In episode 2 of our Understanding Engineering Intent with AI podcast series, we met with Supplyframe’s chief marketing officer, Richard Barnett, and discussed the role AI plays in making vast amounts of data comprehensive amid an increasingly complicated and interconnected supply chain. For episode 3, our conversation expands from untangling data to how Supplyframe achieves an impressive level of accuracy in their long-range predictions through the examination of user-oriented and attribute-driven training data sourced from the millions of interactions their platform receives every year. Additionally, we look at the possibilities of expanding this type of predictive AI technology beyond the electronics industry and into the mechanical design industry, resulting in the propulsion of innovation and interaction with AI.

The significance of training data to long-range predictions

Data is the lifeblood of any AI solution, and Supplyframe excels at harnessing a diverse array of datasets to create accurate predictions that allow for informed decision making. They do this by finding unique ways of taking structural and attribute-based dimensional data with search and user engagement behavior to forecast upcoming shifts within their industry—keeping them well ahead of the curve.

Supplyframe’s first method of acquiring datasets is through tracking user-oriented behavior. They leverage AI to analyze the search behavior, engagement with technical content, and interactions of engineers and supply chain professionals. By doing this, Supplyframe can aggregate patterns amongst industry experts that provide valuable clues into which direction future trends are headed. The other technique for data collection is attribute driven. Using AI, Supplyframe examines behaviors towards items such as resistors, diodes, and IC’s, to forecast, predict or recommend components to clients. In addition, they also leverage this knowledge by selling it to vendors, allowing industries to stock up on parts in anticipation of growing trends. Not only does this give Suplyframe an edge over their competition, but it drives innovation as vendors can design and produce products faster since they have insight into what engineers and other industry professionals are looking for.

Additionally, Supplyframe’s ability to evaluate supply market dynamics, including the relationship between supply chain factors like cost, lead time, and inventory, help them to discover emerging relationships within the supply chain. Ultimately, mastering these types of data collection give Supplyframe a head start they can use to predict trends, produce products, and overcome obstacles before they happen.

Beyond electronics

Currently, Supplyframe’s predictive AI models predominantly serve the electronics industry. However, they plan to expand their predictive insights into the world of mechanical design. Including areas such as, additive manufacturing, CNC machine fabrication, industrial equipment, and heavy equipment. To achieve this, Supplyframe plans on applying the same AI and data-driven approach as they did with electronics to this new territory.

Naturally, breaking into a new industry comes with challenges such as navigating geopolitical trade, tariff policies, and costs. Nevertheless, by analyzing engineering intent and supply market conditions, they seek to build predictive models to identify cost drivers and input costs for commodities. This helps them navigate obstacles and respond effectively to supply chain disruptions—further demonstrating the critical role AI and data have in the success of a business.

AI and the user experience

As AI rapidly weaves its way into our every-day vernacular, you can’t help but wonder about where the future of this promising technology is headed. For Supplyframe, the future is within improving the user experience. Today, their engineers are experimenting with generative AI-powered user interfaces, through integrations with systems like ChatGPT, that can present insights in a faster and more conversational manner. Their goal is to make vast quantities of data easily consumable and accessible, essentially reimagining the way users interact, engage, and interpret information and technology.

AI is proving to be a valuable way for organizations to take control of their future success—and Supplyframe is putting its capabilities to use. By leveraging AI to sift through, analyze, and elucidate hidden trends within their data, they are armed with foresight and readiness to adapt. It is this type of forward thinking that will spark innovation across industries. To take a deeper dive into the role of AI in understanding engineering  intent, listen to the full podcast here.

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Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.