Interview: How digitalisation is turning coatings data into smarter R&D decisions

Özlem Ünlü Işık (R&D Director, Kansai Altan)

Kansai Altan operates across laboratories, production sites and markets worldwide. How are digital tools changing the way R&D teams collaborate, share experimental knowledge and avoid duplicating work across locations?

Digital tools are transforming collaboration from a location-based activity into a knowledge-based activity. In the past, valuable experimental know-how was often embedded within individual laboratories, project teams or even specific researchers. Today, digital platforms allow us to capture and share that knowledge much more effectively across functions and locations.

 

For an R&D organisation operating in diverse markets ranging from automotive and industrial coatings to protective applications, one of the biggest benefits is increased visibility. Scientists can access historical formulations, test results, project documentation and lessons learned from previous developments without needing to rely solely on personal networks or informal communication channels. This significantly reduces the risk of repeating experiments that have already been conducted elsewhere.

 

Digitalisation is also improving collaboration between R&D, manufacturing, technical service, regulatory affairs and commercial teams. Instead of exchanging multiple versions of documents by email, teams can work from a common data environment where information remains traceable and transparent throughout the product development lifecycle.

 

Ultimately, the goal is not only to work faster, but also to build a collective organisational memory that remains accessible regardless of location, function or personnel changes.

Many coatings companies have valuable know-how stored in lab notebooks, spreadsheets and local databases. What has been Kansai Altan’s biggest challenge in turning this legacy knowledge into structured, usable data—and how are you addressing it?

The greatest challenge is usually not the lack of data. Most coatings companies have accumulated decades of experimental results, formulation records and technical observations. The real challenge is that much of this knowledge was created for human interpretation rather than machine readability.

Historical information often exists in different formats, naming conventions and levels of detail. Similar raw materials may be described differently over time, test methods may have evolved and critical experimental observations may be stored as free-text comments in notebooks or reports. Before data can be analysed effectively, it must first be harmonised and contextualised.

 

At Kansai Altan, we have invested significant effort in establishing the right foundations. Rather than pursuing digitalisation as a large-scale IT exercise, we have focused on building lean structures, practical methodologies, and sustainable workflows that fit naturally into the daily work of our scientists. This approach has helped us progressively create common terminology, consistent data models, and digital processes that ensure new knowledge is captured in a reusable and traceable format from the moment it is generated.

 

We view digitalisation as a continuous journey rather than a one-time data migration project. The quality of future decisions depends largely on the quality of the knowledge base we create today.

Where do you already see the most tangible benefits of digitalisation in the daily work of a Kansai Altan formulation scientist: faster documentation, smarter experiment planning, regulatory assessment, data analysis, or something else?

We see significant value in regulatory assessment. The increasing complexity of global chemical regulations requires more systematic management of substance information, sustainability expectations, compliance requirements and customer-specific restrictions, especially automotive OEMs. Digital tools help ensure that regulatory considerations are integrated earlier into development projects rather than becoming obstacles at later stages.

 

All of these areas benefit from digitalisation, but if I were to highlight the most immediate impact, I would point to knowledge accessibility and decision quality.

Scientists spend a considerable amount of time searching for information, identifying past references and evaluating alternative formulation routes. When historical data becomes easier to access and analyse, researchers can focus more on scientific problem-solving and less on information hunting.

 

Another area where we expect to see significant benefits in the future is experiment planning. We have accumulated a substantial amount of formulation and testing data over the years. We are currently working closely with our IT teams to make this knowledge more accessible, structured, and connected.

 

Our goal is to leverage historical experimental outcomes more effectively to support scientists in identifying promising formulation pathways, learning from previous projects, and making better-informed decisions at an earlier stage. While this journey is still ongoing, we believe that combining accumulated technical knowledge with modern digital tools has the potential to reduce unnecessary laboratory iterations, accelerate innovation cycles, and allow researchers to focus more of their time on creating value rather than searching for information.

 

We see this as one of the most exciting opportunities for the future of coatings R&D: transforming historical data into actionable knowledge that supports scientific creativity and expertise.

Digitalisation and AI can accelerate decisions, but coatings development still depends heavily on practical experience and chemical intuition. How do you ensure that new digital systems support scientists’ judgement rather than simply adding another layer of complexity to their work?

This is a very important question because successful digitalisation is ultimately a people challenge rather than a technology challenge.

In coatings development, experience remains indispensable. Formulation science involves understanding complex interactions between raw materials, processing conditions and end-use requirements. Not all knowledge can be fully captured through algorithms or databases.

 

Our philosophy is that digital systems should augment scientific expertise rather than attempt to replace it. The purpose of digitalisation is to provide better visibility, stronger evidence and faster access to relevant information so that scientists can make more informed decisions.

 

When implementing new systems, usability is crucial. Researchers will only adopt digital tools if they clearly improve everyday work. Therefore, we focus on solutions that reduce repetitive tasks, improve data quality and facilitate knowledge retrieval. If a digital tool creates additional administrative burden without creating value for scientists, adoption becomes difficult regardless of its technical capabilities.

 

AI can be a powerful assistant for identifying patterns, summarising information and generating insights, but scientific judgement remains the responsibility of experienced researchers who understand the broader technical and business context.

Looking ahead, what would an ideal digitally connected working environment at your team R&D look like—from the first formulation idea through lab testing, scale-up and production—and what role will people play in that environment?

In an ideal setting, innovation would flow smoothly from the earliest research stages all the way through to commercial production. Researchers would start with immediate access to everything they need, including past project knowledge, customer requirements, market expectations, sustainability goals, and regulatory considerations. AI-enabled tools could then help uncover promising formulation concepts, recommend experimental directions, and build on lessons learned from previous work.

As experiments are conducted, results would be captured automatically and connected within a shared digital environment. Insights from the laboratory, pilot trials, and production processes would continually feed into the same knowledge network, ensuring that information remains linked, traceable, and available to everyone who needs it. Rather than existing in isolated systems, data would evolve alongside the project and become increasingly valuable over time.

As a company with strong collaboration between R&D and production teams, we see significant value in detecting scale-up risks earlier and incorporating manufacturing knowledge from the start of the development process.

The future, however, is not about machines replacing scientists. If anything, it will make human expertise even more important. By automating routine and repetitive activities, digital tools will free researchers to focus on what they do best: applying creativity, solving complex problems, driving innovation, and creating greater value for customers.

Digitalisation should ultimately free scientists to do more science, not less.

• Save article in bookmarks
• Print this article