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Accelerating science-led-innovation-whitepaper


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Accelerating science-led-innovation-whitepaper

  1. 1. Accelerating Science-Led Innovation for Competitive Advantage WHITE PAPER Sponsored by: Accelrys J oe Ba r k ai Fe bru ar y 2 0 "The ability to learn faster than your competitors may be the only sustainable competitive advantage." — Arie De Geus (as quoted in The Fifth Discipline by Peter Senge) As globalization expands in scope and complexity and economic andF.508.988.7881 competitive pressures intensify, discrete and process manufacturing companies experience increasing price pressure from customers and suppliers, low-cost competition, and high expectations for profitable growth from investors and shareholders. To respond to these challenges, product companies in all industry sectors must accelerateP.508.988.7900 innovation and learning and achieve a higher level of innovation efficiency to remain competitive and drive top-line growth. However, there is a growing consensus that innovation is stalling or even decreasing in its effectiveness, and evidence concerning the highGlobal Headquarters: 5 Speen Street Framingham, MA 01701 USA failure rate of product innovation and commercialization is abundant. Across industries, only about 25% of projects result in a product that reaches the market, and of those projects, two-thirds fail to meet the companys original expectations. Fully 20% of projects take too long and miss their market targets, and 35% of product companies have experienced at least one runaway project in their history. All in all, about 45% of the resources allocated to product development and commercialization are wasted. In certain industries, such as pharmaceuticals, these numbers can be considerably higher. In the current competitive environment, frivolous and wasteful innovation is a luxury no company can afford. While effective and efficient innovation is critical to competitiveness, and top-notch innovation resources are an increasingly scarce commodity, many companies still treat innovation as an inherently unstructured and therefore unmanageable process. This is especially true in scientific innovation, where the available tools have been inadequate to address domain complexities and process disciplines. As a result, many companies do not invest in productivity tools to productively manage innovation and experimentation and maximize the value of their human capital and enterprise resources. February 2012, IDC Manufacturing Insights #MI233313
  2. 2. The Innovation Information GapIn the course of innovation, design, and manufacturing of products,companies make extensive use of software tools that generate aplethora of complex scientific and technical information: Food andbeverage companies utilize formula and specification managementsoftware; engineering companies invest in CAD and CAE tools;pharmaceutical companies use bench chemistry tools, and so forth.While many of these tools are well designed and highly optimized fora purpose, they are not generally designed to interoperate with othersystems and data repositories. Furthermore, the science inherent inmany of these processes has been a key factor in creating an R&Denvironment that is too reliant upon informal, unconnected, and highlycustomized personal productivity tools such as email, spreadsheets,and homegrown software tools.The impact of these internal bottlenecks is further exacerbated by thedynamics of enterprises in the global economy. Many productcompanies rely on partners for external innovation and to provideadaptation and localization in new markets. Joint ventures are formedand companies merge to leverage presence in emerging sectors. But alltoo often, new participants in this elongated innovation chain bringwith them different knowledge processes, practices, and tools.The result is a highly decentralized and fragmented environment,where critical knowledge is scattered across departmental informationsystems and geographic silos that introduce waste and impedeorganizational learning and sharing of past methods and experience,further impeding new product introduction and eroding the value ofcritical intellectual property.To systematize and control scattered information, companies utilizeERP and product master data management (MDM) software tools thatare designed to coalesce multiple data and serve as the "single versionof the truth" for the enterprise. These tools, which have strong roots inmechanical and electrical engineering disciplines, are much lesseffective in supporting science-led innovation.Standard MDM tools store scientific data as semantics-poor documents,large binary objects, or simple text-based design attributes. They areunable to represent attributes and complex interrelated hierarchies inscientific data: chemicals, materials, and scientific experimentationprotocols and results. Consequently, information indexing and retrievalare predicated primarily, sometimes exclusively, on text.While a certain level of free-form innovation is justified, even necessary,forward-looking companies recognize that all innovation must beunified under an enterprise process. Mature organizations ensure thatinnovation is not an independent activity that happens in isolation.Page 2 #MI233313 ©2012 IDC Manufacturing Insights
