Posted by George Huhn on Fri, Jun 04, 2010 @ 11:36 AM
Wondering why your teams aren't more creative and innovative? Maybe they are stopping themselves.
When it comes to innovation "what really matters is not getting many good ideas, but getting one or two exceptional ideas. That's really what innovation is all about," says Terwiesch.
But if you're getting lots of ideas, shouldn't that naturally lead to more "exceptional ideas?"
Not necessarily.
"The evaluation part is critical.," Terwiesch says. "It's no good generating a great idea if you don't recognize the idea as great. It's like me sitting here and saying I had the idea for Amazon. If I had the idea but didn't do anything about it, then it really doesn't matter that I had the idea."
And project teams are not very good at recognizing and developing great ideas. For example, here are 4 reasons that team dynamics can get in the way of innovation:
- Team members self-censor themselves to avoid upsetting a colleague or to go along with the status quo
- Too many people in the room doesn't provide enough time for everybody to be heard and have their ideas discussed
- "Build-up" or the tendency of people to expand on ideas that have already been accepted by the team at the expense of considering other ideas
- The tendency to defer to authority or up the hierarchical team structure, i.e. "the boss is always right"
Also, Terwiesch says, "We find huge differences in people's levels of creativity, and we just have to face it. We're not all good singers and we're not all good runners, so why should we expect that we all are good idea generators?"
What are some things that teams can do to work around the innovation-killing team dynamics?
In their book "
Innovation Tournaments: Creating and Selecting Exceptional Opportunities," Terwiesch and Ulrich suggest that putting more structure around the early idea generation process can help ensure that quality ideas bubble to the surface. They recommend using a virtual "suggestion box" to collect ideas and "coordinated competitions" to filter exceptional ideas.
Terwiesch adds, "People like having a process because they understand that it's fair. In a typical brainstorming meeting, it's not fair and everybody knows it: The boss is always right."
He also says that it is important to prevent the group dynamics in brainstorming sessions from killing early ideas. "Your initial thoughts are very vital to the company because they are your unbiased opinion."
According to Ulrich, "We have found that, in the early phases of idea generation, providing very specific process guideposts for individuals [such as] 'Generate at least 10 ideas and submit them by Wednesday,' ensures that all members of a team contribute and that they devote sufficient creative energy to the problem."
The next time you're looking for innovative ideas, consider trying the virtual suggestion box, and then focus on evaluating the ideas that were generated. If you have a large team, consider breaking the team into smaller groups while being conscious of the social and organizational hierarchy and personalities within each small group.
Finally, consider using a "Positive, Negative, Interesting" (PNI) parallel thinking structure around the discussions. Using this approach, the team evaluates each idea in only one area at a time. So when the "Positive" idea characteristics are evaluated, only positive attributes of the idea are discussed and recorded, and when "Negative" characteristics are evaluated, only the downsides of the idea are discussed and recorded. The "Interesting" characteristics are the neutral or "just the facts" attributes of the idea.
Once teams get used to this approach and experience the value in it, they become more self-directed and peer pressure alone keeps the discussions on-track.
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Posted by George Huhn on Fri, Apr 30, 2010 @ 10:38 AM
This last paragraph caught my attention:
Karl Popper, the great philosopher of science, once divided the world into two categories: clocks and clouds. Clocks are neat, orderly systems that can be solved through reduction; clouds are an epistemic mess, “highly irregular, disorderly, and more or less unpredictable.” The mistake of modern science is to pretend that everything is a clock, which is why we get seduced again and again by the false promises of brain scanners and gene sequencers. We want to believe we will understand nature if we find the exact right tool to cut its joints. But that approach is doomed to failure. We live in a universe not of clocks but of clouds.1
Our businesses are mixtures of clocks and clouds, but it is often difficult to distinguish which parts are clouds and which parts are clocks. The clocks are things that we can measure and control but clouds are things that we can only try to predict.
