The inception of industry 4.0 and smart, autonomous and data driven processes will bring significant opportunity for the agri-food sector to leverage value-based decisions with regards to productivity, waste, risk and efficiency. AI and ML capabilities form part of the greater autonomy that industry 4.0 affords manufacturing sectors, providing the necessary knowledge (from data) to enable humans to make business efficient decisions for them and the wider supply chain. AI has the potential to bring positive social impacts. A Fronter AI report suggested that agriculture was least exposed to labour market disruption because of AI, suggesting that AI will positively enhance the work lives of people in agri-food. Identified as a high potential growth sector, there are endless opportunities for AI application within agri-food; from helping drive prevision agriculture to predict disease outbreaks, weather and yield, livestock monitoring and anomaly detection and supply chain optimisation through predictive algorithms for managing waste, inventory and demand and detection of food safety outbreaks. This project proposes working with 2 large, digitally mature organisations involved in the manufacture and retailing of food in the UK, to develop bespoke generative AI chatbots, housed within the existing Foods Connected (FC) demonstration platform. Housing within a demonstration platform and using representative data will highlight use cases of AI in a safe and secure environment, paving the way and de-risking future AI projects with live data. Both these businesses have used the platform for a number of years to collect various data points, e.g. audits, supplier management, technical due diligence and quality/safety checks in their supply chain, and have a wealth of data of which dummy data can be modelled off. Generative AI chatbots can analyse large amounts of data significantly faster and more precisely than humans, improving data driven decisions and automate administrative tasks and reducing resource waste. From a range of inputs, chatbots using pattern recognition can make predictions that inform users that make key business decisions. Combined with representative data, external sources including food authenticity-based ML models for origin discrimination and alerts or horizon scanning services, could all act as inputs to the chatbot. Users could ask the chatbot, 'How many deliveries of soya in the last month have been sourced from Brazil?'. Outputs or responses could help users determine sourcing strategy risk both from an economical and ethical (social) perspective and adjust practices accordingly in a significantly more efficient way than done so currently.

A trusted and transparent food supply chain is an inherent expectation of consumers from all farmers, food manufacturers, food service providers and retailers. The speed at which data from the supply chain can be accessed varies depending on the complexity of the supply chain, the product involved, and the digital maturity of the systems being used across the different supply chain partners. Therefore, we have identified the need for an interoperable platform which integrates with existing systems to capture traceability information in a way in which it can be demonstrated and challenged in real time. By integrating with existing software platforms, the innovative Trace Connected solution has the potential to reduce the requirement for duplication of data entry and removes manual intervention of data wherever possible. By making traceability available in real-time and removing the requirement for manual intervention, the risk of data manipulation for financial gain and food fraud incidents is dramatically reduced. The project will deliver a digital framework which will enable farmers, food manufacturers and retailers to have faster information on the products they are producing, purchasing and selling, enabling them to make faster, evidence based decisions which will result in reduced costs, waste, risk and increased value. The technology solution will have the capability to alert users to potential compromises within their supply chain, mitigating reactionary responses and promoting proactive issue management. Ultimately, taking the labour out of traceability exercises and enhancing control and visibility of the supply chain. The system will deliver traceability and supply chain mapping functionality which will track and trace products and defined product attributes from farm to fork. This will result in a fully interoperable traceability system which operates in real time to present a visualised trace constituting of critical data elements, such as animal identification, batch codes and retail pack codes. Through the supply chain mapping processes and 'walk the trace' exercises stakeholder value propositions will be defined to include but not limited to; rapid product recall, improved efficiency and promotion of a circular economy and proactive validation of sustainability parameters. With increasing consumer awareness and demand for information on a variety of product attribute's on areas such as animal welfare, sustainability, pesticide usage, food miles and antibiotic usage, the technology solution will provide a framework, enabling businesses to digitally link this information to specific product batches, enhancing a consumers ability to be better informed on their food purchases.