The paper aims to investigate the reasons why Augmented Reality (AR) has not fully broken the industrial market yet, or found a wider application in industries. The main research question the paper tries to answer is: what are the factors (and to what extent) that are limiting AR? Firstly, a reflection on the state of art of AR applications in industries is proposed, to discover the sectors more commonly chosen for deploying the technology so far. Later, based on a survey conducted after that, three AR applications have been tested on manufacturing, automotive, and railway sectors, and the paper pinpoints key aspects that are conditioning its embedding in the daily working life. In order to compare whether the perception of employees from railway, automotive, and manufacturing sectors differs significantly, a one-way analysis of variance (ANOVA) has been used. Later, suggestions are formulated in order to improve these aspects in the industry world. Finally, the paper indicates the main conclusions, highlighting possible future researches to start.

Purpose: Providing a comprehensive literature review to consolidate existing knowledge, advancements and future directions in the field. By synthesizing the state of research, this work enhances the understanding of Prescriptive Maintenance (PsM) methodologies, applications and potential benefits to assist researchers in identifying fruitful avenues for further investigation, and guide practitioners in implementing PsM strategies to improve maintenance outcomes in their industries.Design/methodology/approach: Through a systematic, multistage, specialists audited analysis of peer-reviewed articles, conference papers, books sections, thesis, magazines and industry reports, this work provides a literature review analyzing PsM origins, definitions, enablers, outputs and emerging trends.Findings: PsM concept evolved in recent years representing a shift from traditional maintenance, leveraging prescriptive analytics, data-driven modeling and optimization techniques to enable proactive decision-making and optimal resource allocation. By harnessing PsM, organizations can anticipate and mitigate failures, optimize maintenance actions and enhance asset reliability.Research limitations/implications: Existing literature points out the following challenges for PsM implementation: prescriptive analytics improvement, scalability of frameworks, development of prototypes, processes integration; PsM maturity assessment; asset health prognostics assertiveness, real-time data availability and adoption of cost functions to grasp business and environmental, social and governance (ESG) costs.Practical implications: Optimal deployment of resources with little or no human intervention in the maintenance decision process and the creation of new services improving reliability and operational performance.Social implications: By optimizing maintenance, not only direct costs diminish but also environmental, social and governance (ESG) related costs decrease by reducing energy waste during equipment’s operating phase, assessing the ecological impact of providing maintenance to operators and line maintenance stakeholders and, consequently, minimizing or even eliminating harmful effects on the environment and the human.Originality/value: Work consolidating existing PsM-related knowledge and indicating future work is a gap in the literature. This paper fills this gap.

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Manufacturing and assembling aircraft require hundreds of different machines for various process applications. The machines have different complexity and often different ages; however, they have to ensure a higher precision than other industrial fields. Recent technology advancement in maintenance approaches offers a wide range of opportunities to provide performance and availability. The paper discusses how the maintenance technologies applicable to the various machines need to be appropriately supported by a production environment, called “ecosystem”, that allows their integration within the process and their synergy with the operators. (1) A background analysis of the aircraft production environment is offered. (2) A possible framework for designing a proper ecosystem 4.0 for integrating maintenance activities with design solutions and data gathering is provided. (3) A case study based on the assembly line of specific aircraft is adopted for testing the validity of the framework. (4) Finally, a discussion highlights the critical points of the research, underlying future work.

Aerospace systems are composed of hundreds or thousands of components and complex subsystems which need an appropriate health monitoring capability to enable safe operation in various conditions. In terms of monitoring systems, it is possible to find a considerable number of state-of-the-art works in the literature related to ad-hoc solutions. Still, it is challenging to reuse them even with subtle differences in analogous subsystems or components. This paper proposes the Generic Anomaly Detection Hybridization Algorithm (GADHA) aiming to build a more reusable algorithm to support anomaly detection. The solution consists of analyzing different supervised machine learning classification algorithms combined in ensemble techniques, with a physical model when available, and two levels of a decision to estimate the current state of the monitored system. Finally, the proposed algorithm assures at least equal, or, more typically, better, overall accuracy in fault detection and isolation than the application of such algorithms alone, through few adaptations.

Purpose: A framework is being developed to help Integrated Electronic Technical Publications (IETP) consultation inside and outside the aviation maintenance hangar. The expected results are the reduction in time to access the desired IETP and to assist mechanics while performing maintenance tasks using voice recognition.Design/methodology/approach: The work is being conducted based on literature review and consultation with mechanics from the aviation industry, through questionnaires. The development will be made through study cases by building a core search engine and mobile applications to support the mechanics during the maintenance activities.Findings: The identified problem in small maintenance shops and defence organizations suggests that IETP are not entirely accessible before and during the maintenance activity. Such organizations suffer from information and communications technology (ICT) low infrastructure capability and demand access to multiple IETP databases as they usually support different aircraft. To have access to the IETP through voice assistant application will help mechanics to access the IETP, including when they would be with dirty hands and having difficulty in using mobile devices with touch displays.Originality/value: The framework being developed will give mechanics the ability to quickly find any existing IETP to support its maintenance task at any time and in any place with low demanding for ICT infrastructure. The architecture will support different applications, and the identified priority is for IETP viewers to the most demanding functionality of specification ASD S1000D. This approach could also help in troubleshooting activities since COVID-19 brought new demands for the social distancing for mechanics.