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Accelerating Science-Led Innovation for
                                                               Competitive Advantage
                                                               WHITE PAPER
                                                               Sponsored by: Accelrys

                                                               J oe Ba r k ai
                                                               Fe bru ar y 2 0 12
www.idc-mi.com




                                                               "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 and
F.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 accelerate
P.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 high
Global 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
                                                               company's 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
The Innovation Information Gap

In the course of innovation, design, and manufacturing of products,
companies make extensive use of software tools that generate a
plethora of complex scientific and technical information: Food and
beverage companies utilize formula and specification management
software; 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 for
a purpose, they are not generally designed to interoperate with other
systems and data repositories. Furthermore, the science inherent in
many of these processes has been a key factor in creating an R&D
environment that is too reliant upon informal, unconnected, and highly
customized personal productivity tools such as email, spreadsheets,
and homegrown software tools.

The impact of these internal bottlenecks is further exacerbated by the
dynamics of enterprises in the global economy. Many product
companies rely on partners for external innovation and to provide
adaptation and localization in new markets. Joint ventures are formed
and companies merge to leverage presence in emerging sectors. But all
too often, new participants in this elongated innovation chain bring
with them different knowledge processes, practices, and tools.

The result is a highly decentralized and fragmented environment,
where critical knowledge is scattered across departmental information
systems and geographic silos that introduce waste and impede
organizational learning and sharing of past methods and experience,
further impeding new product introduction and eroding the value of
critical intellectual property.

To systematize and control scattered information, companies utilize
ERP and product master data management (MDM) software tools that
are designed to coalesce multiple data and serve as the "single version
of the truth" for the enterprise. These tools, which have strong roots in
mechanical and electrical engineering disciplines, are much less
effective 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 are
unable to represent attributes and complex interrelated hierarchies in
scientific data: chemicals, materials, and scientific experimentation
protocols and results. Consequently, information indexing and retrieval
are 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 be
unified under an enterprise process. Mature organizations ensure that
innovation is not an independent activity that happens in isolation.


Page 2                                 #MI233313                   ©2012 IDC Manufacturing Insights
Instead, they connect front-end innovation to processes further
downstream and assess the value and impact of innovation on the entire
product life cycle before long-term design and manufacturing
commitments are made. For example, a single change to a formulation
ingredient can have an impact on any number of later-stage downstream
activities, including process instructions, the selection and calibration of
plant equipment, safety procedures, environmental compliance, and
package labeling.

In a similar vein, efficient innovation must be informed by and benefit
from previous innovation activities — whether successful or not — so
that best practices are implemented and mistakes are not repeated.

The gap between organizational needs for efficient and effective
innovation and availability of effective information tools leads to
tremendous waste and organizational burden. We estimate that as
much as 40% of all R&D experiments are repeated — unnecessarily
and often inefficiently — delaying projects and increasing costs and
risks. As companies' R&D organizations adopt global innovation
models with geographically dispersed project teams and third-party
partners, these problems intensify, challenging organizations' capacity
to build process velocity and improve the success rate of innovation
commercialization through learning and sharing of past methods.

Closing the Gap Between "R" and "D"

Accelerating the innovation process during the early stages of product
ideation is critical. But no less important is the ability to drive
effective innovation collaboration among various parallel innovation
and experimentation processes and, perhaps more critically, between
the "R" and the "D" of R&D and then between innovation and product
commercialization.

An effective approach to R&D must facilitate the context and handoff
between innovation at the fundamental science level and its
application in a final design, formula, or part geometry. For example,
chemistry-level information gathered during product development is
coalesced and stratified in a way that it can be applied effectively by
ingredient testing and sourcing or engineering in designing a part or a
manufacturing process.

Furthermore, as products enter volume production, this framework
hands off critical data to the PLM and ERP systems that govern
product manufacturing, supply chain management, and distribution.
For example, detailed chemistry-level information gathered during
product development and volume production ramp-up is used to
support activities such as quality and traceability, regulatory
compliance, and sourcing.



