HOW EFFECTIVE IS YOUR INNOVATION LIFECYCLE?

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1 WHITEPAPER HOW EFFECTIVE IS YOUR INNOVATION LIFECYCLE? A MATURITY MODEL AND ROADMAP FOR CONSISTENT, REPEATABLE SCIENTIFIC INNOVATION Businesses the world over put a lot of stock in innovation, and for good reason. Whether a shampoo, drug, computer chip or composite for an airplane wing, products that do something better, cheaper, faster or differently than the competition are the Holy Grail for organizations that seek to thrive and profit in today s fast-moving global economy. Yet all too often, somewhere along the innovation lifecycle that starts with a novel idea and ends with a viable, ready-to-manufacture product, something falters. According to IDC Manufacturing Insights, across industries, currently only a quarter of R&D projects reach the market. Of those, two thirds fail to meet customer expectations. And almost half the resources R&D companies allocate to product commercialization are wasted because of manual and disconnected processes that create bottlenecks, slow time-to-market and damage profitability. 1 Part of the problem is that most organizations have no clear roadmap for ensuring that their R&D investments consistently lead to successful new products. Scientific innovation is often viewed as something ephemeral, like lightning, rather than as an end-to-end process that can be streamlined, optimized and continuously improved upon. But in a world where being first-to-market is increasingly one of the most important drivers of competitive advantage, businesses simply don t have the luxury of waiting for innovation to strike. 1 IDC Manufacturing Insights: Accelerating Science-Led Innovation for Competitive Advantage 1

2 This paper will explore the key components of the scientific innovation lifecycle, examine the challenges that are getting in its way, and introduce a model that can be employed to assess your organization s innovation maturity level. It will also discuss how the maturity model may be used to create a framework for improving the alignment of people, processes, tools and data across the R&D enterprise, with the ultimate goal of realizing faster, more profitable and repeatable innovation. THE SCIENTIFIC INNOVATION LIFECYCLE IS BROKEN What, exactly, is innovation? In R&D-driven industries, it s the process of leveraging science to invent something that customers will want to buy. Whether the invention is a more effective anti-aging skin creme, a new drug, a longer lasting battery or a strong yet lightweight material that will improve the performance of a host of different products, the key to success is meeting a need and getting it to market... fast. This kind of scientific innovation isn t (or shouldn t be) a flash in the pan, but rather a lifecycle that is systematically managed from that first glimmer of an idea all the way through to the hand off to manufacturing. There are numerous steps in between, however all of which involve diverse scientific activities, contributors and disciplines. These include: the heavy experimentation that comes with early research and discovery; design optimization, validation and qualification; QA/QC testing and regulatory compliance; and production scale-up and beyond. Smart management of the scientific innovation lifecycle can provide a competitive edge that enables companies to get novel products into the hands of customers quickly and cost effectively. It helps businesses both do the right things and do things right by aligning profitable market opportunities with streamlined and efficient R&D. The problem is that, in reality, many science-driven enterprises are having trouble managing innovation activities in a way that delivers optimal results. Organizations are increasingly challenged by disjointed and manual processes, siloed data and IT systems, as well as a lack of collaboration and information visibility between R&D and business functions all of which cause multiple productivity gaps that slow product development, drive up costs and negatively impact competitiveness. As an example, consider the impact of data being locked within technology, departments and disciplinary silos. While one project team within an organization may have already spent a great deal of time and resources researching a compound that has turned out to have a significant stability flaw, there s nothing to stop another team from repeating costly experiments on the same compound if they can t find and access this critical historical data. Or, without insight into downstream sourcing data, chemists may waste weeks designing a formula that s not a viable candidate for large-scale production. These kinds of issues come up all the time during the innovation lifecycle, resulting in bottlenecks, errors, added expense and delay. Complicating matters further is the fact that scientific complexity and data volumes have increased while product cycle times have shrunk, so there s also the added burden of having to do more, much faster and with fewer resources. 2

