A leading industrial equipment manufacturer produced complex, high-precision components across multiple plants. Scrap rates were rising due to variability in material quality, machine performance, operator techniques, and process conditions. Quality teams relied heavily on manual checks, isolated SPC charts, and retrospective reporting—making it difficult to detect issues early or identify the conditions that contributed to defects.
We implemented the quality analytics platform generated measurable improvements to yield, consistency, and operating costs, enabling faster, more proactive decision-making.:
15% reduction in scrap across targeted product lines
Faster root-cause analysis, cutting investigation time from hours to minutes
More consistent production quality, with fewer process deviations
Lower material and rework costs, improving unit economics
Improved operator decision-making, supported by real-time insights
THE SITUATION: Production environments across plants were highly heterogeneous, with different machines, process controls, and data collection systems. Sensor data lived in historians and PLCs, while MES transactions, QC results, and operator observations were siloed in separate systems. Without a unified view of process behavior, teams struggled to correlate machine parameters with product outcomes. Investigations were slow, inconsistent, and dependent on tribal knowledge—leading to excessive scrap, rework, and material waste.
THE PROBLEM: The manufacturer faced significant visibility and consistency challenges that limited its ability to control quality and reduce scrap, including:
Data scattered across historians, PLCs, MES, QC systems, and spreadsheets
Limited ability to correlate machine parameters with product defects
Slow root-cause analysis due to manual investigations
No predictive visibility into process deviations
Inconsistent quality practices across shifts and plants
High material waste driving up production costs
The Frame Solution
Delivered a modern Quality Analytics solution that unified operational and quality data, introduced predictive intelligence, and enabled real-time visibility into process deviations. Our approach combined strategy, change management, lakehouse architecture, machine learning, and automated insight delivery to transform end-to-end quality performance.
Unified machine sensor data, MES transactions, QC test results, and operator inputs into a Lakehouse
Built predictive models to identify process conditions that lead to defects or rework
Developed anomaly detection for machine parameters outside optimal ranges