  3. 3. Instead, they connect front-end innovation to processes furtherdownstream and assess the value and impact of innovation on the entireproduct life cycle before long-term design and manufacturingcommitments are made. For example, a single change to a formulationingredient can have an impact on any number of later-stage downstreamactivities, including process instructions, the selection and calibration ofplant equipment, safety procedures, environmental compliance, andpackage labeling.In a similar vein, efficient innovation must be informed by and benefitfrom previous innovation activities — whether successful or not — sothat best practices are implemented and mistakes are not repeated.The gap between organizational needs for efficient and effectiveinnovation and availability of effective information tools leads totremendous waste and organizational burden. We estimate that asmuch as 40% of all R&D experiments are repeated — unnecessarilyand often inefficiently — delaying projects and increasing costs andrisks. As companies R&D organizations adopt global innovationmodels with geographically dispersed project teams and third-partypartners, these problems intensify, challenging organizations capacityto build process velocity and improve the success rate of innovationcommercialization through learning and sharing of past methods.Closing the Gap Between "R" and "D"Accelerating the innovation process during the early stages of productideation is critical. But no less important is the ability to driveeffective innovation collaboration among various parallel innovationand experimentation processes and, perhaps more critically, betweenthe "R" and the "D" of R&D and then between innovation and productcommercialization.An effective approach to R&D must facilitate the context and handoffbetween innovation at the fundamental science level and itsapplication in a final design, formula, or part geometry. For example,chemistry-level information gathered during product development iscoalesced and stratified in a way that it can be applied effectively byingredient testing and sourcing or engineering in designing a part or amanufacturing process.Furthermore, as products enter volume production, this frameworkhands off critical data to the PLM and ERP systems that governproduct manufacturing, supply chain management, and distribution.For example, detailed chemistry-level information gathered duringproduct development and volume production ramp-up is used tosupport activities such as quality and traceability, regulatorycompliance, and sourcing.©2012 IDC Manufacturing Insights #MI233313 Page 3
  4. 4. Indexing of scientific data found in logs, patents, and designs, such asthe properties of a compound or a molecular structure, should be"science aware": Indexing and searching mechanisms should be ableto handle a broad range of domain-specific scientific terminology suchas molecular structures and substructures, chemistries, sequences,applications, experiment results, and so forth.Forward-looking organizations need a structured framework for allinternal and external experimentation so that critical information canbe organized and shared to facilitate effective collaboration inside theenterprise as well as with suppliers, partners, and academia. Thisnecessitates that the system be able to access and unify differentinformation types from multiple domains and disciplines, both internaland external.ESSENTIAL GUIDANCECompanies must rethink their entire innovation and R&D processes,especially science-led activities, and strive to manage scientificexperimentation the same way and with the same rigor and precisionthat they manage engineering, manufacturing, and supply chaindisciplines.Specifically, they need to facilitate an enterprise approach to R&Dinformatics to manage all critical scientific data and make it availablein a usable, structured format, to diverse stakeholders, to exploit itefficiently through the design-test-manufacture pipeline.Furthermore, an enterprise R&D management system must also beable to hand off critical pertinent data, in a usable, structured format,to the PLM and ERP systems that govern product manufacturing,supply chain management, and distribution without compromisingcompleteness and fidelity. For example, utilizing detailed chemistry-level information garnered during product development andproduction ramp-up, a company is better equipped to supportdownstream volume production activities such as yield management,regulatory compliance, and sourcing.Companies adopting an approach that effectively connects theinnovation cycle and commercialization cycle with high fidelity datathat maintains the context as a project moves through discovery intomanufacturing should experience greater operational visibility andimproved decision making in all areas:● Optimized experimentation and real-time results improve decision making. Decisions can be made early in the product life cycle by having insight into downstream activities.Page 4 #MI233313 ©2012 IDC Manufacturing Insights
  5. 5. ● Collaboration can be enhanced within globalized R&D organizations and across decentralized partners innovation centers.● Design goals, hypotheses, and ideas can be shared, annotated, and discussed across dispersed teams in an unbiased, data-driven fashion.● Accelerating experimentation through the identification of prior work and intellectual property inside and outside the enterprise and leveraging approved experimentation methods lead to better screening and prioritization of experiments.● Data collection evaluation and analysis processes and methods are structured and standardized across teams and projects. Use of proper statistical methods and standardized reports and dashboards improves accuracy and usability of results.● Aggregating and processing large volumes of structured and unstructured data from multiple disparate research areas in a single environment reduces manual work, rework, and data errors.Copyright NoticeCopyright 2012 IDC Manufacturing Insights. Reproduction withoutwritten permission is completely forbidden. External Publication ofIDC Manufacturing Insights Information and Data: Any IDCManufacturing Insights information that is to be used in advertising,press releases, or promotional materials requires prior written approvalfrom the appropriate IDC Manufacturing Insights Vice President. Adraft of the proposed document should accompany any such request.IDC Manufacturing Insights reserves the right to deny approval ofexternal usage for any reason.©2012 IDC Manufacturing Insights #MI233313 Page 5