Competitive Intelligence Data for Manufacturing & Industrial Companies

We help our clients to draw connections between companies and facilities, map markets and supply chains and uncover and visualize hidden relations between companies. Given a specific U.S. or Europe-based company, we draw a connections map, including all detected relationships between that company and other companies and facilities nationwide, statewide, and between countries. Markets we can address We can identify and detect connections between facilities and companies across the United States, and across countries and within them in Europe. What makes us different? We have developed a brand-new technology that combines the use of alternative data and machine learning methodologies, what allows us to perform competitive analysis in a non-traditional way, by studying a specific facility and detecting and drawing all the connections between the facility and other locations. We use publicly available information such as location data and building footprints to identify and geolocate company facilities. With a machine learning algorithm, we identify movement patterns between these locations. Already Validated Use Cases - Market Intelligence & Due Diligence Company: A large multinational company that provides specialized materials to different industries. Challenge: How to find a cost-effective way to analyze the competition and identify potential customers? Solution: Relationship data with a geolocation approach to unveil the DNA of a facility. Results: Identify competitors' clients and suppliers. - Risk Assessment Company: A leading data analytics provider serving customers in insurance, energy and specialized markets, and financial services. Challenge: How to anticipate and prevent possible disruptions in all the tiers of the supply chain? Solution: Inter-company data with a geolocation approach to detect and measure relations between companies and facilities in the US. Using the movements of physical goods between different companies and locations, the supply chains can be inferred and analyzed. Results: Anticipate potential disruptions by understanding behavior in all the tiers of the supply chain. - Asset Protection & Loss Prevention Company: A large multinational corporation in the industrial and logistics sector. Challenge: How to optimize the asset recovery strategy in a network of more than 1 million locations nationwide? Solution: A machine learning model that combines client´s own data and a geolocation approach to track assets´ movements between facilities. Results: Improvement in the identification of facilities where the presence of assets to be recovered is higher. Other Use Cases - Monitor company supply chains (paired with import-export data if additive). - Provide a proxy for manufacturing/production (e.g., monitoring activity from factories to warehouses).

搜集汇总

背景与挑战

背景概述

该数据集旨在为制造和工业公司提供竞争情报分析，通过结合替代数据和机器学习技术，非传统地绘制公司与设施间的关系图、映射供应链。它支持市场情报、风险评估等应用，利用公开位置数据和算法识别移动模式，以帮助客户识别竞争对手、预测供应链中断并优化资产回收。