Confusion begins when we try to measure and control clouds as if they are clocks or we focus only on trying to control clocks without trying to predict and mange the clouds.
Like Leher's comment on science, it is also a mistake of modern business to try to pretend everything is a clock. While I am a strong proponent of using business analytics, such as
simulation and
optimization in
project portfolio management tools, the analytical tools that we use should be able to manage data from both clocks and clouds. And when we're using these tools, we should be ever mindful of the inherent uncertainty (cloudiness!) in the data and the resulting predictions. Business analyses should rarely be
chiseled in stone. Too often, they are treated like they are.
Look around your business today. Where are your clocks? Where are your clouds?
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Posted by George Huhn on Mon, Apr 26, 2010 @ 09:55 AM
Looking for new and better ideas from your project team? Thinking about having a typical "
brainstorming" session? Don't bother. New research shows there's a better way.
"Brainstorming" usually consists of people meeting specifically for the purpose of coming up with new ideas around a specific topic. Any and all ideas are recorded, and evaluation of individual ideas is postponed so as not to inhibit idea generation or discard ideas prematurely. Brainstorming is proposed to work on the basis that ideas from each person in the group will stimulate new and different ideas from others, therefore, more ideas will lead to better ideas. That's the theory, anyway.
However, in a recent article published in the
INFORMS journal
Management Science entitled
"Idea Generation and the Quality of the Best Idea," researchers found that most of the literature on brainstorming focused on the number of ideas generated, but not the quality or the selection of the best ideas. Furthermore, they found that the literature showed the opposite of what brainstorming promised: "research has unequivocally found that the number of ideas generated (i.e., productivity) is significantly higher when individuals work by themselves, and the average quality of ideas is no different between individual and team processes."
In other words, brainstorming produces fewer ideas than individual efforts, but about the same quality of ideas.
1) the average quality of ideas generated,
2) the number of ideas generated,
3) the variance in the quality of ideas generated, and
4) the ability of the group to discern the quality of the
ideas.
In their study, they compared two processes: a typical brainstorming structure and a "hybrid" structure. In the brainstorming structure, each team of 4 was given 30 minutes to complete an idea generation challenge. At the end of the 30 minutes, each team was given 5 minutes to develop a consensus-based selection and ranking of the team's five best ideas. In the hybrid structure, individuals were asked to work separately on an idea generation challenge for 10 minutes and then asked to rank their own ideas at the end of the 10 minutes. These individuals were then randomly placed in teams of 4 to share and discuss their ideas and generate new ideas for 20 minutes. At the end of this phase, each team was given 5 minutes to develop a consensus-based selection and ranking of the team's five best ideas.
The ideas were then evaluated by a panel of 41 MBA students who had received formal training in the valuation of new products.
Their conclusion? They found the "hybrid structure" produced 3 times as many ideas per unit of time compared to brainstorming, and the average quality of the ideas was significantly higher.
So, the next time you're trying to generate some breakthrough ideas on your project team, be sure to let your project team members generate some new ideas on their own before you try to do it together as a team. This research shows you'll get much better results doing it that way.
And if you're involved in
project portfolio management, be sure to share this information with your project managers. Sometimes it only takes one breakthrough idea to make the difference between a project that is delivered on-time and on-budget and one that fails to meet its goals.
Follow-up: Two of the researchers who wrote this brainstorming article were interviewed to discuss their work in developing systems to help teams find great ideas.
Read more about it here.
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Posted by George Huhn on Tue, Apr 13, 2010 @ 03:24 PM
Do you want to get a higher price when you sell something at auction on eBay?
After running hundreds of controlled auctions on
eBay, researchers reported in the
INFORMS journal
Decision Analysis that longer auctions yielded more bidders and more bids resulting in an 11% average final price increase compared to the shorter auctions.
