Personalizing Industrial Maintenance Operation Using the Model of Hierarchical Complexity DOI Creative Commons
Gonçalo Raposo, Nuno Araújo, Marco Parente

и другие.

Journal of Manufacturing and Materials Processing, Год журнала: 2025, Номер 9(4), С. 132 - 132

Опубликована: Апрель 15, 2025

The rapid advancement of Industry 4.0 technologies has transformed industrial maintenance operations, introducing digital work instructions as a critical tool for improving efficiency and reducing errors. However, existing digitalization approaches often fail to account variations in worker expertise, leading cognitive overload, frustrations, overall inefficiency. This study proposes novel methodology dynamically personalizing by structuring task based on complexity levels proficiency. Using the Model Hierarchical Complexity (MHC) framework ensures that operators receive guidance tailored their skill capabilities. is implemented evaluated an environment, where are adapted profiles. results show significant improvements including reduction completion time, decrease error rates, enhanced engagement. Comparative analysis with conventional static reveals personalized contribute more effective knowledge transfer process, strain enhancing procedural adherence. Additionally, integrating predictive strategies could further enhance operational enabling proactive decision-making. Addressing potential challenges, such resistance adaptive data privacy concerns, will be crucial widespread implementation. In conclusion, leveraging personalize represents step toward optimizing workflows. Tailoring instructional content individual abilities enhances workforce productivity, reduces errors, contributes broader objectives 4.0.

Язык: Английский

Personalizing Industrial Maintenance Operation Using the Model of Hierarchical Complexity DOI Creative Commons
Gonçalo Raposo, Nuno Araújo, Marco Parente

и другие.

Journal of Manufacturing and Materials Processing, Год журнала: 2025, Номер 9(4), С. 132 - 132

Опубликована: Апрель 15, 2025

The rapid advancement of Industry 4.0 technologies has transformed industrial maintenance operations, introducing digital work instructions as a critical tool for improving efficiency and reducing errors. However, existing digitalization approaches often fail to account variations in worker expertise, leading cognitive overload, frustrations, overall inefficiency. This study proposes novel methodology dynamically personalizing by structuring task based on complexity levels proficiency. Using the Model Hierarchical Complexity (MHC) framework ensures that operators receive guidance tailored their skill capabilities. is implemented evaluated an environment, where are adapted profiles. results show significant improvements including reduction completion time, decrease error rates, enhanced engagement. Comparative analysis with conventional static reveals personalized contribute more effective knowledge transfer process, strain enhancing procedural adherence. Additionally, integrating predictive strategies could further enhance operational enabling proactive decision-making. Addressing potential challenges, such resistance adaptive data privacy concerns, will be crucial widespread implementation. In conclusion, leveraging personalize represents step toward optimizing workflows. Tailoring instructional content individual abilities enhances workforce productivity, reduces errors, contributes broader objectives 4.0.

Язык: Английский

Процитировано

0