Maintenance in any manufacturing organization is critical, given its significant role in ensuring business continuity. Maintenance plays a crucial role and has a significant impact on the results of industrial companies. Therefore, it is essential to manage maintenance, observe, understand, and improve actions by adopting well-chosen performance indicators according to the company's needs. These indicators are known as Maintenance KPIs or Key Performance Indicators, which allow for gathering knowledge and exploring the best means to achieve the organization's goals. Maintenance KPIs are critical to keeping track of the function, monitoring performance, and ensuring fulfillment of business expectations. In addition, KPIs drive reliability growth while guiding decisions to improve maintenance efficiency and performance. A helpful maintenance KPIs help to identify the problems causing the maintenance effect and help to select the right strategy to support or correct the actions that produced the results. They also allow to identify the causes of equipment failures (measure the influence of life cycle factors), direct what maintenance does with its time and resources (measure the efficiency and effectiveness of the maintenance group) and identify if maintenance removes failure causes ( measure the improved reliability and operational risk reduction results of maintenance effort) and help drive the business benefits provided by maintenance (measure the contribution to the business value of maintenance).

Essential maintenance KPIs are the most commonly used for maintenance management and are adopted by most industries; among these primary KPIs which are essential for maintenance management, we cite Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), and Overall Equipment (OEE). Nevertheless, it is crucial to continuously redefine and update KPIs to ensure they are appropriate for the organization's current environment, significantly when the constant market or research methodologies change. Hence, researchers and the industry propose several other maintenance KPIs outside the essential ones used in the industry according to the needs and within the performance improvement framework. These proposed KPIs aim to compensate for the lack of maintenance data, the absence of decision support, and the problems related to specific equipment, also in the context of improving the management strategy, the application of predictive maintenance, and the quality control of a maintenance process or the monitoring of systems reviews. Unfortunately, these indicators are not sufficiently known and are, therefore, not used by the industry. However, we believe that some of them should gain maturity and reach the status of widely used traditional indicators, such as the KPI of obsolescence management in maintenance operations and schedule compliance KPIs that aim to link maintenance planning with production. In addition, although not all proposed KPIs in the literature are generalizable, it has been identified that they can sometimes be specific to problematic situations, equipment categories, and even sectors of industry activity. Therefore, this work aims to inventory the most widely used maintenance KPIs and some of the KPIs proposed by researchers and the industry. In addition, we study the trends and challenges of selecting these KPIs and for what purposes they are used to help their understanding and usability. Indeed, Maintenance managers need to select relevant KPIs aligned with the maintenance strategy and the company objectives. 

Whereas ontologies are formal knowledge representations, conveying a shared understanding of a given domain, databases are a mature technology that describes specifications for the storage, retrieval, organization, and processing of data in information systems to ensure data integrity. Ontologies offer the functionality of conceptual modeling while complying with the web constraints regarding publication, querying and annotation, as well as the capacity of formality and reasoning to enable data consistency and checking. Ontologies converted to databases could exploit the maturity of database technologies, and databases converted to ontologies could utilize ontology technologies to be more used in the context of the semantic web. This work aims to propose a generic approach that enables converting a relational database into an ontology and vice versa. A tool based on this approach has been implemented as a proof of a concept.