What is Research & Development (R&D)?

At InspiRD we get to visit R&D organizations across many industries. It is interesting to see that there is some confusion around what is R&D. Some firms define R&D as only scientific research. Others include New Product Development in R&D, but not sustaining product development. Yet others exclude incremental product development from R&D. Other names for R&D include Research and Engineering or Science and Technology. Even Wikipedia acknowledges that there is some variability about what is considered R&D:

The activities that are classified as R&D differ from company to company, but there are two primary models, with an R&D department being either staffed by engineers and tasked with directly developing new products, or staffed with industrial scientists and tasked with applied research in scientific or technological fields which may facilitate future product development. In either case, R&D differs from the vast majority of corporate activities in that it is not often intended to yield immediate profit, and generally carries greater risk and an uncertain return on investment.

There are many reasons why definition of R&D changes across companies: Corporate Culture, History, Organization Structure, etc. So, is there a good way to define what is R&D?
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Gartner selects InspiRD as a Cool Vendor in R&D

Gartner, the global leader in Information Technology research, has selected InspiRD as one of four “Cool Vendors in R&D for Manufacturing 2015”.  Here is a link to Gartner Research Director Michael Shanler’s blog post:  

 

Gartner Cool Vendor 2015

:  

  Here is what Gartner Research Director Michael Shanler said in the recommendation section of his research report:

“Consider these Cool Vendors when evaluating technologies to drive R&D innovation and give greater insight into decision making during NPD processes.”

To obtain the complete research, please contact your IT Department to see if they subscribe to Gartner where you can obtain the full report.  Or browse our Solutions page to learn more about our innovative products.

 

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.


How to measure R&D Effectiveness?

Metrics that measure effectiveness of ongoing R&D have been difficult to obtain. Four types of R&D metrics have traditionally been used:
  • R&D Investments / Expenses
    • Total R&D Headcount
    • Total R&D Expense
    • R&D Expense as % of Revenue
    • R&D Expense increase/decrease from prior year
    • R&D Expense compared with peers/industry average
  • Project execution status
    • Performance relative to plans (costs and schedule)
    • Concept to Market Time
    • Number of Projects in the Pipeline
  • Historic results-based Metrics
    • Fraction of Revenues from New Products
    • Number of Patents generated
    • Number of Papers Published
    • Customer satisfaction with new products
  • ROI-based metrics
    • Return on Innovation Investment
    • Target NPV for each new product
The problem with most of these metrics is that they are not actionable. As we discussed earlier, R&D effectiveness needs to drive real business results. It is hard for managers to take concrete actions using these metrics to improve R&D effectiveness. All metrics need to be able to aggregate information across the R&D portfolio to help managers see trends and make decisions. Good metrics should allow segregation of management decisions into individual project level actions.
The first two types of metrics are not directly related to ongoing R&D and its projected results. For example, what would happen if we increased our headcount or reduced it? There is no easy way to understand the impact on R&D pipeline. Will it improve revenues? When? Are there key technologies that will take time to develop no matter how much money you invest? It is not easy to tie headcounts to R&D results in the future.
Historic results-based metrics such as number of patents generated are all approximate indicators of R&D performance several years in the past. Managers can gain little direct insight into ongoing R&D effectiveness from these metrics. Nothing a manager can do now will impact number of patents issued or number of papers published. More importantly, it might be better for
ROI-based metrics tend to work better with product development projects near delivery, but they are very hard to use for early stage development. Furthermore, it is hard (if not impossible) to develop ROI on technology development effort that might impact a feature in several different products (think a new type of metal that can be used in different types of cars). ROI computation becomes even more difficult for disruptive innovation.
Over the next few weeks, we will discuss new kinds of metrics that can help managers improve R&D effectiveness…

How to Manage R&D Risks?

