2 posts tagged with "capacity-utilization"

Every production manager has been asked the same question by their VP of Operations: "How much more capacity do we have?" And every production manager has given the same answer with varying degrees of confidence: "We think we have about 15-20% more capacity, but it depends."

It depends on downtime. It depends on changeovers. It depends on which products are running. It depends on whether the Tuesday night shift actually gets 7.5 hours of production out of their 8-hour shift or whether they lose 90 minutes to startup, cleanup, and that recurring alarm on Press 4.

The gap between planned production time and actual productive time is the single largest source of hidden capacity in manufacturing. According to a study by the Aberdeen Group, the average manufacturer operates at 65-72% capacity utilization — meaning 28-35% of available production time is consumed by downtime, changeovers, slow cycles, and other losses that are rarely measured accurately.

IIoT platforms close this gap by measuring exactly what happens during every minute of planned production time. Not what is supposed to happen. Not what operators report happened. What actually happened, based on real-time machine data.

You have four plants. Three states. Two countries. 200 machines total. And your Monday morning report is a spreadsheet cobbled together from four different plant managers who each use slightly different metrics, slightly different definitions of "downtime," and slightly different opinions about what counts as an alarm.

This is the multi-plant visibility problem, and it's universal in manufacturing organizations that have grown through acquisition, geographic expansion, or capacity scaling. Each plant has its own SCADA system, its own HMI panels, its own maintenance practices, and its own way of reporting performance. Getting a unified view of your manufacturing operation feels like translating between four different languages — because it is.

Modern IIoT platforms solve this by creating a single data model across all locations — but only if the platform was designed for fleet management from the ground up.