Case Study — Nexora
Turning Machine Data Into Margin: How One Manufacturer Added $6M in EBITDA
From 4TB of untapped sensor data to $6M EBITDA improvement through industrial analytics
Industry
Precision Manufacturing
Timeline
14 weeks
Team
6 engineers
Tech
Python + ClickHouse + React
The Challenge
A precision manufacturer was generating 4TB of machine sensor data daily but had no analytics infrastructure to extract value from it. Process engineers made quality and throughput decisions based on intuition and shift supervisor experience. Scrap rates were running at 8% — twice the industry benchmark — with no systematic root cause analysis capability.
Our Approach
How We Solved It
Sensor Data Lake Architecture
Built a scalable data lake on AWS S3 with automated ingestion from 240 machines, normalizing timestamp formats, unit conventions, and sensor naming conventions across 3 manufacturing lines.
Process Parameter Analytics
Developed statistical process control (SPC) analytics that monitor 1,200 process parameters in real time, flagging parameters trending toward out-of-control conditions before defects occur.
Root Cause Analysis Toolkit
Built a visual root cause analysis toolkit that allows process engineers to correlate any quality outcome (defect type, dimension deviation) with upstream process parameters across the full production history.
Quality Prediction Models
Trained ML models that predict final product quality 3 process steps in advance based on early-stage process parameters — enabling proactive scrapping of parts that will fail final inspection.
Engineering Process
How We Built It
ClickHouse for Time-Series Analytics
ClickHouse's columnar storage handles 4TB/day ingestion and ad-hoc analytical queries across 18 months of process history in under 2 seconds — critical for real-time SPC monitoring.
Delta Lake for Historical Reliability
Implemented Delta Lake on S3 for the raw data store, providing ACID transactions, time travel queries, and schema evolution support as sensor configurations change.
Streaming SPC With Apache Flink
Apache Flink computes real-time Western Electric rules violations across all 1,200 process parameters, emitting alerts within 30 seconds of a statistically significant process shift.
Architecture Decisions
Key Technical Choices
Lambda Architecture for SPC vs Full-History Analytics
Real-time SPC runs on the streaming Flink layer while historical process analysis uses the batch Delta Lake layer — different latency requirements justify separate data paths.
Process Parameter Taxonomy Before Data Model
Spent 2 weeks with process engineers defining the canonical parameter naming taxonomy before building the data model. Retrofitting semantic names onto raw sensor IDs is 10x harder after the fact.
Quality Prediction as Risk Score, Not Binary
Outputting a defect probability (0-100%) rather than a binary pass/fail prediction gave process engineers the ability to apply engineering judgment rather than blindly accepting model decisions.
Results
What We Delivered
Solution Blueprint
How It All Fits Together
- 240-machine sensor connectors
- Delta Lake on S3
- 4TB/day processing
- Streaming SPC (Flink)
- ClickHouse OLAP engine
- Quality prediction ML models
- Real-time SPC dashboard
- Root cause analysis toolkit
- Process engineer alert system
Lessons Learned
What We Improved
Data Quality Requires Process Engineer Involvement
Process engineers knew which sensors were unreliable due to calibration drift and which were deliberately excluded from previous analysis. That domain knowledge saved 4 weeks of dead-end analysis.
SPC Adoption Needs Statistical Education
Process engineers unfamiliar with SPC concepts initially dismissed control chart violations as 'noise.' A 2-day SPC fundamentals workshop increased alert response rate from 20% to 87%.
Quality Prediction ROI Is Front-Loaded
The first 2 weeks after deploying quality prediction delivered 60% of the annual scrap reduction benefit — the highest-frequency defect modes were immediately identified and addressed.
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