R&D is about building something new and delivering it in the future. The process is inherently uncertain, and hence involves risk. The more disruptive the technology, the more risk is likely to be introduced. The risk comes from several sources:

  1. Technology Uncertainties
  2. Integration Uncertainties
  3. Manufacturing Uncertainties
  4. Market Uncertainties

It is not possible to eliminate these risks, but it is definitely possible to manage them. An interesting case study can be found in The mysterious story of the battery startup that promised GM a 200-mile electric car

Image Source: Quartz.com

Image Source: Quartz.com

Energy storage systems are probably one of the most important current R&D challenges. From mobile phones to automobiles to airplanes, the implications of better performance are huge — both for the companies that develop new batteries and companies that start providing products using them. Because of this promise, a small company called Envia got an opportunity to work directly with GM:

It was to contain a deal rare to an industry newcomer—a contract worth tens and possibly hundreds of millions of dollars to provide the electric central nervous system for two showcase GM models including the next-generation Chevy Volt. Untested small suppliers almost never get in the door of the world’s major automakers, which regard them as too risky to rely on. But GM was won over by what seemed to be the world’s best lithium-ion battery—a cell that, if all went well, would catapult the company to a commanding position in the industry with a middle-class electric car that traveled 200 miles on a single charge and rid motorists of the “range anxiety” that disquieted them about such vehicles. GM would have the jump on the high-end Tesla S, the only other major model with that range but one that would cost much more. For Envia, the contract could lead to an IPO that would make both men rich.

The article points out many steps that both GM and Envia took to manage the four R&D risks we laid out above. Let us get started with Market Uncertainty. Clearly, GM would have needed to take some risk in projecting demand for a new extended range car. However, for start ups and small firms like Envia, market uncertainties potentially overwhelm other types of risks. The discussion around a potential agreement with GM took a long time (as many such agreement with large companies take):

But the talking had gone on so long and with such uncertainty that neither man had even told Envia’s staff scientists of the impending deal. Even if they felt more confident, they could not have said anything, since such news could affect GM’s share price. Word had leaked around the Envia lab anyway. An edginess hung over the lunch. … Bay and Redpoint invested another $7 million in the company a year or so after the first tranche. But the startup was burning through the cash while potential customers were slow to commit.

Unfortunately, market risks are not easy to mitigate. Pretty much the only approach might be diversification: to pursue many alternate paths so that the impact can be managed if there are any changes. Many startups such as Envia may not get the opportunity to do so. Larger companies can try alternate sources of technologies or alternate product roadmaps to mitigate market risk. For managers, this means gaining visibility into, guiding and synchronizing many alternate development paths. For R&D organizations, this means managing multiple projects. Product and Technology roadmaps are a key solution that organizations can use to address the challenge of increased R&D complexity. That brings us to the biggest source of risk in this case: Technology Uncertainty. Most new technologies take years to develop. Most disruptive technologies might even take decades to mature. Both Envia and GM took several steps to manage technology uncertainty.

  1. Acquire Partially Developed Technologies: Less mature the technology, more risky it is likely to be. An easy approach to mitigating that risk is to find out if someone else who has solved the problem. Open Innovation is a common term used for this access. There are many challenges to accessing innovation and technology (differentiation, control, innovation valley of death), but that is a topic for another post.

    Kumar and the company’s early team perused patents and journal papers and consulted experts before settling on a promising cathode invented by Argonne National Laboratory outside Chicago. The cathode combined nickel, manganese and cobalt into an exceptional composite that astonishingly had not attracted a single licensee

  2. Modify Technologies Developed Elsewhere: If a small manufacturing or processing step can let a company build on research performed elsewhere, it can mitigate technology risk while maintaining differentiation.

    The cell reported to Arpa-E and sent to GM contained anode material purchased in a confidential deal from Shin-Etsu, a Japanese supplier. Kumar said Shin-Etsu’s role was unimportant—the anode’s true value emerged in the processing steps he had developed that allowed the anode to cycle hundreds of times without shattering.

  3. Leverage new sources for funding disruptive R&D: Many sources of funding such as government research grants may be available for funding development of disruptive / innovative technologies. However, other sources of funding can bring many distractions to a company and can potentially dilute the focus and energy.

    So Kumar applied for the Arpa-E competition in collaboration with Argonne. … It won a $4 million Arpa-E grant for the work to be carried out jointly with Argonne.

  4. Setup multiple intermediate gates: Divide development in phases and only commit resources for the next phase. Develop alternate plans to mitigate technology risks. Key challenge to deploying effective intermediate gates is: quality plans; clearly defined pass /fail criteria; and misaligned development pace/cycle across different components.

