When it comes to lab processes, it is generally frowned on to make decisions without reliable data to back them up. Yet, when it comes to laboratory equipment optimization, critical decisions are often made based on incomplete information. Understanding the OEE (overall equipment effectiveness) of your instruments means understanding how they are being used and, ultimately, how their ROI can be enhanced. In this post, I briefly outline 3 outputs from analyzing your lab equipment utilization data that can help you to cut costs, minimize idle time, and improve your laboratory’s overall performance.
Optimize equipment uptime and throughput
Equipment is expensive, so you need to ask yourself: are we using our equipment as efficiently as possible? Is it giving us the desired return on investment? Should we invest in new equipment or solve capacity bottlenecks by better utilizing the equipment we already have?
To fully optimize equipment uptime and increase throughput, you need to answer those questions based on historical instrument usage patterns, or even better: prevent equipment under-utilization before it happens.
The obvious requirement here is to have the data readily available to generate insight into your Overall Equipment Effectiveness. From there, you need to understand the root-cause of issues and identify reasons for downtime by answering questions like:
Can we increase the use of idle equipment? Are there opportunities to make better use of our available equipment?
Can we increase uptime? Why was equipment unavailable? Can we work on the root cause?
Can we increase the throughput of the equipment that is being used? Can we increase the average run size? Reduce the amount of reanalysis? And what are the root causes of reanalysis?
Then you can start modeling and use forward-looking capacity planning to prevent equipment idle time before it even occurs.
Most common reasons Overall Equipment Effectiveness is low
Here are three types of insights that our customers come across when they analyze instrument utilization data in their Binocs environment:
1. Unnecessary equipment idle time
Having a system that captures and visualizes data about instrument usage—both historically and forward-looking —is essential to understand better when, where, and why instruments are being under-utilized. That system should enable you to visualize the relevant data easily and use these insights to increase efficiency and minimize idle time.
2. Hidden usage patterns that cause productivity problems
When you see that utilization drops, the reasons can vary. For example, instruments sometimes have a greater tendency to fail after being used non-stop for several hours; staff might be more tired at night and therefore less productive; analysis runs may be delayed because the necessary samples are not arriving in the laboratory on time; some methods may be more error-prone depending on the matrix… If you have better visibility of when and how equipment is being used, you can identify and troubleshoot problems more effectively. What might seem like obvious solutions to increase productivity often turn out to be ineffective because they don’t address the problem’s root cause. To spot patterns and uncover the true root causes, labs need to visualize and analyze the available instrument utilization data more easily.
3. Inefficient usage of resources across labs
When managing resources across multiple labs, it is crucial to have an accurate overview of productivity, workloads, performance fluctuations, and overall quality. It is easy to lose sight of the big picture when operating many different lab instruments across multiple sites. In such cases, it makes sense to look for a solution that enables global data access, visualization, and analytics. Better visibility of instrument usage across your entire lab network can improve transparency, allow analysts to compare system performance more easily, and improve efficient deployment of resources.
The data needed to provide answers to those three questions are provided by a digital lab twin and covers what we call the ‘3-layer approach’ of actionable KPIs:
Having high-level visibility = measure overall equipment efficiency.
Identify opportunities for improvement: get to the root-cause of equipment idle time/inefficiency.
Simulation capability: to have forward-looking capacity planning to prevent equipment idle time before it even occurs.
Frederik is VP of Sales at Binocs and has over 25 years of experience in laboratory systems, including as a Project Manager for major LIMS rollouts at various global enterprises. When he joined Bluecrux in 2017, his experience and business development skills significantly boosted the commercial success of Binocs.