## 4 Results and Discussion
[9], also drastically reduces operating costs. The second case is very similar to the first one; however, the percentage of savings is much higher, which was about 200% of the initial investment. Condition-Based Maintenance Techniques in Renault Cacia  
In the CBM concept, several parameters can be controlled through monitoring. That is, CBM involves making decisions about maintenance or repair based on the actual deteriorating conditions of the components [72]. The most advanced and impactful predictive techniques in the industrial sector to date are vibration analysis [45], ultrasound [42], thermography [44], voltage [43], dynamic pressure, and visual inspection. In the case of Renault Cacia, namely in M1, it started by implementing pressure and temperature measurement techniques. In particular, the pressure technique was implemented in the hydraulic system of all 12 manufacturing machines to detect irregular parameters in the exchanger, the oil pump, or the hydraulic material. As for the temperature analysis, it was implemented in three different areas: refrigeration, where malfunctions can be caused by leaks, poor ventilation, and low temperatures; hydraulic, where anomalies detected can be associated with several hydraulic materials; electrical panel, where the detected problems may be related to the door not being closed, poor ventilation, and inadequate power modules. The implementation of these techniques in Renault Cacia reinforces what has been described in the literature that CBM is practical to implement [57; 58], notably in the automotive industry. Nevertheless, it increasingly requires greater use of various disciplines in the maintenance field, such as AI [73]. * Condition-based maintenance and predictive maintenance practices in Renault Cacia  
PdM essentially aims to complement the CBM purpose by assisting in decision-making regarding equipment repair actions. The monitoring of parameters above gives rise to a large set of data, which is then collected by the Renault server (i.e., Smart Observer). The monitoring system is composed of sensors, which continuously collects data of critical parameters of each machine. Subsequently, this data is sent via corporate networks to the central server. The Smart Observer then automatically collects the data in real-time, analyzing and presenting it via dashboards, which allow a graphical depiction of the values collected by the sensors. Thus, it is expected to eliminate most of the periodic inspections, a characteristic of the preventive practices, maximizing the life cycle of the machine components. With this system, it is also possible to promptly detect failures by early identifying anomalies in the equipment, resulting in financial and operational benefits. In short, it is possible to infer that this practice (i.e., monitoring systems and Smart Observer) automates and digitalizes maintenance activities, resulting in greater speed and operational efficiency with regard to maintenance actions. It also eliminated mentioned periodic inspections and manual troubleshooting operations of the machines in Module 1, as maintenance is now performed only when a particular component reaches a state that will lead to its failure. The CBM consists of defining limits that the measured values must not exceed so that machines work in optimal operating conditions. This was Renault Cacia's main challenge in the server operationalization process since each asset (machine) is particular and must operate within a specific range of values, which means that its functional limits will also be different. Indeed, this may be similarly true to most automotive industry companies, as they have machines with different production specifications, which increases the degree of complexity of the analysis. Following [PERSON] and [PERSON] [5], the Renault Cacia team defined two upper limits, the alert, and the alarm. The first, when exceeded, will give rise to an alert notification, meaning that certain equipment started to operate at values higher than those allowed under normal conditions.