Digital-centric R&D Laboratories

To have a transformative impact, labs must reinvent workflows through digital technologies and skills, adopting a strong data culture. Innovation through digital-centric systems confidently produces new materials that meet customer specifications orders of magnitude faster than before, enabling broader business transformation. 

Authors: Chris Farrow, Ph.D., VP Materials Science Solutions and Michael Heiber, Manager, Materials Informatics

Leveling Up

Digital technologies are having a significant impact on R&D labs across all technology driven industries, in particular in chemistry and materials science. The bigger challenge is how to evolve R&D labs in a way that delivers value early and continuously, while creating an environment for innovation that can deliver orders of magnitude improvements in performance, and ultimately, business value.  

The white paper ‘The Journey to Digital-centric Chemicals and Materials Laboratories’ posits that the transformation of R&D labs takes place in a well planned journey through five distinct levels, taking a holistic approach to data capture and usage, infrastructure and digital processes, introducing increasing levels of autonomy.

The levels are: 

  • Level 1: The Human-centric Lab
  • Level 2: The Data-informed Lab
  • Level 3: The Data-driven Lab
  • Level 4: The Transforming Lab
  • Level 5: The Digital-centric Autonomous Lab

The Transformed R&D Lab 

Transforming a lab in today’s digital world is a journey. Scientists must acquire new skills, adopt a strong data culture and be empowered to bring digital innovation into the lab. Digital technologies that can rapidly evolve in lock-step with the lab must be adopted. An R&D system that is too rigid, inefficient, or adopted as a quick fix must be avoided, as it will be incapable of broader transformation and unable to adapt as business needs change. 

When the lab arrives at a point where scientists can dial-in desired material or chemical properties, and samples with those properties are produced quickly and automatically, there has been a true transformation. It is now possible to develop highly customized products for each customer, bring speciality services into new markets, and stave off commoditization. 

From there, the business must decide how to leverage this new capability. The challenge flips from a technical one of creating samples, to a business one of scaling production capacity, creating new customer-focussed digital sales tools, expanding into new markets and generating increased revenue – a good set of challenges to have. 

Key to advancing to a Digital-centric Autonomous Lab is that technological and cultural changes progress concurrently. Technological initiatives generate value, while cultural and organizational initiatives accelerate value, increasing the potential beyond incremental steps, and ensuring a foundation for future progress. Once a given level has been mastered, the lab is positioned to move to the next. 

At the final level, entirely new possibilities can be explored and a new future envisioned in line with broader digital business transformation goals. 

Access the white paper by registering here.

About the Authors

Chris Farrow, VP Materials Science Solutions, holds a Ph.D. in physics from Michigan State University and degrees in physics and mathematics from the University of Nebraska.

Michael Heiber, Manager, Materials Informatics, holds a Ph.D. in polymer science from The University of Akron and a B.S. in materials science and engineering from the University of Illinois at Urbana-Champaign with expertise in polymers for optoelectronic applications.

Share this article:

Related Content

Enthoughtが定義する、製薬会社の研究開発ラボにおける真のDX

Enthought GKチームは、東京で開催されたライフサイエンスカンファレンス「ファーマIT&デジタルヘルスエキスポ2022」に出展し、技術的な見識と市場成長の活性化を求めて集まる製薬業界のリーダーたちと会談しました。三日間の会期中に200社が出展し、6700人以上の参加者が集まりました。 デジタルトランスフォーメーションが主要テーマである本展示会は、当社のターゲットとする企業に、製薬業界の新薬開発を加速させる当社のサービスを

Read More

科学における大規模言語モデルの重要性

OpenAIのChatGPTやGoogleのBardなど、大規模言語モデル(LLM)は自然言語で人と対話する能力において著しい進歩を遂げました。 ユーザーが言葉で要望を入力すれば、LLMは「理解」し、適切な回答を返してくれます。

Read More

ライフサイエンス分野におけるデジタル化拡大の課題

研究開発におけるイノベーションの規模拡大は、ラボか…

Read More

Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More

By Mike Heiber, Ph.D., Di…

Read More

Life Sciences Labs Optimize with New Digital Technologies and Upskilling

Labs are resetting the tr…

Read More

From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions

Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.

Read More

The Importance of Large Language Models in Science Even If You Don’t Work With Language

OpenAI's ChatGPT, Google's Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language....

Read More

Leveraging AI in Cell Culture Analysis

Mammalian cell culture is a fundamental tool for many discoveries, innovations, and products in the life sciences.

Read More

Scientists Who Code

Digital skills personas f…

Read More

Making the Most of Small Data in Scientific R&D

For many traditional innovation-driven organizations, scientific data is generated to answer specific immediate research questions and then archived to protect IP, with little attention paid...

Read More