Escaping OEE Purgatory: Why Micro-Level Tracking Matters

“We’re already at 90% OEE, above the benchmark, so there’s little room left for improvement.”

Comments like this one are common when discussing Overall Equipment Efficiency, the core KPI that measures production performance across three dimensions:

• Availability – Actual runtime versus planned production time (downtime losses)
• Performance – Actual speed compared to designed speed (speed losses)
• Quality – Good parts versus total parts produced (scrap/rework losses)

Improving OEE by even one percentage point often requires substantial effort: process mapping, operator training, poka-yoke mechanisms and optimized production planning.

And yet, despite decades of lean programs, Six Sigma, predictive maintenance and analytics, many manufacturers remain stuck in what can be called OEE purgatory—incremental gains at best, while true efficiency breakthroughs remain elusive.

Where Traditional OEE Falls Short

The core limitation lies in granularity. In complex automated or semi-automated lines, OEE is usually measured at the overall line or major station level. But inefficiencies often originate much deeper—in the dozens of micro-steps within the line.

Even with advanced industrial process control/SCADA systems, underperformance at individual steps may go undetected. As Martin Nepovim, CEO of Mainware, notes: “If even a small portion of production data is unreliable, Houston—we have a problem.”

The human factor adds another layer. Operators may not report every disruption. During ramp-up, when responsibility shifts from integrator to shop-floor engineers, unreported micro-stoppages quietly erode OEE. Even something as simple as leaving a machine running during a break can corrupt OEE data and waste hours of analysis time.

The Data Gap

Global research reinforces this point. Rockwell’s 2025 “State of Smart Manufacturing Report” highlights a widening gap between data collected and data effectively used:

• Manufacturers are collecting more data than ever, yet only 44% is utilized effectively.
• The top barriers to improvement include deploying new technology (21%) and balancing quality with profitability (21%).

Micro OEE (mOEE) measures efficiency at the smallest possible production step. Examples include:

• A single screw assembly
• Glue application
• Bolt testing

Unlike traditional OEE, mOEE does not measure the entire subassembly or finished product. Instead, it pinpoints tiny steps that collectively shape line performance.

Though mOEE requires additional technical capabilities, once implemented it reveals bottlenecks that accumulate into major macro-level inefficiencies. It allows manufacturers to:

• Identify exactly where time, speed or quality is lost
• Optimize human–machine interaction
• Reduce costs and improve predictability

In short: mOEE transforms OEE from a broad indicator into a tool for actionable improvement.

From Data to Practical Insights

The challenge lies in capturing and analyzing data without overwhelming complexity. A practical approach combines:

• SCADA/sensor data mapped into a digital twin of the production line
• Camera captures of individual steps for visual confirmation

This allows engineers to analyze issues without depending solely on AI. At scale, however, AI becomes valuable—helping to replicate the approach across multiple lines or plants.

A high-granularity digital twin linked with self-analytics tools can detect deviations in both technical and human performance. While often seen as aspirational due to investment needs, digital twins are becoming the next big leap in manufacturing efficiency.

AI’s Role

AI is best understood as an enabler—but not a substitute for reliable data.

For instance, during commissioning, when systems are being tuned, unaddressed issues become costly micro-stoppages later. At this stage, even a 1% OEE improvement requires disproportionate effort compared to earlier phases. AI cannot reconstruct missing PLC data or fill in blind spots.

Where AI excels is in scaling insight once the data foundation is solid:

• Highlighting alarm sequences from programmable logic controllers (PLCs)
• Detecting deviations at scale
• Supporting engineers and managers with contextual insights

However, the first priority must be data quality and harmonization. Clean, reliable data ensures that AI, visualization tools and business-intelligence dashboards deliver real value.

Manufacturers that ensure data quality, digital infrastructure and close coordination with integrators from the start will stabilize OEE and unlock real efficiency gains. Without data granularity, manufacturers risk mistaking a ceiling for a benchmark—and leaving major improvements on the table.