Making the right sourcing decisions is vital to the success of any hardware project. We believe that Data Science provides the key to better decision making and can help build better products.


Search Intelligence

Most of the component research activity now happens online - from early-stage part discovery and parametric data lookup to distributor selection and procurement. An aggregate view of this activity provides unique insights into component performance and future market demand.

Learning from 100M+ Monthly
Part Searches

Supplyframe Network spans the range of over 70 different component search engines and procurement websites. This unique, continuous stream of component research activity provides real-time insight into global demand for any given part.

Aggregate demand of 6.5 million
monthly active users

Our component search data stream consist of activity of a massive number of unique users from across the globe, allowing us to spot local trends and better understand the overall demand signals.

Part Popularity

To efficiently manage insights from the aggregate search stream, our Data Science team has created PartRank - a benchmark index of the component demand over time, which provides critical insight throughout the component selection process.


Price Analytics

Understanding individual component prices across distributors and over time is vital information any electronics-related business. However, this information is often scattered and rarely allows for an efficient comparison and analytics. Our platform is designed to address this challenge.

Web Inventory and Pricing

Our platform solves the challenge of aggregation and normalization of pricing, availability and metadata information from hundreds of different electronic component suppliers.

Discount Curve Interpolation

In modeling the relationship between price and quantity for a given component, we address a wide range of challenges in managing sparse data, high-dimensional statistics, and transfer learning.

Benchmark Price Modeling

As a way to help evaluation of purchasing decisions, our Data Science team has developed PriceFX - a market benchmark price for each component at the desired quantity.


Recommender Systems

Though electronics research and design activity can get quite specific, there's still a lot to be learned from what other engineers have done when faced with the same set of decisions. Recommending a commonly searched component in a given scenario can provide just the right inspiration at the right moment to keep the project moving forward.


Discover the commonly used alternative selections when given component is not available. Personalize these recommendations in the context of the entire Bill of Materials.


Discover components from other categories commonly searched with the selected part. Customize recommendations according to personal or project preferences.


Discover distributor or manufacturers commonly selected by similar users for a given component. Find an optimal trade-off with existing vendor preferences.


Quote Modeling

At high volumes, components are generally procured via a formal request for quote process. This results in a deluge of data that can be leveraged for smarter negotiations, strategic bid awards and internal performance benchmarking. Aggregating this data across the entire supply chain can allow us to start thinking about the entire quoting process in a whole different way.


Quoting for the purpose of "price exploration" represents a big challenge in the electronics supply chain. Our QuoteEstimate simplifies this by providing a quick range estimate of the expected quote values.


Having a benchmark price to compare received quotes against can be critical for making optimal bid award decisions. Using the QuoteEstimate as a "virtual quote" allows for efficient comparison and an expedited awarding process.


Tracking quote awards values over time and benchmarking against historical PriceFX index allows for a properly calibrated PPV analysis, better negotiations and smarter strategic decision making.


Behavioral Modeling

User activity on research and procurement websites contains valuable insights into decision-making and can be used to better understand product performance in the marketplace. It also provides another dimension by which all other data analysis can be segmented.


Constructing a “click graph” of user actions allows clustering users into different behavioral segments. Supporting real-time infrastructure allows acting given estimates to personalize the user experience.


A large-scale historical activity dataset, spanning tens of millions of different users allows development of predictive models that generalize user behaviors and even anticipate user's intent.


Information Retrieval

Electronics industry spans hundreds of millions of components, datasheets, parametrics, reference designs and schematics. It is a robust repository of technical information, but the disparity of sources makes efficient access to this data a massive challenge. Our platform aims at transforming this data into a universally accessible knowledge repository.

Start Making Smarter Sourcing Decisions

Supplyframe Sourcing Intelligence platform is embedded across all the products in the Supplyframe portfolio and available via APIs and custom data integrations. Whether you're looking for components for a new project, updating an existing part library or trying to improve strategic quoting efficiency, our tools can help bring more analytic capabilities to your decision-making process.

Contact Us