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.
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.
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