©2012 IDC Manufacturing Insights       #MI233313                               Page 3
Indexing of scientific data found in logs, patents, and designs, such as
the properties of a compound or a molecular structure, should be
"science aware": Indexing and searching mechanisms should be able
to handle a broad range of domain-specific scientific terminology such
as molecular structures and substructures, chemistries, sequences,
applications, experiment results, and so forth.

Forward-looking organizations need a structured framework for all
internal and external experimentation so that critical information can
be organized and shared to facilitate effective collaboration inside the
enterprise as well as with suppliers, partners, and academia. This
necessitates that the system be able to access and unify different
information types from multiple domains and disciplines, both internal
and external.


ESSENTIAL GUIDANCE
Companies must rethink their entire innovation and R&D processes,
especially science-led activities, and strive to manage scientific
experimentation the same way and with the same rigor and precision
that they manage engineering, manufacturing, and supply chain
disciplines.

Specifically, they need to facilitate an enterprise approach to R&D
informatics to manage all critical scientific data and make it available
in a usable, structured format, to diverse stakeholders, to exploit it
efficiently through the design-test-manufacture pipeline.

Furthermore, an enterprise R&D management system must also be
able 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 compromising
completeness and fidelity. For example, utilizing detailed chemistry-
level information garnered during product development and
production ramp-up, a company is better equipped to support
downstream volume production activities such as yield management,
regulatory compliance, and sourcing.

Companies adopting an approach that effectively connects the
innovation cycle and commercialization cycle with high fidelity data
that maintains the context as a project moves through discovery into
manufacturing should experience greater operational visibility and
improved 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
● 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 Notice

Copyright 2012 IDC Manufacturing Insights. Reproduction without
written permission is completely forbidden. External Publication of
IDC Manufacturing Insights Information and Data: Any IDC
Manufacturing Insights information that is to be used in advertising,
press releases, or promotional materials requires prior written approval
from the appropriate IDC Manufacturing Insights Vice President. A
draft of the proposed document should accompany any such request.
IDC Manufacturing Insights reserves the right to deny approval of
external usage for any reason.




©2012 IDC Manufacturing Insights     #MI233313                             Page 5

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Accelerating science-led-innovation-whitepaper

