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.
Some flaws are too subtile & too small to be detected by the human eye. Indeed, the likelihood of production errors and quality problems typically increases when dealing with a product that has many different components all varying in size and function. Manufacturers are facing strict regulatory environment to ensure consumers safety & guarantee standards of quality. In cases of non-compliant products, it can lead to significant losses from dissatisfied customers to fines and class action lawsuits.
On the production line, cameras scan the product in 360° simultaneously, then the edge computing server collects, processes data in real time. BullSequana Edge offering the highest inference capabilities outside the datacenter in the plant. This solution dramatically cuts the costs of real time in-line inspection are answers to these use cases:
- Defects detection
- Package inspection
- Object classification & identification
- Ensure regulation compliance
- 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.
There are about 100 deaths per month on the job in 2019 in the USA, which has a direct impact on the company’s reputation, attractiveness, but moreover on employee’s safety feeling & productivity. It’s a high priority for manufacturers to ensure safety at all stages. The key is to ensure compliance with safety standards to prevent workplace accidents.
A set of cameras is connected to BullSequana Edge servers, in case of a detection of a worker is not wearing his/her personal protective equipment (PPE) like ear plugs, helmet, gloves.. the server analyzes this information in real time and triggers an alert to production site managers. It can also detect:
- Workers are in a hazardous and life-threatening situation
- Environmental risks or hazards at the right time
- Real-time abnormal situation (People on the ground..)
- Dangerous driving situations with forklifts, trucks…