When you sell an item on eBay, you can choose auction durations ranging between 1 and 10 days. The researchers studied the differences between 1 and 3 day auctions by auctioning identical items simultaneously with one item in a 3-day auction and the other in a 1-day auction, but both ending on the same day. The also ran an additional 241 control auctions in which the durations for the identical item pairs were the same.
In total, there were 1,223 unique bidders and only 202 of them placed bids simultaneously in a paired auction. Interestingly, they analyzed their data set to see if the higher prices from longer auctions could be attributed to more bids and/or more bidders, but they found that could only partially explain the results.
They also conducted another set of studies on local auction sites with similar formats as eBay, but that had much smaller groups of potential participants. These studies compared auction durations of 1 day versus 10 days. Here they found the opposite effect: shorter auctions resulted in average final prices that were 16% higher than the longer auctions.
The authors caution that these studies are not directly comparable due to a variety of differences in experimental controls and design considerations, and should be therefore be considered primarily as "two separate investigations of the effect of duration on prices in two auction platforms."
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Posted by George Huhn on Thu, Apr 08, 2010 @ 10:42 AM
Not quite, although that was the promise in the title of an
article in the March 2010 "Wired" magazine about a recently invented algorithm called "Compressed Sensing" or CS. That isn't to say CS isn't a really cool algorithm for certain applications - it is and you should know about what it can do – but it can't make something out of nothing.
To understand the concept behind CS, think of a digital image. Different kinds of compression technologies (jpg, gif, png, etc.) are used to shrink the size of these images so they use less memory to store and process. Basically, these technologies work by using clever ways to reduce redundant or repetitive data points to a much smaller number of data points. For example, a large area of a single color can be saved without saving each data point since the color is identical. But even though compression can reduce an image size significantly, the compressed file still holds all of the essential information to display the image in its original detail and resolution.
Therefore, a digital image that is compressed to, say, 10% of its original size has essentially discarded 90% of its original data as unnecessary. So if 90% of the data that was originally collected by the image sensors is unnecessary, why collect it in the first place? Why not just collect the essential 10%?
The idea behind CS is that you can collect digital image data using far fewer physical sensors than would normally be used and then use the CS algorithm to reconstruct the digital image as if you had used a conventional number of sensors. So you lose the computationally expensive overhead of collecting all the data, analyzing it, and then discarding most of it. Instead, you only need to collect a small amount of it, and then use CS to reconstruct the rest. And CS can do a remarkable job of reconstructing an image from very little data.
The CS algorithm isn't just applicable to digital images. It can be used on all kinds of digital data processing from music to interstellar radio waves to scrambled radio communications.
CS works based on a concept called "sparsity," which describes the density of data. Conceptually, a floor that has a few balls spread out over it would be considered sparse whereas a floor covered with many balls of different colors all touching each other would not. It turns out that reconstructing an image using CS means finding the sparsest image that can be constructed from the dataset.
However, there is one key point that needs to be stressed: CS cannot reconstruct data that isn't there - you can't make something out of nothing. In other words, if you take a digital image using far fewer sensors than normal and there is a critical detail that is missed entirely by the sensors, it cannot be recovered using CS. Unlike CS, conventional image compression works well because it looks at all the detail first and throws away the data it doesn't need.
Nevertheless, the promise for CS is exciting, particularly in areas where full data collection can be difficult or impossible because of volume of data or physical constraints. These can be sampled using far fewer sensors than otherwise might be required and then reconstructed using CS to obtain a resolution that is adequate for extracting information. One of the major challenges in applying CS is determining the minimum number of sensors required to sample a given data set.
I have been thinking about how CS technology might be applied to business, process development, and manufacturing data. Can you think of any potential applications in your business?
Post script: The original title in Wired magazine was "F_ll _n T_e Bl__ks: A revolutionary algorithm can make something out of nothing." The on-line version's title was changed to "Fill in the Blanks: Using Math to Turn Lo-Res Datasets Into Hi-Res Samples." Much better.
