Sense supply chain risk before it happens

A revolutionary method of sensing network-based supply chain risk without revealing every participant's data

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Team

Misson

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Our Mission

01 / Sense delivery risk

As the Suez Canal crisis in 2021 showed us: many companies cannot see upstream. In fact, most companies cannot see past Tier 1 in their supply chains. This means that they are vulnerable to anything that might disrupt upstream supply.

Since over 98% of the upstream supply chain are SMEs (Small and Medium Entities), it follows that we need a method to incentivize these companies to join a digital supply chain network and contribute to overall risk sensing.

Providing new working capital solutions and digital marketplaces for SMEs are part of the solution.

02 / Sense payment risk

03 / Ensure data privacy

Similar to logistical delivery risk, we are trying the answer the question: "What is the probability of a customer paying a particular invoice line on time and in-full?"

The reason we are interested in this is that we believe that supply chain performance holds the key to defining a better financial credit risk score for SMEs. This is going to lead to better short-term, working capital lending solutions in the future.

All SMEs need capital to grow. This work is essential to solving that problem.

The problem with sharing supply chain data is that most suppliers are unwilling to do so. They feel that customers will take advantage of them and their sources of supply.

This is where the beauty of Federated Machine Learning comes in.

Federated Machine Learning keeps the SME's data private (via encryption) and passes local (at the SME's node) to the overall Machine Learning algorithm to process the final results - keeping the SME's data safe.

TRY A DEMO

What is Federated Machine Learning?

OUR STORY

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