This dataset provides an in-depth view of any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental US. 

We map US facilities (including factories, warehouses, and retail outlets) to companies. 

With this dataset, it is possible to track the movement of trucks and devices between locations to identify supply chain connections. Machine learning algorithms ingest 7-15bn daily events to estimate the volume of goods transported between locations. Consequently, we can map supply chain connections between:

•Different companies (expressed as a  percentage of volume transported).

•Locations owned by the same company (e.g. warehouse to shop).

With this novel geolocation approach, it is possible to "draw" a knowledge graph of any private or public company´s relations with other companies within the country.

This solution, in the form of a dataset, provides an in-depth view into any specific company’s truck-based supply chain and its relationships with other facilities and companies within the continental United States. 

Use cases:

- Identification and understanding of relations company-to-company: It helps to identify and infer relationships and connections between specific companies or facilities and between sectors/industries.

- Identification and understanding of relations place-to-place: A logistics and domestic distribution supply chain can be mapped, both nationwide and state-wide in the US, and across countries in Europe.

- Visualization and mapping of an entire supply chain network.

- Tracking of products in any distribution or supply chain.

- Risk assessment

- Correlation analysis.

- Disruption analysis.

- Analysis of illicit networks and tracking of illegal use of corporate assets.

- Improvement of casualty risk management.

- Optimization of supply chain risk management.

- Security and compliance.

- Identification of not only the first tier of suppliers in the value chain, but also 2nd and 3rd tier suppliers, and more.

Current largest use case: global corporation using it to model risk at a facility level (+100,000 locations).