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Predict multi-tier supply chain risk 

A revolutionary method of identifying supply chain risk across an ecosystem based on a collective learning model

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Project Cores is sponsored by Innovate UK and includes a diverse team of academia, manufacturers and supply chain software providers


01 /

Sense delivery risk

Receive early warning that a supplier might be late with a delivery or a product doesn't meet quality requirements 

02 / 

Sense payment risk

Understand a customer's credit risk profile via high-fidelity supply chain data as an additional verification to ratings from credit rating agencies

03 / 

Ensure data privacy

Protect the data of business partners to overcome the barrier of data sharing

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 sized Enterprises), 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.

Similar to logistical delivery and quality risks, we are answering 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 improved due diligence process and 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 our approach comes in.

The solution that we are developing 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.



Despite billions spent on Digital Transformation, larger companies have limited visibility of their upstream supply chain



Data privacy


Low digital adoption


Lack of incentives

Smaller suppliers fear that their customers will take advantage of them if they share their data. Therefore, there is a general reluctance to collaborate around data sharing

Contracts are often written to punish suppliers for being late and therefore suppliers are reluctant to share risk information until an actual issue has arisen

Most SMEs are unable to digitally connect with their customers and suppliers. Most of these companies do not have full-time IT staff and therefore lack the capabilities to perform technical integrations

Most of today's supply chain management software is aimed at the higher end of the market and is either too complex or too expensive for SMEs to adopt

SMEs have a perception that there is no real upside for integration with their customers and suppliers.

Stakeholders are unclear of how integrated supply chain might help increate efficiencies, decrease cost burden and improve customer relations

98% of the world's manufacturing supply chain are SMEs* that are less sophisticated and have the lowest levels of digital adoption

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* SME - Small and Medium sized Entities

In order to calculate supply chain risk, a customer would need access to all the data of all of their critical suppliers in a centralized model

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We propose a system where risk parameters are calculated locally at each 'node' (supplier) and are passed into an overall risk model that can be accessed by all

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We will be publishing our results and lessons learned so that you too can learn how to build your own supply chain risk monitoring program


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Thank you for your interest. Stay tuned for updates.

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