  • 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 12 www.idc-mi.com "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 and F.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 accelerate P.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 high Global 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 company's 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. The Innovation Information Gap In the course of innovation, design, and manufacturing of products, companies make extensive use of software tools that generate a plethora of complex scientific and technical information: Food and beverage companies utilize formula and specification management software; 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 for a purpose, they are not generally designed to interoperate with other systems and data repositories. Furthermore, the science inherent in many of these processes has been a key factor in creating an R&D environment that is too reliant upon informal, unconnected, and highly customized personal productivity tools such as email, spreadsheets, and homegrown software tools. The impact of these internal bottlenecks is further exacerbated by the dynamics of enterprises in the global economy. Many product companies rely on partners for external innovation and to provide adaptation and localization in new markets. Joint ventures are formed and companies merge to leverage presence in emerging sectors. But all too often, new participants in this elongated innovation chain bring with them different knowledge processes, practices, and tools. The result is a highly decentralized and fragmented environment, where critical knowledge is scattered across departmental information systems and geographic silos that introduce waste and impede organizational learning and sharing of past methods and experience, further impeding new product introduction and eroding the value of critical intellectual property. To systematize and control scattered information, companies utilize ERP and product master data management (MDM) software tools that are designed to coalesce multiple data and serve as the "single version of the truth" for the enterprise. These tools, which have strong roots in mechanical and electrical engineering disciplines, are much less effective 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 are unable to represent attributes and complex interrelated hierarchies in scientific data: chemicals, materials, and scientific experimentation protocols and results. Consequently, information indexing and retrieval are 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 be unified under an enterprise process. Mature organizations ensure that innovation is not an independent activity that happens in isolation. Page 2 #MI233313 ©2012 IDC Manufacturing Insights
  • 3. Instead, they connect front-end innovation to processes further downstream and assess the value and impact of innovation on the entire product life cycle before long-term design and manufacturing commitments are made. For example, a single change to a formulation ingredient can have an impact on any number of later-stage downstream activities, including process instructions, the selection and calibration of plant equipment, safety procedures, environmental compliance, and package labeling. In a similar vein, efficient innovation must be informed by and benefit from previous innovation activities — whether successful or not — so that best practices are implemented and mistakes are not repeated. The gap between organizational needs for efficient and effective innovation and availability of effective information tools leads to tremendous waste and organizational burden. We estimate that as much as 40% of all R&D experiments are repeated — unnecessarily and often inefficiently — delaying projects and increasing costs and risks. As companies' R&D organizations adopt global innovation models with geographically dispersed project teams and third-party partners, these problems intensify, challenging organizations' capacity to build process velocity and improve the success rate of innovation commercialization through learning and sharing of past methods. Closing the Gap Between "R" and "D" Accelerating the innovation process during the early stages of product ideation is critical. But no less important is the ability to drive effective innovation collaboration among various parallel innovation and experimentation processes and, perhaps more critically, between the "R" and the "D" of R&D and then between innovation and product commercialization. An effective approach to R&D must facilitate the context and handoff between innovation at the fundamental science level and its application in a final design, formula, or part geometry. For example, chemistry-level information gathered during product development is coalesced and stratified in a way that it can be applied effectively by ingredient testing and sourcing or engineering in designing a part or a manufacturing process. Furthermore, as products enter volume production, this framework hands off critical data to the PLM and ERP systems that govern product manufacturing, supply chain management, and distribution. For example, detailed chemistry-level information gathered during product development and volume production ramp-up is used to support activities such as quality and traceability, regulatory compliance, and sourcing. ©2012 IDC Manufacturing Insights #MI233313 Page 3
  • 4. Indexing of scientific data found in logs, patents, and designs, such as the properties of a compound or a molecular structure, should be "science aware": Indexing and searching mechanisms should be able to handle a broad range of domain-specific scientific terminology such as molecular structures and substructures, chemistries, sequences, applications, experiment results, and so forth. Forward-looking organizations need a structured framework for all internal and external experimentation so that critical information can be organized and shared to facilitate effective collaboration inside the enterprise as well as with suppliers, partners, and academia. This necessitates that the system be able to access and unify different information types from multiple domains and disciplines, both internal and external. ESSENTIAL GUIDANCE Companies must rethink their entire innovation and R&D processes, especially science-led activities, and strive to manage scientific experimentation the same way and with the same rigor and precision that they manage engineering, manufacturing, and supply chain disciplines. Specifically, they need to facilitate an enterprise approach to R&D informatics to manage all critical scientific data and make it available in a usable, structured format, to diverse stakeholders, to exploit it efficiently through the design-test-manufacture pipeline. Furthermore, an enterprise R&D management system must also be able 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 compromising completeness and fidelity. For example, utilizing detailed chemistry- level information garnered during product development and production ramp-up, a company is better equipped to support downstream volume production activities such as yield management, regulatory compliance, and sourcing. Companies adopting an approach that effectively connects the innovation cycle and commercialization cycle with high fidelity data that maintains the context as a project moves through discovery into manufacturing should experience greater operational visibility and improved 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. ● 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 Notice Copyright 2012 IDC Manufacturing Insights. Reproduction without written permission is completely forbidden. External Publication of IDC Manufacturing Insights Information and Data: Any IDC Manufacturing Insights information that is to be used in advertising, press releases, or promotional materials requires prior written approval from the appropriate IDC Manufacturing Insights Vice President. A draft of the proposed document should accompany any such request. IDC Manufacturing Insights reserves the right to deny approval of external usage for any reason. ©2012 IDC Manufacturing Insights #MI233313 Page 5