You’re probably familiar with the term overall equipment effectiveness (OEE), but it means different things to different people. It’s often used interchangeably with factory information systems (FIS) to refer to production process analysis, measurement and management.
At its most basic, OEE is a simple calculation that can be the foundation of great process analysis. It is used to evaluate one machine, one station, or an entire factory. It’s understandable by operations, engineers and executive management, and it can guide real-time and long-term process improvements.
In this post, I’ll define OEE in its strictest terms, and then talk about how measuring OEE can be part of critical manufacturing improvements.
How is OEE measured?
There are three factors that go into calculating OEE: availability, performance and quality.
Availability: Is the line/station/machine operating? As an example, consider:
- Planned downtime: Rest and lunch breaks = 90 minutes/shift
- Unplanned downtime: Unexpected maintenance = 60 minutes
- Total downtime: 150 minutes
- Actual run time (330 minutes) / planned run time (480): 68.8 percent availability
Performance: What are the product completion rates (throughput)? Performance is based on availability because your throughput is inherently limited by hours available.
- 100 percent performance: 1 can of cola per second: 330 minutes of available time = 19,800 cans of cola
- 66.7 percent performance: 1 can of cola per 1.5 seconds
Quality: How many completed products met their specifications?
- Quality Score: 19,800 total production – 4,000 defective products / 19,800 = 79.8 percent
The OEE score is 36.7 percent, which is figured as availability (68.8 percent) x performance (66.7 percent) x quality (79.8 percent). You’re probably not going to be happy with an OEE score of 36.7 percent. According to LeanProduction.com, manufacturers typically fall within the 60 percent range. If you’re scoring 85 percent or above, you’re exceptional!
The major issues I see impacting my customers’ OEE are probably the same ones that you see:
- Unplanned stops: equipment failures, starved stations or unplanned maintenance.
- Planned stops: changeovers, machine cleaning, tooling swap outs, etc.
- Small stops: minor or idling stops usually less than two minutes that could include things like a sensor obstruction, jams, feeds, etc.
- Slow cycles: anything slowing the production time from its maximum speed, like a worn out conveyor belt, poorly maintained equipment or an inexperienced machine operator.
- Process defects: defective parts produced during stable production due to equipment handling errors or incorrect equipment settings.
- Reduced yield: defective parts produced after an equipment failure until equipment has returned to a steady state.
What benefits can you expect from measuring OEE?
Plants typically work quickly to identify and attack problems, but fail to take the time to understand what’s at the root of the problem. OEE can provide visibility to both the big picture and the details.
By measuring OEE, you can identify your potential losses and understand where you’re falling short. Is it performance, quality or availability? From there, you can pinpoint the problem and correct it.
OEE provides valuable insight to in-depth process analysis, like root cause, where operational studies are required rather than quick fixes. The following sections provide examples I’ve seen where plants used their OEE data to improve availability and performance.
Reduce downtime without buying new equipment
One of our automotive manufacturing customers was having unexpected downtime issues that turned out to be related to their assembly line tool track.
Using station-specific OEE metrics, they identified an operation that was frequently experiencing small stops. Upon further investigation, they realized that when vehicles advanced beyond a certain point in that operation, the torque tool hose reach was too short. When the torque tool could no longer reach the desired part, the line would stop. This is the kind of thing that’s likely to be accepted as an inherent, structural limitation and ignored. The longer an operator experiences it, the more they’ll take it for granted as an unavoidable stoppage.
To eliminate this downtime, we simply rewrote the customer’s automated work instructions to begin torquing the part earlier in the line. This was a quick change that improved availability at minimal cost. Without the station-specific OEE metrics, it might not have been identified as a root cause.
Identify and correct causes of performance shortfalls
The example above showed improvements based on station-specific OEE analysis. OEE is also a great tool for gaining larger scale insights, like comparisons of shift productivity.
One of our customers needed to increase line speed from 17 jobs per hour to 23 jobs per hour. One shift had production rates considerably lower than the other. Possible causes included a lack of operator training, lack of just-in-time parts, or machine faults. Without specific data, it was hard to know where to start.
OEE was evaluated for operators, machines and operations, and ultimately they were able to determine that the issue was cycle slowing caused by insufficient operator capability on the second shift. Second-shift operators were given additional training and performance targets were reached.
Leverage OEE data to solve real-time problems
OEE data is not only valuable for longer-term studies and analysis; it can have real-time and near-real-time impact.
Your method of communicating OEE data will influence its effectiveness. Plant floor PLC data blocks can drive machine data to a cloud-based or locally hosted server database. If you’re using the Industrial Internet of Things (IIoT), you’re already gathering that data into your information infrastructure.
Most of our customers use media casting or andon boards to communicate real-time data to the plant floor that’s easily viewable throughout the factory. Andon boards and large screens are perfect for throughput rates and completion targets, and to identify blocked or starved stations.
Equipment fault data from the PLC can be sent via pager or SMS for maintenance requests. Notifications can go to a specific maintenance group or individual, with escalation steps automatically defined by time elapsed and severity codes.
Equipment health monitoring can also take advantage of OEE targets and metrics by tracking actual part consumption and wear data. This information can become part of the machine history, allowing plants to replace supplier recommendations with historically accurate information on true equipment maintenance. More accurate maintenance schedules will obviously reduce unplanned downtime.
Whether you choose to use OEE for machines, stations, lines or all three, it’s one of those brilliant basic tools that can help you improve production short and long term.
Greg Giles, firstname.lastname@example.org, is an executive director of manufacturing execution systems (MES) and Argonaut for RedViking. He leads a team of electrical and software engineers who design and implement the Argonaut manufacturing performance platform and apps, including OEE/FIS, track and trace, error proofing, IIoT gateway, part kitting and sequencing, and the HMI bridge for third-party app integration. He graduated from the University of Michigan Dearborn with a B.S. in electrical engineering. RedViking is a member of the Control System Integrators Association. Visit RedViking’s profile on The Industrial Exchange.