Six building blocks for digital R&D in chemicals

These building blocks can be applied at key points in the core R&D process to help move innovations from concepts to products ready for commercialization and marketing:

Figure 1: R&D stage-gate process enabled by digital technology and integrated with digital marketing

1. Search and content analytics
   By monitoring the patents coming from key academic groups and start-up companies, R&D departments can identify opportunities to collaborate on innovation—but this patent analysis process is often labor-intensive. Automating it can help increase efficiency and speed in uncovering innovations. For example, automated processing based on intelligent semantic search algorithms—which considers context and intent in language—can be applied to internal and external sources of information. This type of automation not only speeds up the work, it also frees up specialists, such as patent attorneys and researchers, to focus on higher-value tasks that require creativity and judgment.
2. Lab automation
   While chemical companies have made significant use of lab automation technology, it has usually been deployed in standalone situations within the lab. There is now an opportunity to link systems to create end-to-end automated lab workflows tied into the company’s ERP system. Doing so has the potential to help eliminate idle time, make measurement procedures more repeatable and enable labs to test more samples, among other things. Altogether, we have found that more effective lab automation can help reduce time-to-market and increase quality and reliability in the lab, while cutting costs 10 to 25 percent.
3. Artificial intelligence
   AI can enhance the ideation funnel in a number of ways. Machine learning, for example, can be used to quickly sort through large amounts of structured and unstructured information, significantly enlarging the universe of ideas that can be considered for further development. And natural language processing can be used to assess possible new materials and identify the most promising candidates for further development. These capabilities can significantly accelerate R&D and the delivery of new products to market.
4. Quantum computing
   Large, multifaceted computations are handled much more quickly with quantum computers than with traditional computers. As a result, they can compare larger and more complex molecules, which can ultimately lead to increased speed and reduced costs in R&D. Quantum computing is not yet in wide use, but it is advancing quickly. Accenture Labs has collaborated with a quantum software company to conduct quantum business experiments through newly available quantum hardware platforms and software application programming interfaces (APIs). With one pharmaceutical company, for example, this technology was used to improve the molecular comparison model, and comparatively weigh different molecular variables, providing a clear advantage over the traditional “black box” comparison model.
5. Intelligent knowledge management
   Capturing and sharing knowledge is central to R&D, and chemical companies can enhance those capabilities with AI-powered knowledge management solutions. These solutions can help address some of the key challenges of conventional knowledge management, such as struggling to keep up with ever-expanding amounts of information or the difficulty involved in finding the specific knowledge needed to solve a given problem. Intelligent knowledge management can improve the ability of those in R&D to efficiently capture, retain and leverage information, giving decision makers real-time access to critical knowledge to help drive innovation.
6. Co-creation platforms
   Understanding and incorporating the customer is key to effective R&D and, ultimately, growth. As a result, many chemical companies now collaborate with customers—and suppliers—on innovation. Innovation management platforms can enhance this process by integrating R&D and IT and connecting them with partners. These platforms can help companies tap into the knowledge and expertise of suppliers, startups and others, and provide access to a wide range of skills, technologies and data. This can support an agile innovation-incubation process and help companies complete innovation projects more quickly, from the identification of new ideas to proofs of concept and deployment.

As chemical companies take advantage of these technologies, they will need to take a systematic approach to assessing and implementing them. This will be key to controlling costs, keeping a sharp focus on achieving the expected benefits and, of course, avoiding the traditional fragmentation of the R&D technology landscape.

With that in mind, R&D teams should begin by working with IT to create a clear vision and roadmap and build the appropriate data and technology platforms—all in alignment with the company’s overall digital strategy. With a planned, comprehensive approach, chemical companies can put themselves into position to create an R&D function that is integrated, highly automated and AI-enabled—one that is able to move with greater speed and efficiency, and put innovation on a faster track.