AG Solution and Canvass AI Partner to Drive Process Automation and Industry 4.0 Adoption
By: Canvass Analytics
According to Gartner, 85% of AI projects fail due to the lack of internal AI and digital skills, understanding AI use cases, and concerns with data scope or quality. To overcome these challenges, AG Solution and Canvass are partnering to provide a unique solution that combines Canvass's powerful pre-built AI applications with AG Solution's deep knowledge of process manufacturing. Together, manufacturers will benefit from a proven solution that allows for a fast, scalable implementation of AI into their operations that empowers their workforce.
"Accelerating process automation and achieving the benefits of industry 4.0 has become a key priority for manufacturers across the world. Canvass's ability to productionize and scale AI quickly equips industrial manufacturers with the capabilities they need to come out of the COVID-19 crisis stronger than ever," commented Eric Billiard, CEO of AG Solution (https://www.agsolutiongroup.com/
"This new partnership with AG Solution marks another milestone in our growth strategy that focuses on empowering manufacturers with AI and the digital workforce they need to support the sustainability of their business. By working with industry leaders, like AG Solution, Canvass will expand our footprint with some of the world's leading manufacturers,"
Industrial organizations around the world are increasingly digitalizing their operations floor in order to improve availability, performance and quality. By putting Machine Learning and AI on top of their operational data layer, manufacturers are making data-driven decisions to support every aspect of their assets and process workflows. With the Canvass AI Platform, one of the world's largest food and beverage companies is saving $100,000s each year by optimizing asset utilization, reducing unplanned downtime, and preventing energy loss. Likewise, a leading global automotive supplier is utilizing Canvass' machine-learning templates to predict the failure of its robotic welding stations, which has led to the manufacturer generating cost savings by minimizing manual quality inspections, increasing production throughput and improving customer satisfaction by reducing defective parts.