario of losing market share and business because of poor supply chain segmentation decisions.