Supply Chain Mapping & Company-to-Company Relationships Dataset

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).

本数据集可深入展现美国大陆境内任意特定企业基于卡车运输的供应链，及其与其他设施和企业的关联关系。
我们将美国境内的设施（包括工厂、仓库及零售门店）与企业进行关联映射。
借助本数据集，可追踪卡车与设备在不同地点间的移动轨迹，从而识别供应链关联。机器学习（Machine Learning）算法每日处理70至150亿条事件数据，以估算各地点间的货物运输量。据此，我们可绘制以下场景间的供应链关联图谱：
•不同企业之间（以运输量占比表示）
•同一企业旗下不同地点之间（例如仓库至门店）
通过这一创新的地理位置定位方法，可绘制任意私有或公有企业与国内其他企业关联关系的知识图谱（Knowledge Graph）。
本数据集形式的解决方案，可深入洞察美国大陆境内任意特定企业基于卡车运输的供应链，及其与其他设施和企业的关联关系。
应用场景：
- 企业间关联关系的识别与理解：助力识别并推断特定企业或设施之间、以及行业/产业之间的关联关系。
- 地点间关联关系的识别与理解：可绘制美国全国及各州范围内、以及欧洲多国间的物流与国内分销供应链图谱。
- 完整供应链网络的可视化与图谱绘制。
- 任意分销或供应链中的产品追踪。
- 风险评估
- 相关性分析
- 中断分析
- 非法网络分析与企业资产非法使用追踪。
- 伤亡风险管理优化。
- 供应链风险管理优化。
- 不仅可识别价值链中的一级供应商，还能识别二级、三级及更高级别的供应商。
当前最大应用场景：跨国企业利用其在设施层面（覆盖超10万个地点）进行风险建模。

搜集汇总

背景与挑战

背景概述

该数据集通过分析卡车运输活动，利用机器学习量化美国境内公司间及设施间的货物运输量，构建供应链关系知识图谱。它支持供应链网络可视化、风险管理等多种应用，覆盖美国和欧洲地区，包含公司、地理位置等关键属性。