Predictive Maintenance Algorithms, Artificial Intelligence Digital Twin Technologies, and Internet of Robotic Things in Big Data-Driven Industry 4.0 Manufacturing Systems DOI Creative Commons
Marek Nagy,

Marcel Figura,

Katarína Valašková

и другие.

Mathematics, Год журнала: 2025, Номер 13(6), С. 981 - 981

Опубликована: Март 17, 2025

In Industry 4.0, predictive maintenance (PdM) is key to optimising production processes. While its popularity among companies grows, most studies highlight theoretical benefits, with few providing empirical evidence on economic impact. This study aims fill this gap by quantifying the performance of manufacturing in Visegrad Group countries through PdM algorithms. The purpose our research assess whether these generate higher operational profits and lower sales costs. Using descriptive statistics, non-parametric tests, Hodges–Lehmann median difference estimate, linear regression, authors analysed data 1094 enterprises. Results show that significantly improves performance, variations based geographic scope. Regression analysis confirmed as an essential predictor even after considering factors like company size, legal structure, Enterprises more effective cost management net were likely adopt PdM, revealed decision tree analysis. Our findings provide benefits algorithms their potential enhance competitiveness, offering a valuable foundation for business managers make informed investment decisions encouraging further other industries.

Язык: Английский

Enhancing Circular Supply Chain Management (CSCM) in Manufacturing SMEs: An Integrated CODAS-ISM-MICMAC Approach to Big Data Analytics Capability DOI
Rangga Primadasa,

Noor Nailie Azzat,

Elisa Kusrini

и другие.

Process Integration and Optimization for Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

4
Predictive Maintenance Algorithms, Artificial Intelligence Digital Twin Technologies, and Internet of Robotic Things in Big Data-Driven Industry 4.0 Manufacturing Systems DOI Creative Commons
Marek Nagy,

Marcel Figura,

Katarína Valašková

и другие.

Mathematics, Год журнала: 2025, Номер 13(6), С. 981 - 981

Опубликована: Март 17, 2025

In Industry 4.0, predictive maintenance (PdM) is key to optimising production processes. While its popularity among companies grows, most studies highlight theoretical benefits, with few providing empirical evidence on economic impact. This study aims fill this gap by quantifying the performance of manufacturing in Visegrad Group countries through PdM algorithms. The purpose our research assess whether these generate higher operational profits and lower sales costs. Using descriptive statistics, non-parametric tests, Hodges–Lehmann median difference estimate, linear regression, authors analysed data 1094 enterprises. Results show that significantly improves performance, variations based geographic scope. Regression analysis confirmed as an essential predictor even after considering factors like company size, legal structure, Enterprises more effective cost management net were likely adopt PdM, revealed decision tree analysis. Our findings provide benefits algorithms their potential enhance competitiveness, offering a valuable foundation for business managers make informed investment decisions encouraging further other industries.

Язык: Английский

Процитировано

2