D5.3 Use-case Plan, Reports & Recommendations

This deliverable outlines the methodologies for piloting and evaluating the EFRA use-cases and also describes the specific scenarios that will be tested, along with the activities and Key Performance Indicators (KPIs) associated with them. The methodology comprises two distinct evaluation phases: formative and summative, each targeting different stages of the use-case development. The formative evaluation focuses on the early development stages, involving user interaction with preliminary tool versions. This phase is pivotal for gauging initial performance through KPIs like accuracy, AI model efficacy, and usability. Feedback from this phase, particularly from decision-makers, is integral for refining the tools, paving the way for the second cycle. The summative evaluation then assesses the final tool versions, aiming to validate their compliance with targeted KPIs, such as enhanced accuracy, user satisfaction, and overall business impact. This phase is crucial for confirming the technical robustness and practical utility of the developed tools in real-world settings. Parallel to these methodologies, the deliverable presents concrete use-cases that will undergo these piloting phases. These include scenarios like root cause analysis for salmonella in poultry farms, enhanced predictive capabilities for pest alarms, and automated regulatory analysis. Each use-case is accompanied by specific activities designed to test and refine the tools in question. For instance, the poultry farm use-case involves leveraging historical data and AI algorithms to predict salmonella outbreaks, with KPIs focusing on model accuracy and user satisfaction. Similarly, the pest alarm enhancement scenario aims to improve prediction algorithms using diverse data, with real-life tests planned to evaluate the model's accuracy and efficiency. The automated regulatory analysis module targets reducing manual effort in data interpretation, with KPIs focusing on the accuracy of automated summaries and user time saved. In summary, this deliverable lays out a structured approach to piloting and evaluating EFRA tools and details the specific use-cases that will be put to the test. It highlights the planned activities and KPIs for each scenario, ensuring a comprehensive and focused approach to validating and enhancing the project's outputs. Through this dual emphasis on methodology and practical application, EFRA aims to deliver solutions that are technically sound and directly applicable and beneficial in addressing realworld challenges in the food safety and risk prevention sectors.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-20
DOI
https://doi.org/10.5281/zenodo.14626568
Primary Topic
Statistical Methods and Applications
Type
article
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article

D5.3 Use-case Plan, Reports & Recommendations

Zenodo (CERN European Organization for Nuclear Research)
Statistical Methods and Applications
article

D5.3 Use-case Plan, Reports & Recommendations

article en

Abstract

This deliverable outlines the methodologies for piloting and evaluating the EFRA use-cases and also describes the specific scenarios that will be tested, along with the activities and Key Performance Indicators (KPIs) associated with them. The methodology comprises two distinct evaluation phases: formative and summative, each targeting different stages of the use-case development. The formative evaluation focuses on the early development stages, involving user interaction with preliminary tool versions. This phase is pivotal for gauging initial performance through KPIs like accuracy, AI model efficacy, and usability. Feedback from this phase, particularly from decision-makers, is integral for refining the tools, paving the way for the second cycle. The summative evaluation then assesses the final tool versions, aiming to validate their compliance with targeted KPIs, such as enhanced accuracy, user satisfaction, and overall business impact. This phase is crucial for confirming the technical robustness and practical utility of the developed tools in real-world settings. Parallel to these methodologies, the deliverable presents concrete use-cases that will undergo these piloting phases. These include scenarios like root cause analysis for salmonella in poultry farms, enhanced predictive capabilities for pest alarms, and automated regulatory analysis. Each use-case is accompanied by specific activities designed to test and refine the tools in question. For instance, the poultry farm use-case involves leveraging historical data and AI algorithms to predict salmonella outbreaks, with KPIs focusing on model accuracy and user satisfaction. Similarly, the pest alarm enhancement scenario aims to improve prediction algorithms using diverse data, with real-life tests planned to evaluate the model's accuracy and efficiency. The automated regulatory analysis module targets reducing manual effort in data interpretation, with KPIs focusing on the accuracy of automated summaries and user time saved. In summary, this deliverable lays out a structured approach to piloting and evaluating EFRA tools and details the specific use-cases that will be put to the test. It highlights the planned activities and KPIs for each scenario, ensuring a comprehensive and focused approach to validating and enhancing the project's outputs. Through this dual emphasis on methodology and practical application, EFRA aims to deliver solutions that are technically sound and directly applicable and beneficial in addressing realworld challenges in the food safety and risk prevention sectors.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 53%
Statistical Methods and Applications
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