3 For many organizations, the scientific innovation lifecycle is broken. The question is, what can be done to fix it? A study conducted by Booz Allen Hamilton 2 on global innovation has shown an ambiguous link between investment in science and technology and business growth. Yet the same report also correlates underspending on R&D to competitive disadvantage, including lower profit margins and shareholder returns. What conclusions can we draw from this? Innovation clearly has competitive value, but perhaps managing the activities that drive it with greater discipline will yield better results than simply spending more. Businesses that seek to optimize their investments in scientific innovation must find ways to manage it more efficiently, consistently and economically. This will require making fundamental changes to how people, processes, technology and data are aligned across the innovation lifecycle. From early research at the chemical and molecular level, all the way up 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 the scientific information and activities that drive innovation, so that novel products can be delivered to market before the competition. But to know where your organization needs to go, and what steps need to be taken to get you there, you need a baseline. As the saying goes, You can t manage what you can t measure. A MATURITY MODEL FOR SCIENTIFIC INNOVATION While many of today s science-driven organizations want to innovate more successfully, most have little insight into what, exactly, they need to focus on to improve. Accelrys is addressing this issue with a Maturity Model for Scientific Innovation, created to provide a consistent measuring stick that businesses can use to assess the current maturity level of their innovation practices against the desired maturity state that they would like to achieve. Accelrys Maturity Model for Scientific Innovation is inspired by the process improvement approaches outlined in the Capability Maturity Model Integration (CMMI) 3, which is the result of more than 20 years of ongoing work by members of industry, government and academics at Carnegie Mellon University s Software Engineering Institute (SEI). CMMI has been successfully deployed and validated by thousands of companies from a variety of industries. Users have realized benefits across numerous areas of business performance, including product and service quality, forecasting accuracy, productivity, customer satisfaction, return on investment and more. With the Maturity Model for Scientific Innovation, Accelrys aims to apply the approaches put forth by CMMI to the innovation lifecycle, focusing on benchmarking 2 3 The Booz Allen Hamilton Global Innovation 1000: Money Isn t Everything; by Barry Jaruzelski, Kevin Dehoff, and Rakesh Bordia; strategy+business issue 41, Winter

4 Figure 1: Maturity Model levels, which are assessed by organizational categories the R&D tactics and strategies of participating companies against the best practices consistently employed by successful organizations. The model is also informed by Accelrys many years of experience working in partnership with science-driven businesses to build practical technology solutions that help users get the most out of R&D investments. The Maturity Model for Scientific Innovation includes five increasingly advanced levels, and assesses innovation maturity across four main organizational categories: 1) People, 2) Processes, 3) Technology and 4) Data. (See Figure 1.) Innovation practices related to people are evaluated by the amount of collaboration and teamwork that exists within an organization, training practices, and by how individuals are incentivized and rewarded for their contributions. With processes, variability is considered, as well as whether procedures and steps are documented, monitored, measured and continuously improved. Technologies and tools are evaluated by how effectively they support complex scientific activities, whether they support common/shared standards, and by how easily they can be integrated with other tools and information systems. And finally, data is evaluated based on how efficiently information is stored, protected and shared, by how accessible (and useful) it is to users throughout the organization, and by how well it supports innovative decision-making. 4

5 An organization s maturity level is based on its performance in each of the four areas described above. The five levels of scientific innovation maturity are: Level 1: Initial At this level, innovation practices are driven by individual needs and preferences, rather than by clearly defined processes and strategic organizational direction or oversight. This means that individual stakeholders whether chemists, biologists, engineers or other experts are given free reign to choose their own tools, create their own processes and store the data they are creating in any way that they see fit. As a result, valuable knowledge becomes isolated and/or hidden from other stakeholders within the organization, contributors view their expertise as a source of individual value and power rather than as something to be shared, and variable processes lead to unpredictable, inconsistent and poorly controlled results. While there may be a few superstars using industry or locally developed best practices, the end-to-end innovation process is ad-hoc, chaotic, disjointed and rife with bottlenecks and productivity gaps. Level 2: Defined Here we see organizations begin to define and manage innovation processes, but only at the disciplinary, team or departmental level. Within these groups, best practices are loosely documented, data policies are in place and knowledge and expertise is accessible and shared. Innovation practices still vary widely, however, across functional areas, scientific disciplines and organizational departments. Because there is little consistency from group to group, knowledge is tribal, rather than institutional and access to prior learning is dependent on personal relationships. Re-creation of previous work is common and there is a lack of data visibility at the enterprise level, which negatively impacts the alignment of business objectives and R&D activities. Level 3: Integrated This is where the integration of people, processes, technology and data becomes manifest across the enterprise, enabling the science and business sides of the organization to more effectively collaborate in managing and optimizing the end-to-end innovation cycle. At this level, consistent, repeatable organizational policies and scientific procedures are defined, documented and perhaps even automated. Additionally, all relevant project information is captured and made accessible to stakeholders up and down the discover-design-test-manufacture value chain, ideally with the help of an information architecture that supports the integration of multiple types and sources of scientific data. Most importantly, scientific contributors have clear insight into how the projects they are working on are relevant to the business, and the business has visibility into what the R&D organization is learning, so that it can link even early discoveries to potential market opportunities. 5

