Industry & Manufacturing

Artificial Intelligence for Industry

Elevate your production efficiency. Control quality, anticipate machine failures, and manage raw material purchases autonomously and predictively.

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Before

  • ✕Quality control by visual sampling
  • ✕Unforeseen machine stops (downtime)
  • ✕Reactive raw material stock management
  • ✕Shift reports done on paper

With Mobizze

  • ✓Computer vision for 100% accurate inspection
  • ✓Predictive maintenance based on sensors
  • ✓Just-in-time and automated provisioning
  • ✓Full digitization and data structuring in ERP

Frequent Problems Solved

Downtime (Unplanned Stops)

When a production line fails by surprise, the labor and logistical costs of the stoppage are overwhelming.

Manufacturing Defects

Human inspection at the end of the line is exhausting, fallible, and causes costly recalls.

Lagging Procurement

Raw material stockouts or overstock tying up capital, due to the purchasing department's slowness.

How It Works in Practice: Workflow Example: Visual Quality Control

1

Continuous Capture

Cameras installed along the conveyor belt photograph each piece in seconds.

2

AI Analysis

Computer Vision models process the image looking for micro-cracks, color deviations, or assembly flaws.

3

Automatic Rejection

If a defect is found, the AI sends a signal to the robotic arm or pneumatic system to remove the piece from the line.

4

Analytics and Adjustment

The system alerts the production manager (if the defect rate suddenly rises) to stop and recalibrate the machine.

Software & Integrations

SCADA/PLC SystemsSAP ERPVision Cameras (Cognex, etc.)IoT Platforms (AWS IoT, Azure)Power BI

Required Data

  • Machine failure history
  • Image catalog of normal/acceptable vs. unacceptable defects
  • Inventory policies and supplier lead-times

Solution Limitations

The AI does not perform physical repairs on machines nor makes long-term strategic decisions about factory layout changes without process engineering input.

Human Validation

Automatic purchase orders above a certain threshold and total factory shutdowns always require validation by the Production Manager.

Implementation Estimate

8 to 12 weeks, as it frequently integrates computer vision hardware or industrial sensors (IoT).

Case Study

Scrap Reduction of 35%

"

A plastic components factory for the automotive sector integrated our computer vision into injection molding machines. The AI not only rejected pieces with bubbles but identified heating patterns, warning the team to lower the temperature before dozens more defects occurred. Material waste dropped 35% in the semester.

Sector FAQs

Do we need to replace our machine park? +
In most cases, no. We can attach non-invasive IoT sensors to old machines (motors, belts) to measure vibration and temperature and predict failures.
Is it safe to connect production to the cloud? +
You can opt for Edge Computing. The AI runs on a closed local server in your factory, not depending on an internet connection to operate and reject pieces in real-time.
Doesn't this eliminate line jobs? +
It usually reconverts them. Tiring inspection workers transition to acting as system operators and calibrators, reducing injuries and errors.

Ready to transform your business efficiency?

Discover how our AI agents can optimize your operations and scale your revenue.

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