Without the aid of computers, calculating production volume in a tobacco plant is troublesome and error-prone. PC-based monitoring systems that automatically collect production data and keep records can greatly improve in-factory logistics management and machinery maintenance. Combined with the use of networking communications, a Manufacture Executive System (MES) can be developed to improve the overall management of the factory. Our customer is a branded tobacco producer in China. When they came to Advantech, their production lines were already automated with the deployment of Programmable Logic Controllers (PLCs). However, they were still dependent on manual labor to count the amount of cigarettes and other materials, and their engineers had to manually check and record the data-logs stored in the PLCs to produce analyses for repair and maintenance purposes. But with Advantech’s PC-based monitoring machines, installed next to manufacturing machines, related data is shown on touch screens and transmitted to the control center, helping to reduce errors and improve management efficiency.

Gary Bruner, the president of Carolina Precision Manufacturing, a contract manufacturer that specializes in small-diameter, close-tolerance CNC Swiss turned parts, had a problem. Over breakfast that morning, Gary had logged onto the MachineMetrics monitoring system on his laptop at home to check the status of his machines assigned to a lights-out operation, but saw immediately that two of his machines were not in production. “What’s wrong with machines 35 and 36?” he thought to himself. Upon arrival to the shop, Gary learned from his operator on duty that this type of hold up was nothing out of the ordinary, and was in fact a product of inefficient startup procedures that had simply never been analyzed or augmented previously. In an industry with razor thin margins, Gary understood that the keys to growth and success were in efficiency and quality. He understood the importance of keeping tabs on production stats, job status, uptime, and setup. However, there was no way to know how well machines were doing in real time. What was causing this additional downtime? Furthermore, CPM’s current methods of measurement and data collection were not only time consuming, but had quickly becoming outdated. Historically, CPM had an employee dedicated to the collection of utilization data. This employee would walk around to each of the machines, collect scrap tickets post-production, talk to operators, and record yesterday’s data into their current ERP system; not to mention that this manual data collection was prone to errors, and would take upwards of 2 hours per day. Without the ability to visualize their results, the recorded data was not very actionable.

McKinley faced several challenges to implement these product improvements: - Which device hardware is best? - How do they connect their products and put them online? - How will their products communicate? - Which sensor protocols work best? - How will they develop the proper visualizations? - How do we integrate it with their field service technology? - How do they set up rules and their associated actions?