Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.

The rise of Artificial Intelligence (AI) enables enterprises to manage large amounts of data in order to derive predictions about future performance and to gain meaningful insights. In this context, descriptive and predictive analytics has gained a significant research attention; however, prescriptive analytics has just started to emerge as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time. Although machine learning for decision making has been identified as one of the most important applications of AI, up to now, prescriptive analytics is mainly addressed with domain-specific optimization models. On the other hand, existing literature lacks generalized prescriptive analytics models capable of being dynamically adapted according to the human preferences. Reinforcement Learning, as the third machine learning paradigm alongside supervised learning and unsupervised learning, has the potential to deal with the dynamic, uncertain and time-variant environments, the huge states space of sequential decision making processes, as well as the incomplete knowledge. In this paper, we propose a human-augmented prescriptive analytics approach using Interactive Multi-Objective Reinforcement Learning (IMORL) in order to cope with the complexity of real-life environments and the need for optimized human-machine collaboration. The decision making process is modelled in a generalized way in order to assure scalability and applicability in a wide range of problems and applications. We deployed the proposed approach in a stock market case study in order to evaluate the proactive trading decisions that will lead to the maximum return and the minimum risk that the user's experience and the available data can yield in combination.

The emerging capabilities of artificial intelligence (AI) and the systems that employ them have reached a point where they are integrated into critical decision-making processes, making it paramount to change and adjust how they are evaluated, monitored, and governed. For this reason, trustworthy AI (TAI) has received increased attention lately, primarily aiming to build trust between humans and AI. Due to the far-reaching socio-technical consequences of AI, organisations and government bodies have already started implementing frameworks and legislation for enforcing TAI, such as the European Union’s AI Act. Multiple approaches have evolved around TAI, covering different aspects of trustworthiness that include fairness, bias, explainability, robustness, accuracy, and more. Moreover, depending on the AI models and the stage of the AI system lifecycle, several methods and techniques can be used for each trustworthiness characteristic to assess potential risks and mitigate them. Deriving from all the above is the need for comprehensive tools and solutions that can help AI stakeholders follow TAI guidelines and adopt methods that practically increase trustworthiness. In this paper, we formulate and propose the Trustworthiness Optimisation Process (TOP), which operationalises TAI and brings together its procedural and technical approaches throughout the AI system lifecycle. It incorporates state-of-the-art enablers of trustworthiness such as documentation cards, risk management, and toolkits to find trustworthiness methods that increase the trustworthiness of a given AI system. To showcase the application of the proposed methodology, a case study is conducted, demonstrating how the fairness of an AI system can be increased.