The Efficiency Imperative in Scientific R&D

Scientific R&D organizations have always been under tremendous pressure to “discover and innovate” on-demand, but this pressure has exponentially increased over the last few years. In fields like pharmaceuticals, biotechnology, and materials science, R&D is tasked to build a continuous pipeline of groundbreaking discoveries and then transform them into viable products—all faster than ever.?

Balancing the rigor of scientific inquiry with the pragmatics of business efficiency, however, is no small feat, especially in an environment where the stakes are high, and the competition is fierce.

Tick Tock, It’s 2024

The current pace at which new discoveries and technologies emerge has drastically shortened the window for exclusivity in today’s markets. Companies are racing not only against each other but also against time, to be the first to discover, patent, and market new innovations.?

As a result, the concept of 'time-to-market' has become an increasingly critical metric in R&D. Delayed project timelines can mean losing a competitive edge or missing critical market opportunities. This time pressure is particularly acute in industries like pharmaceuticals, where the duration of patents and the speed of competing research can dramatically affect a company's market share and profitability.

Ultimately, this race fuels the relentless pursuit of speed and efficiency in R&D processes.?

How effectively companies can optimize their internal teams, workflows and processes, and technological resources without compromising on quality or compliance not only defines their immediate success but also sets the course for their long-term sustainability and growth in an ever-competitive world.

R&D Tech: No More Status Quo

The concept of efficiency in R&D extends beyond mere cost-cutting. Efficiency in this context also means reevaluating the status quo around workflows and the technology systems in which R&D organizations and teams have to operate. Despite the pressures and expectation of the actual science being conducted faster, most industry labs still operate using traditional methods and within bureaucratic infrastructures.

Here are a few strategies to consider that challenge the status quo and expose new opportunities to increase efficiency, and you will find they are also intertwined.?

1. Simplify internal processes

Initiatives for increasing efficiency in R&D typically start with optimizing the scientific workflows within the lab. Enthought has been helping customers achieve this for over 20 years. We find that what is often overlooked, however, are the workflows outside of the lab that impact how R&D uses, or doesn’t use, their technology assets.

It is common, especially in large companies, for R&D to be forced to slow their work by weeks, months, and even years just to get the access and technology resources they need.?

What causes these external bottlenecks? With the traditional IT infrastructure, standardization, efficiency, and risk aversion are prioritized, and not aligned with how scientific research is conducted. R&D’s technology needs to be structured around experimentation and discovery, where failure is not just a possibility but an integral part of the iterative research process. This is in stark contrast to the more rigid and standardized systems used in other areas of the business, where consistency and predictability are the goals. What often results is a mismatch of digital tools to actual need, and the resulting waste of valuable time and resources assessing and implementing the wrong solutions. The right tech stack can help with this ??.

2. Reevaluate the R&D tech stack

The wrong tech stack is wasting dollars and time long after deployment. Most R&D tech stacks are a myriad of specialized data and analysis tools and platforms. Some work well; some are extremely frustrating. There are also unique needs that no tool helps with, often the result of the misalignment mentioned above. This patchwork of data tools and solutions contribute heavily to inefficient R&D and should be reevaluated regularly.?

This reevaluation is especially vital today considering the unprecedented pace of technological advancement. Technologies such as cloud computing, advanced data analytics, and secure collaboration platforms significantly enhance the efficiency and scalability of R&D. It's also crucial to ensure that the tech stack is user-friendly and aligns with the skill sets of the R&D team, minimizing disruption and facilitating quick adoption. This ongoing process of tech stack optimization ensures that R&D organizations are not just equipped with the latest tools but are also positioned to adapt quickly to emerging scientific challenges and opportunities.

3. Leverage AI

Of course, AI. The power of artificial intelligence (AI) to revolutionize the way research labs operate and conduct science is no secret. Studies show that the total spend on AI in pharma alone is expected to grow to over $3 billion by 2025. That’s just next year ??.

AI can be leveraged to optimize processes and enhance decision-making, enabling breakthroughs that were once unimaginable. AI algorithms can automate data analysis, literature reviews, and experimental design, significantly reducing time and effort. AI can also effectively manage large volumes of data and cross-correlate findings with other data sets, all at rates beyond human capabilities. Through AI-driven automation of repetitive tasks and optimization of workflows, processes that previously took days, weeks, or months can be reduced to minutes and seconds.

Pressure-driven labs can no longer effectively compete in the time-to-market race with time-consuming and resource-intensive traditional research methods. Modern science calls for AI (and machine learning) to augment and accelerate the work of the scientist.

Start with these questions

The strategies discussed here hopefully help you think differently about what impacts the efficiency game for your lab and R&D organization. These are just a few of many but top of mind for technologists. To help spur some conversations, below are some questions to ask yourself:

• Strategy 1: Assess the coordination challenges for your lab: How much time is spent requesting compute resources? Do your scientists have self-serve access to their data and tools or do they have to go through IT for setup each time? How long is it taking for your computational scientists and data teams to get their data products from development to deployment to use??
• Strategy 2: Think about your tech stack for R&D at a systems level: Where are the workflow bottlenecks and which tools are contributing to the inefficiencies? Which tools are feature rich but aren’t flexible or scalable, requiring workarounds? Which tools empower the end user (scientists) out-of-the-box vs. the ones that require more people involved to implement?
• Strategy 3: Assess if your lab is ready for AI: Is an off-the-shelf AI solution adequate or do you have a platform in the R&D tech stack (strategy #2) that has the capability to tailor AI to your needs? Are there internal processes (strategy #1) that can hinder access and adoption of your AI tools?

Heading to #SLAS2024 in Boston?

Bring your questions to us at Booth #763 at SLAS 2024—we'll also be giving live demos of Enthought Edge!