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Home > AI for Supply chain > Predict Supply Chain Disruption

Predict Supply Chain Disruption

AI can detect possible Risks in Supply Chain Networks by mining different data sources including: news outlets, weather feeds, social media, unstructured reports, and more. It looks for natural disasters, country policies and events, supplier news, weather events, and quantify potential impact on different “nodes” in the network

Business value: better visibility for supply line managers, improving reaction time to supply line disruptions

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ROI Examples
Data Needed

1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

More details on the technical aspects of the use-case

3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

Twitter Datasets from Crises

Twitter data collected during 19 natural and human-induced disasters. Each dataset contains tweet-ids and human-labeled tweets of the event.

Low Altitude Disaster Imagery (LADI) Dataset

Scenes of destruction and the negative aftermaths of disasters. It includes scenes of destroyed infrastructure, neighborhoods, and public spaces.


Off-the-Shelf Products using AI for Predicting Supply Chain Disruption

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