Industry4.0 powered by machine learning
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 has been 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)
By 2022, 99% of video/image content captured for enterprise purposes will be analyzed by machines rather than humans. (Deloitte)
of CEOs of large companies consider artificial intelligence as a key technology.
Atos Computer Vision Platform use cases
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…
Outcome-driven AI Platform use cases
- Machines going unexpectedly out-of-order can be costly for smooth business operation. Knowing when a part is going to fail can prevent a lot of headache with delayed deliveries, machinery failures or unhappy clients.
- Successful predictive maintenance enables to schedule maintenances smartly instead of having to wait for a failure.
- We offer predictive maintenance solutions for a wide range of applications – from real time monitoring using IoT to smarter maintenance based on historical data.
- Our solution will help you reduce production disruptions, reduce costs and extend longevity of your equipment.
- Cetin – predictive maintenance for in-the-field repairs optimalization for the largest Czech cable provider.
- Flowpay – prediction of company’s healthiness for prioritization of investment.
- Train producer – predictive maintenance of rolling stock bearings using real time monitoring (currently in RFP phase)
How do you know how much you will sell, how many machines or how much energy will you need? Inaccurate forecasting in manufacturing and warehouses causes a 5-10% loss on the margin. In the worst case, it can slow production due to out-of-stock.
With our powerful models, you can go from simple predictive methods to cutting-edge algorithms, giving you the power to account for seasonality, trends and holidays. On top of that, we offer our expertise by combining the historical data of production or sales with a wide range of data sources, such as geolocation data, promotions, competition and many more.
Join our satisfied customers like Asahi, who moved to data-driven demand forecasting with us.