From Innovation To Commercialization: Closing The Productivity Gap

Today’s science-driven enterprises are increasingly challenged by a “productivity gap” that exists within the innovation-to-commercialization lifecycle. Errors that slow innovation to a halt are all too common due to disjointed processes, siloed information systems, and a lack of data visibility across the product discovery-design-test-manufacture continuum.

Imagine that your company is preparing to launch a composite that promises to revolutionize the performance of a wide range of products with its strength and flexibility. The entire R&D organization has been working tirelessly for more than a year to design and test the new material. Aircraft and auto manufacturers are already calling. The supply chain is in place, the plant certified and production equipment has been purchased, calibrated, and is ready to roll. Then the news begins to trickle down through the enterprise: a key ingredient is unavailable in the larger quantities needed for industrial-scale production. The manufacturing side of the house reaches out to R&D. R&D scrambles to find and search a year’s worth of experimental data to identify a cost effective alternative. New rounds of testing will need to be done and different processing equipment may be required. Suddenly, a hot opportunity is squandered by months of rework and delay.

Today’s science-driven enterprises—particularly those developing products based on research at the atomistic or molecular level—are increasingly challenged by a “productivity gap” that exists within the innovation-to-commercialization lifecycle.

Errors, missteps, and delays that slow and even grind innovation to a halt are all too common due to disjointed processes, siloed information systems, and a lack of data visibility and access across the product discovery-design-test-manufacture continuum. In fact, according to IDC Manufacturing Insights[1], only 25 percent of R&D projects ultimately result in new products. What’s going on? And what can organizations do to close this gap and bring novel products to a global marketplace more quickly and cost effectively?

The Innovation Lifecycle is More Complex than Ever

Most companies view innovation as a competitive asset, and for good reason. Being first to market with a better, safer, cheaper, or more effective product can translate into big profits—an opportunity to dominate before the other players catch up. At the same time, the increasing sophistication of R&D can often get in the way of speed and efficiency.

Today, advanced chemistry, materials science, formulations, lab experiments, virtual experiments, Q/A Q/C test results, patent documentation, and more form the basis of an ever-growing data pyramid that leads to a new product. The volume of scientific information that organizations need to capture, track, analyze, share, and report on is enormous, and unlike the structured content that is commonly processed through PLM and ERP systems, it’s exponentially more varied and complex. Beyond standard row and column-based data sets, it may include text, images, 2- and 3-dimensional models, and more, and be generated by a multitude of diverse software systems, laboratory equipment, sensors, instruments, and devices. While this data is just as critical for the manufacturing and business sides of the house to have access to as it is for R&D, the traditional IT systems used by downstream functions like processing or procurement can’t easily search, capture or make sense of it. As a result, key insights gained during the innovation lifecycle are lost, which can have a negative impact on product quality, regulatory compliance, and time-to-market.

Complex and iterative process inputs across the New Product Introduction cycle create excessive and unnecessary labor-intensive rework with too much reliance on “tribal knowledge”. Further complicating matters is the increasingly global and fragmented nature of the innovation-to-commercialization value chain. For example, it’s not uncommon for a consumer packaged goods company to contract with 30-40 external factories, as well as a host of ingredient suppliers in varying geographical locations. The R&D organization and manufacturing operations may be half a world away from one another, while product lines need to be customized for multiple markets with diverse purchasing motivators and environments. (A skin crème’s shelf life and efficacy may be different in a humid locale than it is in an arid climate, for instance.) Widespread data visibility and process agility is needed to capitalize on outsourcing and emerging market opportunities. 

Closing the productivity gap requires a new approach to R&D informatics, one that incorporates many of the successful data and process management capabilities of ERP and PLM systems, but also supports the complexity and variability inherent in the innovation lifecycle. From early research at the chemical and molecular level, all the way up to safety and QA/QC testing and production scale-up, the goal is to empower stakeholders—including scientists, engineers, lab managers, plant managers, business executives, and more—to extract maximum value from the scientific information and activities that drive innovation, so that competitive products can be delivered to market faster and more cost-effectively. Following are three key areas that need to be addressed:

