Nov 22, 2010

Nokia’s Bureaucracy Stifled Innovation

This New York Times article on Nokia’s Bureaucracy Stifled Innovation has several interesting lessons for R&D managers.  It appears that Nokia had a smart phone before others, but cancelled it:
A few years before Apple introduced the iPhone in early 2007, the prototype of an Internet-ready, touch-screen handset with a large display made the rounds among upper management at Nokia. The prototype developed by Nokia’s research centers in Finland was seen as a potential breakthrough by its engineers that would have given the world’s biggest maker of mobile phones a powerful advantage in the fast-growing smartphone market.
I am not sure that having a prototype in 2004 and choosing not to bring it to market was such a bad decision. Apple itself had a prototype for a smart phone working with a large consumer electronics company in 2004.  They too chose not to bring it to market.  I do not think technology existed to actually build successful smart phones in 2004 - that included fast enough processors, low power LED backlit screens and abundant DRAM/FLASH. R&D Managers need to make touch choices at times and they can all not be the right choices.  This one example does not directly prove that all decisions made at Nokia were bad - or that even this decision was a bad one.

On the other hand, the article mentions a couple of times that Nokia got complacent because of its own success:
... former employees depicted an organization so swollen by its early success that it grew complacent, slow and removed from consumer desires. As a result, they said, Nokia lost the lead in several crucial areas by failing to fast-track its designs for touch screens, software applications and 3-D interfaces.
“Nokia in a sense is a victim of its own success,” said Jyrki Ali-Yrkko, an economist at the private Research Institute of the Finnish Economy. “It stayed with its playbook too long and didn’t change with the times. Now it’s time to make changes.”
This is clearly a problem.  How do R&D mangers keep from falling into this trap?  I guess one has a better more formal portfolio balancing process that allows decisions to be based on qualitative and quantitative criteria that can be discussed rationally.  This was NOT the case at Nokia:
Juhani Risku, a manager who worked on user interface designs for Symbian from 2001 to 2009, said his team had offered 500 proposals to improve Symbian but could not get even one through.
“It was management by committee,” Mr. Risku said, comparing the company’s design approval processes to a “Soviet-style” bureaucracy. Ideas fell victim to fighting among managers with competing agendas, he said, or were rejected as too costly, risky or insignificant for a global market leader. Mr. Risku said he had left in frustration at its culture; he now designs environmentally sound buildings.
The key phrase in portfolio balancing is BOTH qualitative and quantitative criteria.  A strict focus on ROI will kill high-risk high-return innovative projects.  Fundamental technology development is also a difficult area to measure ROI because technology has impact on multiple products and ROI is impossible to compute (Symbian could be seen as a fundamental technology with impact on multiple product platforms):
Proposals were often rejected because their payoffs were seen as too small, he said. But “successful innovations often begin small and become very big.”
 In fact, R&D managers should set a portion of their budgets for innovation (10-20%).  These projects should not have any ROI requirements.  Another way to encourage manager risk taking is to reward failure.

Nov 21, 2010

Meetings are a waste of time

Here is some data that would make your day  First a survey that suggests Businesses Waste 4.8 Hours Per Week Scheduling Meetings:
By the time the year ends, many have spent the cumulative equivalent of six weeks scheduling meetings, and that doesn't include time spent attending them.
That sounds excessive and not realistic.  But what do I know...  On the same note, here are some suggestions from HBR to improve reduce waste with my 2 cents added:

  • Meetings always shorter than 90 minutes
  • Meeting materials delivered at least day before and everyone comes prepared
  • One page executive summary for the briefing package that everyone must read
  • Clearly defined roles and responsibilities for each participant in the meeting (no one attends the meeting unless they have a stake)
  • Predefined agenda for the meeting (no meeting requests without agenda)
  • Clear conclusion at the end of the meeting (As per HBR: Where are we going to go from here? What are the to-dos and who is going to do them? When will they be delivered?)
I remember reading and article about how P&G's strategic reviews took place.  The business unit sent out the briefing package at least a week before the review.  All obvious questions were discussed over email.  The meeting was for making decisions and having real debates.  I really like that idea.

