Decision support system for the life cycle optimisation of food manufacturing value chains

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.

• The entities, relationships, temporal and spatial scales representing the considered food value chains will be defined through a multidisciplinary ontology model. 
• Multidisciplinary Indicators and metrics will be classified, to homogeneously cover all aspects of sustainability (using also results from WP2.3.1).
• User-friendly and interactive monitoring dashboards will be created to analyze the data.
• Scalable open-source data collection platforms to connect to existing open data sources and to store new data collected along the food production chain.
• Based on the Value Stream Mapping approach, metrices are captured and converted into a CE index along the food value chain. 
• The detailed structure and mechanisms of the decision support tool are defined and a prototype software tool integrates the developed concepts. 
• Features are verified through adequate case studies in industrial context of food industry (e.g. manufacturing of sweets or fruit processing).
   

• An ontology model for an appropriate visualization of existing data along the value chain as well as indicators for circular cross-process evaluation visualizing them in multidisciplinary and multi-objective monitoring dashboards.
• Scalable data collection infrastructure based on open-source technology.
• An extended framework for circular value stream mapping for data-based determination of circularity potential.
• A Decision Support System (DSS) algorithm and prototype for life cycle optimization taking in account the previously multi-objective indicators.
• The linkage of the results from the developed DSS with a Digital Twin platform will lead towards an automated generation of decision-making proposals for self-optimization.
• Application and validation of the developed DSS and the digital twin platform in one or two industrial case studies from food manufacturing industry (e.g. confectionary & sweets or fruit processing).
• Recommendations and best practice guidelines for the implementation by practitioners.