1. Data capture. It’s no secret that a lot of organizations that depend on science to drive product innovation have issues with “knowledge management.”All too often, critical information gets trapped in departmental, technology and disciplinary “silos”—the chemists keep all their experimental data in paper lab notebooks, the design engineers save their work in the modeling software system, and so on. This disjointed approach not only negatively impacts collaboration and data sharing, it also can result in a great deal of time and money being wasted on re-work, redundant experiments and other forms of duplicate effort. An essential first step toward streamlining the innovation cycle, therefore, is systematic, consistent data capture. This helps enterprises more effectively document and protect intellectual property, keep track of the learning gained through experimentation and modeling, prepare for patent filing and take better advantage of the power of “collective intelligence.” Today there are a host of informatics technologies, like electronic laboratory notebooks (ELNs), as well as chemical and biological registration systems,that enable users to do this in a more controlled fashion. The idea is to take a master data approach to data capture—one that will ensure that the “who, what, when, where and how” of each piece of information is catalogued in a consistent manner, across all areas of the organization. Having a single, central version of the truth can help organizations avoid many of the errors and delays that crop up in R&Dand beyond due to missing, incomplete or inconsistent information, much like the product specification helps decision-makers track activities throughproduction, manufacturing and distribution. Keep in mind, however, that any technology solution deployed to capture the work of scientific contributors like chemists and engineers also needs to be simple to use, and flexible enough to allow them to work the way they want to work, or it won’t be adopted. Experts mustbe able to leveragetheir specialized tools—which could be anything from sophisticated microscopes and high throughput experimental equipment to advanced modeling software—while having an easy way to export their findings.

2. Data access. Data capture is only the first step, however. Improving innovation productivity also demands that stakeholders throughout the enterprise—from chemists, lab techs and design engineers on up to plant managers, procurement specialists and top-line execs—be able to access and understand core scientific information. This requires an informatics solution capable of not only capturing the data generated in the lab, out in the field or at a user’s desktop; it also needs to be able to integrate it, give it context and run processes across it. With such a system in place, the insights that have been accumulated during the ideation and design phases of product development can be applied more systematically to downstream activities such as ingredient sourcing, quality control, manufacturing, compliance, claims support and more.

Thanks to the advent of service-oriented architecture and the use of web services, this cross-enterprise approach to data access is now possible. Web services can, for example, be used to support “plug and play” integration of multiple data types and formats without requiring customized (and expensive) IT intervention. As data previously scattered throughout the organization is made accessible through a single informatics platform, a number of time, cost and efficiency benefits can be realized. First, information, no matter where, when or how it was generated, can be utilized by numerous contributors across the organization, enhancing collaboration and speeding cycle times. Toxicologists can make their history of assay results available to formulators developing recipes for a new cosmetic, for example, or chemists can work more closely with sourcing experts to ensure that the compounds they are developing in the lab are actually viable candidates for large-scale production. Second, processes, such as product specification management, that were previously disjointed due to critical data being locked within isolated databases and proprietary systems, can be streamlined and automated without hampering the unique R&D methods deployed by individual contributors. And third, the company as a whole can better track and re-use valuable data, as well as feed it into PLM and ERP systems that are deployed to bring the final product to market.

3. Intelligent search. Finally, innovation stakeholders need to be able to quickly find what they are looking for, and have it presented to them in a form that they can intuitively understand.This is where emerging technologies such as advanced, scientifically-aware semantic search and text analytics come in. These types of artificially intelligent categorization tools can help remove the time and cost constraints involved in extracting the context from complex content and meta data so that collaborators can capitalize on all the valuable stores of data available to them in an accelerated and automated fashion. Key to this is an ability to search data across the many sources and applications that have generated it throughout the enterprise—including everything from product and ingredient information to technical specifications and social data—and to be able to do so in a way that’s flexible enough to accommodate frequently changing search terms and research requirements. Running intelligent searchfunctions in conjunction with an integrated informatics platformis an especially powerful approach, as it can help solve some of the technical master data management issues associated with disconnected data.

Let’s go back to the case of the unavailable ingredient described in the introduction of this article. Imagine that way back in discovery, formulation experts captured detailed ingredient data in a dedicated informatics platform that’s accessible to participants up and down the innovation lifecycle. As a result, plant managers are able to quickly search through the data (rather than asking someone else to go find it and interpret it for them), identify an alternative ingredient, see that the ingredient has already been tested (avoiding unnecessary rework) and set in motion the process that will engage a nearby supplier. Production is back online within a few days rather than weeks or months. The end result is a more coordinated, focused and proactive response instead of a reactive and chaotic scramble.  

Today’s organizations need to close the productivity gap that exists between innovation and commercialization. Rather than looking at early research and development as something separate from downstream activities, organizations are beginning to find ways to ensure wider connectivity and deeper, more integrated informatics across the R&D, PLM and corporate decision-making landscape. And they are doing this with solutions that facilitate systematic data capture, sophisticated search, and broad information access across the entire innovation-to-commercialization lifecycle.

About the Author

Michael Doyle, Ph.D., is Director of Product Marketing and Principal Scientist at Accelrys, a provider of scientific informatics software and solutions for the life sciences, energy, chemicals, aerospace, and consumer products industries. His blog can be found at:


[1]IDC Manufacturing Insights: Accelerating Science-Led Innovation for Competitive Advantage