Entire industries and manufacturers are realizing the opportunities but also experiencing challenges in the transition to “Industry 4.0”. Smart, connected equipment and data analytics tailored to the needs of different industries and individual companies enable better use of data to drive efficiency.
Such smart solutions provide obvious benefits provided they are properly designed, validated, and integrated to the business. However, many companies are in need of support, expertise, and appropriate tools to successfully take the steps to exploit the data assets and apply intelligent analytics to enhance their business.
Our solution: AI Robot
Awarded by the European Union, with an Artificial Intelligence R&D funding of 2.5M EUR, Heaven Solutions is aiming to solve challenges of the Fourth Industrial Revolution.
AI Robot is a data analysis and decision support platform which can help industrial/manufacturing companies with use cases as:
- Preventive Maintenance — improved overall equipment effectiveness by predicting unplanned downtime, wear level and optimizing asset availability
- Spare parts stock and inventory optimization
- Reduced Costs through real-time production monitoring and control, predictive maintenance, and optimized energy consumption
- Enhanced Quality Control by reducing defects in products with real-time quality control
- AI assisted business decision making through real-time data analytics for support and optimization
The performance is assured by developing machine learning algorithms and methods based on the research work of our Data Scientists in collaboration with the local Computer Science University.
The vision behind a quality-improving solution
With AI Robot we aim to build an AI-assisted data analysis and decision support platform for manufacturing companies looking to optimise important areas in their business, such as:
- Quality Assurance via video streaming
- Automatic detection of usage level
- Spare parts stock inventory
- Featured production parameters in manufacturing
- Predictive maintenance scheduling
Therefore, we will be using the historical operational data and machine learning algorithms and methods that are developed based on the research work done by our Data Scientists in collaboration with the local Computer Science University.