As part of the team that was involved in the development of SCOR 1.0, and in subsequently using the model in 120-plus projects over the past 17 years, I have the great fortune to see how companies have evolved their use of SCOR to improve their supply chain performance.
One of the most frequent questions (and observations) centers around the SCOR Level 2 processes, including the thread diagram and the geographic map.
SCOR Level 2 processes attempt to define the process strategies for each location in a project scope. For example, a manufacturing location may have Source (both stocked and to-order), Make (only stocked), and Deliver (only stocked). This is commonly notated S1, S2, M1, and D1. After each location is defined, a team then defines the material flow path(s).
The combination of the process and material flow definitions generates a thread diagram (process view) and geographic map (material flow view). These two tools originally were intended to generate strategic and analytic discussion around supply chain flexibility, network strategy and order lead times.
In the past three years, the use of the SCOR Level 2 processes have still provided the platform for strategic supply chain discussions but the thread diagram and geographic map have been replaced by different analytical tools that utilize both product and location data.
- Many companies use Excel to summarize the defect analysis for upside supply chain flexibility by product. A pivot table can easily illustrate the stacked lead time for each SKU using both planned and actual lead time at each level of the bill of materials. A SCOR Level 2 process is defined for both current and future state and is noted for each component. “What if” scenarios are generated through substituting lead times associated with changing supply locations.
- Supply chain optimization tools like IBM’s ILOG LogicNet utilize transportation data and can generate accurate geographic flows that incorporate cost and inventory data. Once a supply chain model is set up, decisions regarding manufacturing, supplier and distribution locations are less risky and more data-based than conceptual geographic maps.