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PrismaWerk

Demos

Walk through the workflows we actually ship.

Every demo starts with a problem on the floor, maps the workflow end to end, and ties the work to the business impact it's meant to create.

Talk to us about a demoSee the solutions
Automated factory line used to illustrate an OEE loss breakdown dashboard.
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. 1Combine equipment states, production output, quality events, and shift context.
  2. 2Break losses into Availability, Performance, and Quality views.
  3. 3Drill from line-level OEE to the root causes behind the largest losses.

Expected impact

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

Visual concept: Dashboard card layout with loss waterfall, line comparison, and shift-level drilldown.

Get this on your floor
Electronics manufacturing line used to illustrate predictive maintenance alerting.
Predictive Maintenance

Predictive Maintenance Alerting

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

Workflow

  1. 1Learn normal equipment behavior from sensor and maintenance history.
  2. 2Flag degradation patterns before a failure becomes visible in output.
  3. 3Rank maintenance actions by production capacity at risk.

Expected impact

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

Visual concept: Alert queue with confidence, capacity risk, signal trends, and recommended next action.

See it on your equipment
Semiconductor production environment used to illustrate scheduling and bottleneck optimization.
Production Optimization

Bottleneck and Scheduling Optimizer

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

Workflow

  1. 1Model work orders, equipment, skills, time windows, and production targets.
  2. 2Generate feasible schedules under real operating constraints.
  3. 3Compare scenarios by throughput, delays, and capacity impact.

Expected impact

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

Visual concept: Scenario comparison with schedule timeline, constraint flags, and throughput impact.

Run it on your schedule
Industrial analytics visualization used to illustrate quality anomaly detection.
Quality Analytics

Quality Anomaly Detection

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

Workflow

  1. 1Cluster similar production traces to establish normal behavior.
  2. 2Flag abnormal signals, batches, or defect patterns for review.
  3. 3Show the engineering team the smallest useful set of exceptions first.

Expected impact

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

Visual concept: Exception-first review screen with clusters, anomaly score, and trace comparison.

Try it on your traces

Assessment tools

Size the prize before you share a single file.

Use the calculator and the short diagnostic to frame the OEE conversation, no data-sharing or commitment required.

Estimate Your OEE Improvement Opportunity

Build a preliminary value range from annual revenue, weekly downtime, and defect rate.

Open calculator

2-Minute OEE Improvement Diagnostic

Identify whether Availability, Performance, Quality, or data access is the likely starting point.

Start diagnostic

Next step

Want to see one of these running on your data?

A first call is enough to figure out which dashboard, alert, optimizer, or anomaly workflow is worth spinning up first.

Book a strategy callSee case studies