Overall equipment effectiveness (OEE) is a metric designed to evaluate how efficient a production operation truly is based on the real-world availability of the equipment used. Commonly used as a key performance indicator in Lean production efforts, the OEE concept has been around nearly 50 years. It is now starting to gain a stronger foothold in facilities of all sizes due to advances in automation software.
At The 2012 Automation Conference, Chris Bacon, production manager at Pepsi Bottling Ventures (PBV) in Nampa, Idaho, described how this small facility (which processes just one tenth the amount of cases produced at PBV’s largest facility), combined the use of automation and OEE tracking as part of its improvement efforts. The key for the Nampa facility was leveraging automation with real-time monitoring controls.
Though this PBV example originates in the batch process sector, the approach to OEE and continuous improvement has lessons for continuous process as well.
This project began as a way to improve the OEE on the facility’s bottling line, but also became an important bridge towards improving problem solving and developing a continuous improvement culture.
Step 1: Tracking Performance
Faced with limited personnel resources and numerous low-volume production runs, the No. 1 challenge was to develop a system to track the lines’ performance as a total unit and by each machine center. Measuring and capturing relevant data would provide the ability to evaluate the line at each machine center to formulate real-time impressions of the opportunities, while the background data could be pulled from the system for point-in-time analysis. Having this data, accurate downtime reason codes could be implemented for root cause analysis.
Step 2: How, When and What to Measure
For the OEE calculation to be as accurate as possible, the production line must have a standardized time. As fundamental as this sounds, many facilities do not have this, due to a non-standardized approach to all primary and ancillary activities necessary for quality control, maintenance, and production personnel.
To remedy this problem, a 7 a.m. startup time became the new standard. The tracking system was designed to start OEE calculation at 7 a.m. each morning and capture every minute of non-production time based on release of the bottle stop or filler. A specific reason code was automatically attached to accurately reflect the root cause impeding production. These reason codes were directly pulled from that machine center’s PLC.
Step 3: Putting Data Into Action
Once the facility was finally measuring and capturing relevant data, the next priority was to design a visual representation of the system so that each member of the team could discern some useful information from it.
Opting for a very simple design that mirrored the facility’s machine center flow, color combinations on the HMI screens would correlate to real-time activities of the line in accurate fashion. Red meant a machine center was in stoppage mode with the reason code available in the drop-down visual. Yellow meant a machine or machine centers were idle, either through changeover mode or awaiting upstream or downstream machine centers to be released from stoppage mode.
Armed with this new production insight, one root cause for low performance at the PBV facility was quickly identified—the periodic jamming of a vacuum drum on the labeler. PBV realized this problem because of the reason codes now built in to the downtime tracking system.
With an OEM upgrade to the processor and a few creative internal modifications, PBV was able to reduce downtime by 55 percent at this specific machine center. With this gain in performance, the OEM upgrade was paid off in five months.
Long-term results with improved performance also exhibited a 43 percent decrease in labor over time (from 2009 to 2011).
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