The early adopters of Industrial Internet of Things (IoT) have achieved a 30% increase in productivity! That is in addition to a 30% reduction in maintenance costs due to better predictive maintenance. It is estimated that between 2016 and 2020, the cost reduction in the automotive sector would be around $28 billion,  a 3.9% reduction. This has been possible with increasing levels of automation driving the Fourth Industrial revolution, or Industry 4.0.

Artificial Intelligence and IoT are fast becoming the foundation for Industry 4.0. Be it reducing forecasting errors for supply chain, optimising fabrication operations, automating quality testing, asset management or inventory management – every aspect of manufacturing is experiencing a unprecedented  improvements in better user experience, machine availability, streamlined operations and lower costs.

By 2020, 72% of manufacturers are expected to be highly digitised with an ROI on adopting Industry 4.0 of just 2 years. If you wish to be one of them, reach out to us at info@robonomics.ai

Corrosion detection in telecom towers from Drone Imagery using Deep learning

• Problem Statement: Millions of drone images of industrial assets analysed to detect anomalies like corrosion for preventive maintenance.
• Solution: Use deep learning and computer vision techniques within Machine learning.
• Benefit: Faster, more accurate and efficient image analysis

Automated integration of SCADA, GIS and ERP Systems

• Problem Statement: Data from disparate SCADA, GIS and ERP systems that are too complex, risky and expensive to integrate was being manually extracted and pasted.
• Solution: Software robots log into each system, extracted relevant information at the time of need and performed pre-defined calculations into a report.
• Benefit: Automation of routine manual work, leading to faster processing times, lower errors and enhancing ease of use.

Automation of Reporting for Machine Parameters

• Problem Statement: Machine performance parameters in a manufacturing plant was being manually copied from text files and pasted into Excel sheets for reporting.
• Solution: Automated extraction of plant machine data from sources like historian and SCADA. The extracted data was classified / identified before becoming part of an operational report.
• Benefit: Ease of use, lower errors. Process engineers get to do engineering work, as their reporting work got automated.

If you wish to learn more about how we can help you adopt Industry 4.0, please reach out to us at info@robonomics.ai