Nov 20, 2010

Impact of Incentive Bonus Plan

Here is a cool article from Management Science on Empirical Examination of Goals and Performance-to-Goal Following the Introduction of an Incentive Bonus Plan with Participative Goal Setting:
Prior research documents performance improvements following the implementation of pay-for-performance (PFP) bonus plans. However, bonus plans typically pay for performance relative to a goal, and the manager whose performance is to be evaluated often participates in setting the goal. In these settings, PFP affects managers' incentive to influence goal levels in addition to affecting performance effort. Prior field research is silent on the effect of PFP on goals, the focus of this paper.
The authors studied retails store performance (I believe retail stores have a much better handle on performance bonuses than most R&D organizations I know)
Using sales and sales goal data from 61 stores of a U.S. retail firm over 10 quarters, we find that the introduction of a performance-based bonus plan with participative goal setting is accompanied by lower goals that are more accurate predictors of subsequent sales performance. Statistical tests indicate that increased goal accuracy is attributable to managers 'meeting but not beating' goals and to new information being impounded in goals.
So, managers lower the goals and then meet them!
we find that prior period performance has incremental power to explain goal levels in the postplan period. Our results provide field-based evidence that PFP and participative goal setting affect the level and accuracy of goals, effects that are associated with both information exchange and with managers' incentives to influence goals.
Take home message is to be very careful with setting up an incentive bonus plan.  In R&D organizations, it is even more difficult because the results are often not measurable and incentives tend to get disconnected from performance to start with.  Please let me know if you would like to discuss this further.

Nov 19, 2010

Specification and Design of Embedded Hardware-Software Systems

For last few months, I have been working on developing a new design flow that brings ASIC like reuse and semiconductor like cost curve to all R&D.  The idea is that semiconductors have increased in complexity and performance exponentially, while costs has come down continuously.  How can we replicate the same for all system R&D?

One of the earliest papers on the topic was  Specification and Design of Embedded Hardware-Software Systems.  In retrospect, the place where it should have come up first anyway - system where electronics/semiconductor and other technologies interact.
"System specification and design consists of describing a system's desired functionality, and of mapping that functionality for implementation on a set of system components, such as processors, ASIC's, memories, and buses. In this article, we describe the key problems of system specification and design, including specification capture, design exploration, hierarchical modeling, software and hardware synthesis, and cosimulation. We highlight existing tools and methods for solving those problems, and we discuss issues that remain to be solved."
The paper suggests five tasks:
  1. Specification capture: Specify desired system functionality
  2. Exploration: Explore design alternatives
  3. Specification refinement: Refine specifications based on exploration
  4. Software & Hardware design:
  5. Physical design:
Much more on this in the future.  But a good paper to start thinking about things.

Nov 18, 2010

Top 10 R&D spending Firms

Some good benchmarking data from Booz & Co. and Christian Science Monitor at R&D spending: Here are the Top 10 firms:
Apple, Google, and 3M may top Bloomberg’s list of the world’s most innovative companies, but they’re not the biggest research and development spenders – not even part of the Top 20. Out of 1,000 publicly traded companies with the highest R&D spending in 2009, here are the Top 10, according to a survey by management-consulting firm Booz & Co.
Here is the list for 2009 R&D budgets(Clearly dominated by Drug companies):

  1. Roche Holding $9.b
  2. Microsoft $9B
  3. Nokia $8.6B
  4. Toyota $7.8B
  5. Pfizer $7.7B
  6. Novartis $7.5B
  7. Johnson & Johnson $7B
  8. Sanofi-Aventis $6.3B
  9. GlaxoSmithKline $6.2B
  10. Samsung $6B

