Planned Downtime

Routine maintenance and proper training can extend the life of any piece of laboratory equipment. Unfortunately, even with proper maintenance, and sometimes resulting from lack of maintenance, large equipment can break down—sometimes at the worst possible time.

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Extending Equipment Life Through Overall Equipment Effectiveness

Routine maintenance and proper training can extend the life of any piece of laboratory equipment. Unfortunately, even with proper maintenance, and sometimes resulting from lack of maintenance, large equipment can break down—sometimes at the worst possible time. The subsequent bottleneck and chain of events, including scheduling a service vendor, coordinating purchase orders, and redirecting or halting work planned on that equipment may lead to increased employee frustration, lost research time and materials, and increased equipment cost of ownership. Fortunately, there is a way to reduce equipment breakdowns and related service costs so your team can focus, uninterrupted, on your science.

In addition to preventive maintenance and training, a key performance indicator known as overall equipment effectiveness (OEE) can highlight improvement areas that may prevent unwanted equipment breakdowns in your lab. OEE is a well-known measurement in production environments for capturing equipment performance, and is gaining momentum in life sciences. As part of an overall improvement strategy, OEE is not difficult to calculate, and it will create lasting benefits for your lab.

OEE captures relative equipment effectiveness over a set time period by measuring unplanned downtime, resulting defects/errors, and equipment operating speed. Once calculated, you and your team can investigate issues impacting the OEE value and implement appropriate corrective actions. If you can improve OEE, you can increase overall lab efficiency, reduce equipment repair costs, and focus more on your science.

OEE may be useful in your lab if equipment:

  • Is used more than other equipment
  • Is critically important to processes, thus creating work stoppages when unavailable
  • Has frequent unplanned downtime
  • Has poor quality performance
  • Has a specific purpose that cannot be duplicated in the lab

Calculating overall equipment effectiveness

Take the time to collect data. Calculate the OEE by measuring unplanned downtime, processing speed, and quality of results, and keep a log of all equipment-related issues (Figure 1).


Figure 1. A variety of factors may influence overall equipment issues and costs.

OEE captures unplanned downtime. It is not meant to minimize planned downtime such as routine and preventive maintenance, new user training, and operations that should be regularly scheduled to extend equipment life. Unplanned downtime is time when the instrument should be functioning but cannot complete its assigned tasks. This includes broken machinery, occasional stoppages during a process, and setup and adjustment time prior to and during the process. Users in a lab often tweak scientific equipment to find the optimal setting without realizing that this is unplanned downtime. For example, if an instrument is scheduled to run 35 hours every week and is actually unavailable for five hours during that week due to setting tweaks or other unplanned downtime, it is only available for 30 hours per week, or 85.7 percent of its scheduled time.

Actual processing speed compared to the manufacturer’s specifications is also considered. Measurements may vary based on the nature of the samples used or individual labs and could include microplates per minute, units per second, time to incubate, assay read time, or any other processing speed measurement. Continuing with our example, let’s assume the instrument’s manufacturer specifies a processing speed of 40 units per second and the actual output is 38 units per second. The instrument therefore performs at 95 percent of the manufacturer’s specification.

The third and final OEE parameter is quality of the equipment’s output. Does another sample have to be processed as a result of a previous incorrect outcome? What percentage of this equipment’s processed outcome is correct the first time? Concluding with our example, let’s assume that one out of every four samples has to be reprocessed. This means that the instrument’s correct output is 75 percent.

Using unplanned downtime, processing speed, and equipment output, OEE may be calculated. Using our example parameters of 85.7 percent availability, 95 percent processing speed, and 75 percent quality results, we can calculate that OEE is 0.857 x 0.95 x 0.75 = 0.61, or 61 percent. Fundamentally, the instrument is losing effectiveness, because it has less availability for valuable processing, runs at a slower speed, and produces inferior results.

Once OEE is calculated, a small team or an individual should consider the most significant problem to address. Per the example we used, users may be spending too much time tweaking the instrument (unplanned downtime). When investigating the need for setting tweaks, consider the following possible causes:

  • Worn parts
  • Partial contamination (spills, clogs, dirty optics, etc.)
  • Decreased instrument accuracy
  • Inconsistent methodology from user to user
  • Varying material types

Now research further to determine their root cause. Why are parts worn? Why and where has contamination occurred? Why is there an accuracy issue? Why are employees doing the same test differently? Are the sample materials appropriate for this instrument? This applies to all potential causes, and there could be several levels of subsequent questioning before the final root cause is identified. Once the root cause is identified, brainstorm possible solutions, test them, and measure their impact on OEE and overall lab performance.

In line with any corrective action or improvements, financial benefits should also be considered. Quality improvements could translate to less wasted expensive materials, consumables, or precious samples. Increased available time and processing speed may provide increased overall throughput.

It is important to remember that the more improvements you make to the equipment’s operation, the more results you should get back. With that said, OEE is not a quick-fix method toward instant benefits. The time to recognize impact is dependent on the change implementation time, and benefits are based on sustained, long-term activity.

Other factors to consider

Although a world-class benchmark is 85 percent OEE or higher, this number is not absolute, and attaining it should not be the focus. Manipulating OEE metrics to achieve a high OEE value will only result in a high, meaningless number; it will not contribute to improvements in your equipment’s life or efficiency. Instead, determine and correct root causes where feasible and set a realistic and lab-specific OEE target number.

Regularly scheduled preventive maintenance and user training also significantly contribute to extended equipment life and reduced unexpected breakdowns and related repair costs. Maintenance and training can be performed by internal maintenance and training designees, manufacturer service representatives, or authorized third-party service providers. For lab managers looking for external assistance with a maintenance improvement program, many states offer assistance through the Department of Labor or economic development programs. Many state governments also have funds for Lean and Six Sigma projects, which utilize OEE and Total Productive Maintenance.

In a laboratory accustomed to running equipment despite quality issues and frequent work stoppages, it may seem paradoxical to improve lab efficiency through increased equipment downtime. However, this planned downtime improves quality, minimizes unplanned downtime, saves money, and improves timely results and performance.

Categories: Laboratory Technology

Published In

Is the Rollercoaster Ride Over? Magazine Issue Cover
Is the Rollercoaster Ride Over?

Published: March 1, 2012

Cover Story

Is the Roller Coaster Ride Over?

The laboratory industry enjoyed several years of robust growth from the late 1990s until 2003. Record research and development (R&D) investments by the biopharmaceutical industry, in combination with the doubling of the U.S. National Institutes budget, allowed for continual double-digit growth rates.