Combining human experience, insight, and AI techniques, manufacturers are discovering new ways to differentiate themselves while driving down costs, protecting employees and increasing margins.
Over the last 5 years, manufacturers drove massive data collection, major progress were made on the production line, however drivers of productivity (quality, time, automation, etc.) is still scarce.
By 2035, AI-powered technologies could increase labor productivity by up to 40% in manufacturing. (Accenture and Frontier Economics)
Many manufacturers are facing inconsistencies on the production line in the process so that issues can be corrected in real time. Risks of breakdowns slow production process and deliveries, which affect customer satisfaction and loyalty.
Beyond minimizing downtime, computer vision solutions empowered by edge computing servers reduces maintenance costs and increases productivity. It enables manufacturers to predict issues, purchase replacement parts and plan human resources to maintain machines, without disturbing the production line.
- operational efficiency has plateaued
- operators lack full visibility and control
- occupants aren’t satisfied with their space
- lack the ability to predict and preempt events.
- Using predictive maintenance to maintain equipment, production lines, and facilities
- Getting a better understanding of products by monitoring them in real-time as they are used by real customers or end-users
- Manufacturing process optimisation
- Enhancing product traceability processes
- Testing, validating, and refining assumptions
- Increasing the level of integration between unconnected systems
- Remote troubleshooting of equipment, regardless of geographical location
A digital twin is a virtual replica of a physical product, process, or system. A digital twin acts as a bridge between the digital and physical worlds, using connected sensors and IoT devices to collect real-time data about physical items. This data is then processed within a server at the edge (BullSequana Edge or BullSequana SA) and used to understand, analyze, manipulate, and optimize the item.