The impact of food loss and waste (FLW) generation on food supply chains' (FSC) sustainability represents a challenge embodied in the Sustainable Development Goal (SDG) 12.3. This problem requires a methodology to measure such an impact in a rigorous, holistic, and standardized way that can be applied to any FSC. This paper aims to develop and validate a single index to assess the readiness of FSCs to implement FLW prevention strategies and measure their impact: the so-called FOODRUS index. The co-creation methodology followed incorporates experts and FSC stakeholders feedback. The index has been validated in 3 FSCs: The Slovak pilot scored 74.35%, the Spanish pilot reached 68.79%, and the Danish pilot was rated 61.14%. Its calculation, eased by the FOODRUS index self-assessment tool (described in the Appendix), allows quick diagnosis of the FSC capability to implement FLW prevention strategies considering both the knowledge provided by experts and the experience of the FSC stakeholders that participated in its co-creation process. In this way the FSC can assess its FLW prevention performance at a strategic and management level, with the aim of improving its sustainability impact.

Food loss and waste (FLW) demands urgent attention: over 58 million tonnes wasted annually in the EU, while 828 million people face hunger worldwide. SDG 12.3 targets a 50% reduction in FLW by 2030, requiring rigorous quantification. Governments must quantify FLW, especially at the consumption stage, where most of FLW is generated. However, the limited granularity of existing data emphasises the need for improved quantification methods that enable reliable comparisons and set solid basis to measure the impact of future FLW prevention actions. This paper proposes a FLW quantification methodology at municipal and regional scales aligned with the existing urban waste management requirements. The methodology involved experts and stakeholders to define the optimal FLW quantification framework and measurement method. It was validated in 6 Basque municipalities with a common waste characterisation matrix to compare waste fractions, generator types and waste collection systems. A cartographic analysis, from regional to street level, demonstrates how data granularity shapes FLW pattern interpretation. As in the case of Zamudio, where data at container level permits detailed insights. This approach enables improved waste collection and the design of ad-hoc FLW prevention actions (like economic instruments) according to the generator profile and type of FLW.

This chapter explores the technologies involved in certification systems designed to prevent food waste. First, it presents the state-of-the-art in IoT and blockchain technologies applied to the food supply chain, followed by a summary of the key certifications in this domain. The chapter then provides a comprehensive overview of how these technological innovations are being integrated to tackle the pressing challenges of the food industry, with a particular focus on food waste prevention. Finally, it concludes with insights and conclusions drawn from the analysis of these systems.