6 Level 4: Measured Once people, processes, technology and data are integrated, ongoing measurement and evaluation is necessary to be able to identify where there is room for improvement. At this level, organizations have deployed a standard framework for the consistent measurement of the effectiveness of people, teams, departments, technologies, tools and processes. Scientific and business workflows are fully integrated, activities are managed centrally and routinely audited for quality, and the organization is able to connect all decisions and deliverables to measurable results. Finally, scientific data and information is leveraged not just as a source of knowledge and learning, but also as a legal record of intellectual property and invention. Essentially, the organization can now prove whether or not it is doing things right by tracking compliance with all policies, procedures and processes that have been created to support innovation. Beyond measurement best practices, this level is also characterized by the ability of the organization to share data securely with external suppliers and business partners (and define clear roles and permissions) so that more streamlined collaboration can be extended outside of the enterprise s four walls. Level 5: Optimized At this level, an organization is able to continually and systematically improve and refine its innovation performance, based on the insights gained through consistent measurement and evaluation. People, processes, tools and data are 100 percent aligned, both within the organization and across the extended value chain (encompassing ingredient suppliers, partners, CMOs, distributors, etc.) and tools are in place to not just facilitate open collaboration, data sharing, process automation and information visibility, but also to identify and take action where there are opportunities for the organization to perform even better. This is innovation nirvana a difficult state to reach, but certainly a worthy goal. Where are most companies at on the innovation maturity scale today? It varies, but in Accelrys experience working with science-driven organizations in industries ranging from automotive, aerospace and biotechnology to consumer packaged goods, electronics, energy and pharmaceuticals, many face myriad challenges getting beyond the data and process silos that are typical of Level 2. These challenges include ongoing reliance on manual and paper-based information management processes, as well as a lack of data and technology integration across disciplinary, departmental and geographic boundaries. 6

7 ASSESSING YOUR ORGANIZATION S INNOVATION MATURITY In order to reduce product development costs and risks while increasing innovation success, organizations need to know what areas require attention so that they can create a roadmap for improved performance. The answers to these questions depend on making an accurate assessment of where you are on the innovation maturity scale and specifically defining where you want to go. The Accelrys Maturity Model for Scientific Innovation allows for an objective measure of an organization s current maturity state that can be compared with a desired state that is aligned to business objectives, as well as benchmarked against industry peers and competitors. Most importantly, it enables organizations to identify which gaps between current and desired states will have the most significant business impact if addressed. There are several key steps involved in completing an accurate innovation maturity assessment: Step 1: Define Innovation Lifecycle Objectives Creating a roadmap for innovation improvement demands a clear understanding of the business objectives your organization hopes to achieve with the help of R&D. For example, is speed to market the most important consideration when it comes to new products, or exceptional quality? Is the business looking for incremental product improvements or a complete departure from previous offerings? What areas of research are considered most critical to the company s overall mission? How much is too much to spend on R&D? What types of R&D data would the business like to have insight into on a regular basis? In what form? What information does R&D need from the business in order prioritize projects and allocate resources? These and similar types of questions need to be answered by the executives responsible for R&D decision-making, with the goal of identifying desired innovation maturity levels related to people, processes, technology and data. Step 2: Assess Current Innovation Maturity Level Once maturity objectives are identified, an objective measure of current innovation practices is required. This involves querying a cross section of the organization (including lab techs, scientists, IT support staff, business stakeholders, etc.) to gather feedback about the processes, tools, collaborative activities and data infrastructure currently in place. Accelrys has developed two assessment tools to guide this process; an online self-assessment covering 12 key topics that help to isolate high-level focus areas, and an on-site professional level assessment that is a key part of the Accelrys Scientific Process Improvement project methodology. 7

8 Figure 2: Maturity Model Self-Assessment Questionnaire The self-assessment covers 12 topics that are related to how people, processes, tools and data support innovation. For example, an assessment query focused on Quality Management asks, How well defined are your quality processes with respect to scientific experimentation, analysis, and development? (See Figure 2.) Respondents are then prompted to: rate the degree to which the activity is addressed within the organization; identify whether processes around the activity are documented, followed and audited; and finally, to rank how important the activity is to meeting business objectives. Each question is designed to capture how well (or how poorly) the respondent thinks a certain activity is handled within their organization, to determine whether procedures for the activity are defined, followed and measured, and to get a sense of how much the topic matters to the respondent in the first place. The on-site or Professional scientific maturity model assessment uses a more comprehensive interviewbased methodology that captures insights from the participants to increase the depth and objectivity of the assessment, makes the model relevant to your specific business environment, and provides a detailed gap analysis between your as-is and to-be states of scientific maturity. The professional assessment evaluates over 50 topic areas and includes specific recommendations for improvements backed by objective financial and operational impact evaluation. 8

