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A Data-Driven Approach to Reducing Food Safety Non-Conformances in Ready-to-Eat Food Facilities
Subject area: Science,Engineering and Technology · Area of research: Food Safety Culture
Abstract
Ready-to-eat (RTE) food production plants are a high-risk environment where lapses in food safety procedures by just a single step can result in major public health and regulatory repercussions. In this research, a new, data-driven approach designed to minimize food safety non-conformances systematically is introduced that combines multi-source streams of data such as real-time environmental monitoring, past compliance history, and process control data into a common analytical platform. By leveraging advanced statistical modeling and machine learning-enabled classification techniques, the system is capable of accurately identifying operational pain points and high-risk factors. Deployed at five different RTE facilities within an 18-month timeframe, this methodology achieved a 35% reduction in significant non-conformances, 28% increase in sanitation audit scores, and a 22% increase in worker training efficacy. Besides, the study identifies the strategic role of proactive leadership, regulatory collaboration, and targeted interventions in instilling a robust food safety culture. The proposed model is not only consistent with international best practices such as FSMA and ISO 22000 but also a model that can be scaled for digital transformation in the food industry. These outcomes position the model as a vision-led template for establishing sustainable excellence in food safety management.
Keywords
Food safety, Ready-to-eat (RTE) foods, Data-driven approach, Non-conformance reduction, Environmental monitoring, Sanitation protocols, Employee training, Process control, HACCP, FSMA compliance
How to cite this paper
@article{1709019,
author = {Kikelomo Meshioye},
title = {A Data-Driven Approach to Reducing Food Safety Non-Conformances in Ready-to-Eat Food Facilities},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {140-146},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709019.pdf},
abstract = {Ready-to-eat (RTE) food production plants are a high-risk environment where lapses in food safety procedures by just a single step can result in major public health and regulatory repercussions. In this research, a new, data-driven approach designed to minimize food safety non-conformances systematically is introduced that combines multi-source streams of data such as real-time environmental monitoring, past compliance history, and process control data into a common analytical platform. By leveraging advanced statistical modeling and machine learning-enabled classification techniques, the system is capable of accurately identifying operational pain points and high-risk factors. Deployed at five different RTE facilities within an 18-month timeframe, this methodology achieved a 35% reduction in significant non-conformances, 28% increase in sanitation audit scores, and a 22% increase in worker training efficacy. Besides, the study identifies the strategic role of proactive leadership, regulatory collaboration, and targeted interventions in instilling a robust food safety culture. The proposed model is not only consistent with international best practices such as FSMA and ISO 22000 but also a model that can be scaled for digital transformation in the food industry. These outcomes position the model as a vision-led template for establishing sustainable excellence in food safety management.},
keywords = {Food safety, Ready-to-eat (RTE) foods, Data-driven approach, Non-conformance reduction, Environmental monitoring, Sanitation protocols, Employee training, Process control, HACCP, FSMA compliance},
month = {June},
}