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PrismaWerk
DEMOS

Walk through the workflows we actually ship.

Four walkthroughs: dashboards, predictive alerts, scheduling optimizers, quality anomaly review.

See the solutions
Operator at a production control station, used to illustrate an OEE loss breakdown dashboard.

FIG 01 / OEE Monitoring

01 / OEE Monitoring

OEE Loss Breakdown Dashboard

Plant teams can see OEE is below target but cannot quickly identify which loss category deserves attention first.

Workflow

  1. Step 01

    Combine equipment states, production output, quality events, and shift context.

  2. Step 02

    Break losses into Availability, Performance, and Quality views.

  3. Step 03

    Drill from line-level OEE to the root causes behind the largest losses.

Impact

A shared operating view for daily production reviews, prioritization, and leadership reporting.

Get this on your floor
Electronics manufacturing line with status lights, used to illustrate predictive maintenance alerting.

FIG 02 / Predictive Maintenance

02 / Predictive Maintenance

Predictive Maintenance Alerting

Maintenance teams get too many alarms and too little guidance on which machine will hurt capacity most.

Workflow

  1. Step 01

    Learn normal equipment behavior from sensor and maintenance history.

  2. Step 02

    Flag degradation patterns before a failure becomes visible in output.

  3. Step 03

    Rank maintenance actions by production capacity at risk.

Impact

Earlier interventions, fewer reactive escalations, and clearer maintenance planning conversations.

See it on your equipment
Robotic solar panel assembly line, used to illustrate scheduling and bottleneck optimization.

FIG 03 / Production Optimization

03 / Production Optimization

Bottleneck and Scheduling Optimizer

Manual schedules miss system-level constraints and create avoidable conflicts between resources.

Workflow

  1. Step 01

    Model work orders, equipment, skills, time windows, and production targets.

  2. Step 02

    Generate feasible schedules under real operating constraints.

  3. Step 03

    Compare scenarios by throughput, delays, and capacity impact.

Impact

Faster planning and better use of existing capacity without adding equipment.

Run it on your schedule
Pick-and-place machine handling silicon dies, used to illustrate quality anomaly detection.

FIG 04 / Quality Analytics

04 / Quality Analytics

Quality Anomaly Detection

Engineers spend hours reviewing normal traces while abnormal batches wait in the same queue.

Workflow

  1. Step 01

    Cluster similar production traces to establish normal behavior.

  2. Step 02

    Flag abnormal signals, batches, or defect patterns for review.

  3. Step 03

    Show the engineering team the smallest useful set of exceptions first.

Impact

Shorter review cycles, more consistent decisions, and faster yield investigation.

Try it on your traces

Next step

Want to see one of these running on your data?

One call. We figure out which dashboard, alert, optimizer, or anomaly workflow is worth spinning up first.

Book a strategy callSee case studies

60 minutes. Free. No deck, no pitch.