Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- The chemical industry, which invests significantly in research and development (R&D) across the US and EU, faces economic volatility, geopolitical uncertainty, and sustainability pressures intensified by disruptions like declining demand and Europe’s decarbonization costs.
- Traditional R&D struggles with fragmented informatics, poor data management, knowledge transfer gaps, and adapting to evolving manufacturing demands.
- Digital technologies—AI, GenAI, digital twins, and hyperscale computing–enable in-silico experimentation, driving faster market delivery, optimized R&D returns, and a shift to sustainable portfolios.
On this page
The need to reinvent research and development
Chemistry is ubiquitous in our daily lives, playing a crucial role in everything, from the food we eat to the technology we use.
As a foundational industry, the R&D function is vital for driving advancements.
Companies typically allocate 3-8% of their turnover to R&D, emphasizing the need for innovation. However, heightened customer expectations for product performance and shorter lifecycles, along with market pressures, demand faster speed to market and higher returns on research spending. Sustainability goals necessitate a shift toward greener product portfolios. Amid these significant challenges and transformations, R&D must reinvent itself to meet evolving demands and ensure continued progress.
The challenges with traditional research processes
From siloed data sources and systems to aging workforce, modernizing the R&D function will require companies to address a plethora of challenges.
Traditional R&D processes face ongoing hurdles, such as:
- Fragmented lab informatics: Disconnected and disparate lab systems create isolated workflows, slowing research and reducing efficiency.
- Lack of data governance limiting access to knowledge: Researchers struggle to gather data from varied instruments and databases, where it is often unstructured and scattered. This hampers collaboration and limits insights from past and current projects.
- Manual intricate tasks: Researchers need to manually search through existing literature and internal and external chemical databases, review them, and screen and summarize results iteratively. This is tedious and often leads to researcher fatigue and suboptimal results.
- Knowledge transfer: With an aging workforce, most chemical and process companies are at risk of losing vital organizational knowledge, while the younger, more tech-savvy researchers often find it challenging to effectively use legacy systems.
- New manufacturing methods: Emerging techniques like process intensification and modular plant designs require a certain degree of flexibility in the R&D function in order for the business to adapt quickly to market and production shifts.
The imperative called digital transformation
Digital solutions are reshaping the very fabric of research.
However, the benefits of these innovations have yet to reach a larger audience in the chemicals research field, as companies are still evaluating and waiting for this space to mature.
Digital transformation offers powerful ways to address these inefficiencies, streamlining research and paving the way for commercialization. By integrating advanced technologies, companies can break down silos, harness data, and accelerate innovation. These solutions not only solve R&D challenges but also drive tangible outcomes, as seen in their application from lab to market. The direction is to perform in-silico as much as possible.
Listed below are key solutions for a smooth digital transformation.
- Knowledge engineering and artificial intelligence: These tools speed up molecule screening, accelerating the discovery of new compounds and materials.
- Machine learning models: Machine learning predicts material properties before synthesis, reducing physical trials and speeding catalyst development by up to ten times the usual rate.
- In-silico experiments: Computational methods cut the need for wet lab experiments, saving time and costs while allowing broader exploration of variables. Advanced simulations also streamline scaling processes.
- Market intelligence tools: These tools track trends, customer needs, and regulations, helping research to align with market demands.
- Digital tools and augmented reality: Digital tools and augmented reality (AR) enable real-time training and knowledge sharing, keeping expertise alive in the organization.
Of course, all the above rely on a robust data platform which is scalable, flexible, secure, and offers robust data governance. It should have capabilities for data ingestion, data transformation, and meta data management.
Innovation through collaboration
Industry, academia, and regulators must come together to fuel innovation and make the industry leap forward.
The digitalization of chemical R&D is transforming competitive dynamics by fostering a dynamic interplay between upstream and downstream ecosystems. This shift opens the door to bolder ideas, such as innovation challenges where top minds—even competitors—collaborate to create sustainable solutions. Such initiatives can significantly boost impact and accelerate breakthroughs within a vibrant ecosystem.
Forums like SusChem in Europe and the American Chemistry Council’s CHEMSTAR program exemplify this trend by bringing together industry, academia, and regulators to advance innovation. These collaborative platforms are crucial for driving progress and fostering a culture of shared knowledge and collective problem-solving.
Leap into the future
Establishing a strategy and roadmap for implementation is crucial.
To fully harness digitalization, chemical companies must create a strategy and roadmap for R&D modernization. This would involve aligning digital tools and technologies with business goals, establishment of governance structure and a clear approach to organization change management.
Two areas of significant impact are:
- Foundational infrastructure to connect and collect data from lab experiments and maintain the data in a centralized platform, which can be leveraged for analytics and modeling
- Knowledge engineering tools to assist researchers toward faster and smarter innovation
A well-defined plan will tie these elements together, aligning R&D with sustainability goals and market needs, and positioning chemical and process manufacturers to lead in a rapidly evolving landscape.
