Data sharing in the coatings industry – what’s the status?
In general, a more scientific approach is needed for testing. Legacy techniques have been the norm for generations, but we should aim for more uniformity. We have fluctuations in the measurement methods (and sometimes the raw materials). So, it’s not surprising that there’s often a huge variation in batches of mixtures used in final products, it’s time to see how we can be more scientific about this. Standard practices would deliver scientifically generated results, and a very useful set of measurement points.
How do companies in the industry currently organize their R&D?
There’s a siloed approach at the moment. Experimentation is generally carried out in the R&D department, which exists as a separate entity in the organization. That reduces the connection to clients, production or other departments with insights on the customer’s needs. If people had the right data and visualization tools, innovation could move faster.
Companies in the sector also rely heavily on information provided by raw materials suppliers but that information often lacks detail. Suppliers might want to shield a product’s ingredients from competing suppliers. Coatings manufacturers may also be reluctant to share data on the end product. What if a supplier reveals it to a competing coatings company?
Imagine if there was more transparency along the supply chain. Researchers in coatings companies would get a deeper understanding of materials used in the final product. Suppliers would know what’s needed to make a better end product. Both parties could drive fast-track their continuous improvement efforts.
Simulation software can help, but how?
Simulations offer the capacity to break down the silos and reduce the number of testing cycles. They do so by deepening the understanding of how different scientific parameters interact in a material. A simulation helps to interpret results and shows where to tweak parameters to get a mixture to work. Researchers can use it to understand, ask the right questions, create hypotheses, and verify. Trial-and-error is not totally done away with, but it’s given direction and made more effective.
Simulations also offer a more robust, scientific way of measuring things. We can combine data on materials and visualize the results. Phenomena in a formulation are seen on screen, in graph, or video format. Even non-modeling experts can tweak parameters and see interactions at minute levels. That’s a game changer in today’s fast-moving markets.
If R&D personnel have fixed starting points for measuring phenomena, they can spot deviations earlier and eradicate thousands of lab tests. Imagine being able to see a deviation in a batch, and remove samples that exhibit a particular phenomena. Testing then only continues on the viable options, so the number of testing cycles drops dramatically. The cost and time savings are substantial.
There’s also the benefit of having a place to store knowledge collected over multiple experiments. Insights gained on formulations are kept for everyone’s benefit. A central point like this also reduces duplication of effort. If scientists can access information online, or connect to a person with the knowledge they need, there may be no need to re-do a test. For a given experiment they can see the ingredients, or how a sample was prepared. Shared experiences reduce the workload for everyone.
So, what kind of physics do experimental scientists interact with for coatings?
Stability of dispersion is a good example. At the moment, we are missing metrics on how the chemicals interact. Working like this means the chemistry behind raw materials used in products will be quite foggy. If we dig out those answers however, we get a base of valuable learnings and helpful data, which can be tracked over time. Even if raw material suppliers would share information on the chemistry in their products, there’s a lack of understanding amongst buyers on the right questions to ask. So we are left with complex systems and limited understanding of interactions or other events that happen within them. Mixtures are created and there can be all sorts of acid based, ionic and particle shape interactions going on, yet we don’t grapple with all that valuable information. The magic phrase here is ‘transparency of data’ – this is the key.
Why would coatings companies share data with other organizations in the value chain?
As I see it, there are two key reasons for sharing data. Firstly, there’s no need for everyone in the industry to keep repeating the same experiments in isolation. Rather than re-doing the same research individually, we could create a repository of data, from which we all benefit.
Secondly, the quality of the results we get from using AI to run data increases with the number of data points available. By sharing those data points, we all get better results. Joining forces in this way gives all parties in the value chain a solid base of information. In the future I see an ecosystem, a space where everyone adds, or finds, the data they need.
In general, what kind of data can be shared?
The average SDS is a good starting point. We could share data on formulations, detailed enough so the properties of formulations make sense but anonymous enough not to reveal exact suppliers. That gives all of us a picture of what specific ingredients can deliver the desired properties in an end product. That kind of visibility is an essential ingredient for product innovation. Even though IP protection remains an issue, we can still find a way to anonymize some parts of the data while providing valid, useful information.
In general, shared information should be good enough to help establish structure-property-relationships. When we can connect the chemical structure of raw materials to desired properties in the end product, we start to see the knock-on effect of changing one thing. That kind of practice is standard in pharma and plastics. Of course, regulation has driven pharma to be more transparent, but it works and without hurting individual players in the industry.
What are the quick wins from sharing data?
