Personalizing Industrial Maintenance Operation Using the Model of Hierarchical Complexity

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Raposo, G (Autor) Outra; Araújo, N (Autor) Outra; Parente, M (Autor) Outra; António Mendes Lopes (Autor) FEUP; Santos, A (Autor) Outra; Pereira, F (Autor) Outra; Leite, S (Autor) Outra; Ramos Silva, A (Autor) FEUP

Revista

Título: Journal of Manufacturing and Materials Processing

Vol. 9

Página Final: 132

Editora: MDPI

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-HFV

DOI: 10.3390/jmmp9040132

Abstract (EN): 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 for variations in worker expertise, leading to cognitive overload, frustrations, and overall inefficiency. This study proposes a novel methodology for dynamically personalizing digital work instructions by structuring task instructions based on complexity levels and worker proficiency. Using the Model of Hierarchical Complexity (MHC) as a framework ensures that operators receive guidance tailored to their cognitive and skill capabilities. The methodology is implemented and evaluated in an industrial maintenance environment, where digital work instructions are adapted based on worker profiles. The results show significant improvements in maintenance operations, including a reduction in task completion time, a decrease in error rates, and enhanced worker engagement. Comparative analysis with conventional static instructions reveals that personalized digital work instructions contribute to a more effective knowledge transfer process, reducing cognitive strain and enhancing procedural adherence. Additionally, integrating predictive maintenance strategies with personalized work instructions could further enhance operational efficiency by enabling proactive decision-making. Addressing potential challenges, such as worker resistance to adaptive technologies and data privacy concerns, will be crucial for widespread implementation. In conclusion, leveraging the Model of Hierarchical Complexity to personalize digital work instructions represents a significant step toward optimizing industrial maintenance workflows. Tailoring instructional content to individual skill levels and cognitive abilities enhances workforce productivity, reduces errors, and contributes to the broader objectives of Industry 4.0. © 2025 by the authors.

Idioma: Inglês

Tipo (Avaliação Docente): Científica