Man's eternal quest to predict the future is not limited to the mystical realm.  Many of us encounter this desire every day in the form of customers demanding to know when something is going to be delivered.  It's common in all supply chain efforts for delivery dates to be required as well as in office work for things like reports, projects, or other work products.  If you are frequently surprised by inaccurate delivery date predictions, or you find yourself adding large amounts of "padding" to predictions in order to mask uncertainty, then you have a predictability problem.

To solve this problem you need to know what current and future work needs to be done, when, and with what resources, and then understand how your own capacity (resource constraints), as well as your internal and external dependencies, will allow the work in question to be done.  There are many applications, concepts, and schools of thought dedicated to this challenge because it is the same challenge that has been plaguing manufacturers for well over a hundred years.

We employ a hybrid approach that draws from Theory of Constraints, Scrum, Traditional Manufacturing Resource Planning (MRP) and Capacity Requirements Planning (CRP), Lean Manufacturing, Little's Law, Queuing Theory, and Six Sigma to develop simple but powerful predictive models of systems that allow for not only the accurate prediction of when things will be done, but also to allow decision-makers to run scenarios that tell them how changes in resources and demands will impact delivery dates in the future.

Our modeling effort also inherently reveals and resolves the process improvements required to make the process consistent enough to be predictable.