Manufacturing Process Data

Common Types

Cycle time (per machine/operation)
Throughput rate (parts/hour)
Downtime events (reason codes, duration)
Reject/scrap counts (with defect classification)
Machine utilization percentage

Uncommon/Advanced Types

Tool wear measurements (cutting force sensors, vibration signatures)
Thermal imaging data during process runs
Acoustic emission monitoring for early fault detection
Real time energy consumption per batch or product type

Possible Insights

Bottleneck identification and root-cause downtime analysis
Predictive maintenance scheduling before costly failures
Energy-per-unit cost tracking for sustainability reporting
Process optimization (reduce downtime, increas yield)

Environmental and Facility Data

Common Types

Ambient temperature and humidity in production areas
Air quality and particulate count (cleanrooms, food production)
Compressed air pressure trends

Uncommon/Advanced Types

Localized vibration from building/foundation shifts
Dynamic airflow mapping in cleanrooms
Noise level heat maps across shifts

Possible Insights

Compliance with regulatory requirements
Environmental correlations with defect rates
Safety and ergonomic improvements for operators

Equipment Health and Predictive Maintenance Data

Common Types

Vibration analysis for motors, pumps, fans
Motor current signature analysis (MCSA)
Lubrication/oil quality sampling

Uncommon/Advanced Types

Infrared thermography for bearing/gear wear
High-frequency ultrasound for leak detection (compressed air, steam)
Magnetic flux leakage detection in rotating shafts

Possible Insights

Early fault detection to avoid unplanned downtime
Maintenance cost optimization by replacing parts only when needed
Extending equipment lifespan through condition based maintenance

Quality and Traceability Data

Common Types

Inspection pass/fail counts
Dimensional measurement data (CMM reports)
Barcode/RFID based batch tracking

Uncommon/Advanced Types

In line 3D scanning and surface defect mapping
Vision system deep learning defect classification
Product genealogy tracking with full component trace

Possible Insights

Supplier quality performance monitoring
Linking defect patterns to specific machines, shifts, or suppliers
Reducing recalls by narrowing scope via precise traceability

Operator and Workflow Data

Common Types

Shift start/end logs
Production count per operator
Downtime cause codes by operator

Uncommon/Advanced Types

Wearable device data for ergonomics and fatigue monitoring
Eye-tracking during inspection tasks for training optimization
RFID/RTLS tracking of tool and operator movement

Possible Insights

Identifying training needs for underperforming shifts
Optimizing workstation layout for reduced movement waste
Improving operator safety and reducing fatigue related errors