    But given the stringent deadline, every day counted. GM decided to delay publicly announcing the deal. Instead, the carmaker rushed a planning team to Newark three days later—on Dec. 3—to create a quarter-by-quarter schedule of milestones that ended with delivery of the battery technology for its two signature models. As Envia hit each quarterly target, it would receive $2 million.

Beyond setting up multiple approaches for mitigating technology risks, GM and Envia did many other things right. They set up clear deliverables:

The draft contract went on to be quite specific: For the 200-mile car, Envia was to provide a working battery delivering around 350 watt-hours per kilogram that could endure 1,000 charge-discharge cycles.

They defined a clear timeline:

Kumar’s deadline for the 200-mile battery was October 2013. After that, adjustments could be made to optimize the performance until Aug. 15, 2014. But that was a full-stop deadline—Kumar could make no changes to the battery after the latter date. This point was critical to GM because once the battery was ready, all the other deadlines could follow, ending with the pure electric car’s actual launch in 2016.

And the defined intermediate milestones:

Instead, the carmaker rushed a planning team to Newark three days later—on Dec. 3—to create a quarter-by-quarter schedule of milestones that ended with delivery of the battery technology for its two signature models. As Envia hit each quarterly target, it would receive $2 million.

Envia tested and demonstrated disruptive capabilities:

It was Kumar and his Envia team. Envia, he said, had just reported (pdf) the achievement of “the world record in energy density of a rechargeable lithium-ion battery.” It had produced a prototype car battery cell that demonstrated energy density between 378 and 418 watt-hours per kilogram. Envia said the achievement had been validated by Crane, the Indiana-based testing facility of the US Naval Surface Warfare Center, which cycled the cell 22 times.

That brings us to Number 2 on our R&D risks: Integration Uncertainties. Technology capabilities that are demonstrated in a lab environment may not translate to real world results. More importantly, as the technology is integrated with other components necessary to get it to market, new problems may arise. R&D plans and roadmaps need to clearly account for these uncertainties and allow plenty of time to address them. That did not quite happen in Envia’s case. They had less than a year to move from a hand tweaked battery to mass producible system.

Increasingly alarmed queries piled up from GM in phone calls and meetings. The Arpa-E results could not be reproduced—not by a long shot. Meeting a team from the carmaker on March 4, Kumar “struggled to allay GM’s concerns,” according to Kapadia’s lawsuit. A document provides a sense of why GM was concerned.

The team never even got to a stage where they would need to address manufacturing uncertainties (Number 3 in our list of risks). Many times we can produce small batches of widgets, but processes required for mass production may not be supported. Again, the only way to mitigate this risk is careful planning, intermediate milestones (that verify manufacturability) and adequate resources…

A year later, the deal is in tatters, GM has accused Envia of misrepresenting its technology, and a document suggests why the carmaker may be right. The startup’s unraveling is a blow for GM as it transitions to a new regime next month under CEO-designate Mary Barra, setting back its ambitions in the potentially gigantic future electric-car industry. It also risks making Envia, the recipient of several small federal grants, another punching bag for critics of US government funding of advanced battery companies.

Here is the summary: R&D is risky. Some R&D efforts will fail — and that is a good thing as it means that the organization is encouraging responsible risk taking. However, the risks should be managed carefully and failures should be caught early, before significant resources have been expended. A good R&D planning process and robust tools may help.


Why Improve R&D Effectiveness?

Research & Development (R&D) is a critical component of most business strategies, and a driver for market success. However, executives typically have little visibility into the R&D pipeline or the value being generated in R&D.

Overall, like any other corporate function, improved R&D effectiveness can have two potential impacts: Improve Revenues and/or Reduce Costs. Let us explore how:

  • R&D can help increase revenues by:

    • Gain market share by developing and delivering more innovation to customers
    • Improve Average Sales Price by providing differentiating capabilities to customers
    • Address new market segments by quickly developing new products using existing capabilities and components
  • R&D can help optimize revenue timing by:

    • Align R&D pipeline with corporate strategy
    • Accelerate or slow-down R&D efforts to maximize market impact
    • Efficiently modify R&D portfolio based on new competitive or market challenges
  • R&D costs can be reduced (without significantly impacting delivery) by:

