A guide through the Hardware Engineering Tool Landscape 2019

In an era of “digitization”, “smart factories” and everything “4.0”, it is hard to keep an overview over the evolving engineering tool landscape for hardware development.

This blog post starts with a look at the three types of data that are crucial for hardware engineering. Then we compare key strengths and weaknesses of tools & methods that help managing that kind of data.  A glossary for all abbreviations used in this article is attached at the end of the page.

Hardware engineering = 3 types of data

In hardware development you will typically encounter three kinds of data:

Hardware Engineering: 3 kinds of Engineering Data
  • CAD data
    CAD data are the 3D models of your physical system.
    This kind of data is usually created in CAD systems and then managed in PLM/PDM tools, to create BOMs (Bills of Materials), track as-designed vs. as-built configurations and connect to ERP systems for logistics tracking. While MCAD systems focus on mechanical design, ECAD Systems model electronic circuits.
example of a CAD file
  • Simulation Data
    Specialized models for detailed analysis of forces, fluids, dynamics, stresses, etc.
    These simulations might be executed in specialized CAE/FEM/Thermal/Orbit/etc. simulation software, or can be custom written in MATLAB, Python or other languages.
example of a Simulation File
  • All other non-CAD data (engineering values)
    For example: power consumptions, data rates, velocities, temperatures, capacities, charging-cycles, operating modes, coefficients, efficiencies, etc.
    These data are necessary for all kinds of analysis, trade-offs and simulations, but unfortunately usually not stored in a single place, but instead across hundreds of spreadsheets, reports, emails, wikis and many other files.
Today non-CAD Engineering data is stored in thousands of inconsistent documents, spreadsheets and files

A huge part of engineering data today are buried in hundreds of inconsistent documents and Excel spreadsheets.

Mapping the tools to the data

In a classical approach these three types of engineering data are managed in the following tool families:

DataProduced inManaged in
CADCAD software
(Catia, Solidworks, etc.)
PDM systems
(Dassault SolidWorks, Autodesk Inventor, etc.)
SimulationSimulation tools
(Matlab, ANSYS, etc.)
PLM systems
(Siemens Teamcenter, PTC Windchill, etc.)
Engineering ValuesOffice tools
(Word, Excel, etc.)
Document Management Systems (DMS) / sometimes included in PLM
(Microsoft Sharepoint, Siemens Teamcenter, etc.)

This breakdown results in the following map of tool capabilities and strengths:

Traditional Engineering Toolstack


CAD and simulation tools have continuously been improved over the past decade and PDM/PLM tools have made it far easier to manage them even across big teams. However other tasks of systems engineers, such as the management of engineering values (connecting and comparing them as well as managing requirements and their impact on the system design) could not be solved by spreadsheets and document management systems. Instead, data had to be related and managed through new methods and tools; which led to the introduction of two complementary concepts:

Model Based Systems Engineering (MBSE)

In MBSE, engineers start by modelling the functions of the product that they are developing. These functions are then converted and traced into requirements and system diagrams, that describe the functionality of the product.
MBSE has been used in complex product development, such as for space systems and is gaining popularity in more and more industries, mainly in the Form of SysML.

MBSE tools are usually expert tools, used at the early stages of product development by a small group of engineers. The resulting documents and graphs are the backbone for a large part of the engineering effort of complex products and provide an overview over the system architecture.

Functional Analysis in SysML

Data Driven Systems Engineering (DDSE)

The models of MBSE cover the functional design overview and requirements. After their development and their breakdown into detailed design, the actual implementation begins. This implementation phase can take years for complex products, in which countless data are produced.

In the implementation phase, DDSE tools:

  • Manage non-CAD data & engineering values
  • Integrate different software-tools through one single data hub
    (“Single Source of Truth”)
  • Automatically propagate changes through documents and simulations
    (“Technical Change Management”)
  • Are collaborative tools that allow for simultaneous multi-user access

Documents that are created from DDSE data, are stored in PLM/PDM systems, to record the as-built-status.
DDSE tools are used and accessible by all engineers in the project: providing systems engineers with the necessary technological overview and experts with the up-to-date values and design baseline.

DDSE tools manage engineering data in the implementation phase, provide version control, make it available collaboratively and provide full traceability to the entire engineering team.

