## 1 Introduction  
With the continuous development of Industry 4.0 (I4.0) and its applications, business models have been adapted to keep up with the incorporation of new technologies. Since industrial companies are operating in an increasingly competitive environment, with very short cycles of technological innovation, where process optimization and automation are pivotal to the company's survival [1], researchers and managers are called to contribute to the understanding and description of the phenomenon. While the concept of the intelligent factory has evolved over time, today it is highly digitalized and interconnected [2, 3], supported by emerging information and communication technologies (ICT), and involves the operation of the so-called cyber-physical systems (CPS). In this environment, the main components are usually robotized and connected through complementary technologies, such as the Internet of Things (IoT), which enables measurement, sensing, control, and communication in all the manufacturing processes. As a result, it is expected that facilities can be managed in real-time and with the necessary flexibility to meet continuous changes. The vision of an intelligent factory will only be possible if machines work properly, with the desired precision and efficiency. Herein lies the crucial role of maintenance, which must fulfill the delicate task of keeping machines and their components operational [4]. With the arrival of predictive maintenance (PdM) and condition-based maintenance (CBM) in the context of I4.0, the whole process changed, creating new challenges and opportunities. At the same time, it is expected that productivity will continue to grow through the prediction of failures and consequent maximization of the machines' life cycle [5]. However,these new forms of maintenance represent a very complex process and involve an indeterminate number of variables. Here, the use of artificial intelligence (AI) techniques and advanced algorithms are crucial to gather and interpret the data acquired from the physical world, namely, from machines. The acquired knowledge, often resulting from the analysis of large volumes of data, is instrumental in supporting the decision-making process. In fact, the objective is that the machines reach a self-decision capacity [4] to directly identify which problems occur and when components need to be replaced. I4.0 can, thus, improve maintenance decision-making through the employment of industrial sensors and big data technology, which can lead to a more responsive information system [6]. However, we need viable solutions to assist the maintenance decision-making process. Its application to the automotive industry is of paramount importance, as this industry has to comply with key performance indicators [7] to control the overall manufacturing performance (e.g., IATF 16949:2016) [8]. Indeed, the automotive industry invests significant amounts in new technologies and automation to achieve short lead times, high-quality standards, and high levels of competitiveness [9]. Similar research has been carried out in the context of intelligent, condition-based, and predictive maintenance systems, mainly in the context of 14.0 [10; 11; 12] and in the automotive industry [13; 14]. However, none of those articles used business process management (BPM) and business process model and notation (BPMN) to improve the maintenance systems. The literature shows that BPM and BPMN have gained greater significance, as they promote better communication and transparency in the decision-making process [15], especially with regard to the integration of new concepts that need to be understood and accepted by all the companies' stakeholders. That said, we developed two research questions (RQ), as follows:  
**Research Questions 1 (RQ1). **_How does BPMN contribute to predictive and condition-based maintenance?_  
**Research Questions 2 (RQ2).