9 Integrated Product Development Design for Mfg. & Cost People Topic Process Topic High Importance Data Integrity Risk Management Internal Data Opportunity Management Lessons learned Tools Topic Data Topic Quality by Design Validation & checking Physical/Virtual team integration Supply chain exchange High Gap Figure 3: Gap Analysis enables organizations to identify areas that offer the greatest opportunity for improvement Step 3: Identify the Gaps Once participants have completed the assessment process, Accelrys will aggregate, score and analyze the data to identify the most significant gaps that exist between the organization s current innovation maturity level and desired end state. The final analysis is weighted according to the following factors: 1. Size of the problem. How large is the gap between the current and desired state? 2. Importance of the problem. How important is the issue overall to the respondent? To the company as a whole? 3. Level of consensus. How many stakeholders agree that a specific gap is a both a significant and important problem? For example, an issue that has been identified as a big problem by only a few respondents would not be given as much weight as a productivity gap around which there is a great deal of consensus. Using the gap analysis as benchmarks, organizations can then begin to investigate, plan and prioritize actions (and potential investments) based on which areas offer the greatest opportunity for new approaches to make a positive impact. (See Figure 3.) Final results are not based on a value judgment made by Accelrys (or any other third party,) but rather are determined by the combined assessments made by participating respondents from within the organization. As a result, the level and diversity of participation in the assessment should improve the accuracy of the outcome. 9

10 MATURITY MODEL BENEFITS: DEVELOPING A ROADMAP FOR IMPROVEMENT The Maturity Model for Scientific Innovation assessment tool provides organizations with a qualitative point of reference for appraising current processes and a common framework for building a roadmap for improvement. Because of the level of detail and range of innovation-related activities explored in the assessment, it can be used to determine where incremental changes can be made, to prioritize which issues should be addressed first, and identify actions most likely to yield the highest impact. Based on the innovation practices determined by organizational consensus to be both important and impacted by significant productivity gaps, Accelrys will develop a high-level roadmap with a list of suggested activities that can be taken to eliminate bottlenecks and increase efficiencies. These activities may include process or organizational changes, new tools and technology infrastructure, or approaches designed to realize improved performance from existing resources. Accelrys additionally will provide information and ROI studies on the specific areas where our solutions can add value and work with an organization on developing an implementation strategy. Consider the experience of a large specialty chemicals company that recently measured its practices against the Accelrys Maturity Model. The organization was progressive about investing in and deploying IT solutions in support of innovation-related activities, but was not seeing business improvements at the pace needed to support growth objectives. The Maturity Model assessment indicated several productivity gaps related to the fact that processes and solutions were often deployed in isolation by individual business units, negatively impacting cross-organizational collaboration. In addition, the assessment found that the hand-off of data from research to scale-up was problematic, leading to knowledge gaps and mistakes due to stakeholders at the experimental stage not considering or even knowing about critical production requirements downstream (for example, chemists might use a solvent in early research that would not be affordable at manufacturing scale, with the result being product launch delays and rework.) Based on the identified productivity gaps, recommendations included the following: Put processes and technology infrastructure in place to ensure multi-directional data sharing of lessons learned between R&D, manufacturing and commercial teams. Consider the introduction of formal mentoring or buddy system arrangements to foster better crossorganizational communication and faster development of personal networks. Focus more closely on last mile data management issues to ensure that knowledge is not just stored and available, but searchable and useful for all stakeholders, regardless of when and where it originated. Create auditable policies and procedures for data management that ensure consistency, objectivity and quality. 10

11 This customer is now using the assessment and recommendations to guide its overall innovation strategy, to prioritize technology investments and to engage in a more productive partnership with Accelrys to meet these goals. One of the participating executives has stated, Going through this process helped us to improve insight into the dials and levers that link scientific operations with business performance. We are leveraging the insight gained through the Maturity Model framework to build a continuous improvement culture, both from the bottom-up and top-down. HOW TO GET STARTED Improving the speed and success of science-driven product innovation needs to be less like trying to catch lighting in a bottle and more like a process that can be measured, optimized and continuously improved. Ready to put your business on the path to realizing faster, more profitable and repeatable results from R&D investments? Take the Self-Assessment Online: maturity. Call Accelrys for information about the On-site Professional Level Assessment: To learn more about Scientific Innovation Lifecycle Management, go to 2012 Accelrys Software Inc. All brands or product names may be trademarks of their respective holders. WP