SOLUTIONS

Four systems.One goal: more uptime, more speed, fewer defects.

Pick the problem first: equipment failures, bottlenecks, quality anomalies, or limited visibility. We build the workflow, model, dashboard, or optimizer that moves it.

See case studies

Manufacturing equipment area used to illustrate predictive maintenance and equipment performance analytics.

FIG 01 / Availability

01 / Availability

Predictive Maintenance

PrismaWerk combines sensor history, alarms, maintenance records, and process context to surface the equipment failures that cost the most capacity. The result is a maintenance workflow that moves teams from reactive firefighting to targeted prevention.

Workflow

Step 01
Connect equipment, process, and maintenance data sources.
Step 02
Model degradation patterns and rank interventions by expected production impact.
Step 03
Deliver alerts, maintenance priorities, and validation metrics for daily reviews.

Proof points

+$2.4M annual value identified in a prioritized maintenance platform
+Top machine showed 5.3 hours recoverable over a 72-hour horizon
+30-60 day payback range for targeted interventions

Explore Predictive Maintenance

Automated semiconductor production transfer system used to illustrate production planning optimization.

FIG 02 / Performance

02 / Performance

Production Optimization

PrismaWerk builds constraint-aware models that show where production flow is actually limited. Operations teams use them to compare scenarios, pick better schedules, and claw back capacity without buying new equipment.

Workflow

Step 01
Map operational constraints, resources, timing windows, and production targets.
Step 02
Build optimization models that evaluate feasible schedules and tradeoffs.
Step 03
Deliver decision-ready planning outputs and scenario comparisons.

Proof points

+Sub-second solve times for 400+ activities
+System-level planning across 20+ resources
+Performance-aware timing to create additional capacity

Explore Production Optimization

Engineers inspecting semiconductor production equipment, representing quality analytics and defect review.

FIG 03 / Quality

03 / Quality

Quality Analytics

PrismaWerk helps quality and engineering teams move from scrolling through normal traces to handling the exceptions that matter. Models flag abnormal behavior, compare production runs, and score decisions by real operational cost, not just F1.

Workflow

Step 01
Ingest sensor traces, inspection results, defect labels, and batch context.
Step 02
Cluster normal behavior and flag abnormal runs, signals, or defect patterns.
Step 03
Package findings into review queues, model evaluations, and operational dashboards.

Proof points

+10% faster analysis workflow through automated outlier detection
+40k+ sensor traces screened per batch
+Cost-weighted model evaluation for fab deployment decisions

Explore Quality Analytics

Operations team monitoring technical systems, representing OEE dashboards and manufacturing visibility.

FIG 04 / Visibility

04 / Visibility

OEE Dashboards / Monitoring

PrismaWerk wires equipment states, production logs, sensors, alarms, and OEE calculations into dashboards teams actually use on shift. The goal isn't reporting for its own sake, it's faster decisions on the losses that move the number.

Workflow

Step 01
Define OEE, loss, and escalation logic with operations stakeholders.
Step 02
Integrate MES, ERP, sensor, and alarm sources into a reliable reporting layer.
Step 03
Create dashboards and routines that highlight the next operational action.

Proof points

+200+ sensor signals integrated into manufacturing monitoring
+Daily auto-updating dashboards for operational reviews
+Shift from reactive maintenance to predictive insight workflows

Explore OEE Monitoring

Next step

Not sure which of the four to start with?That's what the first call is for.

One hour. We map your current OEE, your data, and the loss category with the strongest business case.

Book a strategy call See the demos

60 minutes. Free. No deck, no pitch.