Mondra - Hyper-modelled Scope 3 Carbon Accounting across Food Supply Chains

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Under this project, Mondra Global Limited seeks £1.2M innovation loan funding for experimental development (build and real-world-pilot) to deliver a game-changing solution for dynamically assessing and reducing carbon emissions across global food supply chains. The solution leverages automation, machine-learning and predictive analytics to drive system-wide decarbonisation.Business need: Increasing carbon-regulation is forcing food corporations to consider the 90% of emissions "trapped" in product supply chains.Challenge: Information on supply-chain ("Scope 3") emissions is inaccessible or unknown, with major barriers to data sharing and traceability.Opportunity: Retailers making Net-Zero pledges that include scope-3 and collaboration with farmers to improve performance (British-Retail-Consortium, BRC-2020). Lacking a scalable solution for measuring and monitoring 'source to shelf' product eco-performance.Nearest-state-art: Traditional carbon modelling is a manual, consultant-led activity which breaks down product-ranges into ingredient volumes and maps to old/outdated reference-data. Takes days/weeks per product and bears limited resemblance to reality, lacks accuracy and actionable insight.A more accurate/insightful Life-Cycle-Assessment (LCA) approach which targets primary data from supply chains is typically consultant-led, laborious and prohibitively costly at scale.Game-changing solution - Mondra 'Hyper-modelling':Working with consortium partners, we will deliver Mondra Hyper-modelling, an ultra-scalable, 'instantly-available' supply chain environmental insights service for Retailers/Brand-owners.The solution directly consumes a wealth of pre-verified data from 30 Retailers and 250,000 suppliers. Data includes food product composition/packaging/supply-chain down to farmers/growers. Agricultural eco-impact is assessed using cutting-edge remote-sensing/small area estimation techniques based on 350K farmers surveyed annually for field-level farm data that is interpolated for the world and verified with satellite telemetry.Where primary data is unavailable, Mondra ML-gap filling engine completes the tree and 'source to shelf' environmental impact figures are calculated (GHG-emissions/water-scarcity/eutrophication/biodiversity), with insights/recommendations for Retailer/Brand owners to consume 'as-a-service'.Objectives & outputs: Experimental-development and real-world pilots for commercial-grade Hyper-modelling platform.Working with Oracle and our data partners, Mondra's award-winning management team has the experience to lead this partnership, supported by 3Keel (subcontractor), market-leading sustainability consultancy and authors of the British Retail Consortium (BRC) NetZero2040 roadmap.