“When they want you to buy something they will call you.” Poet Wendell Berry’s words from 1973 were prescient for the last decades of the 20th century. Driven mostly by curiosity, researchers and inventors used 'what if' experimentation to create new products, and then sales and marketing professionals would build consumer desire for them and push them out to customers. Today, companies create things because customers ask for them. This is known as pull manufacturing, in which production is based on demand. Demand is escalating from business customers that make specific requests for materials based on their manufacturing needs, and from informed consumers who request what they need and want—with a high degree of personalization.
Manufacturers ask more from their suppliers because their customers ask more from them. A consumer goods company makes a request to a chemical producer for a polymer that can expand to 50 times its size so diapers can be 10 percent lighter. An auto manufacturer extends its network beyond its tier-one supplier of fully assembled seats to include communications with the maker of the foam that goes into the seats. Consumer demands are getting more explicit as they request healthier drinks, purses that last longer, ethically produced clothing, and waterproof phone cases. An increasingly conscientious international community seeks sustainable products, lighter materials, new medications, and improved energy efficiency as people endeavor to meet large-scale challenges triggered by urbanization, climate change, ageing population, and diminishing natural resources.
This is changing the nature of scientific work performed in R&D departments. In many industries, curiosity-driven research is being replaced by purpose-driven research, as scientists must satisfy market-led specifications.
Standardization Supports Holistic Strategies for Product Development
The shift to purpose-driven R&D is supported by a convergence of disciplines. Experts in areas such as physics, chemistry, biology, and materials science who once passed projects and insights from one siloed discipline to the next now collaborate earlier in the product development lifecycle and across product development stages. Many companies are adopting creative strategies such as design thinking and agile methodology that view challenges holistically and bring multiple proficiencies to bear as specialists work together to arrive at a specified goal and a final product. Mathematics, data science, and computer engineering are impacting many other fields in a systems approach where everything augments everything else.
This can lead to more comprehensive knowledge and faster product development that is targeted to specific goals. But there’s a hurdle to overcome. Each of these disciplines typically communicates with an exclusive vocabulary, has unique processes, and uses different data types. Enterprises that were established when chemistry was purely chemistry must adapt to increasing discipline convergence.
Leading organizations strive to eliminate silos within R&D to support a more holistic approach to product development. When they design, they consider everything from materials sourcing to the nuances of the customer experience. A packaging engineer works with a chemist to ensure that the product created through chemistry is safely and securely packaged and can be dispensed as expected.
Specialists can only collaborate at these different stages if they speak the same language. If disciplines are to fully converge, companies must enforce standardization in data models to merge data and processes. Ontologies define a common language across all processes. They allow experts from various disciplines to put information into a digital system in a standardized way so it can be interpreted. The system then presents the information in the appropriate format for stakeholders who need to consume it along the R&D continuum.
Standardization is important not just for data models and language, but also for business processes and the ways people work. Every piece of information is generated through a process. When organizations standardize the processes that create information, they are able to reuse the information more effectively and leverage best practices that accelerate collaborative innovation. There won’t be a need for colleagues to revisit previous steps or discuss how materials were measured, stored, and prepared in earlier development stages. Better yet: they won’t be distracted by incomplete or incompatible information.
Scientists, researchers, and everyone involved in R&D require holistic information systems that can store and interpret information about diverse processes. This provides a firm foundation for identifying best practices and establishing common standards.
Ultimately, standards should extend beyond the organization. They should not be proprietary to any technology provider, knowledge platform, or corporation. As discipline convergence drives deeper collaboration, information must be accessible and reusable, not locked into silos created by proprietary processes and formats.
Leading companies begin by defining internal standards, and then make pre-competitive alliances through entities that bring organizations together to establish best practices that span industries. Pistoia Alliance is a global, not-for-profit coalition of life science companies, vendors, publishers, and academic groups that work together to develop standards, best practices, and technology pilots to overcome common obstacles in R&D. Allotrope Foundation is creating an open, publically available framework comprised of software tools and libraries to utilize, implement, and integrate data standards into analytical laboratory workflows. Pistoia and Allotrope member organizations collaborate on open projects that generate significant value for the worldwide science community.
Discipline convergence and the comprehensive knowledge it produces are increasingly relevant as the real and the virtual realms converge in computer simulations. Advances in compute power now enable organizations to use virtual simulation models (sometimes referred to as digital twins) to improve efficiency, sustainability, and response time to changing consumer behavior. For example, auto manufacturers don’t need to build a car, crash it, and then re-engineer and crash it again until they get it right for production. That’s a waste of material, time, and money. Using virtual models, they can determine how the car will behave in a crash before the production line is even created, thus saving years of development time.
Although virtual modeling is more complex with biological and chemical development processes, the life science and chemical industries also benefit from these approaches. The European Union has outlawed microbeads. So certain consumer personal care products must be reformulated. If companies have standardized test data, they can use virtual models to predict the outcomes of swapping microbeads for other ingredients. By predicting product behavior, manufacturers can minimize or eliminate the need for prototypes and get answers more quickly while saving materials.
Successful predictive science depends on seamless collaboration, unified workflows, comprehensive knowledge, and standardized data that is available and understandable in context. You can’t simply throw data at a problem. There must be a holistic understanding of the data and the practices underlying the data. Most importantly, scientific disciplines can no longer operate in isolation from one another. Instead, contextualized data must flow freely, back and forth, along the digital continuum connecting all processes to the product lifecycle. This can only occur in a data-driven, model-based environment that enhances the scientific work experience and increases productivity across functional, roll-based domains.
Nothing happens in isolation. In a systems approach, people from different disciplines work together to optimize processes across many dimensions. With an effective knowledge platform in place, the discipline convergence we are experiencing today allows organizations to react more quickly to industry and customer demands in the new manufacturing paradigm.