Within supply chains, data can be held in a variety of formats, at different levels of detail, and employing different semantic models. This often makes it challenging to combine data from different sources into a coherent and cohesive form to support the innovation in goods and services.This interoperability is a key cross-sector challenge in situations where data must be exchanged between different digital systems. 

In addition, these systems are often owned and operated by different businesses with often competing interests, which makes it very difficult to determine how to fairly share the risks, benefits, and obligations of any interoperability solution.

This interoperability flagship project focuses on the rail construction industry as they look to deliver the Integrated Rail Plan for the North and Midlands. The ambition is to create a ‘Google Translate’ for the rail construction supply chain, allowing information to be translated semi-autonomously so that disparate software systems and teams can effectively communicate and work together. 

Why is this important?

One of the key blockers for effective information flow across complex systems is the problem of interoperability of the systems between different nodes in the supply chain. The process of data exchange between different nodes within the supply chain is often slow, expensive, and may even exclude key information to the extent that it makes the exchange superfluous because the data is stored in myriad different ways.

AI and machine learning possess the power to streamline data exchange, enabling a seamless flow of information, thereby unlocking the inherent value within the data and improving decision-making. Through the development of suitable business and collaboration models, active engagement of stakeholders becomes a driving force in ensuring the successful large-scale deployment of this transformative technology.