By department · Operations
When stock and capacity are out of balance, a solved bottleneck simply moves to the next step in the chain. A view of the whole flow (not of one department) shows where the time actually leaks away.
We connect the data across the chain, make lead time visible per step, and build a forward-looking model where that pays off.
The work itself is described under data engineering and ai & machine learning.
New Data Student makes the chain measurable: bring data from the systems together so lead time and OTIF become visible per step. New Data Squad steps in when something has to be forecast (demand, capacity, failures) or when the chain has to be redesigned. New Data Search finds the data engineer or supply chain analyst you want in the team permanently.
Because solving a bottleneck almost always means the next step takes over. That is not a failure but the nature of a chain. What does help is measuring where the waiting time sits rather than where the complaints come from: those two rarely point at the same step.
If there is enough history, yes. And that is the honest condition: without a few years of usable data a forecast is an expensive guess. We first check whether that history exists and say so when it does not.
Start with the New Data Scan. One day on site, fixed price, and afterwards you know which step in the chain should be measured first and what that delivers.
These are indicative figures from earlier work and not a promise. What it delivers for you depends on where you are now.