Industry 4.0 technologies in quality and safety control systems in food manufacturing: A systematic techno-managerial analysis on benefits and barriers
Ayşe Selcen Semerciöz, Pieternel Arianne Luning
Wageningen University & Research
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Background The food industry faces increasing demands for improved quality and safety, while conventional quality control methods remain labour-intensive, slow, and limited. Industry 4.0 (I4.0) technologies show promise, but real-world implementation remains limited. Advancing practice requires clear insight into technical/technological and managerial benefits and barriers. Scope This review examines the I4.0 technologies and their implementation status in quality and safety systems in food manufacturing, and their applicability in either product or process quality control, as well as in elements of the quality control circle (data collection and analysis, corrective and proactive actions). Followingly, the benefits and barriers of these technologies that are mentioned in the reviewed studies are categorised using a techno-managerial approach. Key Findings and Conclusions Artificial intelligence (AI) is mainly used for product quality control, while the Internet of Things supports process quality control in the reviewed studies. Data analysis is the most addressed element of the quality circle; AI has the most potential. The reported benefits are primarily technical/technological, focusing on contamination detection and real-time quality monitoring. Managerial benefits, though less emphasised, include cost-effectiveness, better food safety and crisis management, and strategic improvement. Key technical/technological barriers are process and equipment-related , notably the need for high-quality data and time-intensive AI model training for large or complex datasets. Besides, reliable and accurate performance can still be a barrier due to overfitting, misclassification, etc. Managerial barriers are mostly people-related , including manual labelling errors and security issues. A multidisciplinary approach is essential to overcoming these barriers and promoting field implementations.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
工程Digital Transformation in Industry
Food Supply Chain Traceability · Impact of AI and Big Data on Business and Society
参考文献 89
此处列出前 3 条
引用本文 22
按被引量排序,此处列出前 3 条