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:

  1. Scarcity of resources: scarcity of resources shows up in our database as the single strongest driver of innovation in organizations in general.
  2. 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.
  3. 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:

  1. 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.
  2. Dynamic innovators gain more from collaborations than lagging (in terms off innovative product sales and growth)
  3. Dynamic innovators are open and do not try to “appropriate” innovation by keeping it from others
  4. 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:

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

Thats it… 

Disruptive Innovation and IP

A quick article that elaborates on something that I have observed in many firms:

Though it may be difficult to convince a business to invest millions in pursuit of a speculative disruptive innovation, it is much easier for a small team to gain support in pursuing low-cost intellectual assets in the name of mitigating potential threats. 

I have actually seen empirical data that the aggregate investment in these speculative patents far exceeds the net investment in innovation!  The author repeats what many managers believe – patents are cheap so use them to protect markets:

A two-pronged approach is proposed that builds on the authors’ experience at Kimberly-Clark Corporation in dealing with disruptive threats and opportunities. The approach calls for generation of intellectual assets, often using small proactive teams

Specifically, the author suggests:

  1. Protect yourself form other’s disruptive innovation by patenting first; 
  2. Use patents to create new business.
This approach though popular, is not easy to execute.  It takes more than five years for a patent to issue.  If one waits for patent issuance to decide which R&D projects to invest in, they will be far behind competitors and never be able to catch up.  Millions of patents are issued each year.  
It is pretty much impossible to keep up with issued patents, much less figure out who is infringing your patent and prevent them.  So patents are a pretty weak approach to speculatively reducing competitive pressures.  We sometimes see Motorola suing Apple.  But Motorola has 10,000+ patents in cell phones.  The cost of obtaining those patents $300M!
Even if one finds infringement, patent assertions are expensive and a pretty inefficient way to drive R&D strategy.  In most cases, assertions take years to conclude.  So from the time the inventor had an idea to asserting and changing market landscape will take 10-15 years at the minimum.  How can one drive R&D strategy with that kind of a lag?
Finally, if the organization really knows which innovations are valuable, they could develop them in the first place.  This approach of trying to do work around poor management decision making can lead to nothing but wasted effort.
Take home message from me is exactly the opposite of what the author suggests.  Patent ONLY when you believe it is going to help you develop a significant product.  Speculative patents should be limited to foundational technologies not individual products.

R&D Complexity Impacts Number of Concurrent Projects

Here is a quick article with some empirical proof of what we all suspected: Increased complexity of products under development leads to fewer concurrent development projects (Development Resource Planning: Complexity of Product Development and the Capacity to Launch New Products. Abraham Sin Oih Yu. 2010; Journal of Product Innovation Management):

The number of new product families that a firm can effectively undertake is bound by the complexity of its products or systems and the total amount of resources allocated to NPD. This study examines three manufacturing companies to verify the proposed model. The empirical results confirm the study’s initial hypothesis: The more complex the product family, the smaller the number of product families that are launched per unit of revenue. Several suggestions and implications for managing NPD resources are discussed, such as how this study’s model can establish an upper limit for the capacity to develop and launch new product families.

Impact of the Corporate Mind-set on New Product Launch

Here is an article that discusses the impact of the corporate mind-set on new product launch and its subsequent  market performance. (Katrin Talke. 2010; Journal of Product Innovation Management). The article divides corporate mindsets into three types: analytical, risk-taking, and aggressive posture.  Also, the product launch is boiled down to three decisions: Set launch objectives, Select target markets and position the product in the new market.Not sure how there are orthogonal or independent of each other, but lets play along for a minute.

A research model with mediating effects is proposed, where the corporate mind-set determines the launch strategy decisions, which in turn impact market performance. The model is tested with data on 113 industrial new products launched in business-to-business markets in Germany using a multiple informant approach. 

The results show that of course, the corporate mindset has a strong impact on launch decisions.  Analytical firms focus on all three launch objectives, risk taking firms focus on the first two and aggressive firms just go…

It is found that while an analytical posture relates to all three strategic launch decisions, risk taking and an aggressive posture have a significant impact on two, respectively one, launch strategy elements. 

So you know as much as I do…

Categorizing Project Execution Risks

Here is an interesting article in the Project Management Journal about types of risks in project management:

  1. Strategic risks: Those that relate to project goals (short-term or long-term)
  2. Operational risks: Those that relate to project operations, individual outputs and results
  3. Contextual risks: Those from circumstances outside of the project that may influence the scope of work and the performance of the organization. Examples are competing projects, change in ownership and management, legislation and governmental directives, media attention,  market conditions, and accidents.
As is the case in most activities, project managers tend to focus on operational risk at the expense of strategic risks:

In this study, risks are categorized as risks to operational, long-term, or short-term strategic objectives, and, by studying a dataset of some 1,450 risk elements that make up the risk registers of seven large projects, we examine how operational and strategic risks are distributed in the projects. The study strongly indicates that risks to a project’s strategic objectives rarely occur in the project’s risk registers, though project success and failure stories indicate their importance.

Portfolio management organization cultures

The article Using R&D portfolio management to deal with dynamic risk by Serghei Floricel.in the journal R&D Management has some interesting insights into why organizations adopt adopt specific approaches for project portfolio management.  Here is what I took away:

  • They theorize that portfolio management is driven by the competitive environment (characterized by velocity, turbulence, growth and instability). 
  • This competitive environment requires managers to develop  approaches to address it and learn some preferred approaches (what I would call  “gut feelings”).  
  • These approaches to portfolio management can be characterized in four dimensions (structure, commitment, emergence and integration).

They then surveyed 795 firms in a variety of sectors and on four continents to find the following results:

  • high-velocity environments favor structured as well as integrated portfolio management approaches
  • high-growth environments favor approaches that are structured but commit significant resources to each project as well
  • Turbulent environments favor approaches that are emergent, but also, contrary to our expectations, have high resource commitment levels
  • Finally, firms in unstable environments have a marginal preference for emergent approaches
Pretty good stuff to keep in mind when designing or improving product portfolio management processes.