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.

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?

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?

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.

Risk Test: 7 Answers You Need to Know | Big Fat Finance Blog

This blog post has an interesting Risk Test: 7 Answers You Need to Know. The questions are posed for finance/CFO risks, but are equally valid for R&D risk management as well:

The science of risk management continues to evolve. Lessons learned from past failures are being leveraged to ensure that a company’s risk management is built on the right foundation and evolving in the right direction.

Without further ado, here are the questions:

What is our risk taxonomy?
How do we quantify risk?
What is our risk appetite?
What return are we generating for the risks we take?
How do we separate responsibility for risk-taking from responsibility for risk management?
How do we include risk when we compensate risk-takers?
How do we ensure that our risk management is performing well?

Historically, R&D risk management is somewhat fragmented. Some informal risk management during R&D and a lot in quality control at the end of the process. However, the process sometimes lacks rigor and is not comprehensive. A taxonomy for R&D risks will go a long way in helping. As the author points out, no taxonomy is perfect. But any disciplined use of a taxonomy will help answer the remaining questions as well.

Innovation Grows Among Older Workers

Newsweek has an interesting article about research that suggests that Innovation Grows Among Older Workers:

Duke University scholar Vivek Wadhwa, who studied 549 successful technology ventures. What’s more, older entrepreneurs have higher success rates when they start companies. That’s because they have accumulated expertise in their technological fields, have deep knowledge of their customers’ needs, and have years of developing a network of supporters (often including financial backers). “Older entrepreneurs are just able to build companies that are more advanced in their technology and more sophisticated in the way they deal with customers,”Wadhwa says.”

Somethings to keep in mind building an R&D team…

And the age at which entrepreneurs are more innovative and willing to take risks seems to be going up. According to data from the Kauffman Foundation, the highest rate of entrepreneurship in America has shifted to the 55–64 age group, with people over 55 almost twice as likely to found successful companies than those between 20 and 34. And while the entrepreneurship rate has gone up since 1996 in most other age brackets as well, it has actually declined among Americans under 35.

Or:

One of Germany’s largest companies had a researcher examine its system for continuous improvement, expecting the findings to back up its policy of pushing workers into early retirement. The numbers, however, showed that older workers not only had great ideas for making procedures and processes more efficient, but their innovations also produced significantly higher returns for the company than those of workers in younger age groups. Birgit Verwonk, a Dresden University of Applied Sciences economist and author of the study, says the findings were so surprising for the company (which wasn’t named in the study) that it is now phasing out its early retirement program.

The take home message for me is that innovation is not tied to an age group. In fact, younger employees always need help learning the ropes (as in the case of Toyota). The R&D managers challenge there for is to build virtual and focused communities that facilitate knowledge exchange and transfer.

What makes innovation thrive

The blog post Why Innovation Thrives at the Mayo Clinic in Harvard Business Review has a few interesting points to learn about encouraging innovation as learned from the Mayo Clinic:

Yet in the extensive research my team has done to uncover the mystery of successful innovation, we’ve found few track records to rival that of The Mayo Clinic, in decidedly non-urban Rochester, Minnesota. The World Database of Innovation we are compiling, as a collaborative effort between my firm, Generate Companies, and several universities, represents over 20,000 hours of work to date. As well as over 200 in-depth case studies, it compiles the ideas of 4,500 or so innovation experts and consultancies.

And the lessons are:

Scarcity of resources: scarcity of resources shows up in our database as the single strongest driver of innovation in organizations in general.
Connectedness: Internally, Mayo has achieved a high level of connectedness among employees with systems and processes that enable — and oblige — everyone across the organization to find and connect with the expertise they need at any moment.
Diversity: Their approach is sometimes called cross-functional teaming, and is now common in health care and corporate innovation practices.

Of the three factors, connectedness and diversity are challenging in distributed virtual R&D teams. Here are two articles on managing and driving satisfaction in virtual teams.

Lyric Semiconductor Develops a Probability Chip

I have been thinking a lot about how to move R&D from a deterministic description of design parameters to a stochastic one. R&D is essentially tasks undertaken to manage uncertainty around the product’s ability to perform its intended mission. Uncertainties arise from the underlying variation in design parameters (such as manufacturing variations, material properties or environment where the product will be used). Traditionally, designers combine all uncertainties and account for them through margins of safety. These margins actually prevent reuse of R&D into new designs that involve different materials or slightly different objectives. An logical way around this problem is to deal with the uncertainties directly. These stochastic methods are very computationally intensive. I saw this article from NY Times on Lyric Semiconductor Develops a Probability Chip:

Lyric Semiconductor, a start-up that emerged from work at the Massachusetts Institute of Technology, looks to forgo this certainty in favor of probability. It unveiled plans this week to build a chip that can compute likelihoods. Such technology may help figure out which book someone will want to buy on Amazon.com or help create a better gene-sequencing machine.

I am wondering how the chip could be used for more complex stochastic computations. Any thoughts?

Product Innovation collaboration analysis

The article Heterogeneous Firm-Level Effects of Knowledge Exchanges on Product Innovation: Differences between Dynamic and Lagging Product Innovators is quite difficult to read but has some interesting findings (or at least more empirical evidence of some intuitive conclusions).
The article divides firms as either dynamic innovators or those that follow other’s innovation (lagging innovators). It also divides innovation information exchange (or collaboration) along three dimensions:

(1) information gathering applied in new product development,
(2) research cooperation on particular innovation projects, and
(3) managing information outflows to “appropriate” the innovation.

Thar article analyzes performance of firms engaging in knowledge exchange (collaboration for innovation) along three different dimensions:

(1) research intensity (a measure of innovative input);
(2) the share of revenue realized through innovative product sales (a measure of innovative output); and
(3) their impact on the growth in total revenue.

Here are the results:

Amount of innovation (research intensity) is positively influenced by external input or collaborations – regardless of the type of information exchange. Also, this innovation drives innovative product sales (duh) and growth.
Dynamic innovators gain more from collaborations than lagging (in terms off innovative product sales and growth)
Dynamic innovators are open and do not try to “appropriate” innovation by keeping it from others
Lagging innovators try to “appropriate” innovations and benefit from that appropriation – although overall benefit of collaboration remains less than dynamic innovators (2 and 3 above)

Prevent Taking a Bad Day Home with this Simple Step

Here is a quick how to Prevent Taking a Bad Day Home with this Simple Step – John Baldoni – The Conversation – Harvard Business Review:

Feeling frustrated at work, especially late in the day? Most of us feel this way from time to time. The challenge is what to do about it

Here are 3 steps the author recommends:

Take a deep breath
Pick something easy to do (and complete it)
Get up and leave.

Thats it…