So, you’ve done your research and are convinced about the value of automation for your internal workflows and processes. How do you go about planning to automate? What kinds of questions should you be asking?
The first step is to identify your biggest pain points. Which tasks are taking a significant amount of scientists’ time? Where are the biggest challenges within your process? Are there particular processes where automation would offer the biggest benefit?
It’s also important to assess the biggest opportunities for improvement to your process. Would automation of some of the simple, repetitive steps in the lab free scientists to focus on other tasks? For example, experiments that require scientists to manually pipette samples in 384-well microtiter plates are very time-consuming, risk repetitive strain injuries, and create opportunities for errors caused by small inconsistencies. How would automating that process would make your team more efficient and productive?
Once you’ve identified the best points for automation, the next step is to look at commercial solutions that are readily available on the market. For example, suppose one of your pain points is moving plates among multiple systems in the lab at specific times. In that case, you might consider bringing in an automated robotic arm and some scheduling software that handles this task with little to no input from the scientists. Finding an off-the-shelf solution is always helpful, but you may need to consider a custom-built solution instead for some situations.
It’s also important to think about your budget for an automated solution. Weigh the costs of an automation solution against the long-term benefits — speed, increased efficiency, reproducibility, productivity, data integrity, and possibly lower reagent costs for automation that miniaturizes processes. Typically, you can find the right point where automation adds significant value to your lab and is an obvious win.
Another factor to consider is who will be using the system. Is it scientists or automation engineers? How much background knowledge or expertise will they need to operate the system? There is always a bit of a learning curve when it comes to automation — sometimes a steep learning curve.
At Biosero, we routinely work with customers who want solutions that scientists can use with little experience with automation. That’s our sweet spot — we make automation for scientists, not engineers. We build approachable and functional solutions, whether the user has tons of automation experience or none at all. Our team also works closely with customers in the implementation phase to ensure that all users feel comfortable with the new system. We strive to make our solutions simple enough to be used by researchers to do great science and flexible enough to grow with a lab’s needs.