Dr Patrick Courtney, Member of Board of Directors of SiLA, compares the uptake of automation in industries other than drug discovery.  

 

In our industry we’re often deep in our day-to-day work, but once in a while someone will ask a question that stimulates us to look outside and see how other industries are dealing with the challenges of applying automation, robotics and AI.  

Earlier this year, this happened twice. Once on a panel at the NexusXp pavilion, SLAS in San Diego in January and again on a panel at the FutureLabs LIVE show in Basel in May. I attended both and share some of what was discussed. 

A factory for cars and chips or data?

At both events comparisons were made with automotive manufacturing. That industry makes extensive use of robotics and automation to bring us quality products at a price unimagined a few decades ago. So, it is natural to ask if we could learn some lessons. 

However, a closer look suggests this might be deceptive. The lab is not a factory – except as a data factory where we privilege the scientist and their experiment over the asset in use. And as panellists from the pharma industry commented, suppliers from assembly and manufacturing have different terminology, priorities and values. The automotive industry has a reputation for a somewhat confrontational relationship with suppliers. In the lab we remain very multi-disciplinary – from biology and chemistry to engineering and IT. Furthermore, the automotive industry currently is going through some painful changes in the move to making electric vehicles, with very different supplier chains. Indeed, the leaders in this change are companies such as Tesla, whose approach to manufacturing is more inspired by internet technologies than the traditional automotive industry.  

If automotive shows us the limits of direct analogy, the semiconductor industry offers another model. This is another industry that has brought us all an impressive range of products. This has been achieved by the relentless pursuit of perfection in the processes, and collaboration across the supply chain to a shared roadmap. The robots used here are incredibly fast, and the SCARA arms we see in many labs are based on these designs. However, there are some significant differences. Semiconductor automation is asked to handle small hard objects not liquids. In the semiconductor industry yield is everything. In the lab we typically focus on the quality of the data and time-to-results rather than hyper-efficient use of resources. 

Focusing on the data  

Beyond manufacturing, telecom and IT industries are also shaping how labs think about data. Recently we have seen new lab automation players with a background from the telecoms industry. These bring a deep appreciation of the central role of data, and an understanding of the role of standards and agile development processes in building enterprise-level systems that work. Indeed, interoperability standards for the lab such as SiLA2 enable plug-and-play integration. For example, a liquid handler and an analytical instrument can share data seamlessly. It builds directly on modern internet technology such as gRPC and HTTP2 rather than unsuitable and outdated protocols designed for the factory. 

Collaboration and exchange as the way forward

These ideas are certainly valuable and being applied now. However, we should bear in mind differing expectations in terms of governance, for example when we are dealing with patient samples. So while it is fascinating to look beyond our, sometimes siloed, work, ultimately only we know the challenges we face.  

If we combine lessons from these industries while respecting our unique needs, the lab of tomorrow could be both more connected than ever. We can and should address these opportunities, together, in collaborative structures such as the Pistoia alliance and SiLA consortium. 

 

About the author:

Dr Patrick Courtney has 20 years industrial experience in the development of lab technologies. He worked as Director for global firms such as PerkinElmer, as well as at Sartorius and Cap Gemini. He co-leads a European working group on analytical laboratory robotics and AI for science, and is a Member of Board of Directors of SiLA (Standards in Laboratory Automation).

  

 

 

 

From DDW Volume 26 – Issue 4, Fall 2025 – Read the digital issue