In the context of Industry 4.0, process automation and predictive maintenance play an essential role. There is a need to provide more effective and faster maintenance through the integration of industrial tools and processes, to support manufacturing operations, in the perspective of integration standards and architectures. In a typical maintenance system, registration and maintenance requests are made through maintenance orders, which consist of a standard form and usually are created and filled manually. However, predictive maintenance requires a higher level of automatization, from data acquisition to maintenance order generation in a Computerized Maintenance Management System (CMMS) / Enterprise Asset Management (EAM). The proposal is to automate the process of generating maintenance orders, providing for automated form completion. At the physical level, assets are monitored by sensors, and, based on a set of rules, the respective predictive maintenance order will be issued in CMMS/EAS. Maintenance orders may contain variable fields according to each asset, so Machine Learning (ML) and Multicriteria Decision Making (MCDM) will be applied to fill in these fields, as well as the allocation of the maintenance orders to the maintainer that best fit for the maintenance specification. This automatic process will assist the maintenance workflow, leading to a Smart Workflow concept. A serial and parallel framework are presented, the former consists in applying TOPSIS (MCDM method) to extract features for the use in ML classification, in order to automatically fill in the appropriate form fields. The latter consists in a classification using both methods (ML and MCDM), where TOPSIS performs the initial classification, and if the alternatives ranking scores are close to each other, ML is used for more accurate classification. A case study was carried out in a Brazilian company that develops a CMMS/EAM system, distributed worldwide, and the results demonstrate that the concept of Smart Workflows is valuable, simplifying and enhancing maintenance processes.

在工业 4.0 的背景下，过程自动化和预测性维护发挥着至关重要的作用。从集成标准和架构的角度来看，需要通过工业工具和流程的集成来提供更有效和更快的维护，以支持制造运营。在典型的维护系统中，注册和维护请求是通过维护订单发出的，维护订单由标准表格组成，通常是手动创建和填写的。然而，预测性维护需要更高水平的自动化，从数据采集到计算机化维护管理系统 (CMMS)/企业资产管理 (EAM) 中的维护订单生成。该提议是自动化生成维护订单的过程，提供自动化的表格完成。在物理层面，资产由传感器监控，并根据一组规则，在 CMMS/EAS 中发布相应的预测性维护订单。根据每个资产，维护订单可能包含可变字段，因此将应用机器学习 (ML) 和多标准决策 (MCDM) 来填写这些字段，以及将维护订单分配给最适合的维护人员维护规范。此自动流程将有助于维护工作流程，从而形成智能工作流程概念。提出了串行和并行框架，前者包括应用 TOPSIS（MCDM 方法）来提取用于 ML 分类的特征，以便自动填写适当的表单字段。后者包括使用两种方法（ML 和 MCDM）的分类，其中 TOPSIS 执行初始分类，如果备选排名分数彼此接近，则使用 ML 进行更准确的分类。在一家开发分布于全球的 CMMS/EAM 系统的巴西公司中进行了一项案例研究，结果表明智能工作流的概念很有价值，可以简化和增强维护流程。