Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review

被引:0
作者
de Carvalho, Johnderson Nogueira [1 ,2 ]
da Silva, Felipe Rodrigues [1 ]
Nascimento, Erick Giovani Sperandio [2 ,3 ]
机构
[1] Oswaldo Cruz Fdn FIOCRUZ, BR-21040900 Rio De Janeiro, Brazil
[2] SENAI CIMATEC Univ Ctr, Stricto Sensu Dept, BR-41650010 Salvador, Brazil
[3] Univ Surrey, Surrey Inst People Ctr Artificial Intelligence, Fac Engn & Phys Sci, Guildford GU2 7XH, England
关键词
prescriptive maintenance; predictive maintenance; machine learning; deep learning; biopharmaceutical industry; pharmaceutical industry; Industry; 4.0; PREDICTIVE MAINTENANCE; MODEL; MOTORS; STATE;
D O I
10.3390/s24227163
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The biopharmaceutical industry has specificities related to the optimization of its processes, the effectiveness of the maintenance of the productive park in the face of regulatory requirements. and current concepts of modern industry. Current research on the subject points to investments in the health area using the current tools and concepts of Industry 4.0 (I4.0) with the objective of a more assertive production, reduction of maintenance costs, reduction of operating risks, and minimization of equipment idle time. In this context, this study aims to characterize the current knowledge about the challenges of the biopharmaceutical industry in the application of prescriptive maintenance, which derives from predictive maintenance, in the context of I4.0. To achieve this, a systematic review of the literature was carried out in the scientific knowledge bases IEEE Xplore, Scopus, Web of Science, Science Direct, and Google Scholar, considering works such as Reviews, Article Research, and Conference Abstracts published between 2018 and 2023. The results obtained revealed that prescriptive maintenance offers opportunities for improvement in the production process, such as cost reduction and greater proximity to all actors in the areas of production, maintenance, quality, and management. The limitations presented in the literature include a reduced number of models, the lack of a clearer understanding of its construction, lack of applications directly linked to the biopharmaceutical industry, and lack of measurement of costs and implementation time of these models. There are significant advances in this area including the implementation of more elaborate algorithms used in artificial intelligence neural networks, the advancement of the use of decision support systems as well as the collection of data in a more structured and intelligent way. It is concluded that for the adoption of prescriptive maintenance in the pharmaceutical industry, issues such as the definition of data entry and analysis methods, interoperability between "shop floor" and corporate systems, as well as the integration of technologies existing in the world, must be considered for I4.0.
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页数:15
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