At SPREAD, we create engineering intelligence networks — a technology that connects, contextualizes, and analyzes all kinds of product data to help engineers create more innovative and sustainable machines more efficiently.
What kind of machines?
Highly complex mechanical, electrical, and software systems like planes, appliances, trucks, machinery, and cars.
To create a functioning, high quality vehicle that meets the most stringent safety standards and customer demands, it takes around 10,000 mechanical, electrical, and software engineers. Those 10,000 engineers must create, share, and access tons of highly specialized product information.
This type of knowledge is incredibly hard to transfer, and it typically lives in distributed databases, CAD files, 2D illustrations, requirement lists, and extensive instruction manuals. But to understand the engineering logic behind that information, you must get to the person who created the information. In large organizations like those of the global car brands, finding the right person often involves multiple levels of hierarchy and several time zones.
Knowledge transfer is not an engineering task, but this process takes up to 70% of an auto manufacturer’s engineering resources. As the complexity increases, so does the time it takes to manage this process. Existing means of communication simply don’t serve the needs of automotive engineering organizations. Smart engineers need smarter tools. Without them, valuable product insights get lost, which can lead to expensive inefficiencies, material waste, slower development & innovation cycles, and a higher likelihood of errors, or even recalls.
According to the Center for Automotive Management, more than twice as many vehicles were recalled than were sold between 2011 and 2020. The industry is also facing global pressure from legislators and consumers to lower their environmental impact beyond electrification. For every vehicle, 80% of its emissions are decided in the design phase. On top of that, circularity in the automotive lifecycle can be further optimized — with engineering knowledge only flowing incrementally in one direction, a lot of material resources go to waste later in the lifecycle.
As engineers & veterans of automotive industry, we’ve seen the enormous impact of this problem at every OEM we’ve worked with. It’s pervasive in every engineering discipline, and every stage of the product lifecycle. We figured that by combining in-depth product insights, intuitive knowledge sharing, and engineering logic in a single interactive platform, we would address one of the most urgent needs of automotive manufacturers.
We thought: if AI can be taught to understand physical products, we can leverage this to make the automotive & machinery industry more flexible, cost-efficient, and sustainable.
That’s how SPREAD was born.