This research paper investigates the integration of Explainable Artificial Intelligence (XAI) into Predictive Maintenance (PdM) systems, aiming to enhance transparency, interpretability, and reliability in industrial applications. The primary contribution is the introduction of the Explainability Parameters (XPA) framework, which offers a structured methodology for evaluating and applying XAI in PdM. The study systematically reviews recent advancements and challenges in the literature, categorising explanations into pre-modelling, in-modelling, and post-modelling processes. It presents and analyses significant case studies across various industrial sectors to illustrate the practical implications and hurdles of XAI methodologies. Key findings indicate that while XAI significantly improves the effectiveness and trustworthiness of PdM by clarifying model predictions, its implementation is hindered by the complexity of industrial data and the absence of standardised evaluation methods. The XPA framework addresses these challenges by providing tailored metrics for specific applications and advocating for a multi-phase approach to convert technical outputs into actionable maintenance recommendations. The originality of this paper lies in its comprehensive review and the establishment of rigorous standards for assessing XAI methodologies, thereby bridging the gap between theoretical frameworks and practical applications. By promoting adaptable XAI frameworks that cater to real-world industrial needs, this study fosters trust in automated decision-making processes. It enhances the overall understanding of XAI's role in PdM.

Manufacturers employ AI for monitoring vehicle mileage, inspecting components, and scheduling maintenance. Past studies underscore the need for auto-related plans to prioritize environmental protection, concentrating on AI-driven environmental solutions promoted by AI for Good. AI enhances brand success by improving investment, technology, and promotional capabilities. This study emphasizes consistency in AI application across the automotive value chain for brand sustainability. A web-based poll surveyed 120 AI users in marketing, HR, sustainability, as well as 180 sustainability specialists and regulators. The primary goal is to assess, via structural model evaluation, how extraneous variables affect the development of AI-powered brand sustainability strategies. The study highlights AI's sustainability benefits in the automotive industry improving transportation safety, forecasting maintenance, and creating eco-friendly vehicles. However, challenges involve over-reliance on AI, predicting human behavior, and addressing sustainability threats. AI development should consider regional differences, prioritizing openness, policy harmony, and consumer agency. These findings aid marketing and HR professionals in devising customer-centric long-term plans.

The FMCG sector in developing nations is still not prepared to withstand any disruption brought on by the worldwide pandemic. In order to adapt to the new normal, businesses must make both micro and broad changes to their supply chain strategy. The goal of this research is to create plans to minimize any disruptions caused by upcoming pandemics. To restore the broken supply chain, a number of new implications and adjustments to the current attributes were proposed in the areas of sourcing, manufacturing, and distribution. Finding the fundamental drivers that are frequently impacted by the disturbance is part of the technique. The afflicted locations were the focus of the models' development. The ideas work as preventative measures intended to thwart the disturbance when and if it happens. In order to assess the model's viability, the Key Performance Indicators (KPI) value was ultimately retrieved with the aid of 25 industry experts. These suggestions may result in improved transparency, real-time monitoring, cost effectiveness, and responsiveness, among other benefits. Our analysis indicates that the KPI scores for procurement, production, and distribution are 92.86%, 82.14%, and 87.50%, respectively. The models' total viability is 87.50%. The most recent Covid-19 pandemic has provided us with a vivid illustration of what could go wrong in such circumstances. In both pandemic and non-pandemic conditions, the adaptation of stated suggestions at the aspect of sourcing, production, and distribution might result in a significant shift to organization-wide activities.