The Lab of the Future Congress, hosted by Open Pharma Research, brought leaders, speakers, and attendees from pharmaceutical and biotech companies, as well as innovative technology providers together for a two-day conference in Boston, Mass. One recurring topic: the future of lab operations and how to create a strategic and operational framework that allows these companies and the people – especially scientists – to innovate and bring that innovation to market quicker by leveraging data. One session, “Navigating Digital & IoT Transformation Initiatives for Lab Facilities and the Importance of Planning for a Digital Future,” dove deeper into this topic.
The State of Life Sciences Today
In life sciences, R&D budgets are shrinking. Still, the required outcomes are increasing, forcing the industry to get serious about obtaining accurate data quickly and making informed decisions to better manage processes and optimize workflows. That’s why trends like artificial intelligence and machine learning are quickly gaining popularity. However, the vision of where many life sciences companies want to go is stunted without proper planning, a sturdy foundation for data collection, and a culture shift in maintaining accurate data.
Alex Joyner, Product Manager, Digital Lab of Avantor, notes that while the long-term play for pharmaceutical companies is prescriptive, the short-term move is to create the path by putting digitization and automation in place – beginning with data.
Yet, when it comes to data collection, companies find it challenging to identify where to start – what types of data do I want? Where do I get it from? Where should the data live? Who governs the data? What will that data tell me?
The overwhelming consensus the session panelists offered was to start building the foundation for IoT and digital transformation by identifying use cases that will provide valuable data to solve business challenges. And those challenges, for example, don’t have to solve one monumental challenge but a series of challenges that culminate in costly inefficiencies. For the panel, asset tracking became the use case they repeatedly referenced as a great starting point for extrapolating valuable data to solve numerous business challenges.
Jim Sweeney, Product Management Leader/IoT/Digital Transformation/PLM of PerkinElmer, notes that, “Companies tend to want to start at very advanced stages.” For example, many customers he works with desire data for predictive maintenance. Yet, they don’t have complete visibility or a holistic picture of all assets in their labs. “Everyone has these visions of where they want to go, but you have to take a step back and think about how you start building the foundation,” said Sweeney. That’s why he believes that “it’s really understanding which assets you have, and interestingly enough, where they’re located. A lot of times, we see many companies don’t know the assets they have, and they’re surprised where they’ve gone.” In fact, 15% to 30% of assets move per year. The number isn’t shocking when you consider that just about any asset can gain legs and “walk” (i.e., equipment that’s on a cart or is hand-held can move).
Why Asset Tracking Data Is Critical to Building a Lab of the Future
So, what types of business challenges do asset tracking solutions solve? The panel provided a diverse range. For Pam Walker, Corporate Vice President, Global Head of Operations, Safety Assessment of Charles River Labs, tracking shared equipment and instrumentation was vital for expediting their overarching drug development timeline for external customers and internal employees. They wanted to garner efficiencies so they’re working with the best solutions possible to enhance their experience. Joyner shared a similar sentiment stating that, “Any minute that is taken away from a scientist to go and search for a piece of inventory, a chemical, equipment, is time that's taken away from science.”
Joyner also pointed to supply chain challenges and the criticality of asset tracking data noting, “We had become very confident in the supply chain; it was there when we needed it, we knew the challenges, the lead time- then COVID came, and we had to pivot and find new ways on how we needed to approach getting the supplies we needed. And pulling in that data is key to being able to do that prescriptive analysis.” On the topic of supply chain challenges, Walker added, “[knowing where our assets were] became more important to us when you couldn't just go out and buy another one, so we needed to be able to track that equipment and those instruments better than we had before.” She also cited the importance of knowing where their assets are for regulatory compliance purposes. “Being able to understand when [assets] need to be calibrated or verified from a regulatory perspective is very important to us….It’s not the price of the equipment but the value of the data that equipment provides.”
However, additive costs tied to repurchasing equipment because of its mobility can prove costly in some instances. For instance, Joyner notes, “with chemicals, what’s the first thing a scientist does when they don’t see what they need? They order a new one…so now I’ve spent additional money I didn’t need, and I’m cutting into space within my lab, and we all know space is at a premium.” As a result, real-time asset tracking data becomes even more imperative for keeping companies from incurring unnecessary and unwanted repurchasing costs.
Move Beyond Excel: Digital Database for Transparency and Accessibility – The Challenges We Need to Overcome
With the understanding of the types of data life sciences are looking to capture, the next piece is identifying where the data should reside. According to the panelists, there must be a cultural shift towards a digital record of truth; companies must move beyond Excel spreadsheets. By automating and digitizing asset tracking, companies can move one step closer to extracting and utilizing accurate, real-time data for quick speed to market or operational improvements. Additionally, companies should look to put data in the cloud – moving beyond Excel to the use of a database to eliminate silos, gain real-time transparency and streamline accessibility.
Additionally, labs need to get the correct data to the right person, whether a scientist or a lab manager. For example, scientists need access to equipment data to “perform science” rather than spend time looking for supplies or a space to “do the science.” Alternatively, lab managers need a higher level of visibility into whether the lab equipment has been calibrated/maintained and is in good, working order for usage. But if the data is not digitized, “it’s not uniform, everyone changes it,” says Sweeney.
How We Get There
Simplicity will go a long way, and life sciences companies should consider starting by piloting a use case across a few labs, then scaling to a larger, more global deployment. Doing so, will help validate ROI. Moreover, involving multiple stakeholders early on is essential to vet the use case and prove the broader need across the organization.
Where to Next for Life Sciences?
Life sciences companies require a single pane of glass for all their data. On average, most companies have 6-7 sources where different data sources live; it’s not integrated or tied together to paint a larger picture. The next phase is identifying how to connect all these data sources to get to a prescriptive state.
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