Deep modeling and dynamic constraints

The results of any planning engine are only as good as how the system models the environment. Almost all current S&OP solutions use spreadsheet logic for capacity representation with a bucket! This inaccuracy leads to an unnecessary and painstaking adjustment of plans by the planners. It also leads to a fallacy of fixed lead-times. But the latter heavily depend on the product mix.

Furthermore, the product mix and quantity define bottlenecks. Sadly S&OP solutions make a pre-determined assumption regarding the bottleneck resources! All of this leads unreliable commit dates, and erroneous financial projections.

Plans and execution of the plan are a continuum. By separating S&OP from S&OE, one creates disjointed models that do not represent the same assumptions. This means not having a true supply chain digital twin resulting in two incoherent processes.

We believe S&OP and S&OE are a continuum. One evolves from the other by simply adding to the granularity and frequency of the data and vice versa.

Secondary_Solutions_Self-correcting

Digital blueprint to digitalization of your supply chain

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