Many if not most companies who have implemented S&OP solutions have benefited from the long-term visibility that it provides. However, they are facing some real issues with executing the plans for the short term, whether it is today this week, this month or next 3 months. As indicated in a recent Oliver Wight paper, symptoms are many.
Many S&OP vendors are now using Machine Learning (ML) in order to estimate manufacturing cycle times. In this paper we argue that in the presence of an accurate model of the supply chain (a digital Twin), there is no need for this and it can actually be a misleading exercise.
There is risk in almost everything we do. It is unavoidable. Supply chains are no exception facing all kinds of unexpected but inevitable surprises that can be very costly to the company.
SCIM stands for Supply Chain Intelligent Machine. But what does “Intelligent” mean in this context?
If I arrive on-time or early to the airport but there is a problem with the airplane and delay in the scheduled takeoff time, does it get me to my destination on time?
Imagine a world where supply chain planning systems can mold themselves into their environment, adapt and improve as the business changes.
It’s no surprise that the Semiconductor industry is very asset-intensive, with long leadtimes and expensive capital equipment.
How do you plan in the face of uncertainty? Produce more than expected? If so, by how much? Re-plan when an undesired event occurs?
Optimization technology driven supply chain planning solutions are based on a synchronized holistic methodology that seeks executable and optimal solutions for the enterprise at strategic, tactical, and operational levels.
Predict, Not Just Response As important a role as automation has played in increasing business productivity, automation is simply a predictable and repetitive task. It does not improve or adapt its own performance, as humans do, when various aspects of the business change. To this end, most of the current planning systems are preprogrammed to …
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