What utilization target should I set?

High (85–95%) on the bottleneck, moderate (60–75%) on non-bottlenecks. Targeting 90% everywhere creates excess WIP and lengthens lead time.

Is OEE the same as utilization?

No. OEE combines utilization (availability), performance (speed), and quality. Utilization is one component of OEE, not a substitute.

How do I measure utilization accurately?

Read from machine controls or MES, not from estimated setup/run times. Self-reported utilization is consistently 15–20% optimistic.

Machine Utilization — Manufacturing Scheduling Glossary

Definition

Machine utilization is the percentage of available scheduled time that a machine spends actually producing parts (cycle time, not setup or idle). It is one of the oldest manufacturing KPIs and one of the most misused — high utilization on a non-bottleneck resource is often a symptom of over-releasing work, not productivity.

Why it matters

Maximizing utilization on every machine is a classic scheduling mistake. It creates excess WIP, lengthens lead time, and starves the actual bottleneck. The right utilization target is high on the bottleneck and moderate elsewhere, with non-bottleneck slack used for setup reduction, training, and absorbing variability.

Common failure mode

A shop drives every machine to 90%+ utilization. WIP piles in front of the bottleneck. Lead time doubles. The non-bottleneck operators are busy producing parts that wait three days for the next operation. OTD falls while the utilization dashboard looks great.

How Skody approaches it

Skody surfaces utilization in context — by resource, alongside queue and downstream flow — so planners can see which utilization is productive and which is just generating WIP.

Bottleneck

The resource whose limited capacity governs the throughput of the whole factory.

Work-in-Process (WIP)

All started but not-yet-completed work physically on the floor — money tied up in motion.

Available Capacity

The real, bookable hours on each resource after subtracting maintenance, calibration, training, and known constraints.

Load Balancing

Distributing work across alternate resources so no single machine is overloaded while others sit idle.