Predict, Not Just Respond

An Ounce of Prevention is better than…

You know the rest: a pound of cure!  Unfortunately, most supply chain planning systems are based on responding and manual intervention to cure the problems and issues that arise. This is mainly due to the fact that their S&OP solutions are inaccurate and essentially a rough plan that is not feasible for execution. This approach is the antithesis of planning which is supposed to predict the issues and safeguard against potential problems before they occur. S&OP by itself is unable to do this because it does not represent the supply chain accurately. Its modeling simply is not a digital twin of the supply chain. In order to create an accurate model and digitalize the supply chain S&OE is a requirement.

prevention vs cure

Some organizations try to be more proactive and use their S&OP solutions for what-if analysis. Unfortunately, this is a very limited and ineffective approach. In a typical supply chain operation, there are millions of variables that can influence the outcome. By trying a handful of scenarios, it is impossible to predict what the potential issues would be and more importantly how to resolve them. An example is, a sudden and abrupt change in demand for a product. This can potentially change the mix of products. As a result, the capacity requirements on resources and contract manufacturers would change.

The system must be capable of knowing all the substitute materials, alternate suppliers, their capacity or lack thereof, tooling requirements, setup time changes etc. In the absence of this information, the answers are at best 60% accurate which is what typically is expected in terms of accuracy of S&OP plans.  In reality, in addition to the abrupt demand change, there could be a tariff increase, a transportation strike, or even a change in forecast of other products.  As you can see, by examining all these possibilities the complexity increases exponentially and impossible to get a realistic view of what to expect.

Predictable supply chains, examine the likelihood of events in future and their potential causes using advanced ML algorithms. They may not get it right 100% of the time but they can make your supply chain a lot more resilient and less prone to disruptions. Learn more about supply chain resiliency HERE.



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