A good process orientation and organization are beneficial as is an integrated ERP system and availability of data, but analytics that drive specific improvements through higher quality decisions in less time in each of the processes of Plan, Source, Make Deliver are essential to winning supply chain performance.
A couple of releases back, the SCC added a dimension to the SCOR model of Performance Attributes, creating a matrix that relates Level 1 Metrics to those attributes. This can be taken farther as I have done in the Process/Value/Symptom Matrix (see Supply Chain Digest, September 13, 2011). However, to illustrate need and benefits of combining advanced analytics with SCOR, let’s take three Level 1 Metric examples.
Perfect Order Fulfillment is a composite performance metric built from several second level metrics as shown here. Let’s drill down into just one of the level 2 metrics, following the SCOR model.
Drilling down through Perfect Condition into Orders Delivered Defect Free, gives us a clear understanding of what to measure and of cause and effect. However, it does not tell us exactly how to “move the needle” on Orders Delivered Defect Free. In the case of purchased goods, this will involve a statistical evaluation of the performance of each vendor to determine the sampling and inspection procedures for each class of product from each vendor. There are also considerations of risk management. What are the risk scores of each vendor and product and how should they be computed in your industry? How sensitive are these scores to the factors that make them up such as lead time, availability of alternate sources, contribution to revenue, etc. In the case of manufactured goods, this will require statistical process control and all of the concomitant analytical six sigma tools. The metric is critical, as are the best practices to achieve it, but the excellent execution requires advanced and competent analysis to answer these questions.
Taking a look at cascading metrics another way, SCOR gives us many of the constraints that must be considered in this strategic metric which is defined as “The number of days required to achieve an unplanned sustainable 20% increase in production with the assumption of no raw material constraints.” To further illustrate the rather obvious need for advanced analytics in order to arrive at the “upside make flexibility” for your organization that is most strategically valuable, consider the interrelationships in this chart. We have highlighted a few of them with the red arrows. This does not even consider some of the relationships to other SCOR metrics not on this chart such as the expected range for future forecasts and the confidence ranges around those forecasts for each future time period.
Many of the decisions on the previous slide are inter-related and depend on your business strategy and the supporting value network design. Having done many of these projects successfully across numerous industries, we can say with confidence that every project we have done has been dependent on unique considerations. For example, some projects are very focused on the capabilities and configurations of the plants and need a model that operates at that level of detail while other projects are much more transportation, inventory, duty, and tax focuses, requiring an entirely different modeling approach.Takeaway:You cannot afford to take a cookie-cutter approach.
Scc Presentation For Indianapolis 2 March 2012
Achieving the Multiplier Effectwith Advanced Analytics and theSCOR® Model Arnold Mark Wells CPIM 2 March 2012