China's Drones Raise Eyebrows at Air Show -

Here is an interesting article in the WSJ with significant impact on long-term R&D strategy: China's Drones Raise Eyebrows at Air Show
Western defense officials and experts were surprised to see more than 25 different Chinese models of the unmanned aircraft, known as UAVs, on display at this week's Zhuhai air show in this southern Chinese city. It was a record number for a country that unveiled its first concept UAVs at the same air show only four years ago, and put a handful on display at the last one in 2008."
Amazing progress on Chinese front. During the cold war, USA and Russia kept pumping money into R&D.  This long-term research provided sustainable lead to countries and was a source of significant innovations such as ASICs, Interenet, etc.
I think the difference between the cold war and now is the significant increase in the rate at which technology is changing. Slow progress over decades just won't be sufficient against newcomers because they will be starting from a much more advanced computing platform.  They will be able to model new environments/materials and manufacture with increasingly more capable machines.  In fact, in many cases a long legacy is  a drag on new innovations.
The answer, however, is not the complete elimination of long-range research.  The answer is to develop more robust R&D plans, so that impact of changes in one technology can be propagated quickly across the entire system development.  The answer also is a frequent re-balance of R&D portfolios to account for changing technology/market/geopolitical landscapes.
I guess R&D managers need even more powerful tools and processes.

Nov 17, 2010

NSF Innovation Survey

The National Science Foundation has released preliminary results of their innovation survey: - SRS NSF Releases New Statistics on Business Innovation - US National Science Foundation (NSF). Below are some important take aways:
Defintion of what is innovation:
In the Oslo framework, innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations."[6] Further, "The minimum requirement for an innovation is that the product, process, marketing method or organizational method must be new (or significantly improved) to the firm. This includes products, processes, and methods that firms are the first to develop and those that have been adopted from other firms or organizations.
Lots of interesting data.  Definitely a source to get back to whenever you are trying to benchmark innovation.

Nov 16, 2010

Case study on too much of a vision?

The article on Ars Technica about OLPC's Negroponte offers to help India realize $35 tablet could be an interesting case study on how too definite a vision could actually be counter productive to over all results.  The single minded focus on $100 per laptop could have been a good stretch goal, but then project execution problems and may be technology challenges led to pretty big problems.
OLPC designs low-cost education computing devices for developing countries. The project aimed to produce a ubiquitous $100 laptop that would bring constructivist learning theory to the developing world. The project has fallen far short of fulfilling its initial goals due to serious setbacks, ranging from technical and logistical failuresto divisive ideological conflicts. OLPC was forced to reorganize and downsize much of its development staff last year as its funds dwindled. Despite these cuts, the organization was able to continue moving forward by narrowing its focus and pursuing a less ambitious strategy.
Add to it the problems of multi-organizational R&D where goals of "partners" were often at odds with the overall goals of the project and it became a significant mess:
When the OLPC project first launched, Negroponte argued that OLPC's agenda could only be achieved by harnessing economies of scale. The laptops would be sold to governments in massive quantities in order to reduce overall manufacturing costs. Negroponte contended that competing efforts and alternative low-cost laptop products were harmful to OLPC's vision because they would fragment the market and undermine OLPC's ability to achieve the level of scale that he believed was necessary for success. 
This became a contentious issue that isolated OLPC from potential partners—particularly Intel, which parted ways with OLPC and built its own competing Classmate PC. Intel frowned on OLPC's one-size-fits-all approach and argued that diverse offerings were needed in order to encourage adoption of low-cost educational computing. 
None of the problems here are easy to solve and I applaud OLPC for achieving everything they have.  However, there are several lessons we could learn for more effective R&D management in large cross-organizational R&D:
  1. Flexibility in vision (not at the expense of drive and focus)
  2. Ability to identify, express clearly and discuss diverse goals
  3. Ensure that some team members are not going to work to weaken the overall effort
  4. More collaboration and inclusiveness
Looks like OLPC is at least becoming more flexible:
The world needs your device and leadership. Your tablet is not an 'answer' or 'competitor' to OLPC's XO laptop. It is a member of a family dedicated to creating peace and prosperity through the transformation of education," Negroponte said in his letter. "[I offer] full access to all of our technology, cost free. I urge you to send a team to MIT and OLPC at your earliest convenience so we can share our results with you.