The overall overview is therefore given in the completed tool strength map:

DDSE and MBSE complement each other and the classical hardware engineering toolstack. Any modern hardware company should be digitized in 2019 and have their data connected, stored and versioned in the appropriate tools.

Valispace is the leading browser-based DDSE tool, used by the world’s most innovative hardware companies, such as Airbus, GOMspace, OHB LuxSpace, DLR and Tekever.


BOM – Bill of Materials
CAD – Computer Aided Design
CAE – Computer Aided Engineering
DDSE – Data Driven Systems Engineering
DMS – Document management System
ECAD – Electronic Computer Aided Design
ERP – Enterprise Resource Planning
FEM – Finite Element Method
MBSE – Model Based Systems Engineering
MCAD – Mechanical Computer Aided Design
PDM – Product Data Management
PLM – Product Lifecycle Management
SysML – Systems Modeling Language

Are you new to satellite engineering and want to learn more about how to design the Electrical Power Subsystem, Communication Link and Thermal Control System? At Valispace we are developing a detailed satellite model to share with our users and along with it we have written down these sizing and design tutorials to help you get started!

The tutorials shown below are embedded in this blog post using a live, shareable link generated in Valispace. You can share any Valispace analysis with your colleagues using these kind of unique links. The content shown here will be automatically updated whenever someone changes the content in Valispace.

If you want to get notified once we release our detailed satellite model you can subscribe to our newsletter here!

EPS design description

Below you can see the dynamic document containing the Electrical Power Subsystem design tutorial in Valispace. You can also look at it at this link.

Designing a communication link and budget

Below you can see the dynamic document containing the communication link and budget design tutorial in Valispace. You can also look at it at this link.

Thermal Control System sizing description

Below you can see the dynamic document containing the Thermal Control System design tutorial in Valispace. You can also look at it at this link.

In this article our summer intern Carlos describes how he modeled, analyzed and improved the design of a quadcopter with Valispace:

I was interested in modeling and calculating the performance and endurance of a quadcopter performing wind turbine blade inspections. These kind of calculations and analysis are necessary to make drone systems more mature and efficient in the future. The complete model will soon be available for anyone who wants to import it into their Valispace deployment, as well as in our online demo (sign up here!).

Drones are in many ways changing the world, for example as a help in humanitarian aid, for first responders or for safety inspections. For wind farm inspections, drones are starting to become an essential part of performing autonomous inspections of wind turbine blades. They are cheaper than helicopters and their cameras can capture and analyse defects better than the human eye can.

Now, imagine that you have a company providing inspection services using drones that you build in-house. You obviously want to constantly improve your system and have engineers working on making the drone more efficient. While improving the design, they need to redefine equations and simulations. You might make more precise analysis of the propeller thrust coefficient using CFD calculations or run a battery discharge simulation based on the current and temperature. It might be that your structures engineer changes the shape of the drone and now you have new geometric properties and mass properties. And what better software to use than Valispace to keep a consistent model of the drone which serves as a single source of truth for the simulations and analyses?

Basic building blocks

I chose to start the modeling based on an already existing quadcopter, the Elev8-3 made by Parallax. The Elev8-3 has CAD files from which it is easy to identify the parts and components and even directly import the structure into Valispace. I started to model the components tree, which is a breakdown of the parts of the quadcopter. What parts do we need to get the drone flying?

Remember, in Valispace you can always change the tree at a later stage.

The component tree of the model was broken down into the parts shown in the figures below.

The resulting component tree in Valispace looks like this:

Once the basic structure was in place I could start adding parameters and properties to the components.

Adding formulas, modes and simulations

For a wind turbine inspection, it’s important to know the location of the wind turbine and the distance that the quad must fly, if it’s long or short range. And we want a large battery to be able to fly as much time as possible. So the important parameters that we need Valispace to output are:

  • Power consumption depending on modes;
  • Efficiency of the rotors;
  • Endurance of the drone in several modes.

We will choose to optimize the drone for hover mode because it’s the one that we need to inspect. The drone should inspect the wind turbine for at least 15 minutes.

The Valispace software is great because of the structure in which it organizes the important design values. As opposed to using Excel, where you would have to add and subtract between sheets, use VLOOKUP, have more files connected to that sheet and embrace all dangers that they contain, Valispace organizes all of that in One Single Source of Truth and everything is nested and can be invoked by name or ID. The key here is name, instead of using VLOOKUP(parameter) you can choose “MotArm1.Cost” and use the autocomplete functionality, so, by just typing “cost” a list of options that you can choose from appears directly and you can use this name in formulas instead of using Cell references.

