## 1 Introduction
Data-driven PsM scheduling and production optimization cross multiple study domains, including process system engineering and process safety [16; 17; 18; 19]. In PdM studies, it is critical to rely on real data from factory processes in order to control operations much more efficiently and predict evolution over time [15]. There are often limitations in most of the research work performed when PsM and PdM are considered:  
* As highlighted by [20], \"most of the existing methods possess an asset-centric view, i.e., they focus in their evaluation analyses on the asset itself rather than incorporating adjacent processes within the involved stakeholders.\"* Machine learning techniques are also limited by the fact that they always rely on Remaining Useful Life (RUL) predictions, which do not account for uncertainty in the deterioration trajectory [14; 21]. * Another significant limitation of PdM, CBM, and PsM data-driven approaches is that most of their identification methods rely on collected and labeled data used for training, which are scarce [22]. Therefore, the implicit prognosis is to assume there is a constant degradation rate, which is not always true, as discussed in [PERSON] et al. [21] for lithium-ion batteries and depicted in Figure 1. Such variable degradation rates are rather frequent, and they challenge the overall deployment of such solutions. * Although machine learning techniques are widely used, they tend to accumulate errors, and longer prediction durations result in more accumulated errors because of different degradation rates and other effects. Therefore, the process of creating deep learning (DL) models is not as automatic as often believed [23]. The main objectives of this work were to (1) address these last two shortcomings by providing a framework for advanced monitoring that can integrate self-guided decisions about ML model replacement and (2) continuously track system performance. In this way, the different alternative actions linked to PsM can be assessed in a more consistent way as different sets of sequential actions under uncertainty. Markov Decision Processes (MDPs) provide a powerful analytical framework for such settings and are frequently used to represent these problems, and Reinforcement Learning (RL) is a promising tool for completing PsM analysis [7]. The focus of this study was not on performing PsM itself, which can be very case-specific, nor promoting any advanced ML methods, although some of them were used, but on providing a robust context for employing the RL method. To address this aim, we needed to evaluate ML algorithms' ability to model the parameters and variables pertaining to an industrial environment as well as continuously monitor the models' performance and consider the uncertainty involved in predictions, depending on the established time window. To demonstrate the value of the proposed framework, a relevant industrial context was adopted; critically, this application does not imply a lack of generalization. This study differs from previous ones by introducing a self-guided, cloud-based monitoring framework that not only uses established machine learning methods but also incorporates an automated, scalable approach for continuous model monitoring, updating, and  
Figure 1: Different degradation rates throughout the life of a Li-ion battery. Source: [21]. deployment. Unlike traditional prescriptive maintenance solutions, which often lack mechanisms for lifecycle management and sustainability of deployed models, our proposal integrates a Development and Operations (DevOps)-compatible architecture to autonomously handle model retraining and deployment based on real-time performance indicators. Furthermore, this work introduces the concept of trustworthiness monitoring as an integral part of the prescriptive maintenance framework. This capacity enables organizations to quantify and act upon the uncertainty and accuracy degradation of predictive models over time, addressing a critical but often overlooked limitation in current industrial applications.