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<p>Poka-Yoke is one of the fundamental Lean tools used to prevent or detect errors. However, the existing theoretical framework and classification models are neither sufficiently systematised nor confirmed by empirical research. This study therefore provides the first comprehensive evaluation of three classification models based on: function (I), principle (II), and device type (III). The first two models are the most commonly used in the relevant literature, while the third was developed by the author and improved through the research conducted for a clearer understanding and more practical application. The models were empirically tested using two criteria: classification accuracy assessment and ease of application assessment. The research included 21 examples of PY solutions from literature and industrial practice, evaluated by 30 experts of various profiles. Statistical analysis of the data confirmed the existence of significant differences between the models. Classification models III was found to be the most accurate and simplest to use, thus confirming its practical value in modern industrial practice. This study contributes to the development of a theoretical and practical framework for the PY method, offering empirically based recommendations for standardisation and wider implementation. These recommendations create the conditions for more effective error management, thereby increasing process reliability and ensuring compliance with Industry 4.0 requirements.</p>

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Improving Poka-Yoke thinking: A comparative analysis and proposal of a novel classification model

  • 期刊论文

  • 发表日期：

    2026年5月1日

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  • Jovana Peric,
  • Milovan Lazarevic,
  • Robert Cep,
  • Mitar Jocanovic,
  • Ivan Kuric,
  • Aco Antic

摘要

Poka-Yoke is one of the fundamental Lean tools used to prevent or detect errors. However, the existing theoretical framework and classification models are neither sufficiently systematised nor confirmed by empirical research. This study therefore provides the first comprehensive evaluation of three classification models based on: function (I), principle (II), and device type (III). The first two models are the most commonly used in the relevant literature, while the third was developed by the author and improved through the research conducted for a clearer understanding and more practical application. The models were empirically tested using two criteria: classification accuracy assessment and ease of application assessment. The research included 21 examples of PY solutions from literature and industrial practice, evaluated by 30 experts of various profiles. Statistical analysis of the data confirmed the existence of significant differences between the models. Classification models III was found to be the most accurate and simplest to use, thus confirming its practical value in modern industrial practice. This study contributes to the development of a theoretical and practical framework for the PY method, offering empirically based recommendations for standardisation and wider implementation. These recommendations create the conditions for more effective error management, thereby increasing process reliability and ensuring compliance with Industry 4.0 requirements.