Nov 15, 2010

Strategic Thinking and Dependence on Forecasts

An interesting article in McKinsey Quarterly with significant implications or R&D strategy: Applying global trends: A look at China’s auto industry.  The overall idea is that forecasts are important and can drive strategic plans and R&D plans. However, forecasts are often wrong and R&D organizations need to be careful not to be tied to strongly to one particular forecast.  In fact, strategy / R&D plans need to be flexible enough to support multiple outcomes.
Strategists can challenge conventional wisdom and better prepare for uncertainty by analyzing the complex and not-so-obvious ways global trends interact in their industries.
Here is a good example of how forecasts were wrong in adoption of mobile phones in Africa:
Only a dozen years ago, for example, authoritative predictions for the coming decade envisioned no more than a few million mobile-phone users throughout Africa. Local income, consumption, technology, infrastructure, and regulatory conditions seemed to hold little promise for significant growth. Less than ten years later, though, Nigeria alone had 42 million mobile subscribers—80 times more than initial forecasts predicted—as growth skyrocketed, largely as a result of the interaction between just two trends: improved income levels and cheaper handsets.
Scenario analysis is an effective approach to address such vast mismatches between expectations and reality:  The article proposes a four step approach that might facilitate better scenario analysis:

1. Establish the reference frame: Scenarios are difficult to construct.  Sometimes, it is possible to find published scenarios that might form the foundation for analysis (For example, CIA  global economic/political scenarios).  However, in absence of such background material, it helps to have a frame of reference.  For example, as the article points out, if one is worried about Chinese automotive entry in USA, it might be worth thinking through Korean market entry back in the 80s.  Or may be, Japanese entry even before.  It might also be worth looking at other industries and see what has happened in them.
The right frame of reference—a specific problem statement and a clear sense of the industry context for long-term shifts—is a critical starting point.
2. Expand the solution space: Now it is time to modify/update the reference frame based on current geopolitical scenarios, technology and industry specific environment:
Having carefully defined the problem and the industry context surrounding it, the challenge for strategists is to broaden the potential solution space by challenging conventional wisdom through the lens of global trends. Most companies have a broad range of experts who can help, yet these people are often tucked away in organizational silos that make it difficult for them to connect the dots.
3. Define scenarios
4: Quantify Industry Impact (under different scenarios).
The article also had a great listing of the Delphi tool that is helpful.

