Use SOPE© to Clean Up Your Supply Chain Model
The digital twin of your supply chain must accurately reflect both the shape as well as behavior of your supply chain. In the absence of this you end up with a rough model of the supply chain. Rough models deliver rough results in your financial projections, commitment to end customers and making the right response when a disruption occurs. S&OP modeling is one step above spreadsheet modeling, what you are trying to get away from. S&OP systems represent resource capacities with buckets, assume fixed leadtimes and pre-defined bottleneck resources.
For example, the bucketed capacity for 1000 unites per day for product A may translates to only 100 units/day of product B. Planning hundreds of different products in the same bucket, trying to guess how many units can be completed and delivered on time, is impossible. Using averages resembles having one hand in boiling water and the other in ice thinking that on average you are OK. With capacity bucket approach, you end up generating a plan which is not feasible or executable. As a result, the planners need to step in and spend a lot of their valuable time trying to make adjustments to the plan. Even when they make it work, a sub-optimal plan is the result. There are just too many variables for a human to work with and too little time.
The supply chain behavior changes all the time and your digital twin needs to change with it!
On the other hand, with S&OE, a much better digital representation of the supply chain, i.e. the digital twin, is possible resulting in an accurate model. Having an accurate model implies a true representation of the resources and behavior of the supply chain. The supply chain behavior changes all the time and your digital twin needs to change with it. Some examples are equipment efficiencies, seasonal variations in supplier leadtimes, safety stock policies, customer priorities and product mix changes amongst others. A true digital twin has the modeling capability to mimic the look and the intelligence to replicate the behavior and change dynamically. The latter is done using machine learning techniques.
SOPE© combines both S&OP and S&OE in a unified data model. It does not imply two systems. Just one, capable of performing both, based on availability and frequency of data. By simply adding more granularity and increasing the frequency of data, the system becomes more complete, smarter, more predictive and responsive. For more information on SOPE and unified data model click Here.