SynthesisSensors (Basel, Switzerland)2024
Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review.
Synthesis in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Towards Maintenance 5.0: Resilience-Based Maintenance in AI-Driven Sustainable and Human-Centric Industrial Systems.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.