The easiest way to start is inputting all mass related properties and cost. From the beginning, I added modes for ON and OFF in all the electrical components, and then added power and/or current which were depending on those modes. To build the arms, I decided to make four connected copies of the component called MotArm, which has all the properties of the moto, arm and propeller. This means that if I need to change anything in one of those components, the other ones are automatically updated as well since they are connected.

After the components tree was complete I could also import certain Valis from a spreadsheet with the built in Excel importer. I also added a Vali in the Drone component called “number_rotors” containing the number of rotors of the drone. That way we could make an analysis of what would be the optimal number of rotors  of the drone.

In the software, I defined five different flight modes for the drone; OFF, HOVER, UP, FORWARD and CLIMB. These are the basic operational states that the drone can be in and these are used to define values such as current, thrust and thrust-to-weight ratio, which all have different values depending on which mode the drone is operating in.

After organizing the data by modes the formulas were added with the autocomplete functionality as described before.

I also used the Valispace simulation feature, which is a built-in feature where you can run Octave code and .m files directly in the software. The model is designed in a way that the power can be calculated with values in the components but the mathematical model of the motor is a 4th order model, so I decided to use Octave and the fzero() function for those calculations. So I just imported the Valis that I needed on the left column (as seen in the picture below), and defined the output I wanted in the right column. The calculations were written in m-code and after running the simulation it marked the Valis that were used with SIM.IN and SIM.OUT in the components interface.

From this simulation we return the rotational speed that we need for the motor in the different modes. Then that value is used for computing current and after, by dividing the charge of the battery with current we can have an estimate of the endurance.

Analysing the results

With ValiTypes you can easily create automatic budgets to visualize the progress of your work. For example: to keep track of cost budget I used the ValiType “Cost” and added this parameter to all components. On the top level I specified the formula for Cost to be “soc()” (sum of children), which means that the total cost is summed up automatically from the values in the sub-components.

In the Valispace analysis tab, I could then make budgets with the cost and mass breakdown as seen in the pictures below.

I also compared the Valis of power and endurance in the different flight modes:


Power comparison in different flight modes

Endurance comparison in different flight modes

Simulating the inspection sequence

And finally, I used the data from Valispace in Matlab simulations using the Matlab Toolbox to be able to simulate the inspection sequence.

First, the data from Valispace is pulled to a struct in Matlab.

After running the simulation you need to push the data to Valispace again. Here it was used 2 times to push single Valis with ValispacePushValue().

The simulation results were pushed back to Valispace, to a new component called SimResults:

After the values are pushed (or datasets in this case), you can see and analyse the graphs in Valispace.

The final result of the inspection sequence is shown in the figure below.

Valispace together with Matlab results in a powerful modeling and simulation framework, together with a Single Source of Truth to make sure that all data is transparent and easy to work with for all engineers.

We’re currently working on an optimizer feature in our software, which calculates the optimum parameters for your product to achieve your desired results. If you want to be notified once it is released, signup to our newsletter.

If you are interested in doing an internship with us, where you can create models of complex engineering products, feel free to apply here!


We’re feeling refreshed!

As the team grows we start asking ourselves tough questions to make sure we’re moving in the right direction. This is why our team has spent the last few months figuring out the pillars in which we sustain our values and ambitions by answering three important questions: why, how and what.

We were able to reach some conclusions.

  • Why? We believe that everything we do is to accelerate the path to the future in which new technologies and products will improve people’s lives.
  • How? By empowering engineers with a tool that converts complex engineering into simplified processes.
  • What? We develop a smart collaboration platform for engineers.

For the next couple of years, we wish to have an even more talented and motivated team working towards the continuous goal of empowering engineers.

Cooperative, Efficient, Optimistic, Smart and Trustworthy – this is how we define our brand and how we wish to be recognized by our customers. We hope you feel the same way!

We want to thank Sebastian and his team for bearing with us in the brainstorming sessions and Lara for being open to hear our thoughts and ideas and materialize them so well.

