Periodic Reporting for period 2 - tilkal (Next Generation Blockchain-based Supply Chain Traceability & Transparency Platform)
Berichtszeitraum: 2024-01-01 bis 2025-09-30
With the need for resilience, the demand for transparency and the increase in regulatory obligations over the product life cycle, it has become crucial for companies to ensure that their claims and operations rely on a measure of what really happens across their chain, to drive effective and sustainable actions.
Supply chains serve as the foundation of businesses, yet they remain fragile, complex and fragmented, hindering effective communication among participants. The involvement of numerous stakeholders, often challenging to identify, exacerbates the issue, compounded by a dearth of consistent supply chain data. Consequently, it has become imperative for companies to build a solid, holistic, and transparent dialogue between upstream and downstream supply chain stakeholders that will allow to collect and aggregate operational, environmental and social information.
End-to-end, real-time traceability is at the heart of this paradigm shift and is rapidly becoming a new “license to operate.” Tilkal provides the traceability, transparency, and auditability capabilities needed to meet this challenge and support businesses in building trustworthy supply chains. Within this context, the EIC project focuses on developing and testing AI-driven risk analysis algorithms to help companies anticipate and comply with evolving regulatory requirements.
Supply chains serve as the foundation of businesses, yet they remain fragile, complex and fragmented, hindering effective communication among participants. The involvement of numerous stakeholders, often challenging to identify, exacerbates the issue, compounded by a dearth of consistent supply chain data. Consequently, it has become imperative for companies to build a solid, holistic, and transparent dialogue between upstream and downstream supply chain stakeholders that will allow to collect and aggregate operational, environmental and social information.
End-to-end, real-time traceability is at the heart of this paradigm shift and is rapidly becoming a new “license to operate.” Tilkal provides the traceability, transparency, and auditability capabilities needed to meet this challenge and support businesses in building trustworthy supply chains. Within this context, the EIC project focuses on developing and testing AI-driven risk analysis algorithms to help companies anticipate and comply with evolving regulatory requirements.
The main technical and scientific work was focused on developing the core elements of a risk analysis and scoring model for supply chains. This resulted in four key outcomes:
1) A weakness analysis model: this model uses machine learning to “understand” supply chain network topology and identify outliers in supply chain flows, in an the unsupervised context.
2) a correction layer of the weakness analysis model, based on user feedback.
3) a risk model for end-to-end supply chains, mixing external sources and customizable scoring capabilities based on traceability data
4) a basis of visual document understanding (VDU), an AI-based layer to automate the extraction of data from semi-structured business documents.
In the course of the project Tilkal also collaborated to and implemented a new global standard for traceability and digital product passport defined by UNECE (UNTP: UN Transparency Protocol).
Finally two pilots have been achieved in the food and textile industries.
1) A weakness analysis model: this model uses machine learning to “understand” supply chain network topology and identify outliers in supply chain flows, in an the unsupervised context.
2) a correction layer of the weakness analysis model, based on user feedback.
3) a risk model for end-to-end supply chains, mixing external sources and customizable scoring capabilities based on traceability data
4) a basis of visual document understanding (VDU), an AI-based layer to automate the extraction of data from semi-structured business documents.
In the course of the project Tilkal also collaborated to and implemented a new global standard for traceability and digital product passport defined by UNECE (UNTP: UN Transparency Protocol).
Finally two pilots have been achieved in the food and textile industries.
We have been able to go beyond the traditional reporting and scoring tools that characterize software systems that engage with complex supply chains. Data analysis techniques were developed to identify anomalies in data describing a supply chain flow, based on a standard model. AI techniques have been prototyped to build a Visual Document Understanding capability, as well as a LLM to interrogate supply chain data in natural language. Moreover, this system is fully scalable - it learns from the context it is placed in and is capable of improving itself by leveraging relevant human feedback.