    • Leverage cross-product synergies to effectively reduce costs of developing any new feature
    • Reduce R&D management overhead costs
    • Provide R&D teams early and better visibility so they can reconfigure projects at minimal costs
  • Avoid costs of wasted development efforts:

    • Identify and kill non-viable or pet projects that have no planned insertions into new products
    • Find projects with schedule slack and slow them down to ensure parts of a new product are not ready before others
    • Effectively monitor R&D portfolio and find and fix problems early

However, as most managers know, these are hard objectives to achieve. Key challenges are:

  1. Lack of visibility and access: R&D is spread over many teams and locations. It is hard to get quick and easy access to status of the R&D pipeline. It is hard to aggregate the R&D pipeline to make management decisions and then segregate management decisions into individual R&D project-level actions.
  2. Long development timelines: The impact of decisions is not felt for quite some time. That means it is hard to make decisions that address current strategic or market challenges. It is also hard to perform what-if analysis to decide on the best course of action
  3. Complex interdependencies: R&D portfolios require investments in multiple products and technologies at various stages of maturity. Delays in one development project can have significant impact on others and on the overall development schedule. It is hard to make decisions while keeping these complex interdependencies in mind.
  4. Lack of Metrics: Most R&D metrics tend to give information about the past (such as Revenue from New Products or Number of Patents Generated). There is a lack of metrics that allow managers to quickly assess the health of the R&D pipeline and make decisions about improving it.

This blog is going to focus on both the benefits of improved R&D effectiveness and challenges to getting there…


Yahoo!’s new CEO

Sorry I have been gone for a while – starting a business requires a lot more effort than I had thought.  The rewards more than make up for effort, but some important things like this blog get dropped along the way…

A quick post about Yahoo!’s new CEO from INSEAD Knowledge.  The article provides many interesting facts about the new CEO, however one stands out:

“Some Googlers who worked with Mayer found her style abrasive and her pace hard to sustain. In her early years, apparently, her interpersonal skills were inferior to her user focus and technical prowess. Many engineers relished working for her nevertheless, and her management skills improved with experience” 

The key points here is that R&D teams are willing to put up with a lot of hardship as long as the leaders are engaged in the development. As long as the leaders are able to provide the teams with a long-term vision, progress can be made.  Finally, leaders need to provide challenges that the teams can utilize to innovate. Each of these themes we have discussed many times on this blog.

The media frenzy about Mayer’s appointment, however, requires some examination on our part.  The author of the article has summarized it so well, I have reproduced it below (even though it is not quite related to R&D)

The lessons we must draw from this exceptional event, which reveals less about Mayer and Yahoo! than it does about our norms, is the following: Leaders, especially such visible ones, have to accept constant and ruthless scrutiny that won’t stop at their results. Followers, opponents and observers will always question their motives and lives. And they will account for the leader’s story in ways that reveal and serve their interest. Good leaders know it and work with it. 

At the same time, we must take this opportunity to scrutinise, for once, not just the leader but also ourselves. To cast a light on the ways in which the stories we tell about our leaders – the patterns of thinking and feeling, actions and talk, which we take for granted – affect the efforts and opportunities to lead of those who appear different from us, and may not be as different as we make them to be.


Amgen CEO: Why I’m a listener

McKinsey Quarterly has an interview of Amgen CEO Kevin Sharea (Why I’m a listener: Amgen CEO Kevin Sharer) where he emphasizes the importance of listening for leaders.  We have talked about listening a couple of times in the past (here and here).

He says that the best way to listen is to do so with just one objective – comprehension.  It is important not to be focused on criticism or arguments for or against what the other person is saying.

““Because I learned to listen.” And I thought, “That’s pretty amazing.” He also said, “I learned to listen by having only one objective: comprehension. I was only trying to understand what the person was trying to convey to me. I wasn’t listening to critique or object or convince.””

Listening for comprehension can also help demonstrate respect and teach your team to be flexible by example. It builds and environment of trust, partnership and teamwork.

Listening for comprehension helps you get that information, of course, but it’s more than that: it’s also the greatest sign of respect you can give someone. So I shifted, by necessity, to try to become more relaxed in what I was doing and just to be more patient and open to new ideas. And as I started focusing on comprehension, I found that my bandwidth for listening increased in a very meaningful way.

