Making high-quality products at minimum cost is the goal of most companies, and Industry 4.0 initiatives can bring us closer than ever. Although they are in various stages of digitization operations, many players in the manufacturing industry see the huge opportunities these initiatives present. One of the most talked about initiatives is artificial intelligence (AI).
Mckinsey’s 2020 “State of AI” survey found that 22% of respondents who adopted AI saw their income increase by more than 5%, particularly in areas such as finance and management of the supply chain.
AI can also bring benefits to manufacturing, which we will examine in this article.
Improved product quality
Maintaining consistent product quality is a significant challenge in food and beverage production. Using machine learning can maintain higher levels of overall product quality, while enabling faster quality checks through visual inspection.
Video and image recognition tools can detect and analyze products in real time, determining if a product passes quality control based on input specifications. These tools can determine a pass/fail result for a range of needs, such as package fill levels and label placement.
Image recognition tools are now more accessible, which facilitates their implementation. Usually, this does not require an overhaul of current processes, a large-scale installation within your plant, or a large investment to get started.
How AI contributes to quality:
- Maintains 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 machine performance issues before they happen makes a huge difference to a manufacturer’s bottom line.
The use of sensors and past performance data helps anticipate potential 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 report operating data to the cloud, where it can be analyzed and used to monitor equipment condition, triggering an alert if abnormal performance is detected. These types of tools use AI to take the guesswork out of predicting maintenance issues and provide alerts when needed, instead of having someone investigate data logs.
AI can also use sensor data and machine history to predict when maintenance should be performed, allowing it to be planned appropriately to minimize breakdowns, saving money over time. time.
The integration of data analytics tools can then be used to track what the ideal production process (often referred to as the “golden batch”) looks like. For example, equipment that gets too hot can impact product performance. Taking this information to build an ideal “temperature range” for the equipment means that it can be monitored and the data analysis tool can trigger an alert if the temperature exceeds the ideal range.
How AI helps with maintenance:
- Reduces costs with predictive maintenance that minimizes breakdowns and unplanned downtime;
- Recognizes patterns of imperfection or production anomalies and triggers an alert in the event of a problem;
- Reduces waste due to breakdowns.
Sensor data overviews
Most manufacturing equipment already collects data; it adds value to operations when you have a way to make sense of it all.
Using sensors to capture and correlate task-relevant information, such as temperature or flow data, helps improve processes. The benefit of an AI tool comes from taking real-time sensor data and combining it to extract insights and improve situational awareness.
A built-in AI or machine learning tool uses the raw data to start identifying patterns and recommending actions to improve efficiency. For companies operating across multiple production sites or with different teams, this ability to compare operational conditions and learn from them is extremely valuable.
With business intelligence solutions in place, your plant can capture performance data that AI technologies use to identify patterns. These solutions capture a broader business picture, not only in equipment, but also in energy consumption and production line efficiency. You can also get more comprehensive insights into product quality metrics and start combining other data sources like customer feedback and supply chain efficiency.
How AI helps analyze sensor data:
- Extract patterns and identify opportunities from raw data;
- Monitors operational conditions and allows adjustments to be made for optimal production;
- Analyzes the production cycle and identifies the factors that influence production.
All businesses in the food and beverage industry can benefit from reduced operating costs and risk. Machine learning and artificial intelligence tools hold tremendous promise in this area, whether performing visual inspections or monitoring critical manufacturing equipment. The ability to detect quality issues, or even the wrong packaging on a product, through image recognition tools can significantly reduce the risk of a reputation-damaging (and costly) recall.