Laboratory automation is seen as a silver bullet for reducing errors and cutting costs. But determining whether you should invest in automation is a multivariable equation, and the output is not a binary yes/no. The answer hinges on numerous decisions.
The first step to determine if a lab should invest in automation is evaluating its needs, expected return on investment (ROI), and safety considerations. This phase is where the bulk of decision-making and analysis will happen.
Having an acceptable ROI is vital to getting leadership on board with introducing automation. It should be the cornerstone of your argument. While the specifics of what constitutes a good ROI will be different for every organization, the general parameters dictate:
1. You can afford the costs of the equipment and infrastructure upgrades
2. Automation won’t compromise quality
3. The savings yielded from automation will pay for it in a reasonable timeframe
By fulfilling at least these requirements, you have the foundation for an airtight argument.
Note that automation shifts the costs associated with lab processes from operational expenditure (OpEx) to capital expenditure (CapEx). Capital expenses are large, one-time purchases with long-term horizons in mind, which demand more money upfront and limit flexibility to adjust the system after purchase. Processes carried out from OpEx have lower financial barriers in the short term and are more flexible. Lab leadership must take this shift into consideration. “With purchasing and installing new automation equipment, we always have the upfront cost associated with it, but over time...the cost of automation comes out to be cheaper,” says Meghav Verma, product manager at the National Institutes of Health. “However, this is not a given, and we always have to go through the evaluation process of understanding the long-term gains. We also have to consider the time and opportunity cost saved when employing automated solutions. These costs add up in the long run.”
Maintenance and service fees should also be considered. Such fees remain in OpEx because they are recurring, but they should be accounted for in the evaluation phase to ensure that the lab can justify all costs associated with automation.
Evaluating laboratory needs
There is an inverse relationship between flexibility and automatability. If your lab’s processes are not standardized and rely on in-the-moment judgment calls, then it’s possible that your lab isn’t yet suited for automation. Automating prematurely can be more costly than sticking with manual processes until the processes have been better defined. Tesla Motors’ infamous Model 3 car production line is a prime example of premature automation. Tesla designed an assembly line populated with more than 1,000 robots to manufacture the vehicles, but the robots couldn’t handle the changing geometry of parts coming in different positions. Tesla had to hire several hundred employees to keep up with their production quota, undermining the point of all that automation.
Are processes in your lab too reliant on human intervention to automate? If you’re not confident the processes can be automated, work on standardizing them. Don’t neglect documenting the processes, either. Besides making onboarding smoother for new staff, having detailed documentation will be helpful when you do automate and must translate manual processes into protocols executed by machines.
A company is ready to introduce automation when they understand the current process and its bottlenecks, they can justify the investment, and they're ready to support the equipment.
While automation can positively impact safety, such as by reducing the risk of repetitive strain injury, in some cases it can also complicate it. As automation increases your lab’s throughput, you may need to store higher quantities of chemicals in the lab. It’s possible that this extra storage will push your lab’s fire control zone past its maximum allowable quantity. Work with the facility’s safety specialist to accommodate the new equipment and chemicals.
In short, “a company is ready to introduce automation when they understand the current process and its bottlenecks, they can justify the investment, and they’re ready to support the equipment,” says Hayden Allen-Galusha, automation engineer at Kumi Manufacturing of Alabama.
After opting for automation, the implementation phase begins. There are a few things to consider in this phase: where to start automating, using project management tools, and training.
To begin automating, look for the bottlenecks in your lab’s processes. Such tasks will take disproportionately more time to carry out than others and often will be monotonous. An example would be pipetting. Rather than having an employee pipette dozens of times per day by hand, purchase an automated pipettor. From there, you can start expanding outward, streamlining more processes in your lab as you learn how to identify opportunities to automate.
Software automation can also be a good starting point as the barrier to entry is much lower than hardware automation, but still teaches you how to identify automation opportunities. For instance, rather than exporting results from an analyzer with a USB drive manually, explore options for connecting the analyzer directly to an electronic lab notebook so that results are relayed instantly.
Using project management tools
When you begin implementing automation, you’ll need more than just emails to effectively plan, communicate, and delegate all the tasks associated with the endeavor. This is where dedicated project management tools come into play. There are a wide variety of tools and techniques available, ranging from things as simple as Gantt charts to collaborative webapps. Before the implementation officially begins, carve out some time to demo a few project management programs with other stakeholders. Make a group decision on which software best suits your needs and strive to update it consistently throughout the implementation.
Robust training is essential to successful automation. The workers who will be overseeing the equipment must become familiar with not only usage, but also maintenance and troubleshooting. There will be a lot to learn, especially if multiple systems are added simultaneously. “There has to be consistent training programs associated with new equipment to allow the users some time to learn and get used to the system, rather than feeling like they’re being thrown in the deep end,” Verma says. Depending on the backgrounds of your staff, cross-training may be an effective strategy. Those who have carried over experience using automated equipment from previous positions will catch on faster, and they can assist in training other staff members.
Most important is to foster an environment of open communication. Staff must feel comfortable seeking guidance whenever needed. Don’t become too ambitious with the training timeline, as staff may get discouraged if they aren’t meeting your expectations.
Successful automation can be seen as a three-way partnership between hardware, software, and people. To sustain this partnership, consistent effort must be put in all dimensions. For hardware and software, it’s straightforward: keep up with preventive maintenance, update the software and, ideally, integrate both with your lab’s existing informatics platform. But to properly address the most challenging aspect—people—requires consistent communication and trust.
Cultivating trust and communication
“It’s a misconception,” Allen-Galusha says, “that automation’s sole purpose is to replace human operators. Many times, automation is used to make a process more efficient, increase quality, or make a process safer with the same amount of manpower.” As the lab manager, you should communicate that introducing automation will not affect your workers’ job security and ensure that there are opportunities for staff to step into higher-value, more strategic roles. This will imbue their jobs with more meaning and assuage their fears of replacement, boosting morale. Additionally, you should remain open to feedback and ideas. Your staff are the ones most familiar with the newly automated processes, so they will have insight into how the processes can be improved.
Staff must also trust the equipment. “The onus lies on the lab manager to make sure the hardware and software is not completely changing the way the scientists were performing their tasks,” Verma says. “The equipment should feel familiar, which will increase adoption. Trust is an important aspect of this, where users need to build that trust by verifying the data.”
Ultimately, automation is no silver bullet. Efficiency and standardization are the foundation of a fruitful automated system. In the words of Bill Gates, “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.”