This vendor-written tech primer has been edited by Network World to eliminate product promotion, but readers should note it will likely favor the submitter's approach.
CIOs at companies that rely on scientific R&D to differentiate themselves have highly specific information management challenges. In a tough economy, they need to help their organizations reduce R&D costs and do more with less. They need to help shrink time-to-market by streamlining the numerous activities and processes involved in transforming a great idea into a great product, whether that product is a new shampoo, a drug therapy, or a state-of-the-art polymer used to improve the performance of an airplane wing. And in a world where global competition is fierce, they need to deploy tools that support fast, first-rate and cost-effective innovation.
It's clear to businesses that IT has big role to play in driving better, faster and smarter innovation. The problem is that the current R&D value chain is highly fragmented, with gaps in automation that render many processes largely manual, resulting in delays and reworks; as well as inability to fully capitalize on critical data. From early research at the chemical and molecular level, through 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 R&D information and perform more efficiently, collaboratively and cost effectively.
So why not extend the data and process management capabilities of ERP and PLM systems into R&D? The answer is that optimizing R&D is similar in principle to ERP and PLM, but it can be very different in practice. When it comes to the innovation lifecycle, an information management solution that provides an end-to-end framework for automation and governance is important, but it also needs to be able to support the unique requirements of the R&D function. Here are some key considerations for CIOs to think about:
* Innovation is not like an assembly line. PLM systems have been instrumental in squeezing time, cost and waste out of manufacturing and supply chain activities. They are designed to facilitate highly structured, stage-gate processes that move information through the product manufacturing and distribution pipeline as quickly, accurately and efficiently as possible.
The reason it's so difficult to do the same with R&D information is that innovation doesn't work in a linear way. Researchers and other innovation stakeholders need to be able to follow ideas where they lead. Usually this calls for processes that are ad hoc or out of sequence, and information streams that travel backward before moving forward. As a result, rigid attempts at structuring R&D activities will likely fail.
Some kind of information framework is needed to capture, integrate and leverage complex knowledge and also to streamline the activities that support innovation. For example, tedious manual activities and processes associated with moving R&D data between systems and applications, or integrating and formatting it for reports, can and should be automated so that users can also reuse process protocols that may be time-consuming or difficult to recreate.
* R&D data is extraordinarily complex. Advances in R&D technology and techniques have led to leaps in innovation -- consider the evolution of the computer chip or the medical discoveries enabled through the mapping of the human genome. Simultaneously, the increasing sophistication of R&D has also left IT departments with two sizeable issues to contend with: data overload and data complexity.
From chemical structures and biological sequences to outputs from high-throughput testing equipment and other data streams, the sheer volume is enormous. And unlike the structured data that is commonly processed through PLM and ERP systems, R&D information is exponentially more varied and complex.
Beyond standard row and column-based data sets, it may include scientifically meaningful text, images, two- and three-dimensional models and more -- and it's generated by a multitude of diverse software systems, laboratory equipment, sensors, instruments and devices.
All too often, critical information is locked in system, departmental or disciplinary silos making it difficult to share and reuse throughout the organization. This complexity is the No. 1 barrier to realizing greater efficiencies, so any solution deployed by IT to streamline the innovation cycle must be able to capture highly scientific data, integrate information from diverse sources, run processes across it and report it in a way that makes sense for multiple users from executives to manufacturing engineers.
* Flexibility and simplicity are essential. Brilliant scientists, expert modelers, experienced engineers, star formulators -- these are an R&D organization's crown jewels. Any information management system needs to be simple to use and flexible enough to allow them to work the way they want to work.
The tools that R&D experts rely on are varied and specialized and may include things like megapixel cameras and fluorescent microscopes, high-throughput testing rigs, or molecular modeling and simulation software. A framework for innovation lifecycle management should never attempt to replace these tools.
Rather, it should extract the data generated by individuals in the lab, in the field or at their desktops, integrate it, give it context and make it available for use throughout the product value chain. The key to widespread adoption is that all the information management heavy lifting should be done "behind the scenes," with little end-user interaction.
* The time for Innovation Information Integration has come. In today's highly connected information ecosystem, the moment has arrived for R&D to e-enable itself, just as the manufacturing and supply chain side has done with PLM and ERP. Thanks to the advent of cloud computing, service-oriented architecture and the use of Web services and technologies that support advanced search and data mining, innovation management that streamlines R&D, yet respects its complexity, 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 R&D organization is made accessible through a single framework to the ERP and PLM systems, 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 contributors across the product development value chain, enhancing collaboration and speeding cycle times. Toxicologists can make their history of assay results available to formulators developing recipes, for example, or chemists can work more closely with sourcing experts to ensure that the compounds they are developing in the lab are 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 reuse valuable data to bring the final product to market, speeding that process.
Furthermore, cloud technologies provide an ideal forum for stakeholders engaged in product development to interact and share ideas regardless of where they are located or how much data is involved. Advanced categorization techniques such as semantic search and text analytics can also help remove the time and cost constraints involved in extracting the context from complex content.
CIOs have deployed technologies to help their organizations to streamline manufacturing and the supply chain. The next step is to bring the innovation lifecycle into the fold. Solutions that offer an underlying framework for innovation lifecycle management while still providing capabilities uniquely suited to the complexities of R&D will help today's organizations close productivity gaps between the research lab and final product.
Accelrys.com is a leading provider of scientific informatics software and solutions for the life sciences, energy, chemicals, aerospace and consumer products industries. The author's blog can be found at: http://blog.accelrys.com/author/michael/
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