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PrismaWerk

Recover manufacturing capacity from the equipment you already own.

Find the losses hiding in your production data. Build the systems that close them. In 90 days, not two years.

Book a strategy callSee proof of work

60 minutes. Free. No deck, no pitch.

Target result
5 OEE points
Pilot window
90 days
Typical upside
€2-4M / line
OEE Loss Waterfall. From 100% Available time, minus 12% Availability losses leaves 88%, minus 13% Performance losses leaves 75%, minus 8% Quality losses leaves 67% Current OEE. PrismaWerk target: 72% Target OEE, a 5 point improvement.0%25%50%75%100%100%Available time88%−12%Availability75%−13%Performance67%−8%Quality67%Current OEE72%+5%Target OEE
FIG / OEE LOSS BREAKDOWN · ILLUSTRATIVE
DEFINITION

OEE: the real yield of your production assets.

OEE (Overall Equipment Effectiveness) measures how much planned production potential becomes good output, at the right speed, during scheduled production time.

It combines Availability, Performance, and Quality. An OEE of 70% means 30% of the potential is lost to stops, slow cycles, or scrap.

Core solutions

Four ways to take OEE points back.

Maintenance, planning, quality, daily OEE management. All built from data you already collect.

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

01 / Availability

Predictive Maintenance

Catch degradation early, rank maintenance by what it costs you in throughput, and cut unplanned downtime before it hits OEE.

Anomaly detection · Failure prediction · Maintenance prioritization

Explore Predictive Maintenance
Automated semiconductor production transfer system used to illustrate production planning optimization.

02 / Performance

Production Optimization

Find the bottlenecks, kill the micro-stops, and turn real operating constraints into schedules that recover throughput.

Mixed-integer programming · Constraint optimization · Bottleneck analysis

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

03 / Quality

Quality Analytics

Flag quality anomalies, rank review work by risk, and tie defect patterns back to the process conditions that cause them.

Time-series clustering · Outlier detection · Computer vision evaluation

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

04 / Visibility

OEE Dashboards / Monitoring

Give leaders and production teams one shared view of Availability, Performance, Quality, and the losses sitting behind them.

OEE calculation design · MES/ERP integration · Root-cause dashboards

Explore OEE Monitoring

Proof of work

What a 5-point OEE gain actually looks like.

Real projects across Availability, Performance, and Quality. With the numbers attached.

View all case studies
Electronics assembly line representing maintenance prioritization by production capacity impact.

01 /Availability

Prioritized Maintenance Platform: $2.4M Annual Value

A maintenance prioritization system that used simulation to rank interventions by workshop-level capacity impact and quantify annual value.

Annual Value
$2.4M
Payback Period
30-60d
Recovered/72h
5.3h
Read case study
Semiconductor fab transport system representing integrated operations planning and optimization.

02 /Performance

Integrated Operations Planning & Optimization

A constraint optimization framework for production and resource planning, designed to resolve conflicts across maintenance, production, and quality activities.

Solve Time
<1 sec
Activities Planned
400+
Dynamic Opt.
Level 4
Read case study
Technical analytics image representing automated sensor trace review and outlier detection.

03 /Quality

Automated Data Review Platform: 10% Faster Analysis

An automated quality data review platform that clustered sensor traces and surfaced outliers so engineers could focus on abnormal patterns first.

Faster Analysis
10%
Sensors/Batch
40k+
Outlier Detection
Auto
Read case study
PRODUCT

AutoEdgeAI: from labeled dataset to deployable edge model.

Our local-first edge-AI workbench turns a ZIP of labeled images or audio into a trained, int8-quantized TFLite model with a full model card — validated on the STM32N6 NPU. No ML team required, and your data never leaves your machine.

  • int8 + fp32 TFLite export
  • Auto-improve Pareto search
  • Validated on the STM32N6
Discover AutoEdgeAI

Engagement

Pilots from €45K·90-day OEE proof·Multi-line rollout from €180K

See full engagement model

Next step

Start with the loss that's costing you the most.

One call. We review your problem, your data, and whether a diagnostic or pilot fits.

Book a strategy callSee the solutions

60 minutes. Free. No deck, no pitch.