Automation is a boon for many labs, but implementing it can be a significant challenge. Following her insightful presentation at the recent Lab Manager Automation Digital Summit (now available on demand), Tina Yauger, principal consultant at Clarkston Consulting, sat down with Lab Manager to share more insights on how lab leaders can implement automation in their labs.
Tina Yauger
Credit: Tina Yauger
Q. What are the essential factors of a cost analysis when calculating the ROI of an automated process?
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A. It is essential to first consider investments in both capital and operational costs. Regarding capital expenses, items such as hardware, software, equipment, licensing, and any subscriptions, where applicable, should be captured. On the operational side, factor in ongoing maintenance and support of the capital expenditures. Additionally, project costs for both implementation and validation need to be considered. These items are for any consulting, training, and necessary employee backfills. Then, consider areas where there will be gains with automating processes, such as increased efficiencies, productivity, and turnaround times, while reducing data integrity concerns, waste, labor costs, and production times. Soft factors, such as employee satisfaction, inspection or audit readiness, and continuous improvements, should also be considered in the analysis.
Q. What do you find to be the largest hurdle in gaining buy-in for automation from leadership? And lab staff? How do you address each?
A. The biggest hurdle from leadership is often the upfront cost, as it can be substantial depending on the current state of the laboratory. Laboratories have a mixture of equipment and applications, typically with varying capabilities for automation. Ensuring these technologies can interact and seamlessly share data can be a challenge and may require an upgrade or new purchase for software and/or equipment, incurring a potentially large expense, in addition to the time and effort to implement the automation. Automated activities would need to go through the lifecycle of development, testing, validation, and deployment, which can also be time-consuming and costly. Additional subscriptions, maintenance, troubleshooting, and downtime should also be factored into the overall costs of automation. To address this, it is important to showcase a firm ROI that clearly defines the benefits of automating tasks in the laboratory, such as gains in efficiency and throughput and decreases in errors and defects. Leadership can then make informed decisions regarding investments in automation.
With laboratory staff, the hurdle is often the resistance to change. Change is difficult for most people, and implementing new systems brings along a modification of daily processes and procedures to a digital platform. When laboratories adapt [to] change from manual processes, the concern is that the system may not be flexible or efficient enough to successfully perform their laboratory tasks in a timely manner. The way to address this is with a phased approach, so there are many small changes to fully automate as opposed to doing this all at once. Additionally, a robust change management procedure with good communication and solid training will help to ease the transition.
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Q. Automation demands process standardization. How should a lab manager approach standardizing processes?
A. When approaching standardization, several factors should be assessed. First, assess any workflows, processes, and systems in use in the laboratory and review the current state to identify any areas of disparities, inconsistencies, pain points, and constraints. Next, define what the future state of the laboratory should look like and detail areas where changes can be made to have seamless workflows, integrated systems, and reduce constraints within the processes. Once you have the current and future state process maps defined, then a roadmap can be built to standardize and harmonize processes. It is important to have stakeholders buy in early in the process to demonstrate how efficiency can be increased while reducing risks and costs. Finally, implement these changes in phases for better lab user adoption and an easier transition.
Q. You have mentioned in the past that automation in R&D labs may inhibit innovation. How can R&D labs continue encouraging innovation without foregoing the benefits of automation?
A. Research and development (R&D) laboratories need flexibility in processes, [whereas] quality control labs operate in a more structured manner. Automation requires standardization for repeatable results, and standardizing some processes eliminates flexibility. Processes in an R&D laboratory should be assessed for automation where completing a task may be lengthy, have a high rate of error, or is highly repetitive. Often, tasks such as pipetting or liquid handling are automated in an R&D laboratory. An ELN can be used to digitize experiments while allowing flexibility and capturing audit trail data. Processes for sample storage, retrieval, and preparation can be automated to reduce bench time for repetitive tasks. Robotics can provide automation of many parts of the lab workflow and can be flexible by design. This allows time for researchers to focus on the innovation needed for developing new or improved products while still benefiting from automation.
Automation requires standardization for repeatable results. . .
Q. With the advent of automation and AI, some workers are concerned about how these technologies will impact their job security. They cite the idea that even if an automated system cannot fully replace humans, it will displace enough workers that there will still be a net job loss. How would you recommend a lab manager address these concerns?
A. A lab manager should consider the realities of their particular laboratory and the intent of the automation. In some cases, it does result in job displacement as a reduction in labor cost can be an organizational goal of automation; however, automation also creates new opportunities as it transforms day-to-day responsibilities. A lab manager could take the opportunity to train laboratory staff to maintain the automated processes and shift responsibilities to technology or software-based support. Developing laboratory technology expertise is a great opportunity for an experienced lab analyst. Either way, a lab manager should be completely transparent with the staff and open channels of communication for employees to ask questions and express their concerns.
Q. Bill Gates once said, "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." Do you agree with this take?
A. Yes, I am in full agreement. An operation should be streamlined for efficiency before automation is even considered to preemptively capture areas of risk and error. By not doing this, errors will be magnified as there is a reduced or lack of human intervention during the process, meaning these risks and errors would be likely to happen each time the task is performed.
Q. How do you envision AI changing the landscape of lab automation?
A. I believe AI will significantly change the landscape of automation with increasing benefits. Until recently, automation has been based on a standardized process and rules. AI can step in [to adapt] processes by being able to intervene in decision making based on specific result trends. This will only increase efficiency and allow handling of even more complex workflows and tasks. This will also be a large benefit to R&D labs as it can add flexibility to a once-rigid automated process. It is important to remember that AI is only as good as its data, and it needs a strong, robust dataset to make decisions accurately.
Tina Yauger is a principal consultant at Clarkston Consulting. She has more than 13 years of experience working in the life sciences industries, where her clients are primarily in the pharmaceutical, biotech, cell and gene therapy, and research and development areas. Yauger specializes in laboratory information management systems (LIMS) implementations as well as in laboratory informatics and operations. She also leads the laboratory informatics forum at Clarkston to promote innovative thinking and coaching in laboratory informatics and laboratory operations. She earned her bachelor’s in biochemistry from Cedar Crest College in Allentown, PA.
