Improving Resource Efficiency of Agribusiness supply chains by Minimizing waste using Big Data and Internet of Things sensors
- Ram Ramanathan(PI),
- Yanqing Duan(Project Manager),
- Tahmina Ajmal(CoI)
Project: Research
Project status
Finished
Description
The REAMIT project proposes to adapt and apply existing innovative technology to food supply chains in NWE to reduce food waste and hence improve resource efficiency. Reducing food waste is of highest priority for the EU (88Mt or € 143B wasted per year). The EU has committed to halving food waste by 2030 by focusing on all stages in the supply chain. 35% of food waste in EU-28 has occurred in supply chains in 2012. Though technologies exist to reduce food waste, they have not been applied to food supply chains. REAMIT will focus on fruits, vegetables, meat and fish as these are wasted in large quantities. The supply chain includes farms, packaging sites, food processors, distribution, logistics, wholesalers and retailers. The project will be carried out in Ireland, Germany, France, UK and the Netherlands due to the amount of interconnected food supply chains and huge food waste in these countries. REAMIT will adapt existing Internet of Things and Big Data technologies to best fit the needs of the food supply chain management system in NWE. Through testing and adaptation, these technologies will be enabled to continuously monitor and record food quality and signal potential food quality issues. Through analytics, owners of ‘food to be at risk of becoming waste’ will be provided with decision support options to minimise food waste including redistribution to nearby customers. REAMIT project will save 1.8Mt of food waste or €3B per year in NWE, avoid 5.5Mt/yr of CO2 emissions, test and operationalise 8 solutions, and, support 20 enterprises. The technologies will be self-sustaining at the end of the project. They will be made available to the public via REAMIT website and social media.The long-term effects of REAMIT will be optimising (re)use of food and natural resources in NWE economies and a consortium capable of jointly addressing the challenges in food sector.
Layman's description
A large research project funded by Interreg North West Europe, REAMIT stands for Improving resources efficiency of agribusiness supply chains by minimizing waste using Internet of Things sensors. With food waste being a global problem, particularly in the developed world, the project demonstrates the power of IoT sensors and Big Data technologies in improving resource efficiency of agri-supply chains. Since reducing food waste not only increases food availability but has more benefits in the form of saving significant food production resources (water, energy, labour, fertilisers, etc.), there are social benefits too. The project is led by the University of Bedfordshire involving 12 Academic and Industry partners. With the current situation due to the COVID-19 pandemic, many businesses are seeking new ways to improve their revenue streams whilst ensuring food safety and high-quality food to their customers. The project team believes that the REAMIT technology could make significant difference in the way businesses operate.
Project Information
Project Type
ResearchProject Managed By
Project Collaborators
- University College Dublin
- Nantes Université
- Nottingham Trent University
- Ulster University
- Institute of Technology in Tralee
- Images & Réseaux
- Levstone Ltd
- Whysor B.V.
- SenX
- WD Meats
Acronym
REAMIT Time Period
11/01/2019 – 10/07/2022Status
FinishedKey Findings
Implemented 11 technology demonstration projects (pilot tests) across the North West Europe region. Introduced new to firm processes related to storage and transportation of food in 9 agri-food enterprises in the North West Europe region through testing and adapting IoT sensor and Big Data technologies. Improved supply chains of agri-food enterprises.Developed a REAMIT Life Cycle Assessment (LCA) tool offering estimations for all sort of LCA parameters such as land use, ionizing radiation, water consumption.Developed 5 REAMIT Life Cycle Assessment studies to prove the net carbon reduction in the pilot tests. Results of data analytics for Human Milk Foundation (HMF) provides HMF with a prediction model enabling them to design the optimal route for donor human milk bikes taking account of the distance, duration of the journey, outside weather conditions, volume of human milk transported.
Funding Details
Improving Resource Efficiency of Agribusiness supply chains by Minimizing waste using Big Data and Internet of Things sensorsAward
FundersAmounts
Interreg North-West Europe Programme
4875171 GBPDocuments and links
Sustainable Development Goals
- SDG 8 Decent Work and Economic Growth
- SDG 9 Industry, Innovation, and Infrastructure
- SDG 11 Sustainable Cities and Communities
- SDG 12 Responsible Consumption and Production
