To understand how this works, it’s important to realize that computer vision is a type of AI that uses computers to capture images of physical objects — like a packaged cookie or a cut of meat — and analyze them to identify trends. In food manufacturing, computer vision can be used to analyze products on a production line, taking note of information that may have eluded human detection, such as irregular colors or shapes. Then, the vision system reports the potential quality issue back to a central control system like an MES.
This enables manufacturers to comprehensively monitor the end-to-end food creation process to ensure — through a careful combination of human knowledge and technological oversight — that quality errors do not leave the factory floor. Computer vision’s value lies in its ability to catch errors early, allowing workers to make changes before significant waste or quality issues occur.
When implementing computer vision on your factory floor, it helps to think about the high- volume or high-touch places as a first line of quality defense. From there, you can set secondary touchpoints in areas where your floor workers could be better assisted. This way, computer vision can work together with your operations to improve quality and efficiency for better business outcomes.
Step 3: Mix in machine learning
Managing facility maintenance schedules has always been challenging for food manufacturers, as equipment issues not only mean lost revenue but also food safety concerns. But thanks to machine learning, AI can put manufacturers ahead of the curve on facility upkeep.
Machine learning is a subset of AI that allows computers to learn behaviors without being programmed to do so. For example, machine learning can track the temperature of a facility’s industrial ovens and identify whether the equipment is getting too hot over time. It can then flag this detail to a floor operator for a potential maintenance check.
By tracking variables like temperature, pressure and vibration, machine learning can predict exactly when equipment needs servicing, allowing floor operators to build downtime into their plans rather than being forced to react to a crisis when it happens. This is how the insights provided by machine learning help manufacturers develop maintenance programs based on real-time needs.
For manufacturing operations to successfully use machine learning for greater outcomes, it’s best to couple it with predictive maintenance software to create the best chance at preventing breakdowns and maximizing uptime.
Eddy Azad is founder and CEO of Parsec Automation Corp., a member of MESA International.