Nov 14, 2010

Finding Competitive Advantage in Adversity

HBR article Finding Competitive Advantage in Adversity has some important learnings for R&D managers:
Unlike many managers whose instincts are to hunker down and play it safe during difficult times, entrepreneurs like Bush hear a call to action in the oft-repeated advice of Machiavelli: “Never waste the opportunities offered by a good crisis.”
Opportunity 1: Match Unneeded Resources to Unmet Needs - That is to find resources that are not being used because of adversity and then repurpose or redirect them to meet unmet needs. This is extremely important in R&D management. As budgets are reduced, a lot of projects are cancelled midstream. Wise managers will build on projects being cancelled to deliver something new.
Adversity comes in many forms—acute, cyclical, long-term, and systemic. It sometimes affects individuals or single firms; other times it cuts across a wide swath of entities. However, its pathology is consistent: Adversity constrains a key resource, which then depresses demand, supply, or both. That gives rise to unmet need and releases other resources that become redundant. An opportunity emerges for inventive entrepreneurs who can reroute the redundant resources to fill the unmet need.
Opportunity 2: Round Up Unusual Suspects - somewhat less directly relevant to R&D. However, take away is to look for usual areas for cost cutting and unlikely areas for reuse.
Adversity is also characterized by missing or inadequate elements at critical points in the business system. These may include key inputs, capital, technologies, or partners in the supply, distribution, and marketing chains. Entrepreneurs who can creatively identify unlikely, alternative candidates are able to get a leg up. However, the art of aligning the incentives of an unorthodox coalition and maintaining equilibrium among the members is no small challenge.
Opportunity 3: Find Small Solutions to Big Problems - Take away is that do not make a big project out of repurposing R&D projects that are being cancelled (self explanatory).
The more severe the adversity, the harder it is to change the status quo. Comprehensive solutions that require many changes can appear to be dead on arrival, leaving only tiny cracks as points of entry to break the mold. The message for the intrepid entrepreneur: Small innovations can be huge. First, they are potentially more affordable and can be produced with less initial outlay. Second, they economize on features and complexity and may be just good enough to fulfill an unmet need. Third, their size can help minimize environmental effects or other negative externalities. Finally, they may be easier to integrate into the current model, with only minimal adjustments. In fact, four characteristics that, according to, define future consumer priorities may be the tiny cracks to look for: affordability, simplicity/convenience, sustainability, and design informed by local knowledge about product usage. Small solutions that fit within these tiny cracks represent major opportunities.
Opportunity 4: Think Platform, Not Just Product - This is probably the most important consideration for R&D managers.  By thinking long-term and focusing on platforms, the costs can be spread out over longer term.  The benefits can also be larger because platforms have larger revenue potentials.  Finally, stretching out R&D allows organizations to target solutions for when the adversity is mitigated.
In general, the underlying factors that constrain one situation of adversity also constrain others. This offers an opportunity to invest in a meta-solution that can address several unmet needs simultaneously, either in multiple market segments or various product markets. The multifaceted character of the opportunity also hedges the entrepreneur’s risk and helps the venture grow beyond the initial point of entry. Clearly, entrepreneurs can expect varying levels of success, but the broader the venture’s reach is, the greater the value to be unlocked. The profit potential comes from the capacity to enhance the business model at three possible leverage points: customer value, cost management, and growth-vector creation.

Nov 13, 2010

Exploratory and Exploitative Market Learning

A quick note from Using Exploratory and Exploitative Market Learning for New Product Development divides R&D organizational learning into two types: Exploratory and Exploitative:
More specifically, this study argues that exploratory market learning contributes to the differentiation of the new product because it involves the firm's learning about uncertain and new opportunities through the acquisition of knowledge distant from existing organizational skills and experiences. By contrast, this study posits that exploitative market learning enhances cost efficiency in developing new products as it aims to best use the currently available market information that is closely related to existing organizational experience.
So we can think about Innovation as exploratory learning and invention as exploitative learning.  The paper explores this theory based on a survey:
"This study is based on survey data from 157 manufacturing firms in China that encompass various industries. The empirical findings support the two-dimensional market learning efforts that increase new product differentiation and cost efficiency, respectively. The study confirms that exploratory market learning becomes more effective under a turbulent market environment and that exploitative market learning is more contributive when competitive intensity is high. It also suggests that because of their differential direct and moderating effects on new product advantage either exploratory or exploitative market learning may not be used exclusively, but the two should be implemented in parallel. Such learning implementations will help to secure both the feature and cost-based new product advantage components and will consequently lead to the new product success.
To summarize: Innovation is useful (and probably caused by) a turbulent market with lots of changes and discontinuities, while invention or sustaining development is useful in stable / competitive markets.

Nov 12, 2010

Bosses Overestimate Their Management Skills

A self explanatory article from WSJ: Bosses Overestimate Their Management Skills:
A new survey of 1,100 front-line managers suggests many are over-estimating their skills, with surprisingly little self-doubt. Seventy-two percent said they never questioned their ability to lead others in their first year as a manager.

