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:
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?
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.
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.
Takeaways
- 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 decisions, but also to monitor progress and react to unforeseen 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.
Valispace is a single source of truth and collaboration platform for all your engineering data.