Case Study
Reducing Waste, Increasing Yield: A Manufacturing Turnaround with Modern Data Analytics
Client Overview
A mid-sized manufacturer specializing in precision plastic and metal enclosures for medical-device and industrial-electronics customers. The company employs 120 people and operates a 65,000 sq ft facility in the Midwest US running three shifts. Production includes 18 CNC machines, 12 injection-molding presses, and a manual assembly line.
Problem
Although rich data existed across machine logs, quality records, material traceability, ERP, and MES systems, it remained siloed and inaccessible for real-time decision-making or proactive intervention. The client continued to suffer from high scrap rates and inconsistent yields. Their primary issue was a reactive approach to quality:
Delayed Feedback: Quality issues were often discovered days after production during manual inspections.
Data Silos: Machine sensor data was trapped in proprietary PLC systems, while quality logs lived in fragmented Excel sheets.
Vague Root Cause Analysis (RCA): Slow and inconsistent quality root-cause analysis — averaging a week per incident using manual spreadsheets — which allowed repeat defects to persist and triggered growing customer complaints
Solution
Lasso partnered with the manufacturer to get a handle on their yield and quality issues. The data existed, they just did not have the analytics infrastructure in place to turn this data into business value. The project unfolded in phases:
Data Assessment & Strategy: Lasso conducted a plant-wide data audit to identify sources, gaps, inconsistencies, and integration challenges. Together, we defined a roadmap prioritizing high-impact quality and yield improvements that connected operational data to business goals.
Data Engineering & Centralization: Lasso deployed edge connectors to pull real-time data from the factory floor, including temperature, vibration, and spindle speeds. This data was unified with the ERP & MES systems to create a Single Source of Truth.
Real-Time Yield Monitoring: Lasso developed a live dashboard for floor supervisors. By applying a Statistical Process Control (SPC) layer, the system could flag “out-of-control” variables before they resulted in scrap.
Automated Quality Root Cause Analysis (RCA): The core innovation was a Correlation Engine. When a defective part was identified, Lasso’s analytics workflows automatically cross-referenced the precise timestamp of production against 50+ machine variables to identify the culprit.
Outcomes
By implementing a unified data lakehouse to ingest, store, and process the company’s data, the manufacturers was finally able to leverage all their data to drive better business outcomes, including:
Scrap Rate Reduction: After implementing Lasso’s analytics solutions, the manufacturer reduced scrap by more than 20 percent by identifying process drift, tooling wear patterns, and material lot variability that were previously hidden in siloed systems. Real-time monitoring and alerting enabled operators to correct issues before defects were produced, significantly lowering material waste and rework costs.
First Pass Yield Improvement: First-pass yield improved by approximately 10–15 percent as engineers gained visibility into optimal process windows and setup conditions. By standardizing machine parameters, improving operator guidance, and addressing sources of variation between shifts and lines, the company achieved more consistent production outcomes and reduced the need for secondary processing.
Average Root Cause Analysis (RCA) Cycle Time: The average root cause analysis cycle time dropped by over 60 percent, shrinking investigations from multiple days to same-shift resolution in many cases. With unified production, quality, and machine data available in a single analytics environment, engineers could rapidly correlate defects with process conditions, tooling cycles, and supplier lots without manual data gathering.
Repeat Defect Rate Reduction: Repeat defect rates declined by roughly 40 percent as the organization moved from reactive troubleshooting to preventive quality control. Data-driven corrective actions, improved traceability, and continuous monitoring ensured that once a defect source was identified, it could be permanently addressed and prevented from recurring.
Proven Results
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