Graph + AI World:                   

Supply Chain & Logistics Management with Graph DB & AI

Graph + AI World Session - Recorded September 2020
Manufacturers face great challenges dealing with enormous amounts of parts, components and materials needing to be sourced from a great variety of globally distributed vendors, then processed and assembled in many stages, making it exponentially difficult to trace them from origin to final product. This also includes logistics, i.e. transport types, locations, duration, cost etc. By leveraging Graph DB technology to gain transparency across relevant complex and widely distributed data, combined with predictive analytics, these companies can effectively address these challenges, while also optimizing production planning to ensure parts availability, minimize quality fallouts and improve overall assembly and delivery.

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“TigerGraph is highly configurable ands can fit your specific use case. Whether you’re trying to get super fast query times or run heavy graph processing algorithms, setting the right configuration on your cluster will make all the difference.”


About TigerGraph

TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions. 


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