Oswaldo Cruz Foundation FIOCRUZ, Rio de Janeiro 21040-900, Brazil.
Stricto Sensu Department, SENAI CIMATEC University Center, Salvador 41650-010, Brazil.
Sensors (Basel). 2024 Nov 7;24(22):7163. doi: 10.3390/s24227163.
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.
生物制药行业具有与优化其流程相关的特殊性，需要面对监管要求和现代工业的当前概念来保持生产园区的有效性。当前关于该主题的研究指出，在卫生领域投资使用当前的工业 4.0（I4.0）工具和概念，目的是更自信地生产、降低维护成本、降低运营风险并最小化设备闲置时间。在这种情况下，本研究旨在描述生物制药行业在应用源自预测性维护的规定性维护方面所面临的挑战的现有知识，这是 I4.0 的一部分。为了实现这一目标，在 IEEE Xplore、Scopus、Web of Science、Science Direct 和 Google Scholar 等科学知识基础中进行了文献系统回顾，考虑了 2018 年至 2023 年期间发表的评论、研究文章和会议摘要等作品。结果表明，规定性维护为生产过程提供了改进机会，例如降低成本和更接近生产、维护、质量和管理领域的所有参与者。文献中提出的限制包括模型数量减少、对其构建的理解不够清晰、缺乏直接与生物制药行业相关的应用以及缺乏对这些模型的成本和实施时间的测量。在这一领域取得了重大进展，包括实施更复杂的人工智能神经网络算法、推进决策支持系统的使用以及更结构化和智能化地收集数据。结论是，为了在制药行业采用规定性维护，必须考虑 I4.0 中诸如数据输入和分析方法的定义、“车间”和企业系统之间的互操作性以及现有技术的集成等问题。