As a hardware engineering company, you might be excused for not paying too much attention to the latest trends in data science and artificial intelligence. After all, a lot of your daily work consists of exactly the kind of creative thinking and developing that is making these robots possible. You sell products made from aluminium, silicon and high-grade plastics, not click-per-view advertisements. You might not have a huge interest in the typical artificial intelligence stories about predictive maintenance or managing huge warehouses with robots.

On the other hand, what does matter is how good and how efficient you are at finding out what to create, at designing, manufacturing and selling. And that is where good data management is more and more essential. From evidence-based technology roadmaps to customer relationship management, enterprise resource management and many more. Automating all kinds of manual operations through scripts and macros helps to increase efficiency drastically, and reduces the possibility for errors.

So while as a hardware engineering company, you are maybe not focused on ‘big data’ and artificial intelligence in itself, still being able to work with sizable amounts of data and extracting knowledge out of them is quickly becoming an important competitive advantage.

If you went to bed last night as an industrial company, you’re going to wake up today as a software and analytics company.
– Jeffrey Immelt, former CEO of General Electric


Engineering data is typically not of the huge-quantity / low-quality type that gets all the attention. However, there are other reasons why it can still be a big challenge to work with design and manufacturing data. It is spread across many software tools, each performing its calculations, design or analysis with inputs and outputs that are manually copied and pasted. It is contained in documents, spreadsheets, presentations, emails, in different versions and local copies. There is no uniform way of indicating what number represents which parameter, let alone a clear structure in what numbers belong to what actual parts of the design.

At Valispace, we think of data storage, design automation and optimization as parts of a ‘hierarchy of needs’, inspired by ‘The AI Hierarchy of Needs’ as proposed by Monica Rogati from Data Collective. Just as in Maslow’s pyramid of needs, food and shelter are needed before self-actualization, in the engineering data pyramid a basic unified data storage and a certain data structure are needed before it is possible to perform advanced data analytics, apply optimization algorithms and machine learning.

Valispace engineering data pyramid, inspired by ‘The AI Hierarchy of Needs’.

At the bottom of the pyramid is data collection into a single database, available to all engineers collaborating on a certain product or project. Data is exchanged with as many other tools as possible, feeding in and reading it out again. This is the basic set-up to create a single source of ‘truth’ with the latest, up to date values.

Adding structure to that data is one step above in the pyramid. To allow for clear communication and analysis, values are structured using a simple data model. The relations to other values are also stored, for example through formulas. A difference can be made between a physical property (e.g. the mass of an engine blade of 0.3 kg) and other numbers describing such as a design margin or a maximum value. Keeping the data structure simple allows to put in many different kinds of data easily, from back-of-the-envelope spreadsheet calculations to the most elaborate fine-meshed analysis results, from complex state machine logic to large batches of manufacturing, calibration and testing data.

This structure allows for advanced ways of exploring engineering data and its connections, improving the efficiency of daily work and creating new insights. It becomes possible to filter and track data and the sensitivity of one parameter to other values. Interactions with parameters are based on their meaning rather than where they happen to be in a spreadsheet. This opens the door to new user interfaces such as voice, chat or augmented/virtual reality. You could imagine asking Alexa (or J.A.R.V.I.S.) for a breakdown of the latest design changes inside an important subsystem of your product and to detail their impact on the power budget. In fact, we have already set up a prototype Alexa skill for Valispace to do exactly that, get in touch if you would like to be a beta tester.

Building on these layers of data collection, structure and advanced exploration, powerful data analytics can be automated, providing oversight in charts, budgets, timelines or powerful custom scripts.

Finally, having engineering data stored in one place, but also connected, structured and analyzed, many features of a product or project can now be expressed as values inside Valispace. This provides a perfect platform to apply optimization algorithms and automation tools on a real engineering project, the kind of advanced algorithms that have until now been restricted to academic use or small subsets of data. For example, at IAC 2017 in Adelaide, Johannes Norheim from MIT presented a paper on optimization of a satellite design using geometric programming using Valispace and our MATLAB toolbox.


  • Working with engineering data in an efficient way is a key competitive advantage, breaking up data silos and integrating knowledge across your company.
  • Advanced data analytics is based on structured data storage that forms a single source of truth and has automatic data exchanges with other engineering tools.
  • Exciting new user interfaces and methods of advanced optimization and machine learning become feasible with this data structure in place.

