While business operations have matured to help better commercialize new products, an important puzzle piece is missing. Companies must fill this gap to complete the big picture and accelerate innovation. That missing piece is science.
Over the past few decades, process manufacturing industries adapted business operations to effectively manage transformational changes. Trends like increased cost pressures, globalization and externalization called for new software solutions. Enterprise resource planning (ERP) united major functional areas of business, while product lifecycle management (PLM) software optimized design and shortened time to market by improving planning and productivity and better-aligning departments across manufacturing and logistics, inventory, human resources, accounting and shipping.
Most scientific operations, however, have not kept pace with their enterprise counterparts. IDC Manufacturing Insights notes two-thirds of R&D projects that get to market today fail to meet customer expectations. This is because key data and information are lost, along with the actionable insights that could more quickly and cost effectively bring novel products to market.
With large investments going into R&D, more products need to emerge from the pipeline successfully with the help of science. It’s time for scientific operations to mature, following in businesses’ footsteps to better define end-to-end processes that lead to better innovation and competitive advantages.
Like an adolescent who learns from an older sibling’s successes and mistakes, science can borrow three key insights from the “enterprise evolution.”
Break down organizational silos
A first step is recognizing the interdependency of organizational functions and how business processes need to align to support information flow across company divisions. Just like in business operations, in science, one change can have a domino effect on the rest of the chain—upstream and down. Design teams have to ensure that products in development are manufacturable, taking into account customer demands and current market conditions. With a more collaborative approach driving innovation across the full product lifecycle, connected systems need to support input from all departments with built-in workflows to force better communication related to science.
Some leading process manufacturing companies are setting an example using software to enable such collaboration. In the past, their cross-functional teams would meet just a few times a year to review product lifecycle data. A problem might come to light which required reworking designs. Without processes in place for troubleshooting across-company “silos” there was a reliance on individual human heroics to identify problems. For the industry, this begs for more consistent and efficient ways to connect R&D and customer facing functions—and all teams in between—the way successful business operations have done.
Avoid monolithic technology systems and point solutions
The software industry is responding to market needs for this new approach to science. Companies at industry forefronts want efficient technology solutions to connect people, processes, technology and tools across functions and geographies. One lesson from the business software evolution is to avoid the temptation to expand the scope of a single application to become the foundation of an enterprise solution. Early adopters of finance and design software learned this the hard—and expensive—way, eventually moving to ERP and PLM platforms that helped integrate the individual applications they worked with. It required extensive customization, add-on features and re-aligning practices to match software capabilities, along with costly resources to maintain and support ever-bigger systems.
Another inefficient solution included point solutions for human resources and accounting, among other areas. Changes were required to make software work together across an enterprise, adopting restrictive business processes that the systems could support.
As business operations matured, companies migrated to modular solutions and found they could link them to integrate information. Deploying a consistent technology platform—rather than stitching together point solutions—improves communication by making information more available to everyone whenever and wherever.
Science is beginning a similar evolution. Companies now realize to improve innovation they must enable science to mature through systems that support the way their business is changing. New solutions must allow for interconnectedness that provides more consistency in operations and quality to better leverage intellectual property in the name of innovation.
Innovate using best practices
As business operations demonstrated, the right technology solutions must be coupled with adopting new processes. Science maturity can’t happen all at once, but formalizing the initiative makes it more imperative—recognizing that forward-thinking companies are doing this and outpacing competitors.
Organizations can identify isolated islands of expertise and best practices to share across global teams, extending to suppliers and contract research and manufacturing. Processes that help to codify science and make it consistent through better collaboration is key to innovation. So finding better ways of doing work already done—and innovating in terms of processes, best practices and existing expertise—represents a new level of maturity and “grown up science.”
Digital science initiatives that a few large consumer packaged goods (CPG) and petrochemical companies have undertaken offer examples for enabling connections. These companies are digitizing everything to move from paper-based worlds that hold decades of data. Making science digital helps share knowledge about what has worked well, or not worked. For example, modeling how a fragrance ingredient might affect end products’ quality and compliance unlocks new potential for efficiency and optimization. Does a certain catalyst mixed with crude oil impact product yield? Digital records can be more easily searched and analyzed to help with experimental decisions.
By connecting the right people, processes, tools and technology and data, science can mature to a level in which product innovation is calibrated and continuously improved upon. Institutionalizing collaboration across the breadth of the lifecycle can result in fewer product failures, faster development and more efficient scientific operations overall. Science in a digital format is more closely connected to business operations, and data—beginning with R&D—will become an integral part of information leveraged for strategic decisions.