Here in Rochester, as we enter the official start of winter, we are experiencing the weather conditions that herald the season of chill: short days, snowy fields, nasty driving conditions, and festive holiday lights that attempt to take our minds off of all that has gone into hibernation.
At one point, Kodak continuously evaluated over two hundred thousand process and product parameters for all production orders in the worldwide film supply chain. The parameters being evaluated included: key process parameters, such as chemical reactor feed flows and temperatures; product release parameters, such as number of defects in a 10,000 ft master roll of coated film, and the condition of critical process components, such as pump vibration level, heat exchanger approach temperatures, motor current draws and control valve positions. Automatic alerts are generated to alert maintenance when parts need to be replaced or operations when a product just made needs to be held and not released to downstream operations. How did Kodak accomplish this level of aggressive monitoring in a global supply chain with flexible manufacturing systems and thousands of product recipes? In short, the answer is Process Monitor.
Recently a team of Optimation’s web conveyance experts teamed up with a pair of Eastman Kodak’s IT experts to create a means to demonstrate the power of applying a process monitoring tool to a known but illusive problem. The demonstration involved using Optimation’s Thin Web Rewinder, one of the pilot machines located in our Media Conveyance Facility, and Kodak’ proprietary Process Monitor software. We elected to use roller traction as the parameter of interest, because it is a functional attribute that is subtle, making it challenging to measure and status over time.
An interesting manufacturing tool is emerging as more companies seek to mine, or take advantage, of the process data their production systems generate. The tool, called Process Monitor (PM), uses statistical analysis methods to crunch the manufacturing process data, and predict whether or not the production system is operating within preset control limits that indicate an acceptable product outcome.