Finding accurate, repeatable ways to improve machine working conditions and reduce failure in laboratories is critical to the success of scientific processes. By identifying hidden downtime, ensuring key instruments run at correct testing speeds, and limit loss of machine equipment failure, technicians and researchers can ensure test quality and reduce flawed tests and re-runs.
In production environments, engineers and administrators leverage a process known as overall equipment effectiveness (OEE) to measure productivity by analyzing equipment performance against its true potential. By adapting OEE to best suit the unique operational workflows found in lab environments, technicians can be provided with the visibility required to make informed decisions.
By bringing OEE thinking into lab environments and adjusting processes to better serve unique workflows, managers can improve lab-based productivity and knowledge-sharing between core members of laboratory and production personnel. Below, we discuss how OEE can be refined for use in laboratories to reduce blind spots, navigate bottlenecks, and improve overall performance.
Understanding overall equipment effectiveness (OEE)
OEE was originally designed for manufacturing environments to define how well a machine or production line is used in comparison to its true potential. OEE in manufacturing and production environments measures overall performance by analyzing losses across three core categories:
- Availability: How consistently a machine is available during the time it's planned to work.
- Performance: How efficiently a machine runs during the time it’s operational.
- Quality: The percentage of produced items that meet quality control standards.
A final OEE score is produced by multiplying these three measurements together and displaying the value as a percentage; a flawless production process would achieve an OEE score of 100 percent.
The case for OEE in lab environments
Both laboratory and production staff could see significant benefits by translating OEE to lab environments. As both practices exist within a continuous feedback loop, with lab-based formulas developed in controlled environments, scaled by production teams, and passed back to labs for refinement, there are clear benefits to codifying shared terms for performance analyses.
By bringing OEE thinking into the lab, managers across both practices can better discuss failure, downtime, and rework to more efficiently identify areas for improvement. When managers can quickly and accurately communicate specific elements of loss to one another without being hampered by practice-specific terms, process improvements can be made effectively.
Why classical OEE fails in lab-based settings
If there are such potential benefits to adopting OEE in lab environments, then why is this metric not more commonly adopted by researchers and technicians? The answer is the same reason why research and production are unique practices; each has its own workflows and challenges.
OEE can be freely adopted with minimal adjustments. In the lab, applying traditional OEE to controlled tests, sample analyses, and trials that often require retests and holds can produce findings that are wildly unhelpful to external teams; required bottlenecks in lab settings cap line OEE and produce ineffectual results.
What OEE misses in the lab
Teaditional OEE is calculated in reference to repetitive, predictable machine-led cycles; it is not designed to account for human-driven workflows. When OEE is applied to lab functions without bespoke refinements, it fails to account for required downtime that silently caps line throughput.
Traditional OEE cannot reliably account for:
- Analytical rework: Failed batches and spoiled assays would be documented as quality defects under a classical OEE framework, failing to account for required troubleshooting.
- Testing variability: OEE measures ideal cycle times for predictable machines, it is not built to account for the multitude of unique, variable tests performed daily in labs.
- Release tests: OEE is refined to prioritize efficiency, making it an ineffectual metric to measure the success of release tests built around purity and safety standards.
- Micro-stoppages: Necessary prep time and micro-stoppages related to calibration and cleaning are not commonly factored into traditional OEE, inaccurately tanking productivity.
- Near-miss events: Procedural near-misses and safety events that do not immediately halt processes but slow productivity are not fairly represented in traditional OEE findings.
For OEE to be effectively introduced into lab environments, informed refinements must be made to factor necessary bottlenecks, downtime, and human-led troubleshooting into OEE calculations.
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How to refine OEE for lab environments
To refine OEE for effective use in lab environments, managers must shift the focus of the process to account for human-led workflows and required downtime. To ensure OEE results remain translatable to production line and wider personnel, the best way to do this is to map the three core categories of OEE to laboratory-specific functions, practices, and equipment.
To provide an example that can be adopted and refined by a wide array of labs, availability can be mapped to instrument uptime, performance to process speed, and quality to testing accuracy.
Instrument uptime
Rather than calculating availability as a straight measurement of equipment readiness, a usable metric can be produced by measuring actual uptime and dividing that by planned schedule time.
Calculations can exclude necessary micro-stoppages associated with calibration, cleaning, and preventive maintenance, while accounting for unplanned downtime related to errors and system failures, to produce an availability score that accurately reflects productivity in lab environments.
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Process speed
Process speed, or the actual time taken to conduct a test measured against its ideal completion time, can provide an accurate, interpretable analogue for overall performance in traditional OEE.
By leveraging process speed as an alternative metric, managers can appropriately account for manual bottlenecks such as pipetting, environmental monitoring, and centrifuging delays that may slow performance times but remain central to the successful performance of the test itself.
Testing accuracy
Comparing the number of successful, error-free tests to the total number of tests performed can provide an appropriate and actionable analogue for production quality in traditional OEE settings.
In the same way that defective products are counted against production quality, tests failed as a result of sample contamination or assay reruns can be counted against testing accuracy, giving lab managers a process-specific quality metric that can be communicated to the production line.
Establishing real-time visibility across the lab-line interface
By refining traditional OEE to support adoption into lab environments, lab managers can measure productivity and better communicate required improvements to production personnel; this can help to drive practical optimizations and establish real-time visibility across the lab-line interface.
Lab managers can demonstrate how output is impacted by testing delays and bottlenecks using metrics that the production line understands, enabling teams across both practices to align key processes like testing and factory throughput to minimize total downtime across the organization.