Sustainability is a good example. New legislation and changing end customer needs require an assessment of the entire product life cycle – raw materials, intermediates, coatings, and the coated product. Data sharing will prevent duplication of effort here. Things like the environmental persistency of a chemical could be calculated, but once, then shared across the value chain. Raw material providers can kick this off by sharing their data. Buyers of those materials can share the results of using them and so it continues, until we build a base of shareable information. From there, we can start to agree on common standards.
Look at SDS data. It has to be processed anyway but at the moment, that’s only done in formats like pdf. There’s really nothing that can be used digitally. Why not send that information in a more usable format, even add some chemical data? It would show a lot of goodwill, even leadership in the sustainability sphere.
It all starts with raw materials suppliers. They have the facilities to innovate, but fear that data exchange leaves them open. But really, when you look at the coatings industry in particular, the interchangeability of raw materials from different suppliers is very limited. In most cases, we are dealing with unique products, so there’s not really anything to hide.
How does it all work at the moment?
Right now if a company in the coatings industry has an issue with, for example, a formulation, the person in charge won’t scan the entire market, it’s too much. They will get a proposal from their preferred supplier. That proposal however, is only good if the portfolio of the asked supplier matches the problem. If they offer a 60% solution, that becomes the best a coatings company can deliver.
The main focus has to be the end customer. What’s the best product for their needs? Maybe supplier A is best for certain applications, while supplier B works for others? By picking correctly, we can deliver the best end product. A simulation tool helps in finding those optimal ingredients. From there, coatings companies can actively target the right mix of suppliers, rather than falling back on old contacts. Suppliers benefit too. When they know what’s needed they can position their own products better.
So, if we shift our initial focus to the end consumer and create an ecosystem where we share information, it becomes clear who has the best ingredients or experience for different end products. Better still, if the right materials don’t exist yet, there’s a chance to innovate. We won’t get that information at such speed outside of the ecosystem. What’s worse is that we won’t explore the space of what’s not available yet. That’s a lost opportunity for all of us to future-proof our offerings.
Is there also a role for simulations here?There’s an inherent connection between this ecosystem and simulations.All of us could unite on one huge database of information, from which we can extract data to feed into simulations. This lets us create smarter formulations and immediately see the interactions that occur. We can quickly visualize the outcome of changing different variables, both chemical and environmental, all on screen. That combination of robust data and powerful simulations makes a real difference.
We can also start closing the loop by learning from failed formulations. Were the right variables tweaked? Maybe a formulation fits the rheology window, but not the application one? Maybe the shape of a particle was different? A simulation lets you understand your ingredients properly.
Discovering why things work as they do offers a valuable base of information. There’s a lot to be gained from uniting simulation experts and the experimental community. It opens the door to data sharing on a different level.
How will a simulation tool help to keep up with the market?
The way coatings are manufactured is going to change. We have entered a world where compounds must be REACH certified, while banned lists are growing. There’s an urgent need to reformulate in a way that proves a product’s safety. New formulations require new raw material suppliers. At the same time, we must control the cost and environmental impact of everything. To do this at the pace needed calls for an exponential growth in the R&D team. It requires extra capacity to handle many new formulations. So, we need to work smarter and tap into digital tools where possible.
Customer choice is another issue. End products will be increasingly customized for a more discerning consumer. Coatings companies will need the right product, in stock, anytime. So we have a perfect storm where manufacturing processes will change and a fast go-to-market is crucial. That means 1 year R&D cycles simply won’t be feasible. Speedy customization and ability to react to market changes are crucial.
Are there any examples of where sharing has worked?We had a great example at the 5th ECP. A Frankfurt-based bioreactors company wanted to know why results of their compositions differed all the time. Another attendee from a Bavarian-based company had the answer in seconds – “you just need Bavarian water”. That really was the missing link, it was down to the type and concentrations of salts in the water.
“You just need Bavarian water”, it sounds like a friendly joke actually, but that kind of sharing saves hours of work. In today’s uncertain environment, that’s the kind of collaboration we need. We can maintain alone, or thrive together and shared data is the key. That starts with breaking down the silos between departments, companies and the value chain. Then we equip people with smart tools, especially the new digital savvy scientists coming on stream.
Every player in the coatings industry should ask themselves a question – do we wait for this or start using digital to our advantage? It’s my hope that they all jump on the bandwagon sooner rather than later. There is some work to do, but the coatings industry can prevail, if it starts to act now.
The guest authors of this article is employed at Rheocube. The company offers a cloud-based simulation tool for complex fluids.