When I first met Chris Bacon to discuss his presentation at The Automation Conference in 2011, he was working as production manager at Pepsi Bottling Ventures in Nampa, Idaho. In his presentation, Chris detailed the dramatic overall equipment effectiveness (OEE) improvements he oversaw across both legacy and new equipment using a combination of Lean & Six Sigma principles with various automation technologies.
See Chris Bacon’s presentation from The Automation Conference 2011.
Chris and I have stayed in touch over the past couple of years and we recently spoke about his move into manufacturing consulting, where he now works as an operations productivity analyst at ISS Productivity. The production management principals he espoused in his presentation at The Automation Conference form much of the basis of his work at ISS Productivity.
During our discussion, we spoke about the technology decisions made that often prevent a manufacturer’s vision of improved productivity and lower costs from becoming reality. To help ensure the right decisions are made, Chris told me that he believes it is necessary for the decision maker to have a formal understanding of the overall business from the ‘horizon to the windshield’ of the operation.”
This overview of the business requires “an understanding of the current goals of the organization—proactively identifying future business states, and having a clear comprehension of how the operation currently supports those capacities and what is necessary to drive future organizational growth before it is required,” says Chris. It is from this viewpoint that the proper use of industrial technologies can “increase the visibility as to where the opportunities lie to increase uptime, reduce areas of costing aggravations, and improve operational availability,” he contends.
Sharing direct experience drawn from his days at Pepsi Bottling Ventures, Chris says that his use of trending tools (see graphic 1 in image box at top right), helped to “identify variability and deviations in systems and processes that were negatively affecting start-up yields and final quality requirements.” The use of the trending tools allowed Chris and his co-workers to combine Ishikawa discussions with Gemba walks to identify root causes attributable to environment, processes, methods, etc. at the specific point of contact because the data was being captured in the background, allowing the teams to focus at the source of the issue, rather than scheduling time off the production floor to hold meetings to discuss these very same issues.
Having this type of technology in place allowed the data to “come to us,” Chris says, “and provided a more streamlined approach to root-cause analysis.” With this improved operational visibility to identify deficiencies within internal systems and machine centers, improvements included:
• Break/shrink costs reduced from .07/case to less than .03/case on start-up rejects;
• Average finished yields improved from 92 percent to more than 99 percent;
• Flavor changeovers reduced from 65 minutes to 23 minutes; and
• Line operating availability increased by 126 minutes per day.
Despite these improvements, Chris noted that the overall performance of the operation was in a state that did not fully support the organization’s expectations, which led him to focus on improving downtime data tracking systems. It’s important to note here that this focus on downtime to clearly “define operational macro/micro stoppages and constraints was predicated on collecting downtime data to improve the operation and the development of the production staff to ensure both business initiatives worked in symbiotic fashion,” he says.
Downtime tracking systems and visualization reporting (see graphic 2 in image box at top right) not only helped improve OEE on the production lines, the data captured and mined using the DMAIC (Define, Measure, Analyze, Improve, Control) model ensured “optimized repeatability of performance and allowed the operation to self-produce additional products the organization had been purchasing from other facilities,” Chris says. “Having stoppage codes pulled directly from each machine center’s PLCs and providing the duration and reason data into a reporting and database infrastructure (see graphic 3 in image box at top right) allowed for a more effective root-cause analysis approach.”
More importantly, following these procedures also “helped align all departments to work in a cohesive manner with our own data, and eliminated the ‘waste of debate’ that undermines any effective departmental meetings,” Chris says.
Improving the operation’s OEE and effective reporting had numerous beneficial effects throughout the organization. Some of these benefits were:
• OEE improved more than 10 percent in the first year and 21 percent within 3 years;
• Overtime costs resulted in a 43 percent decrease in two years;
• The company grew from hyper-specialization/single-person support, to a minimum 3-personnel bench depth for all key machine centers and systems; and
• Self-produced SKUs more than doubled within the operation to increase flexibility of cost control and additional product offerings to market.
See Part 2 of this Production Insights series where I share Chris’s insights on taking what he learned from downtime tracking systems and visualization reporting to create a continuous improvement cycle.
