Success story
Prewave
Predicting supply chain risks from Twitter data.
Here's the tl;dr
Using the Twitter APIs, Prewave conducts on-demand historical risk analysis of key suppliers and transportation hubs for its clients, which enables them to understand the true risk footprint of any supplier in their supply chain or node in their transportation network. This has allowed Prewave to help uncover many previously unknown risk incidents at its clients’ global suppliers, including incidents of corruption, pollution, or labor disputes.
Endpoints
Challenge
Founded in 2017, Prewave partners with large logistics companies to identify operational risks ranging from supply chain disruptions, factory conditions or environment issues. Prewave relies primarily on Twitter API as the main data pillar for their predictive engine, but combines news from other media sources as well. News and sentiment collected by Prewave range from citizens complaining about pollution or working conditions at a factory, labor unrest from suppliers, or unfair wages for workers in remote locations.
Prewave started with the standard Twitter APIs. Due to the dynamic aspects of their client engagements, Twitter premium APIs opened up an entirely new offering for Prewave to offer as a solution for their clients - on-demand historical analysis. For example, an auto company needed to evaluate a new supplier. They relied on Prewave to run historical analysis on the reputation of the supplier before they signed the agreement.
"It is our goal to make risks happening around the world transparent for the first time. The Prewave predictive engine enables us to detect risk events on a global scale, days and sometimes even weeks before they happen. Twitter is a huge data source for us - the information we provide improve decision-making for various industries, ranging from NGOs to supply chain management, corporate sustainability, and insurance."
Harald Nitschinger, Co-founder of Prewave
Solution
Working with one of the world’s leading logistics providers, Prewave uses machine-learning to analyze global social media and news media data in 36 languages to predict upcoming disruption risks, allowing that leading Logistics company to anticipate and avoid supply chain disruptions before they occur.
More recently, and enabled by Twitter’s premium APIs, Prewave’s approach can now be applied to historical use cases as well: using the full-archive and 30-day search APIs from Twitter, Prewave conducts on-demand historical risk analysis of key suppliers and transportation hubs for its clients, which enables them to understand the true risk footprint of any supplier in their supply chain or node in their transportation network.
Results
Using these new historical supplier checks, Prewave helped uncover many previously unknown risk incidents at its clients’ global suppliers, among them incidents of corruption, pollution, labor disputes or industrial accidents, that allowed its client to understand the true risk footprint of its global supplier base.
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