Evidence-based Technology Roadmapping

Big manufacturing companies for complex hardware usually have a product portfolio of thousands of products and product families. They range from basic technologies, integrated into components or subsystems up until the final products.

Imagine that you are the responsible for the company’s technical strategy at a major drone company. When creating a product strategy or an investment strategy for your R&D efforts, the main question you would want to answer usually is:

How can I ensure that my R&D investments today, lead to competitive products tomorrow?

The status quo

A Classical way of approaching this problem is to rely exclusively on the experience of managers and chief-engineers. A typical simple powerpoint analysis for your drone manufacturing business could look like this:

A gut-feeling-based Technology Roadmapping slide
The problem: Good Questions hardly get answered

This information is a good start, but it should not be the final information that you base your company’s R&D investment decisions on. Any company that wants to survive in a fast-paced market, needs to perform a much deeper technical analysis, to be able to answer questions like these:

> How much further would each of our 8 portfolio drones fly, if we hit the target for all of our R&D investments?

> How will our products compare to drones announced from competitors for critical performances, such as range, cost per flighthour and max-operating windspeed if we only achieve one of the goals?

> Can we perform quick concurrent engineering studies for the long-term potentials or do we need an entire team for months to work on it?

Evidence-Based Technology Roadmapping

To gain insights from your current portfolio and more importantly to plan its future, your engineers need to model the technological interdependencies between technologies and products:

> How does the flight-time correlate to the battery capacity and mass of a specific drone?
> How does the rotor size affect the lift?
> What is the impact of the landing gear to the center of gravity?

These models can also contain financial and process information:

> How is the cost-per-flighthour calculated?
> How much time in the production process is spent on assembly?

Models of technical interdependencies are the key for R&D investment strategies.

This information should be made available as simple formulas or as complex simulations but the key to success is to link them and not analyse them separately. Any change to a technology or product must directly affect all linked entities.
Once the information has been modeled, across technologies and products, insights can be gained:

  • Generate Design Structure Matrices and cluster them to better understand the interdependencies between technologies, projects, products and responsible teams.
  • Perform What-If Analysis, to understand how exactly a change in technology affects each of your products and calculate the sensitivity of your results to know how well you have to succeed with your reasarch for it to be meaningful for your products.
  • Benchmark your future products against the ones proposed by your competitors.
What Could evidence-based results look like?
Flight Range of a specific Drone depending on its landing gear mass

This chart shows the Flight range of a specific drone, dependent on its landing gear mass. Very quickly now can be assessed:

  • Is this potential improvement in flight range worth the money and time that we plan to spend on 3D printing?
  • Or in reverse: How much better do we have to get in our landing gear design so that is has a meaningful impact?
  • During the development: When the original target cannot be met, you can immediately see what it means for your future products.

From your Design Structure Matrix (DSM) you can identify the impacts of Technologies to products and immediately visualize secondary effects: e.g. here, which constellations are affected by which drone or which products could benefit from a new technology.

Market analysis based on technical evidence

With all the information stored and linked, it is an easy task to extract benchmarkings along many axis within seconds. Comparisons of in-house products and competition can be analyzed at any given time, and with a change in R&D result projections be updated and the strategy revised.

  • A Technology Roadmapping excersise can be made far more meaningful by taking evidence-based decisions.
  • Linking all technical interdependencies is the key to generating meaningful insights.
  • These insights don’t only help to make initial R&D funding descisions, but also to monitor progress and react to unforseen breakthroughs or delays.

If you are looking for a tool which can help you perform evidence-based Technology Roadmapping, have a look at our software Valispace. The Airbus Chief Technology office is using it for their roadmapping activities.


Why Valispace?

“Why?” might be the most intriguing question about any startup.

Independently of the technical or economic viability of the idea or product, the answer to the motives of creating something new reveals so much more than a landing page will ever tell you. It sets the foundation as well as the vision, which leads the startup like a guiding star every step along the way.

So why do we build Valispace?

Almost every object a human interacts with in our modern world, has been built by a team of engineers. The screen you are reading this blog from, as well as the microchips inside your device; the wifi that connects you to a network of servers, which make this text available; as well as the power plant which provides the power for them. But also the chair you are sitting on, the lamp that illuminates your room and the window you look through have been carefully designed by engineers. Every screw in your refrigerator has been simulated, selected and its position debated and optimized. Its door has been tested in a refrigerator-door-open-close-simulator machine for thousands of cycles.

The reason why we engineers are able to design objects today that would have seemed like magic to people living only 100 years ago (think of rockets or smartphones), is because we are standing on the shoulders of giants: applying the knowledge of millions of scientists who lay the groundwork as well as millions of fellow engineers who developed tools and methods which we shamelessly apply and improve.

But the limits of today’s engineering are not the imagination of engineers, but the tools for collaboration: the more complex a product becomes, the more engineers need to work together on it. Designing a complex satellite for example involves usually several hundred engineers, spread over dozens of companies and its complete development and testing can take more than a decade.

Valispace aims to radically streamline the engineering process of hardware projects. This will enable small teams to design highly complex systems fast and cheap and big teams to build things which seem like magic to us today.

Software engineering already went through this revolution: while in the 70s you would need hundreds or thousands of engineers to create the simplest programs, small and lean teams nowadays are able to create amazing and scalable software[1] at a rapid pace. In our opinion, mainly two things drove this revolution:

Collaboration tools such as git or svn, which made teamwork truly feasible and are the backbone for practically every small and big[2] software project today.

Open source, which allowed the creation of reusable building blocks and tools, of which millions of software engineers benefit daily in their open or closed projects.

Today hardware engineering works exactly in the opposite way. What seems unimaginable in the software engineering world (such as emailing source code, instead of managing it in one central place), is common practice for collaboration in complex hardware engineering. Outside of the limited world of CAD, there is no common representation of engineering data, to allow collaboration or reuse. In the past 50 years, academic efforts such as SysML or other ModelBased theories have not proven to be suitable for industrial real world problems, which is why there are no useful, widespread tool implementations of these ideas in companies who actually design complex hardware.

The sad reality is that today’s collaborative engineering of the most complex hardware in the world, such as satellites, power plants, robots, etc. is managed with Word and Excel.

Expensive inconsistencies, low reuse, hardly manageable technical complexity and frustrated document updating engineers are only some of the results.

At Valispace we believe that there is a hardware revolution to come: harnessing the power of low costs for electronics, the availability of methods such as 3D printing as well as ever-growing connectivity, we expect the hardware world to make a huge leap forward in the coming years. And Valispace is aiming at becoming the backbone of this revolution.

Read more:

[1] Think of WhatsApp, who – before their acquisition – built and maintained the app for 900 million monthly users with a team of only 50 engineers.

[2] Even google stores all of its code in one single repository.