Calculating the return on investment (ROI) of automation used in biotechnology research and development can be very challenging. It’s a bit easier to do in industries such as manufacturing, where ROI is measured in terms of the number of units produced, how long it takes to make them, and what it costs. In lab settings, assessing ROI is more complicated because it’s hard to attach values to data points without knowing which would result in the next blockbuster drug.
We need different metrics to assess the value of automation in research settings. One way we do this at Biosero is using what we consider the six pillars of automation. These six factors offer an alternative way of thinking about the benefits of automation that work for any project or setting.
Productivity: Perhaps the most obvious goal of automation is boosting productivity and efficiency. Labs can run their experiments for much longer without hiring more staff or requiring employees to work longer hours. Scientists can run experiments around the clock, even when they are not at work.
Safety and ergonomics: A common hazard of working in the laboratory is the risk of workplace injuries due to the repetitive nature of certain tasks. Scientists can suffer repetitive strain injuries from experiments that require repeated pipetting or lower back injuries due to the hours they spend hunched over experiments under fume hoods. Automating some of these repetitive tasks can help prevent these injuries.
In terms of safety, automation can help reduce scientists’ risk of exposure to infectious pathogens; this has been especially important during the COVID-19 pandemic. Rather than have scientists interact directly with the samples, some labs use automated robots for experiments involving the virus, significantly reducing scientists’ risk of exposure. Automation is also valuable in laboratories where scientists work with hazardous chemicals since using robots to handle the chemical makes working with them much safer.
Cost: Automation can help scientists run experiments using vanishingly small volumes of samples, some of which are too small to be seen with human eyes. Today, it is possible to run experiments using samples at nanoliter and picoliter scales. Labs can reduce costs by miniaturizing their experiments, reducing the amounts of reagents, samples, and consumables used.
Speed: The speed at which labs can complete projects is a significant value proposition of automation for many labs. But it is essential to address the common misconception that automating processes makes them faster. Automating does not always make a process faster. However, even when workflows cannot be completed quicker, automation improves the accuracy and precision of experiments compared to what humans would manage.
Quality and data integrity: When scientists perform tasks in the lab, there is always some variability even if they follow the same protocols and standard operating procedures. Automation addresses this by ensuring that repetitive tasks are done the same way every time. This consistency improves reproducibility, one of the six pillars, and the quality of the product and the generated data. Automation also helps with data integrity by capturing detailed information about each experiment. The automation software can track scientific, instrument, and environment data during the experiment, giving scientists access to a digital, trackable audit trail. Data such as whether the pipettes were calibrated, how many tips were used, how the buffer solution was prepared, errors during the experiments, details on sample incubation time, and so on, are automatically recorded. This kind of data capture is far more detailed than scientists would realistically track manually in lab journals. With automation, scientists can get a complete picture of their experiments.
Reproducibility: For scientific results to be valid, others must be able to perform the same experiments and get the same results. A lack of reproducibility hinders scientific progress and wastes both time and money. Automation boosts reproducibility by eliminating variability seen in manual workflows and capturing all of the data associated with the experiment. By reducing the risk that an experiment won’t be able to be repeated, automation accelerates the transferability and validation of scientific processes between scientists, labs, or even across different sites around the world.
With these six pillars of automation, scientists and engineers can better evaluate the value proposition of implementing an automated solution in their labs.