Contenu connexe Similaire à Accelerating science-led-innovation-whitepaper Similaire à Accelerating science-led-innovation-whitepaper (20) Plus de Sergey Lourie (20) Accelerating science-led-innovation-whitepaper1. Accelerating Science-Led Innovation for
Competitive Advantage
WHITE PAPER
Sponsored by: Accelrys
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"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
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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
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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
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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.
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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.
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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.
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