Nov 11, 2010

Learning in Cross-organizational New Product Development Teams

Another interesting article from the Journal of Product Innovation Management: Increasing Learning and Time Efficiency in Interorganizational New Product Development Teams. The thesis is clear - cross organizational product development is on the rise.  However, it is unclear that processes exist to learn and improve performance from these projects:
Despite the growing popularity of new product development across organizational boundaries, the processes, mechanisms, or dynamics that leverage performance in interorganizational (I-O) product development teams are not well understood. Such teams are staffed with individuals drawn from the partnering firms and are relied on to develop successful new products while at the same time enhancing mutual learning and reducing development time. However, these collaborations can encounter difficulties when partners from different corporate cultures and thought worlds must coordinate and depend on one another and often lead to disappointing performance
Some interesting learnings about what drives success: Caring, Safety and Shared Problem Solving (team building?)
To facilitate collaboration, the creation of a safe, supportive, challenging, and engaging environment is particularly important for enabling productive collaborative I-O teamwork and is essential for learning and time efficient product development. This research develops and tests a model of proposed factors to increase both learning and time efficiency on I-O new product teams. It is argued that specific behaviors (caring), beliefs (psychological safety), task-related processes (shared problem solving), and governance mechanisms (clear management direction) create a positive climate that increases learning and time efficiency on I-O teams.
The results seem to have been validated empirically:
Results of an empirical study of 50 collaborative new product development projects indicate that (1) shared problem solving and caring behavior support both learning and time efficiency on I-O teams, (2) team psychological safety is positively related to learning, (3) management direction is positively associated with time efficiency, and (4) shared problem solving is more strongly related to both performance dimensions than are the other factors. The factors supporting time efficiency are slightly different from those that foster learning. The relative importance of these factors also differs considerably for both performance aspects.
Here is the take home message (lets add it to the other learnings from the past - including virtual team performance):
Overall, this study contributes to a better understanding of the factors that facilitate a favorable environment for productive collaboration on I-O teams, which go beyond contracts or top-management supervision. Establishing such an environment can help to balance management concerns and promote the success of I-O teams. The significance of the results is elevated by the fragility of collaborative ventures and their potential for failure, when firms with different organizational cultures, thought worlds, objectives, and intentions increasingly decide to work across organizational boundaries for the development of new products.

Nov 10, 2010

Lessons on innovation Management from DuPont

If you remember the post from a couple of days ago about Chief Innovations Officer, here are Lessons on innovation from DuPont’s CInO:
I think you keep things fresh by continuing to challenge the organization to look for the next new thing. When you maintain high standards and keep track of the frontiers of science — where the rate of change for technologies is the greatest — then the opportunities will follow. Our willingness to follow new technologies and project in advance the markets for these technologies keeps things new and interesting.
I guess this is pretty much along the lines of our discussion about roles of CInO: To scan and access innovation wherever it might be.  On the other hand, there seems to be a significant focus on the market place:

For me, the key is in the marketplace. It is vital to get an in-depth understanding of the needs and wants of the customer, even if they are unspoken needs. That depth of understanding is what guides our innovation. Without it, our efforts would not be successful.
This might be good, but may prevent organizations from driving breakthrough inventions or disruptive innovation - as by definition, the marketplace does not have a clear understanding of what would disrupt it!
For DuPont, it is about getting external input very early on in the process. The clear demarcation between success and failure is that early external input. It means testing externally before we try and perfect it and getting it right the first time. We are constantly working to better collaborate with our customers and get their input early on in the process.
As we have discussed in the past, collaboration with customers is fraught with dangers.  So, may be CInO needs to find ways of understanding market needs AFTER the proof of concept has been developed and the innovative idea is clear?

