An Enterprise Approach to R&D Informatics

Whether they are developing a new drug, dish detergent, airplane parts or computer chips, companies with heavy R&D requirements face a number of tough challenges. The ongoing economic recession means that businesses everywhere need to rein in spending and do more with less.

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Provides Wider Connectivity Across the R&D, PLM and Corporate Decision-Making Landscape

Whether they are developing a new drug, dish detergent, airplane parts or computer chips, companies with heavy R&D requirements face a number of tough challenges. The ongoing economic recession means that businesses everywhere need to rein in spending and do more with less. Global competition (and in the pharmaceutical industry, patent expirations) are undercutting many of their “bread and butter” products—those that are easy and cheap to produce are now even easier and cheaper for someone else to make in places like China or India. To retain a competitive advantage, companies need to up the ante on innovation by designing and developing better, safer and more effective products than their competitors. And they need to get these products to market both fast and cost-effectively.

As if all the above weren’t challenging enough, product complexity has also reached an all-time high. Chemistry, materials science, formulations, lab experiments, virtual experiments, Q/A Q/C test results and more form the basis of an ever-growing data pyramid that leads to a new product, and 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. For example, a single change to a formulation ingredient can have a big impact on later-stage activities such as processing, the selection and calibration of plant equipment, or even the design of package labels, so it’s important that organizations ensure broad data access and collaboration up and down the design-test-manufacture pipeline. The problem is this is much easier said than done. Closing cost and efficiency gaps between the research lab and final product requires a new approach to informatics, one that focuses on “e-enabling” data visibility, integration and sharing across the end-to-end innovation cycle.

Rethinking R&D informatics

It’s no secret that R&D activities involve massive amounts of data from a variety of sources. In addition to output generated from lab experiments, product testing and virtual science like modeling and simulation, organizations also need to capture and share information from previous projects, from publicly available databases and from production and scale-up systems, as well as from contract research, sourcing and distribution partners. The global reach of today’s corporate enterprise means that critical knowledge can easily get trapped in departmental, system and geographic silos. The research scientists don’t end up sharing data with processing engineers, the processing engineers aren’t communicating enough with procurement specialists, regulatory experts aren’t brought into design planning early enough and so on. Project collaborators often turn to manual approaches to leverage multiple information sources—spending hours searching files, reformatting, and cutting and pasting reports together, or they enlist IT resources to hand code customized “point-to-point” connections to move data between systems and applications. But these ad hoc attempts at data and process integration are too timeconsuming and expensive to make sense on a global scale.

In the mold of solutions for PLM and ERP, Innovation Lifecycle Management demands an underlying, open, enterprise-level informatics framework that allows organizations to electronically integrate diverse information silos, make data more visible and accessible to multiple stakeholders, and move it more efficiently through the design-test-manufacture pipeline.

Current State of New Product Introduction Cycle.

Accelerating innovation through e-discovery, e-design and e-development

Thanks to the advent of cloud computing, serviceoriented architecture, the use of web services and of technologies that support advanced search and data mining, more streamlined “e-innovation” is now a real possibility. 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 framework, a number of time, cost and efficiency benefits can be realized.

First, information, no matter where or how it was generated, can be utilized by numerous contributors up and down the product development value chain, enhancing collaboration. 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 specification management that were previously disjointed due to critical data being locked within isolated databases and proprietary systems can be streamlined and automated, speeding cycle times. And third, institutional knowledge can be captured and archived, promoting re-use and reducing unnecessary rework.

Openness, flexibility and simplicity are key to making e-innovation work, however. Your informatics framework can’t be proprietary or at some point it just won’t be able to use information from some small, yet critical, system or application without IT intervention. It also can’t force scientists and other specialists into a rigid way of doing things. Individuals should still be able to use the tools that are most suitable to their work (for instance, advanced modeling software or high-throughput testing equipment) while being able to more easily share their knowledge and results with the rest of the organization. And finally, the reporting of complex data should be simple enough that it can be leveraged by a number of different users, whether a PhD chemist working on the discovery of lead compounds, a processing engineer getting a product ready for scale up, or a business executive making decisions related to marketing and distribution.

Here’s a good example that illustrates the benefit of using an underlying informatics framework for e-discovery: extending the reach and value of virtual science. Sophisticated modeling and simulation software can be very helpful in augmenting standard wet chemistry. By allowing researchers to design and test compounds, formulations, mixtures and more in silico, organizations can speed timeto- innovation and reduce lab costs. They can look at, for instance, how a new ingredient will impact the viscosity of a shampoo or identify molecules that are cheaper to use in a drug and narrow down the options before doing a series of far more costly and time-consuming live experiments. Now imagine the additional value that could be realized if this discovery research data were captured and made available much earlier to product development and production stakeholders further up the innovation chain. Once the potential new ingredient is identified, plant engineers could use the information to make sure they have the right specs for large-scale production, procurement specialists could start sourcing from suppliers, or maybe even the person in charge of product packaging can begin working on new labels that list the additional ingredient—increasing efficiencies that translate directly into faster time-to-market.

E-enabling development is also much simpler with integrated informatics. Consider all the processes that go into clearing a new ingredient compound out of discovery and into development. At a big CPG company, this may involve multiple steps that touch numerous information sources and applications. Product safety and efficacy tests need to be reviewed, which includes data from three to four different systems. The new formulation needs to be compared with historical data and scenarios. Conclusions and recommendations are written up and formatted so the next contributor to interact with the information can actually use it. Without an integrated informatics framework in place, a reviewer will likely have to jump back and forth between several different screens and information systems to get through the process, with each step taking several ?Current State of New Product Introduction Cycle. IAC Industries, Inc. minutes. Multiply the number of steps in the process, the number of systems involved and the number of times the process needs to be completed (sometimes for each of the 30 to 40 ingredients making up a formulation) and it’s easy to see how just this one activity alone can eat up an enormous amount of time and resources.

Design BoM contains Material, Formula, Testing & Regulatory.

An enterprise informatics platform, on the other hand, can compress process cycle time by freeing participants from tedious manual tasks such as searching for data, prepping information for analysis, formatting reports for different users and bouncing around between multiple applications and systems. When all the data related to product discovery, design and development is captured within a single information framework, processes can be automated across disciplines, applications, systems and departments, which in turn speeds the flow of information along the product innovation value chain.

The last mile: Marrying R&D with systems supporting PLM and ERP

In a climate where budgets are tight, competition is fierce and time-to-market timelines are continually shrinking, product innovation must be more closely aligned with the nuts-and bolts activities that are involved in bringing a great idea to life. In addition to accelerating innovation processes and enhancing collaboration from product ideation through production scale-up, an enterprise approach to R&D informatics must finally be able to hand off critical data, in a usable, structured format, to PLM and ERP systems that govern product manufacturing, supply chain management and distribution. When complex research information can be pulled into these more structured practices (and vice versa) product specification will become more complete, consistent, global and streamlined. For example, if an organization can consider chemistry-level details during product development and deployment activities, activities like claims support, regulatory compliance, sourcing and more can be made more efficient and accurate.

Rather than looking at the innovation cycle as something separate from the product life cycle, 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. The critical layer is a scientifically aware informatics and data-pipelining platform that shepherds the knowledge that drives innovation all the way from the research lab to the final product.

Categories: Business Management

Published In

Budgeting 101 Magazine Issue Cover
Budgeting 101

Published: January 18, 2012

Cover Story

Budgeting 101

Tips for managing your laboratory research dollars.