Is OEE Too Abstract?

OEE has long been a popular method for calculating asset performance, but some are beginning to question its value in providing direction to staff on how to improve production.

Since its introduction in Japan as part of the Total Productive Maintenance (TPM) concept in the early 1980s, overall equipment effectiveness (OEE) has steadily been gaining acceptance across industry. In the past 20 years, OEE has increasingly been relied on as part of both TPM and Lean initiatives around the world. Even outside of such formal continuous improvement concepts, OEE has become a popular gauge of plant performance.

As a refresher OEE = availability rate × performance rate × quality rate. Availability is calculated by taking scheduled operating time minus downtime and dividing it by scheduled operating time. Performance rate is the actual output divided by standard output; and quality rate is right-first-time output divided by actual output.

While few people argue against the use of OEE as a helpful guideline, some are questioning its effectiveness in terms of how much it can help direct specific production improvements.

“OEE is, by definition, an abstract metric,” says David McKay, president of Wave7, a supplier of equipment monitoring and analytics software. “It takes quality, availability, and speed and mashes them all together. So, if you run a report and it says Line 1 is at 61% OEE and Line 2 is at 56% OEE, what does that really tell you? How do you go from an abstract metric to better performance?”

McKay notes that OEE advocates suggest forming local teams to take ownership of each production asset’s OEE, dig into it, discover what the problems are and then build other teams to address each identified problem. McKay argues that doing this not only requires years of effort, it likely dooms those efforts to fail for three reasons:

  • No time. “Manufacturers and machine builders run skinny,” he says. “Every person on staff is tasked with a day job. Your faithful Continuous Improvement staffer can’t build a team out of people that don’t have any availability. In addition, the time between the first meeting and implementing the fixes the team recommends is measured in months—at best. Yet batch and discrete production have changes that occur daily.”
  • No method. McKay notes that continuous improvement teams aren’t always led by manufacturing performance experts. “In most cases they are led by folks who know how to make the product well, which means that every team will come up with their own method for improvement, and it will be tied to the biases of the group,” he says. “The result is that the resulting method(s) for performance improvement won’t consist of best practices.”
  • No specific and actionable information. “OEE does not provide specific and actionable information,” says McKay. “It doesn’t show you when machines are starved for material. It doesn’t tell you when machine adjustment and maintenance is required. It doesn’t provide specific machine or production line problems; and it doesn’t show specific downstream blocks or problems. In fact, OEE invites the team to make their own interpretation of what is happening. To have any long-term success with this approach would require validation of each new interpretation.”

Given these shortcomings of OEE, how do you improve manufacturing performance? McKay recommends focusing on the employees and what they can do, not on how poorly they are doing. “Don’t put OEE on the plant floor and point it at your operations staff as a tool,” he says. “If you want to use it, keep it for the management team and away from your operations staff. Instead, give your operators a tool that is both a method for action and actionable information.”

To do this, McKay suggests focusing on your top three causes of loss, since they are responsible for 90% of your missing output.

“If you can identify that your top loss for the last two hours was an infeed jam at carton erector 1, could you fix it? Probably, and that’s the whole point, because that kind of information is very specific and not at all abstract,” McKay says. “When the information you provide your team is abstract, it requires diagnoses and becomes historical and irrelevant quickly. To be useful, losses must be highly specific, real-time, and actionable. However, most folks end up with a daily or weekly report that shows the number of stops, or the top losses for the day, or the OEE for the line. What’s missing from this kind of information is that nothing is actionable, nothing is real time, and everything requires analysis. What you need are specific, real-time losses sorted by impact to production.”

As an example of how to do this, McKay says to first identify the top three losses that have been impacting operations for the last 10 minutes, last hour, and current shift. Then, focus operations, maintenance, and production on fixing any one of those top three issues.

He stresses that, for this information to be beneficial, it has to be specific. For instance, it’s not enough to announce that Machine 2 is starved. What needs to be communicated is that Machine 2 is starved because of a jam at infeed gate lane 3.

“Obviously, there are steps between where you are now and getting this kind of specific, impact-sorted info on real-time losses in front of your people, but realizing you need to do this is half the battle,” McKay said. “Once you have the key factors identified, put this info in front of operators, maintenance, production, and management by placing big screen TVs everywhere.”

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