Robotics

Benefits of collaborative robots and AI in the food & beverage industry

Collaborative robots and artificial intelligence (AI) are being fused together to provide an even greater return on investment (ROI) for companies in the food & beverage industry.
By Robotic Industries Association (RIA) May 10, 2019
Courtesy: Bob Vavra, CFE Media

Collaborative robots offer potential benefits to the food and beverage industry by reducing  the need for humans to perform strenuous, repetitive tasks. Food & beverage is also using artificial intelligence (AI) to enhance decision-making. The two technologies are being fused to provide an even greater return on investment (ROI) for companies.

Collaborative robots in food & beverage

Collaborative robots are able to work safely alongside humans without the need for fencing. Most collaborative robots support payloads up to 10 kg, which is suitable for a wide range of applications, including pick and place.

Collaborative robots are often programmed by having an operator move the robot to desired fixed points. The collaborative robot memorizes these points and movements, and it repeats the task. They can also be moved to other parts of the production line without disassembly. Since they never forget their preprogrammed tasks, once in place they can go right to work without retraining. Collaborative robots can also be programmed to handle a multitude of SKUs on the line.

Artificial intelligence in food & beverage

Artificial intelligence relies on a continuous process of learning from experience. AI algorithms come up with alternative options to conventional, time-consuming A/B testing. Unlike humans, AI’s ability to adapt to its environment is unlimited, and it has no problem with performing repetitive tasks indefinitely.

  • Data analysis. Machine learning is helping to analyze data quickly. Retailers need to cluster and identify the interests and wants of their main customers. AI can repeatedly analyze this data in real time and inform retailers and suppliers of demand, shortages, and waste.
  • Forecasting. Retailers also want to know what products are best to deliver last-minute and which should be kept in stock. AI-powered algorithms analyze what may influence buyer behavior, including promotions, social media, and weather.
  • Waste reduction. Some food processors have turned to AI to calibrate machines to reduce waste on their production lines. These machines identify the optimal use of raw materials based on size and variety.

Collaborative robots and AI

Vendors now integrate the capability of collaborative robots to perform repetitive physical tasks and artificial intelligence’s ability to perform repetitive data analytics. This results in better decision-making for the use of collaborative robots.

Some applications held out for AI and collaborative robots include:

  • Reducing the collaborative robot’s learning curve to complete new tasks by the use of network-connected collaborative robots learning together
  • Analyzing and adjusting collaborative robot movements with the use of vision system and sensor data to improve efficiency
  • Decreasing downtime by ordering replacement parts and scheduling maintenance ahead of time.

This article originally appeared on the Robotics Online BlogRobotic Industries Association (RIA) is a part of the Association for Advancing Automation (A3), a CFE Media content partner.

Want this article on your website? Click here to sign up for a free account in ContentStream® and make that happen.


Robotic Industries Association (RIA)