Listening can help leaders immerse themselves in the organization and gather the right information, generate new connections and spark creativity / innovation. Leaders need to talk with different people – not just their direct reports because useful pieces of information reside in different places.

My method of gathering the tiles involves regularly visiting with, and listening to, people in the company who don’t necessarily report to me. I also read as much as I possibly can: surveys, operating data, analyst reports, regulatory reports, outside analyses, and so on. I meet with our top ten investors twice a year to listen, and at shareholder conferences I consider the Q&As very important. The key is making yourself open to the possibility that information can and will come from almost anywhere.

Listening can help us become more engaged and innovative leaders. Listening can also help us question assumptions and get our teams to experiment more.


Steve Jobs: Innovation is the only way to succeed

INSEAD Knowledge has published an interview with Steve Jobs from 1996 which has a few very important points for R&D managers:  Innovation is the only way to succeed – you can not cut costs to get out of problems.

“All I can say is I think it was true back when we built Apple and I think it is just as true today which is innovation is the only way to succeed in these businesses. You can’t stand still.
You can’t cut expenses and get out of your problems. You can’t cut expenses and get out of your problems. You’ve got to innovate your way out of your problems.

image from Insead Knowledge

So, lets dig in…
We have discussed many of these points in the past, but this interview provides a few more details.  First is the recurring theme of user-centric design – products should not require customers to learn underlying technology:

Well, one of the reasons I’m so interested in graphics is that it makes things accessible to people without them having to know how it works. So as an example, the Macintosh was really that – we used graphics to make it easy to use; it was the computer for the rest of us. And you didn’t really have to know all this computerese to use it because of the great graphics and user interface.

Even more interesting is the fact that Jobs took the same approach with Pixar: Movie goers should be able to enjoy the experience without worrying about 10 years of R&D that went into creating the movie. We have discussed this in detail in the post about focus on your niche.

And it’s the same way with Toy Story at a much higher level. An audience between 80 and 100 million people will hopefully see Toy Story by the time it rolls out throughout the world, and yet none of them had to read a manual before they saw the movie to appreciate it. None of them had to understand the technology and the ten years of R&D and investment that went in to be able to create that movie to enjoy it, and that’s what’s so wonderful.

Another foundation of successful R&D management is a long-term vision. Steve Jobs again demonstrates his ability to think long-term.  He was working towards removing keyboard input back in the mid 90s:

And I see more and more of that infusing society where you have a tremendous technology but it has a face which is very approachable and you don’t have to understand the technology to interact or use the product….

You know I think that’s the potential of the Internet. We’re certainly not there today. Typing an H-T-T-P slash slash colon w-w-w, you know, is arcane. I mean, you shouldn’t even need a keyboard to use the Internet but we still do. And I think we’ll get to where it really is very simple, but we have a few years to go.

The next lesson for us R&D managers is that of hands-on involvement.  An engaged leader is critical to motivating teams and delivering innovation (by overcoming problems such as valley of death).  Jobs was not had the vision of where products need to go, he was involved in detailed technology development and the business models that need to be developed to support the new technology.  In this case, he was developing a vision about iTunes in mid-90s…

We look at the internet and it looks very exciting to us, but we don’t see how to make any money from it. We haven’t seen any business models emerge where we can put content on the Internet and end up being rewarded for that. And since our talented people always have opportunities to work on things where we do get financially rewarded, we’re not about to take them off that and put them on the Internet until we see a business model that makes sense. And I think we will, you know, in the next one to two years.

 We have a lot of interesting posts about innovation management


Unilever’s Kees Kruythoff: Enthusiastic Employees Key to Success

A quick post about a lecture by Unilever’s Kees Kruythoff in Knowledge@Wharton (Global Leadership Lessons from Unilever’s Kees Kruythoff ).  Kruythoff mentions that a sense of enthusiasm and excitement is key to a company’s success and makes progress possible.  He sees that sense of enthusiasm has been a key to his own success:

“Kruythoff said that his enthusiasm for his job has always been what has propelled him. There is really no substitute for that, he noted, and, in reality, enthusiasm should be the primary reason anyone should work for an organization. “When you join a business, the most important part is to ask yourself how you can improve the values of the company,” Kruythoff stated. ” 

One way to get an enthusiastic workforce is to hire employees that clearly demonstrate the sense of excitement:

A new employee should have a sense of excitement, he added, and make sure that he or she is a good fit with the company. “Wherever you go, if it feels like the place where you want to be, then in all likelihood it is.”  