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Posted by George Huhn on Mon, Jan 04, 2010 @ 02:08 PM
If you don't know the values and costs of not executing your projects then you're probably not maximizing the value of your project portfolio and you may be working on the wrong projects.
When project portfolio managers meet to decide which projects that their businesses are going to execute and which they are going to reject, they often have a summary business case for each project that includes the business value and attributes. Business attributes can include selection criteria such as
net present value (NPV),
return on investment (ROI), costs, resource requirements, and risks.
Thus, when the managers select a project to execute, the value and associated costs of the project are added to the total portfolio value and costs, respectively. When they reject a project, usually the identical "if-executed" values and costs are subtracted from the total portfolio because there is no separate evaluation of the value and costs of not executing the project. Therefore, the value of a rejected project is essentially set to zero by default and the total portfolio loses value.
When they reject a project in this way, any intrinsic positive or negative values and costs derived from not executing the project are not factored-in to the final portfolio. And when these values and costs are not factored-in, the total portfolio value and cost can be dramatically over- or under- estimated.
There are many ways a project can add or subtract value from a portfolio. Even projects that have negative individual ROIs can add value, such as a project that adds revenue to a product line because of its strategic fit. Analogously, there are many ways that not executing a project can add or subtract value from a portfolio. For example, positive value can come from increased revenue streams if the rejected project would have cannibalized revenues from other products; and negative value can come from a loss of revenue from a product line that could have been enhanced by the executing the project. Costs that can be incurred from not executing a project might include costs associated with contract terminations, closing facilities, and reassigning resources.
So, perhaps counter-intuitively, you can see that rejecting (not executing) a particular project may actually add more real value to a project portfolio than selecting another project!
How can you ensure that you're capturing the value and costs of not executing a project?
For each potential project in your portfolio, you could create an associated "Not" project that includes the overall value for not executing the project calculated using the identical attribute categories (rewards, costs, risk, etc.). Then, before
optimizing the portfolio against constraints, you could set up a mandatory dependency between these two projects such that either the actual project is selected
or its corresponding "Not" project is selected. In this way, either the value and costs of executing the project OR the value and costs of not executing the project are included in the portfolio totals.
Of course, if the value and costs of not executing a project are truly "0" and do not impact the total portfolio value and costs, then you don't need to create an associated "Not" project.
In our
project portfolio management tool Optsee®, you can perform rigorous
project portfolio optimizations against multiple constraints (such as limited money and resources) while maintaining four different types of project dependency relationships, including an "Or" relationship. When you select the "Or" dependency relationship between two projects, either one project or the other (but not both) are included in the optimized portfolio. This way it is easy to set up and accurately analyze the real value and costs of your portfolios under different constraint combinations because you're factoring-in the values and attributes of both selected and rejected projects.
Do you currently assign values and costs to not executing projects in your project portfolios? What other suggestions do you have for capturing these values?
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Posted by George Huhn on Tue, Dec 22, 2009 @ 12:11 PM
Remember how mapping the human genome was going to lead to cures for different genetic diseases? The idea was pretty simple: compare the genes of healthy people to the genes of people with diseases ranging from cancers to allergies and – voila – fix the genes that were making them sick. Instant cures, right?
Well, maybe not.
It seems like things turned out to be a lot more complicated than that.
In
The Gene Bubble published in November's Fast Company, David Freedman explains that in spite of the billions of dollars poured into mapping the human gnome "with precious few exceptions virtually no promising new treatments or even highly useful diagnostics have emerged."
Why?
Because of "junk DNA." Apparently, junk DNA "accounts for 80% of a genes influence over disease and is incredibly difficult to sort out."
"It's very discouraging, but we don't have any kind of code for understanding junk DNA. I can find the switches, but I don't know what they do. There are switches for the switches, and switches for those switches. It's endless."
So remember: even in nature, how you fold your junk matters.
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