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7.3 Managing innovation projects: enlightened experimentation
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Compétences que vous apprendrez
Social Network, Management, Innovation, Innovation Management
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Sep 06, 2018
This course covers a lot of ground and the time commitment is very well balanced. The diversity of topics revolves around the same focus, it feels very well structured.
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May 26, 2018
A wonderfully put together course. Very practical insights and useful interviews included. The course material is also quite relevant to today's work place.
À partir de la leçon
Execution - Implementing Innovation
By studying the materials of this week and doing the associated exercise you will learn how innovation projects can be managed. You will understand and describe the different elements of stage-gate models. Also, you will familiarise yourself with the concepts of experimentation and enlightened experimentation within the context of innovation project management. As a result, you will be equipped with an understanding of the peculiarities of innovation projects, and how companies deal with these peculiarities.
Enseigné par
Serge Rijsdijk
Assistant Professor
Jan van den Ende
Professor
Sandra Langeveld
Teaching and research coordinator
Murat Tarakci
Assistant Professor
Stefano Tasselli
Assistant Professor
Daan Stam
Associate Professor
Dirk Deichmann
Assistant Professor
Hi.
In this video, we will focus on this specific
perspective on how individual innovation projects should be manage.
This perspective is called Enlightened Experimentation.
Experimentation refers to testing ideas and products in the physical world.
Enlightened experimentation culture is the testing of ideas and
products in the virtual world of simulations, and computer models.
This methodology is especially suited for innovation projects,
because they involve high levels of uncertainty.
As you may remember, we looked at a stage gate processes in the previous video.
Stage gate processes are tools that help firms to decide whether
project should be continued or shutdown.
In this picture, individual projects are represented by smileys.
In this video, we will zoom in on these individual smileys
that represent individual projects.
Now, what is a project?
A project is something temporary, because it has a start and a finish.
Also, there should be some level of organization and
that you'd have certain goals and be aimed at solving certain problems.
In evasive management, a project concerns developing a new product,
a new service, a new process or a new business model.
In this video, we'll discuss the specific concept of enlightened experimentation.
Stefan Thomke, an MIT Professor introduced the ideas of experimentation and
enlightened experimentation.
He claims that it's very important for
firms that want to be innovative to have systems in place
that allow them to systematically test their ideas and solutions.
Such testing can then be used to refine and create new products.
Early input on what you plan to develop is very important.
It's highly effective to reduce the number of ideas that you may have as a firm and
then refine those ideas that are remaining.
Early prototyping for those ideas is important as well, because this allows for
developed early feedback on ideas.
For example, BMW still makes clay models like you see on the bottom left.
Through these clay models, designers can get a good view and fuel for
what a new car looks like.
BMW engineers also use enlightened experimentation for
the interior design of a new car.
For example, they build virtual reality rooms in which they can simply handle
different controls on the dashboard.
You see a picture of that on the bottom right.
The engineers can sit together and
jointly evaluate their designs right there on the spot.
As I indicated in the beginning of this video, then clay models are an example
of experimentation, because it concerns physical representation of products.
The virtual reality rooms at BMW are an example of enlightened experimentation.
Now, making ideas and designs explicit in the form of physical or
virtual prototypes is a very important means of facilitating
communication across different stakeholders.
You have an idea, you make it explicit.
And only once you make it explicit, you can get proper feedback on your idea.
And once you get the feedback, you use it and try it to improve your idea up
to the moment where you do not see any room for improvement anymore.
Now, why is it important to make things explicit and
create realistic representations of your ideas?
Maybe you know these joints.
Imagine that a firm is planning to develop a certain software package.
The concept of the software package might be based on a certain customer request.
Well, if the idea of that request is not that explicit, this is what might happen.
On the left, you see how the customer may have tried to explain it.
Of course, this is not a software package, but
we draw an analogy with a swing on a tree.
In the middle, you see how the project leader understood it.
And on the right, you see how the analyst designed it.
So, people all have different perceptions of the same thing and you can go on.
On the left, you see how the programmer wrote it.
It doesn't look very effective.
In the middle, what the beta testers received.
And on the right, how the business consultant described it.
Really great, but not what the customer said and we can continue.
Here, you see how the project was documented not at all.
What operations installed?
And how the customer was billed?
Also, very interesting.
And finally, how it was supported?
Just partly, how marketing advertised it?
Look very fancy.
And then finally, this is what the customer really needed,
which is actually also different from what this customer initially describes.
So, the message is try to make representations of what you want to
develop as early and complete as possible.
Only when ideas are made explicit, the different parties involved can verify
whether they have the same understanding of what they are talking about.
Quite often, people will find out that they see things differently and
that they can work together towards a shared and common understanding.
Now, Stefan Thomke has a couple of rules for enlightened experimentation.
These rules are applicable for firms that already conduct experimentation, but
they want to move that experimentation to the virtual world of enlightened
experimentation.
When moving toward enlightened experimentation,
the interrelatedness of different elements of a product come into play.
In car safety, for example, everything plays a role.
The structure of the car, the shape of the engine, all elements and
how they are linked is what determines whether a car is safe or not.