However,  the leaders still need to maintain and fuel that excitement.  A sense of excitement will help overcome any hurdles in the organizations path and build a positive environment.

Enthusiasm makes progress possible, Kruythoff said, and leaders must build that excitement and fire among their employees. Not every decision is a winner, but when employees are optimistic about the future of the firm, that atmosphere will help move the company in the right overall direction.

The article does not quite talk about how to build and maintain this sense of excitement.  Here is what we have learned in this blog:


Why Open Innovation is Hard to Implement (Netflix Example)

We have discussed the difficulties in implementing open innovation.  Netflix did an amazing job of leveraging open innovation with Netflix Prize. For a while they were receiving amazing results from the exercise. That is why, I was surprise when I read the article Netflix never used its $1 million algorithm due to engineering costs:

“Netflix awarded a $1 million prize to a developer team in 2009 for an algorithm that increased the accuracy of the company’s recommendation engine by 10 percent. But today it doesn’t use the million-dollar code, and has no plans to implement it in the future,”

Let us dig in to see what we can learn…
First of kudos to Netflix for engaging a very wide community in the innovation.  But more importantly, Netflix was great at setting up tools to keep the community engaged. The second point is overlooked by many organizations engaging in open innovation portals.  Let us go through each of the four challenges we identified about Open Innovation and see how Netflix was able to address them:

  1. Valley of Death is when organizations are unable to incorporate outside innovation into delivered products even after acquiring it. Netflix focused open innovation around a problem critical to their business – predicting what movies customers will like.  If the outside innovators were able to demonstrate those results, it would be hard for internal experts to resist implementation (because of not-invented-here mentality).  More importantly, there would be management attention on the subject because of its importance to overall business – which would surely help overcome the valley of death.
  2. Trade Secret Protection: Netflix was in a unique position because they did not have to disclose their current implementation in anyway.  All they had to do was to publish the output of their algorithm.  They were able to provide data to the outside innovators to test performance relative to Netflix’s performance.  In most Open Innovation problems, it will be hard for organizations to set up a problem so that they do not have to disclose any internal know-how.  But something everyone should consider…
  3. Evaluation / Management Costs: Although Netflix had to set up an extensive infrastructure to administer Open Innovation, the costs were some what mitigated.  Netflix was able to device an approach where the community was able to test their algorithms internally before sending it to Netflix.  Furthermore, Netflix provided clear guidelines and test data for the outside innovators.  This self evaluation by inventors reduced the overhead required to manage/test innovation ideas submitted for consideration
  4. IP Liability: Netflix bypassed the entire IP problem by only requiring the “implemented” algorithms be provided for evaluation against the test set.  The details of the algorithm need only be discolosed if the algorithm actually produced required results.  Furthermore, by requiring that the results be published (thereby leveraging advantages open source provides to open innovation). I am not sure how many companies will be able to the IP liability issues this way…

Even so, Netflix was not able to get full benefit from the $1M prize because of two factors. First, the cost of implementing the algorithm was so high that they could not close the business case:

We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.

Second, the market had changed from DVD rentals at the time of innovation challenge to to on-line streaming so that the benefit of the innovation was minimized:

Also, our focus on improving Netflix personalization had shifted to the next level by then.

This is an important lesson for all R&D managers: Even if we can overcome most of the challenges in implementing Open Innovation, several other factors may still prevent us from gaining full benefit of the investment.  However, all was not lost. Netflix was able to use some of the algorithms developed at early stages of the challenge to gain significant benefits.

Netflix notes that it does still use two algorithms from the team that won the first Progress Prize for an 8.43 percent improvement to the recommendation engine’s root mean squared error (the full $1 million was awarded for a 10 percent improvement).

That too is an interesting idea: Set up intermediate goals for open innovation and incorporate them into the overall R&D planning process.