The project, titled the Network Insight Collaboration Environment, aims to demonstrate that an innovative approach, based on new and existing technology, will allow supply chains to share insights and data across multiple data platforms, enhancing the efficiency of the supply chain network.
The project will create a collaboration environment demonstrating a range of new concepts addressing data driven manufacturing use cases relevant to the critical minerals industry, such as- “are my EV batteries using conflict free minerals?”.
Some of the key elements of innovation within the collaboration environment include:
Using cryptographic techniques such as zero-knowledge proofs it is possible for one party to prove to another party that a given statement is true, without conveying additional information and without the need for sharing the underlying data.
An environment can be created allowing the sharing of information across a supply chain that overcomes the challenge or the need to share the underlying data.
This project aims to develop an agent-based systems approach in order to achieve the development of data-driven use cases.
Agents are individual goal-seeking software entities with partial visibility over the ecosystem. As an AI, they autonomously execute actions to achieve the objectives of different stakeholders, obtaining knowledge from each other. Their use already includes a wide variety of use cases such as spare parts management, collaborative forecasting and replenishment and inventory management across supply chains.
An agent-based system will allow the differing organisational aims and functions required within the system to be executed in order to achieve the overall purpose of the collaboration environment. We will use principles of ‘cooperative AI’ to enable agents to take actions on behalf of organisations, such as query formation, value attribution to queries, and the conservation of privacy, whilst achieving adequate response to genuine queries.
Even if information can be shared across a supply chain without exposing the underlying data, there is limited incentive for organisations to do so.
If a data-driven use case has business value for an actor in the system, then the insight used to satisfy that use case has value associated with it. The value may be proportional to the number of different insights required to satisfy the overall use case in question. The environment can attribute value across the system to the data providers who support different use cases, creating a commercial incentive to share insight.
With any system involving multiple organisations, trust will always be a major factor in adoption, use and ultimate success. Even with the inclusion of advanced techniques for data privacy and incentive models, all members of the system need to trust that the system is working in the way that they believe it should. The project will aim to deliver a performance monitoring element that will provide participants of the system with transparency over the activities within the system.
By bringing together these different functions the project will demonstrate the foundation of a collaboration environment comprising a range of digital tools, which are able to span multiple data stores or data provenance systems.
Through our partners we will leverage a variety of protocols they already have in market interoperability for identity, asset transfer, tokenisation, and consensus, enhance these with suitable business and operating models, a system governance mode, and explore the regulatory landscape to enable the infrastructure to grow and become financially self-sustaining beyond the end of the project, enabling new users and developers to adopt these innovations, or build upon them.
