Funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3, Theme 10.
Digital-twin and IoT solutions for food and packaging waste reduction. Aim of this task is the design, development, validation, and application of intelligent decision support systems for the control and optimisation of manufacturing, packaging and distribution issues/decisions in food & packaging product life cycle. Best practices of food waste reduction (through surplus food reuse, recycling, and recovery according to the Food Waste Hierarchy framework) will be identified towards a circular economy and social responsibility approach (in connection with Spoke 1).
Novel multi-disciplinary ontology framework for circular food & packaging supply chain and production (M12).
Innovative dashboard for a multi-disciplinary and multi-objective analysis and assessment of food & packaging supply chain (M12).
Scalable data collection infrastructure based on open-source technologies for waste management and reduction (M12).
Decision support and expert systems for life cycle assessment of food and packaging supply chain (M30).
Digital-twin demonstrators and pilots/applications to case studies (M36).
Best practice definition, adoption guidelines for the distribution and food service stage segments and recommendations for environmental and social responsibility of all stakeholders involved in the agri-food chain (M36).
This project foresees to develop an intelligent decision support system for the holistic optimization of food and packaging manufacturing value chains towards circular economy. Life cycle assessment (LCA) has become widely used to evaluate the environmental sustainability of products. Although it has been increasingly used, in its current state it may be inadequate for steering decision making for the design of value chains (Yang & Campbell, 2017). Combining digital twin frameworks (Kamble et al., 2022), life cycle assessment methods and value stream mapping (De Mattos et al., 2022) can support in collecting relevant data of products along their value chain providing a decision-support functionality (Mangers et al., 2022) for designers of food and packaging industries, reducing waste, costs and identifying the potential for circularity. As an example the EU-project INCOVER is developing a Decision-Support System (DSS), where evaluating the environmental, social and economic performance forming a multi-criteria decision support tool (Incover, 2022). Existing ontologies for data architecture design are not adapted to the domain of food manufacturing industry. Further there is a lack in multidisciplinary data monitoring and the usage of open source technologies.
The research will involve different activities as reported below.