AI & BI use case · Industrial AI

Computer-vision quality control

Automatically inspect products visually.

What it's about

Computer-vision quality control checks products for colour, shape, damage and packaging errors, rejecting faulty items in real time.

  • Higher quality
  • Cost reduction
  • Faster processes
Business case & ROI
−40 %

scrap

−30 %

manual inspection effort

6–12 Mon.

payback

99 %

detection accuracy

Calculated from reduced scrap, lower inspection staffing and fewer complaints.

How we do it
  1. 01

    Data integration

    Consolidate data from ERP, MES and further sources for Computer-vision quality control.

  2. 02

    Data quality

    Clean, harmonise and validate data for plausibility.

  3. 03

    Model development

    Train and validate the Computer-vision quality control model on historical data.

  4. 04

    Pilot

    Pilot Computer-vision quality control in one area and collect feedback.

  5. 05

    Rollout

    Scale the solution and integrate it into operational processes.

5

Steps

6

Data sources

4

Stakeholders

From first data access to production – every step delivers a tangible interim result.

Data typically needed

Camera images

Images from inspection cameras on lines.

MES data

Production orders, feedback and machine data.

Quality data

Inspection results, lab values and complaints.

Product data

Bills of materials, variants and life cycles.

Order data

Customer orders, line items and dates.

Maintenance data

Faults, maintenance and spare-parts consumption.

Stakeholders
  • Quality management

    Secures compliance with standards and regulations.

  • Production manager

    Uses insights directly in daily operations.

  • IT

    Builds on a scalable and secure data infrastructure.

  • Controlling

    Quantifies effects and supports budgeting.

Typical business value
01

Higher quality

02

Cost reduction

03

Faster processes

04

More safety

05

Higher efficiency

The data platform advantage

With a solid data foundation this use case gets faster, cheaper and far more stable.

Central data platform

Real-time data integration

Scalable analytics pipelines

Reusable data products

Build a data platform
Synergies & positive side effects

Predictive quality

Same data reveals quality deviations.

OEE monitoring

Availability data complements process metrics.

Automated reporting

Metrics are provided without manual effort.