Why Factory Optimization Falls Behind Operational Reality
Factory optimization often underdelivers because it assumes a level of operational stability that rarely exists in practice. Modern manufacturing environments change continuously across product mix, labor availability, equipment behavior, and process configuration. Optimization approaches, however, remain structured around periodic review, static metrics, and human-led analysis that cannot keep pace with this variability.
This mismatch becomes easier to understand when viewed through the lens of human attention.
During his PhD work at Stanford, Andrew Scheuermann, CEO and co-founder of Arch Systems, spent significant time maintaining research equipment, adjusting sensors, and reviewing test results. Large volumes of data were available, yet progress depended on how much time and focus one person could apply to interpreting it. Generating plots was easy. Turning them into clear direction required sustained attention.
Factories operate under similar conditions, only at greater scale.
Human Attention as the Limiting Factor
Most plants generate extensive production, quality, and test data. The constraint lies in the availability of experienced engineers, planners, and process experts who know where to look and how to act. These roles rarely appear clearly on a P&L, yet their absence limits throughput, yield, and utilization more than equipment capability does.
A production line running at 20 percent utilization instead of 80 percent reflects a decision bottleneck rather than a technology gap. The issue sits in how quickly deviations are recognized, understood, and addressed.
Continuous Improvement at Operational Speed
Traditional continuous improvement models were designed around scheduled intervention. Teams reviewed operations every few months, identified issues, and delivered recommendations. That structure aligned with environments where processes changed slowly and predictably.
Current factory operations evolve daily. Defects emerge unexpectedly. Changeovers adjust frequently. Equipment performance drifts gradually through wear and reconfiguration. Labor turnover introduces new variability. These changes accumulate continuously rather than appearing as discrete events.
Human-led optimization struggles under these conditions because attention is finite. Engineers investigate problems once performance degradation becomes visible, at which point issues often span multiple shifts or lines.
Optimization as Ongoing Operational Support
Optimization systems aligned with operational reality evaluate process behavior continuously rather than periodically. They analyze relationships across parameters, test outcomes, and production results as conditions evolve. Instead of presenting more dashboards, they surface specific risks and guide action where attention is most needed.
In some cases, systems adjust parameters directly. In others, they provide clear, prioritized guidance that reduces the time experts spend searching for root causes. The value comes from compressing the time between signal detection and corrective response.
From Data Review to Decision Enablement
Early industrial analytics improved visibility. Machine learning added prediction. The next step focuses on decision enablement under constant change. Optimization functions increasingly act as an extension of scarce indirect labor, supporting faster and more consistent execution across shifts and sites.
Factory optimization performs best when it reflects how operations actually run: continuously, under constraint, and shaped by limited human attention.
About the author
Lucian Fogoros is the Co-founder of IIoT World.