Interested? Take the first step today: centralize engineering data from fragmented spreadsheets and emails into a single place where they create value. To learn more about Valispace, have a look at our website and our documentation page, or try out the tutorials in the online demo.

When explaining the basic concepts of Valispace – collaboration, technical data exchange, connecting data with formulas – a question that often comes up is how Valispace compares with spreadsheets, and especially with cloud-based spreadsheet such as Excel Online and Google Sheets.

It is not a coincidence that spreadsheets are popular: storing data in tables is such a generic approach that it allows for thousands of different applications. Formulas that calculate the value in any particular cell depending on the other cells form a powerful calculation tool that is still relatively easy to learn (although hard to master). Charts can be created easily. Advanced functions like pivot tables and macros allow to set up more elaborate data analysis and automation scenarios.

A spreadsheet is the digital equivalent of a swiss army knife, a flexible tool to ‘get stuff done’ in our digital lives. Working with spreadsheets has become an important skill to survive and thrive in a digital world, so much so that it forms a mandatory part of secondary school education in many countries.

Swiss army knife

It is not a big stretch to imagine using spreadsheets to set up an engineering data exchange, and in fact this is exactly what is in place in many engineering companies: gather all data in one or multiple big ‘master’ tables, including the formulas to recalculate cells if other data changes. Put these on a local shared drive, a document management system or a cloud service such as Excel Online or Google Sheets, to allow for simultaneous editing by different users.

However, large and complex spreadsheets that are in use by multiple people are not a new phenomenon, and the challenges with them have been significant enough to merit quite some academic research and an annual conference. These challenges can include:

  • To keep the spreadsheet from becoming a data jungle and allow to retrieve the essential bits of information, typically some structure needs to be set up initially and carefully maintained through rules, active cleaning and review.
  • The data contains just numbers. For these numbers to be useful in calculations and formulas correction factors are often needed, for example to convert to the correct unit. Data manipulations like this need to be clearly documented to ensure other users work with the data in the correct way.
  • Changes are automatically propagated through the formulas, but their effect remains ‘hidden’ until the correct person happens to looks at the relevant data.

As with any human process, manually maintaining spreadsheets inevitably introduces mistakes. Some of these spreadsheet mistakes have become infamous, but it is increasingly clear that almost every spreadsheet has some bigger or smaller errors. The different types of mistakes in spreadsheets are included in the “Panko-Halverson Taxonomy of Spreadsheet Errors”. For example, ‘execution errors’ can include typos and wrong copies, and ‘planning errors’ can include logic errors inside formulas.

Taxonomy of spreadsheet errors, from Revising the Panko-Halverson Taxonomy of Spreadsheet Errors, R. R. Panko and S. Aurigemma, Decision Support Systems 2010.

While the rate of errors in spreadsheets is in itself not much different from human errors in other forms of data storage, reviewing a spreadsheet is notoriously difficult. Have a look at the picture below for a (still relatively straightforward) example. Difficult reviewing is part of the reason why many errors remain hidden and are even copied to other spreadsheets. It is not surprising that these spreadsheet mistakes are often the root cause of expensive last-minute fixes, leading to schedule slips and budget overruns.

Spreadsheet error example. How much time did it take you to find and fix the mistake?

Adding structure manually, performing data manipulations and taking care of changes all introduce errors in spreadsheets that are difficult to find and fix. How can this be solved? It is essential to set up a clear data structure that is shared between all collaborators, as well as automating as many actions as possible. It is exactly these two solutions that Valispace provides:

  1. Data structure: any data point inside Valispace is more than just a number. It has a name, a unit, a history, margins and many more. It typically is part of a data ‘product tree’ (see picture below). Formulas are constructed based on the name of the input values, rather than referring to an abstract cell number, which makes these formulas much easier to review. In every description or calculation where this value is used, it is always referring to the ‘single source of truth’, ensuring that calculations are consistent among each other.At the same time, very rigid data structures can easily make it complex to set up the data storage system and require significant training from all users. That is why a lot of flexibility remains within the basic Valispace ‘tree’ structure of components, Valis and their properties.

    Example component tree from Saturn V advanced tutorial.


