3 Ways AI is Changing Food and Beverage Manufacturing

March 1, 2022
Learn how utilizing AI can bring improvements to a company's product quality, maintenance procedures, and data analysis.

Manufacturing high-quality products at minimum cost is the goal for most companies, and Industry 4.0 initiatives can get us closer than ever before. Despite being in varying stages of digitizing operations, many in the manufacturing industry are seeing the huge opportunities these initiatives offer. One of the most talked about initiatives is artificial intelligence (AI).

Mckinsey’s “State of AI” survey in 2020 reported that 22% of respondents who adopted AI saw revenue growth of more than 5%, particularly in areas such as finance and supply chain management.

AI can also bring benefits to manufacturing, which we’re going to look at in this article.

Improving product quality
Maintaining consistent product quality is a significant challenge in food and beverage production. Using machine learning can maintain higher levels of product quality overall, while enabling faster quality checks through visual inspection.

Video and image recognition tools can detect and analyze products in real-time, determining whether a product passes the quality check based on input specifications. These tools can determine a pass/fail outcome for a range of needs, such as packaging fill levels and label placement.

Image recognition tools are more accessible today, making implementation easier. Usually, it doesn’t require an overhaul of current processes, a large-scale installation within your plant, or significant investment to get started.

How AI helps with quality:

  • Maintains a high accuracy of visual inspections;
  • Detects quality issues in real time;
  • Identifies the root cause of quality issues, thereby improving future production processes.

More efficient maintenance
Predicting issues in machinery performance before they arise makes a huge difference to a manufacturer’s bottom line.

Using sensors and data on past performance provides the ability to anticipate possible failures, allowing action to be taken before equipment fails. For example, using sensors to monitor machine vibration and trigger alerts when the vibration range changes.

Condition-monitoring solutions have become popular because they simply attach to the machine and communicate operating data to the cloud, where it can be analyzed and used to monitor equipment health, triggering an alert if abnormal performance is detected. These types of tools use AI to take the guesswork out of predicting maintenance issue and deliver alerts as required, instead of requiring someone to investigate data logs.

AI can also take sensor data and machine history to predict when maintenance should be performed—allowing it to be scheduled appropriately to minimize breakdowns—delivering cost savings over time.

Integrating data analysis tools can then be used to track what the ideal production process looks like (often referred to as a ‘golden batch’). For example, equipment becoming too hot can have impacts on the outcome of the product. Taking that information to build an ideal ‘temperature range’ for the equipment means it can be monitored and the data analysis tool can trigger an alert if the temperature increases above the ideal range.

How AI helps with maintenance:

  • Reduces cost through predictive maintenance which minimizes unplanned breakdowns and downtime;
  • Recognizes patterns of imperfection or production anomalies and triggers an alert when there is an issue;
  • Reduces waste due to breakdowns.

Insights from sensor data
Most manufacturing equipment is already collecting data; it adds value to operations when you have a way of making sense of it all.

Using sensors to capture and correlate information relevant to the task, such as temperature or throughput data, enables process improvements. The benefit of an AI tool comes from taking real-time sensor data and combining it to extract insights and improve situational awareness.

An integrated AI or machine-learning tool takes the raw data to begin identifying patterns and recommending actions to improve efficiency. For companies operating across multiple production sites, or with different shifts, this ability to compare operational conditions and draw insights is hugely valuable.

With business intelligence solutions in place, your plant can capture performance data that AI technologies use to identify patterns. These solutions allow the capture of a wider business picture, not just into equipment but into energy use and efficiency of the production line. You can also derive more comprehensive insight into product quality metrics and begin combining other sources of data such as customer feedback and supply chain efficiency.

How AI helps analyze sensor data:

  • Extracts patterns and identifies opportunities from raw data;
  • Monitors operational conditions and allows for adjustments to be made for optimal production;
  • Analyzes the production cycle and identifies which factors influence output.

All food and beverage organizations can benefit from reducing operating costs and reduced risks. Machine learning and AI tools offer huge promise in this area—from performing visual inspections to monitoring essential manufacturing equipment. The ability to detect quality issues, or even the wrong packaging on a product, through image recognition tools can dramatically reduce the risk of a reputation-damaging (and costly) recall.

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