So, the safety of a car also concerns the manufacturing of the car.
Therefore, people from manufacturing need to be involved in virtual testing
of the safety of a car.
So if you are going to do simulations on whether your car is safe or
not just using a computer,
you want to make sure that you have people from manufacturing at the table.
They should be involved when evaluating the data
that comes out of the simulations, as well.
So, this is what Thomke means with the first rule that what you have
to do is reorganize and bundle small groups of experts.
Enlightened experimentation implies that people from different
disciplines may have to be involved in a project more early on.
The second rule is that you fail early and often, but yet, you should avoid mistakes.
Now, do you have any idea what would be the difference between
a failure and a mistake?
Thomke sees failure is something that happens, because you are exploring.
When exploring, you may find out that something doesn't work.
That's a failure.
However, a mistake implies that you are doing
something while you could have known that it is not going to work,
because maybe because you did not collect all the relevant information.
So, that's the difference between a mistake and a failure.
Mistakes can be prevented.
Failures are just part of the exploratory process of innovation.
So, Thomke argues that you should embrace failure.
Take a look at the models at the bottom of your slides.
These are models of a PDA, a personal digital assistant.
The forefather of the smart phone.
Imagine that you would like to try what is the most appropriate way of developing
a stand in which you can put down a PDA.
If you really want to know what works, you may want to try and
make relatively extreme concepts, because that will enable you to find out
what is still acceptable and what is not acceptable.
You may fail and find out that things do not work,
but at least you will find out what works and what doesn't.
Another recommendation is to test with clear goals and hypotheses in mind.
For a PDA model like this, you may want to test whether stand functions properly.
There's no need in developing a very detailed interface for that test.
You can just limit yourself to making a model that roughly has the physical
properties of the PDA and then you try to put it in the stand.
And of course, if there is something that you cannot control such as
subjective consumer opinions that may vary a lot,
always make sure that you do multiple tests.
Another thing that's important for
experimentation is that you want to do front-loading.
So in the top of this graph,
you see the traditional sequential development process.
So we have the design phase, engineering phase and production phase.
In this case, the company design the product,
they move to the engineering phase and then something goes wrong.
This means that they have to go back to the design phase.
Redesign a product and then move on to the engineering phase again.
The same applies to production.
Now imagine that you could make simulations of,
let's again say, a car and you put the entire car in the simulation and
take into account all its technical properties.
You will then see that in the safety desk, engineering problems already come out,
because all the information has to boot both in the simulation.
So what actually happens if you move into this virtual simulation area,
hope in the light of the experimentation is that all relevant parties
are already involve earlier on in the project.
And as a result, you will know about many of your products problems a lot sooner.
As such, the solving of these problems implies
that the workload of solving problems is moved to the front.
The advantage is that once you have solved all the different problems that are there,
you can move on more easily and launch your product.
As a result, you have a shortened lead time,
a shortened cycle time and your project is finished sooner.
Another big advantage is of course, the early detection of problems.
This means that you don't have to go back to a previous phase, which can save money.
Finally, one thing to realize is that low fidelity experiments should be done first.
And generally, these are simulations.
They are not reality, so the fidelity is somewhat lower.
At BMW, for example, we first, to simulations or testing car safety.
They crashed the car in the real world later on.
By using simulations, you can maybe be done after one physical crash test,
because the car is already pretty close to perfect.
As such, using virtual simulations can definitely reduce costs.
In the next movie, you will see a mix of enlightened and
conventional experimentation.
BMW does early crash tests first and
later crash tests are physical, and more expensive.
In the video, you can see a great mix between virtual tests and
real world testing.
You can see that BMW even continues collecting data on
its car models after a car has been sold.
Let's take a look.
So, they have a really great mix of virtual testing and
actual physical testing.
Now the final recommendation being made by Thomke is that you should combine new and
old technologies.
Why?
Well, not everything can be simulated.
There are always going to be things that need to be tested in the physical world.
For example, at BMW, they dedicate a lot of attention to what a car sounds like.
How does the door handle sound when you use it?
And what is the sound of the door when you close it?
They want to ensure that these sounds reflect the quality of the car.
Such sound tests,
however are all being done in the real physical world in a sound laboratory.
BMW, first has to design the car physically,
then starts measuring real sound and engineers also use their own ears.
Based on these observations in the physical world, they perfection the car.
So, what have we seen in this video?
We actually zoomed in on the ideas of experimentation and
enlightened experimentation.
Experimentation, we first do testing ideas and products in the physical world.
Enlightened experimentation is the same that takes place in the virtual world of
simulations and computer models.
Innovation projects require this experimentation approach,
because they involve the design of new things and
this implies that there is a lot of uncertainty.
Dealing with this uncertainty is best dealt with by making things explicit and
doing tests.
Now firms moving into enlightened experimentation should organize for
it, be prepared to fulfill early but
avoid mistakes, anticipate and exploit information.
And finally, they should remember that both old and new technologies
have their strengths and weaknesses as you, therefore, be use together.
I hope you enjoy this video.
Thanks for watching.
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