Using (big) data to reduce risk while building hardware.

Description

From the time of creating a prototype to the day manufacturing begins, there are myriad opportunities for things to go wrong. One of the primary risks for companies (startup to enterprise level) is the supply chain. Shifts, shortages, and uncertainties can derail the best-planned project. One ill-timed component supply issue means you miss your manufacturing and shipping deadlines.

What if you could reduce those risks early on in the design cycle, by getting a larger picture of the electronics supply chain and designing your product accordingly? Not just the best performing product…but the best performing product that can be reliably manufactured.

So what has changed? Why now? Over the past five to ten years there has been an unprecedented amount of data coming online for analysis. We have seen this in parallel marketplaces like real estate, cars, travel, and other vertical markets; think how sites like Kayak, redfin, and Truecar have reduced the risk for consumers in the marketplace. As the slower and larger electronics supply chain continues to move more of its data online, we have seen an increase in opportunities for analysis and prediction.

Other trends such as open source hardware and a general increase in the amount of knowledge about the “whys” and the “hows” of electronic design and manufacturing online have enabled interesting analysis. Taking all available information and compiling it in one place brings large benefits:

• Analysis of whether the popularity of a component is trending up or down (and what that means for price/availability/longevity of a component)
• Match user behaviors and alerting them to components that few others would know or care about
• Show weak and strong correlation between components
• Bring relevance and sanity to the long tail of components available

In this presentation, we will seek to answer the question: “Do more data and software tools reduce the risk for hardware design?” We will do so by including case studies, teardown component analysis, data investigations, relevant datasets from our work in the space, and trend analysis on the industry at large.