  2. Automation: avoiding manual data manipulations greatly reduces the possibility of human errors. Valispace provides a range of different automation tools:
    • A lot of important but tedious calculations are performed by the Valispace algorithm behind the scenes, for example: automatic unit conversion, automatic propagation of design margins, notifications to the relevant users when something has changed that they need to be aware of.
    • Comparison charts, budget tables, relation charts between values and other data summary visuals are automatically to up to date.
    • Custom automation or scripting can be implemented through the Valispace Python and REST APIs.
    • Values included in other tools can easily be kept up to date with the Valispace add-ons for Microsoft Word, Excel, MATLAB,…


Through data structure and automation, Valispace solves the key issues of engineering data processing. While a ‘swiss army knife’ spreadsheet provides the ultimate flexibility in one tool, more complex data construction work requires dedicated tools to limit the amount of manual labor. Valispace is such a tool, a power drill for engineering data that provides efficient data storage and analysis.

Power drill


You might have heard that satellites are massive objects that weigh several tons and cost millions of dollars to launch into space. Now, imagine scaling down both size and cost until you could hold a satellite in your own hands – a miniature satellite! The so called cubesats are already revolutionizing the satellite industry by making access to space more affordable. These mini-satellites are made of one or multiple standardized 10x10x10 cm cubic units which only weigh a little more than 1 kg each. They make it affordable for small companies, universities and research centres all over the world to build their own satellites carrying custom-made experiments. In 2017, there will be over 350 cubesats launched to space and the prediction is over 400 for next year.

Artist’s rendering of a cubesat in space.

The idea of MIST is to allow students to play an active part in designing, testing and launching a cubesat satellite.


The Royal Institute of Technology (KTH) in Stockholm is building MIST or MIniature STudent Satellite – a satellite designed only by students. “The idea of MIST is to allow students to play an active part in designing, testing and launching a cubesat satellite”, says Daniel Bogado, student in Aerospace Engineering and responsible for System Budgets in the MIST project. MIST was proposed to the KTH Space Center in 2014 and the work started in early 2015. Currently the 6th team is working on the development of the satellite. Each team stays with the project for half a year.

The experiments on the MIST are from different institutes and companies with many different objectives. Two of the experiments have their focus on radiation analysis, there is one biological experiment, one experiment is testing their self-healing/fault-tolerant computer system in space, another experiment is testing a semiconductor of silicon carbide. Furthermore, there is a piezoelectric motor being tested in space, a new propulsion technology and finally a camera. All these experiments pose different requirements on the satellite making the combination of these an interesting task for the students.

3D-printed model of the MIST satellite.

It’s very important that each new team member quickly gets up to speed in all areas necessary for their work.


Although the satellite is tiny, building a cubesat is a big challenge. The biggest hurdle in the MIST project is the change in personnel involved in the project each semester, many students stay only for half a year. It’s very important that each new team member quickly gets up to speed in all areas necessary for their work. With 8 experiments the MIST satellite has a high number of custom parts for a 10x10x30 cm (3 Unit) cubesat. Keeping a clear overview about all aspects of each experiment is a major challenge, as well as keeping every team member up to date with the most recent values and what the impact is if something changes.

Valispace allows everyone on the team to have the latest results available from everyone else’s work.


The MIST team has found a solution to this challenge by using Valispace to store their engineering data. They use Valispace as the central platform for saving all data regarding power consumption, mass budget and other important characteristics of each experiment and subsystem of the satellite. This allows everyone on the team to have the latest results available from everyone else’s work and therefore make the right assumptions in their calculations and analysis.

With small satellites, mass and power constraints are very strict. You want to maximize the number of experiments and their operation time, but there is only so much power that the tiny solar panels can generate. On top of that, part of the satellite orbit is in eclipse, meaning that the Earth blocks the sunlight, and the satellite must rely on the power stored in batteries. A small change in the power consumption of an experiment can completely change the overall performance of the mission. The MIST team uses Valispace for the creation of a mass and power budget for the satellite. The stored data is used to create a power schedule for the experiments and allow for an easier overview of the system budgets, to ensure that the system is performing at its best capacity.

MIST mass budget created in Valispace.

Right now, the mechanical design of the MIST satellite is being finalized and after that the required thermal analysis of the whole system will be done. Even a small cubesat must be carefully tested before being launched into space to be sure that all experiments work as they should. Once the satellite is in space, no repairs can be done. MIST will be launched sometime after 2019. You can read more about the MIST satellite and find the newest updates on their blog.

The 6th MIST team together with the Swedish astronaut Christer Fuglesang.

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?” 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.

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[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.