Make Every Plant, Line, and Asset Measurable
Unify production, quality, and maintenance data to improve OEE, reduce downtime, and protect delivery commitments.
Vibration Anomaly
Press #4 exceeded threshold.
Selected by discrete, batch, and process manufacturers where every downtime event, quality escape, and schedule change hits the P&L.
Manufacturing Data Engineering
Industrial data foundation engineered for noisy, heterogeneous environments.
- Ingest OT and IT data: PLCs, historians, MES, SCADA, ERP, CMMS, and quality systems.
- Standardize tags, part numbers, and routing logic into a coherent data model.
- Establish lineage, observability, and SLAs for production-critical pipelines.
Decision Intelligence & BI for Plants
Reporting designed around how plants are actually run. Dashboards for plant managers, line leaders, quality heads, and finance in one semantic layer.
- Standard KPIs: OEE, FPY, changeover loss, material variance, maintenance backlog, and on-time delivery.
- Automated daily and weekly packs replace manual spreadsheets and ad-hoc extracts.
Operational AI & ML
AI deployed where it reduces downtime, scrap, and working capital.
- Predictive maintenance models on top of condition data, events, and work orders.
- Quality models that catch patterns before defects reach customers.
- Forecasting models that support capacity, staffing, and inventory decisions.
OEE Loss Tree
Production and Line Performance Analytics
Objective: expose true bottlenecks and loss drivers across lines, shifts, and plants.
OEE and Loss Accounting
- OEE and loss trees built from machine events, MES states, and manual inputs.
- Breakdowns, micro-stops, speed loss, changeovers, and quality loss are quantified.
Throughput and Cycle Time Analytics
- Cycle time distributions and WIP profiles computed per routing and product family.
- Constraints on stations, cells, and lines are identified using real data.
Schedule Adherence and Plan vs Actual
- Plan adherence tracked at order, line, and shift level.
- Deviations tied back to specific losses, not generic “plant issues”.
Quality and Scrap Analytics
Objective: reduce non-conformance cost while protecting customer risk.
Defect and FPY Analytics
- First pass yield, scrap, and rework tracked by product, line, tool, supplier, and lot.
- Data from quality systems, lab results, and inline checks are aligned.
Root Cause and Pattern Detection
- Statistical and ML methods applied to identify drivers: machine, material, operator, shift, and environment.
- Support for 8D, FMEA, and CAPA processes with evidence, not anecdotes.
Supplier Quality Analytics
- Supplier performance tracked across incoming defects, PPM, and impact on line stoppages.
- Data ties supplier issues directly to downtime and scrap.
FPY by Product Family
Asset #4002
Maintenance and Asset Analytics
Objective: protect uptime, extend asset life, and control maintenance cost.
Asset Health Monitoring
- Sensor, event, and work order data combined into asset health indicators.
- MTBF, MTTR, and failure modes visible in one place.
Predictive Maintenance Models
- Models trained on failure history, usage, and condition indicators.
- Predictions integrated into CMMS with lead time for planned interventions.
Maintenance Performance BI
- KPIs for backlog, planned vs unplanned work, schedule compliance, and overtime.
- Dashboards shared across maintenance, operations, and finance.
Supply, Materials, and Inventory Analytics
Objective: align material availability with plan while protecting working capital.
Inventory Visibility and Classification
- On-hand, WIP, and finished goods inventory consolidated from ERP, WMS, and shop-floor systems.
- ABC and criticality classification applied across items and locations.
Demand and Production Forecasting
- Forecast models connect demand signals, historical production, and seasonality.
- Outputs flow into MRP, S&OP, and capacity planning processes.
Material Variance and Yield Analytics
- Standard vs actual consumption analysed by product, line, and campaign.
- Losses linked to specific processes and conditions.
Treat Manufacturing Analytics as
Plant Infrastructure
Downtime, scrap, and schedule changes already hit the P&L. An unreliable data and AI layer increases that exposure. Rudder Analytics architects and runs manufacturing analytics environments that hold up in shift reviews, audits, and board meetings.
