Automation has transformed laboratories over the past few decades, as replacing manual procedures in the lab with technological substitutes has become more common in modern laboratories. The major advantages of laboratory automation are improved productivity and consistency in the lab whilst minimising the influence of individual working styles on laboratory processes. Laboratory automation through replacing human labour with intelligent devices and machine learning - examples of which include dedicated companies such as Synthace as well established corporations like Siemens and Roche - may be effective for large organisations. However, resorting to high-end automation solutions may not be cost-effective for smaller R&D labs.
Process automation may seem exclusively suited for large-sized labs but there are many ways of automating processes at smaller scale, depending on how one interprets automation. For instance, at smaller scale, it could mean automating smaller labour-intensive tasks to boost productivity or digitising the current workflows, so that scientists are able to focus time on tasks where expertise is required. Whichever approach is used, planning a strategy for deploying automation is equally important.
An effective way for driving process automation for small- and medium-sized R&D labs is through implementing software-driven solutions for laboratory inventory and data management. Through implementing such strategies, a growing R&D lab can expect to:
- Increase productivity
- Improve reproducibility and quality of experimental results
- Simplify processes
- Protect instruments and samples from overloading and contamination
- Enable data collection of difficult-to-record data
- Receive help with compliance and regulations
Taking various forms in the modern lab environments, process automation has a wider reach than ever before. Even in scientific environments like microbiology labs, where process automation used to be difficult to imagine, there are now many opportunities to include it in various workflows. In this article we will provide 4 tips on how to maximise the value of automation for small to medium-sized R&D labs.
1 - Automate individual processes
Finding a starting point for lab process automation can prove difficult. This is why breaking down the lab processes into smaller components before planning automation strategy can be the key to efficient deployment. Individual processes which form a part of a larger lab project can be handled more efficiently through implementing data and asset management software. For example, in a cell culture lab, this can be accomplished through employing a single software to track metadata such as expiry date, concentration and volume for cell culture ingredients. By having an automated system that tracks materials and consumables, ordering new ingredients will become exponentially more efficient. Similarly, automating protocols will boost efficiency and minimise the risk of skipping or forgetting a step on an experiment level. A software-backed system will smoothen laboratory management - related processes and will have an impact on the whole lab by saving time and maximising productivity. You can read this article to find about other benefits a software-backed lab management system can bring to a R&D lab.
2 - Make automation work for your company’s unique scenarios
Referring to standard routes for automation may not be the most efficient way to reap its benefits for small and medium R&D labs. Companies need to identify points of intervention for automation which are specific for a business model and lab operations. Implementing an adjustable software for lab management can be a good way of attaining flexibility in automation deployment. It is crucial for lab leaders to understand what the workflow is and identify how automation can improve it. For example, if a laboratory specializes in sample testing, the process of testing is likely to already be automated if the sample is analyzed by a machine. What lab leaders should focus on is automating the specimen handling and other manual operations aiming to boost productivity and minimize human error.
Lab leaders should get a detailed overview of minute lab operations to identify potential intervention points. A good way to attain this is to record every operation occurring in the laboratory throughout the project cycle, which can itself be difficult without software-backed solutions. Start recording your data with digital lab management software to overcome this obstacle and get a full understanding of the routine workflows at the lab.
3 - Automate manual processes
Start a lab automation journey by automating labour-heavy manual tasks. This covers the spectrum of processes from simple pipetting and sample preparation to assay development and clinical testing. Automating routine manual tasks is a good way of keeping expert talent concentrated on research issues which require expertise. However, make sure that you are automating the processes rather than augmenting them. These concepts may be easily confused in R&D environments which require scale. The key aim of lab process automation is increasing productivity whilst minimizing human error and maximizing the benefits of human talent. Whilst increasing the process output by implementing new technology may be good for companies, it does not necessarily mean that lab automation is taking place.
4 - Explore the options
Once the process where lab automation can be implemented is identified, it is advisable to look into more than one providers or vendors. Find a system which suits the ubique requirements of your lab. Shortlist the potential vendors of an automation solution and systematically narrow it down by establishing a back and forth communication with the provider. Software providers often offer trial periods where a client lab can try out the system without committing to it. Similarly, hardware providers often provide workflow analysis for client labs, designing the automation system and evaluating potential benefits. Fully relying on a vendor’s analysis may not be ideal, but adding it to your own evaluation may be beneficial. Visiting or surveying the labs which have benefitted from the vendor of interest will also help, as it will allow you to gain a first-hand understanding of the benefits or issues the vendor will present.
Labstep is a provider of scientific data management software for R&D organisations across industries (Biotech, Pharma, Biology, Chemicals, Agriculture etc) who need to manage, capture, share and use data effectively.
The Labstep platform is an end to end flexible research environment that connects your notebook, inventory, applications and data in one collaborative workspace.
To learn more about Labstep’s lab inventory management module, get in touch. Contact us or book a demo today.