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Lean into efficiency and quality

Across the automotive, manufacturing, energy and utilities industries, VirtusLab helps clients use digital technology to streamline operations and deliver new levels of quality.

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Technology partners

Connect, optimize and automate

A lean, digital approach to production opens the door to data-enabled management and operations while simplifying the use of technology.

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Redefine the use of technology

Integrate existing equipment and ERP software into a comprehensive digital system
Create an accurate production coordination method from order to delivery
Configure a system where supply chain orders are based on demand
Develop a single, easy-to-use interface for end-to-end planning and management

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Expected outcomes

Increase efficiency of production processes through fast and frequent use of data
Improve profitability by reducing waste and downtime and increasing overall plant efficiency
Increase customer satisfaction through higher overall quality and fewer defects
Adopt adaptive learning principles for higher employee satisfaction

Industry 4 success stories

1
Reduced analysis time by suggesting the data that needs to be reviewed and reducing manual intervention
2
Enhanced the system’s value over time by learning which drift detections are worth alerting
3
Machine learning processes operated without the need for continuous monitoring or manual intervention

Machine Learning

Accelerating manual defect detection through ML-enabled feedback loop

1
Reduced analysis time by suggesting the data that needs to be reviewed and reducing manual intervention
2
Enhanced the system’s value over time by learning which drift detections are worth alerting
3
Machine learning processes operated without the need for continuous monitoring or manual intervention

1
Reduced analysis time by suggesting the data that needs to be reviewed and reducing manual intervention
2
Enhanced the system’s value over time by learning which drift detections are worth alerting
3
Machine learning processes operated without the need for continuous monitoring or manual intervention

Accelerating_manual_defect_detection_through_ML-enabled_feedback_loop_cover

1
Reduced analysis time by suggesting the data that needs to be reviewed and reducing manual intervention
2
Enhanced the system’s value over time by learning which drift detections are worth alerting
3
Machine learning processes operated without the need for continuous monitoring or manual intervention

1
Utilized your resources better
2
Increased agility
3
Reduced costs

Industry 4

Advanced planning and scheduling - data-driven production planning

1
Utilized your resources better
2
Increased agility
3
Reduced costs

1
Utilized your resources better
2
Increased agility
3
Reduced costs

Data-driven_production_planning_cover

1
Utilized your resources better
2
Increased agility
3
Reduced costs

1
Simulated the performance of machines on a worldwide scale
2
Read a possible example of how to create a Digital Twin simulating any process or machine worldwide
3
How to overcome challenges when implementing a digital twin

digital transformation

Digital Twin meets IoT

1
Simulated the performance of machines on a worldwide scale
2
Read a possible example of how to create a Digital Twin simulating any process or machine worldwide
3
How to overcome challenges when implementing a digital twin

1
Simulated the performance of machines on a worldwide scale
2
Read a possible example of how to create a Digital Twin simulating any process or machine worldwide
3
How to overcome challenges when implementing a digital twin

1
Simulated the performance of machines on a worldwide scale
2
Read a possible example of how to create a Digital Twin simulating any process or machine worldwide
3
How to overcome challenges when implementing a digital twin