How has recent AI data collection and analysis innovation affected the packaging industry? What does it take to utilize this technology and create an effective and innovative plant through predictive maintenance and automation? What are the challenges and opportunities?
For an edible oil processor, the best way to efficiently manage unique characteristics of different products was by adopting modern control loop performance monitoring software with the ability to recognize different operating states.
Most manufacturing operations are constantly faced with change, and therefore they must be designed to adapt. Change routinely presents itself in the form of different production recipes, varying material properties, irregular equipment availability, weather extremes and many other factors. What’s more, change often includes several of these variables concurrently, and each may interact with others—adding still more complexity.
A line not running at optimum efficiency and producing less than expected might only be second to unplanned downtime in terms of headaches faced in manufacturing—both of which can be caused by a weak link in the production line. We spoke to Craig Souser, president and CEO of JLS Automation, about overall equipment effectiveness (OEE), about maintenance best practices and strategies to use in order to keep a line operating at its fullest.
Like a 15th century expression, “Children should be seen but not heard,” artificial intelligence (AI) is typically an embedded software technology that operates quietly behind the scenes keeping a production or packaging system running smoothly—and is silent unless something goes out of control and is not easily corrected. Then, the system sounds an alarm, possibly shutting down the process until the problem—which AI has already identified—is remedied by humans.