Nov 9, 2010

The Role of the Chief Innovation Officer

A short article in Business week on The Role of the Chief Innovation Officer:
A primary task then, of the chief innovation officer, is to oversee someone who is responsible for executive training and can make sure that the company's language of innovation and the principles it embodies are widely disseminated and practiced.
 Interesting concept.  I clearly agree that a consistent definition of innovation is critical to measuring innovation effectiveness and ensuring that innovation delivers results.  However, I would hope that a Chief Innovation Officer (CInO) would do more than provide a consistent language. As per the author:
Managing the learning process when innovating for new-business growth is the second critical area of responsibility for the chief innovation officer. Core-business innovation proceeds largely on established knowledge about markets, customers, competition, and capabilities, which can be extended to bring something new to market at scale. New-business innovation proceeds in small-scale, controlled experiments conducted in a foothold market—a small geographic region or customer group that will serve as a low-cost laboratory.
This is interesting.  We have discussed the valley of death before, organizations face significant challenges in bringing innovation to fruition.  The author points out that it is the CInO's job to close the valley of death.  The problem with this broad statement is the overlap in roles between CInO, CTO and VP of R&D.  An organization will need to think through this conflict clearly before establishing a CInO (more on it below).  Lastly, as per the author:
The failure rate, a critical learning metric, is likely to be high. Generally, in the absence of a structured approach to new-business innovation, about 1 in 10 new ideas works out. By taking the test-and-learn approach, the chief innovation officer can increase the hit rate to as much as 3 out of 10—a batting average that might be unacceptable in core-business innovation but can get you into the Hall of Fame in new-business innovation.
 The author is acknowledging what we discussed above and points out that CInO can help improve organizational learning based on the failures of innovation project.  A pretty good idea.

Here are my thoughts on the role of a CInO:

  • Encourage disclosure of innovative ideas from in-house engineers
  • Work with partners and customers on identifying and accessing innovation (and needs for it)
  • Scan external environments (universities, other small businesses) for accessing innovation
  • Provide seed fundings to develop proof-of-concept projects for innovation ideas
  • Monitor innovation projects and transition them to CTO / R&D for development
  • Nurture innovation projects and ensure they are not killed by not-invented-here mentality
  • Measure results of innovation projects and maintain metrics
  • Institute organizational learning from external and internal innovation projects
  • Develop and implement and innovation IP strategy
  • Others?

Nov 8, 2010

The Valley of Death in Product Innovation

Journal of Product Innovation Management has a great article on The Valley of Death as Context for Role Theory in Product Innovation:
The Valley of Death is used as a metaphor to describe the relative lack of resources and expertise in this area of development. The metaphor suggests that there are relative more resources on one side of the valley in the form of research expertise and on the other side by commercialization expertise and resources. Within this valley a set of interlocking roles are examined that move projects from one side to the other.
The idea is pretty clear: Companies acquire or generate innovation and then do not find a way to nurture it to the stage that it delivers results.  The underlying problem is that existing product lines and culture rejects home grown innovation.  The innovation accessed from the outside (open innovation) is rejected because of the traditional "Not Invented Here" problem.  The authors provide empirical evident to support these conclusions after studying 272 PDMA members.
"The data also support the roles of champion, sponsor, and gatekeeper as major actors that work together to develop and promote projects for introduction into the formal process. Champions make the organization aware of opportunities by conceptualizing the idea and preparing business cases. Sponsors support the development of promising ideas by providing resources to demonstrate the project's viability. Gatekeepers set criteria and make acceptance decisions. The data also reveal a dynamic interdependence between role players. It is concluded that the Valley of Death is a productive tool for identifying and understanding a critical area of development that has not been adequately addressed"
This research finds a dynamic interplay between roles to accomplish tasks that are not well understood in practice or the literature. The implications of this research are far-ranging. It suggests that companies must understand the challenges in the valley, must develop the skills, and must make resources available to master the front end of product innovation. Recognizing roles, providing resources, and establishing expectations and accountability in this area of development become manageable in light of these results. Theoretically, this research informs role theory of a dynamic set of relationships previously treated as static. It also empirically investigates an area of product development where there is limited data. This paper opens profitable inquiries by focusing on an area of development not adequately researched yet drives the activities and investment made in subsequent steps of product development.