There are two common Lean metrics for determining just how efficiently equipment is being utilized, and both are relevant for scientific research operations. Equipment covers a wide variety of capital items, including automation platforms (liquid handlers, integrated robotics, etc.), scientific instruments (e.g. microscopes, cytometers, mass spectrometers), or even entire specialized facilities (like a biologics pilot plant). All of these can be analyzed using this methodology.
The first metric is Overall Equipment Effectiveness, or OEE. The second is Total Effective Equipment Performance, or TEEP, which builds on OEE (both acronyms are a lot easier to remember than their full definitions!).
OEE = Availability x Performance x Quality
Availability = Available Time / Scheduled Time
Performance = (Parts Produced x Ideal Cycle Time) / Available Time
Quality = Good Units / Units Started
Available time is scheduled time minus any outages or other maintenance time. Performance measures how closely the number of parts produced (or experiments run) matches the time that was available divided by the ideal cycle time. Quality measures what fraction of runs is successful.
TEEP adds another factor, the extent to which you are fully loading your equipment.
TEEP = Loading x OEE
Loading = Scheduled Time / Calendar Time
Loading is the time the equipment was scheduled for use divided by calendar time. If equipment is scheduled for use 8 hours a day, 5 days per week (40h/168h), loading is about 24%.
For a modern manufacturing facility, the target for TEEP is 24 h x 365 d operation (loading = 100%), with world-class OEE of 85%. OEE of 85% reflects availability, performance, and quality all of about 95% each. Generally, local operations managers try to achieve high OEE, while more senior managers drive better TEEP through increasing and smoothing product demand and efficiently managing the facilities footprint. In Lean, smoothing is an important concept and key goal for operations. Interestingly, OEE and TEEP have qualities of geometric averages, and thus variability is more easily detected.
The real strength of this kind of analysis is that it helps a manager figure out where the best opportunities for improvement are. For example, let’s say you manage a sequencing facility that has more orders than you can handle (loading is high), excellent up time (high availability), low numbers of failed runs (high quality), and really efficient processing times (runs completed x ideal cycle times = available time). In such a case the only significant way to get extra capacity is to buy more machines. On the other hand, if an operation has problems with quality, down time, or excessively long run times, the component OEE parameters will identify which areas need attention and, importantly, how much of a percentage gain you can hope to achieve by improving each parameter. Monitoring OEE and TEEP and their component parameters thereby facilitates better management by focussing attention on performance parameters that will have the greatest impact.