Operating performance advisory that starts with a capability study, not a target.

Variation is the cost nobody budgets for. It shows up as scrap and rework, unplanned downtime, expedited freight and the working capital sitting in buffer inventory, and it is usually attacked with more capacity or more people when the process was never capable of the target in the first place. We open every operating-performance engagement with a capability study, whether the process can meet the specification at all, then apply statistical process control and workflow analysis to remove the variation and hold the result. The practice is led by principals who have carried the P&L in industrial manufacturing and pharmaceuticals, not observed it.

Is the target throughput achievable with this equipment and this labor?

Every operating-performance engagement opens with that question and ends with one of three answers: feasible, feasible with conditions, or not feasible. Answering it means defining the characteristic that actually matters (the dimension, the cycle time, the yield, the turnaround), establishing a measurement system capable of seeing it, and determining whether the target is reachable with the equipment and labor you have. If it is not, no amount of scheduling or supervision will get there, and the finding says so before you spend on either.

Call before you buy capacity, and when margin is leaking and nobody can say where.

Typical starting points: a new line or product that has to hit a specification it has not yet hit; a plant where scrap, rework, downtime or expedited freight are rising and the causes are argued about rather than measured; a capacity purchase or second shift under consideration to chase throughput; working capital tied up in buffer inventory that exists only to absorb variation; or a site decision that rests on throughput assumptions nobody has tested. In healthcare and life sciences the same discipline applies to service lines and batch processes where turnaround time is the margin.

A written capability finding, then statistical control installed on your line and handed to your people.

The finding states whether the target is reachable, under what conditions, and what the variation is currently costing you. When the answer is feasible, the same principal stays on the floor: charting the process, separating common cause from special cause, removing the assignable variation, and standardizing the workflow that holds the result. Then we hand the chart to the line. Control that depends on a consultant is not control, so your supervisors own the charts, the reaction plans and the review cadence before we leave, and the measure of the engagement is whether the improvement holds after we do.

Nothing proprietary and nothing black-box: measurement-system analysis, capability indices, control charts and workflow analysis, applied by people who have had to live with the results.