Your R&D organization is under the gun to create a new line of eco-friendly skin care products ahead of the competition. Yet designing safe and effective formulations involves handling an enormous amount of data -- everything from information on which suppliers can source some of the thousands of possible ingredients available, to highly complex molecular simulations that predict how various combinations will perform. This data is also spread across a diverse array of formats and proprietary systems, such as text documents saved in an electronic lab notebook and images of skin cell cultures generated by a microscope. Without a leaner way to manage it all, innovation can easily slow to a crawl while costs spiral.

Today's top consumer packaged goods, materials, electronics, oil & gas and chemical companies face a host of competing pressures -- get products to market faster, keep up with regulatory requirements, slash expenses and do more with less. And just as these organizations have turned to technology to streamline the supply chain, they now must tap into opportunities to improve R&D and early production efficiencies. How can IT help scientists, production engineers and other product development stakeholders better leverage the vast wealth of research information available to speed the cycle of innovation and improve time-to-market?

Target Processes that Can be Automated
While some of the more creative elements of product development are difficult to control, the reality is that the bulk of the process is taken up by the methodical discovery, integration and analysis of massive quantities and types of data. Currently, much of this information and managed in an ad-hoc or manual manner, which leaves the door open for big efficiency improvements. Automation can greatly improve the speed of routine workflows and repetitive tasks such those involved in accessing, sharing, using and re-using research intelligence.

Look Beyond 'One Size Fits All' Business Intelligence
Retrofitting traditional business intelligence, data warehousing or product lifecycle management tools is not the answer. These "one size fits all" solutions were built for transactional data, which is generally structured and numerical in nature. The information required during product development and early production is far more complex, spanning disparate formats (text, images, models,) information silos (such as proprietary systems and lab equipment) and scientific disciplines (including chemistry, material science, engineering and nanotechnology.) In order to unlock the vast quantities of R&D intelligence within their enterprises, organizations require a scientifically-aware solution that can support the integration of data across all the formats, systems and applications commonly used during the innovation process.

Transform Data Into Knowledge
Improving product development efficiency involves more than simply accessing and aggregating information, however. A host of stakeholders -- including bench chemists and process engineers, managers in charge of manufacturing and supplier selection, as well as business and marketing execs -- need to be able to transform raw data into the knowledge that drives new discoveries. Thus, an ability to analyze advanced scientific information related to chemistry, process engineering, materials science and more is key. An extensive array of statistical methods should be available, ranging from simple dashboards to advanced modeling methods. Users should also be able to "drill down" to the information behind high-level analysis when questions or problems require more in-depth investigation.

Flexible Information Delivery is Required
Because the product development process is focused on discovery, there is no single way that scientists, chemists, engineers and other enterprise users will be looking at, manipulating and analyzing data. Thus, a flexible approach is required -- one that empowers all levels of users to view information in the manner most effective for their needs, which may range from web portals to sophisticated 3D visualization. Rather than relying on standard templates, users should be able to configure what they want to see and how it is presented. This degree of flexibility leaves room for the innovation so vital to the success of R&D initiatives, but at the same time provides a framework for faster decision making.

In the race to develop a new or improved dish detergent, industrial adhesive or silicon chip, today's CPG, chemical, materials and manufacturing companies need to apply lean principals to the innovation process. But product development efficiency is challenged by the need to manage huge volumes of complex data, derived from many different sources, and in many different formats. This requires a highly specialized, scientifically aware solution, able to integrate, analyze and report on disparate data sources in real time, and expose knowledge in a controlled fashion to all parts of the enterprise. By streamlining R&D and early production activities, today's organizations will be better equipped to speed innovation and achieve faster bottom line results.

Frank K. Brown. Ph.D. is the Chief Science Officer for San Diego-based Accelrys. Accelrys develops and commercializes scientific business intelligence software for the integration, mining, analysis, modeling and simulation, management and interactive reporting of scientific data. http://accelrys.com/