Advances in Asset Management: Strategies, Technologies, and Industry Applications

The use of prioritization analysis techniques allows identifying the level of criticality of physical assets and helps to manage resources: human, economic and technological in a more efficient way. In other words, the process of criticality analysis helps to determine the importance and consequences of the failures of productive equipment in the operational context in which they perform. This article explains the basic theoretical aspects of the equipment prioritization analysis process based on risk matrices (failure frequency and consequences); and the development of the model named Risk Qualitative Criticality Matrix (RQCM). Finally, are presented and analysed the results of a case of application of the RQCM in the sector of ophthalmic lenses (new factory built in Costa Rica – PRATS Laboratory).

As an emerging market, the telecoms sector in Nigeria has undergone a considerable increase in teledensity, internet usage and consumer base over a decade and is still on exponential growth. However, the consequence of this increase in growth has been a continuous degradation of telecom network quality of service (QoS), which has impacted subscribers’ customers’ needs, satisfaction, expectations and added value services. In exploring the quality of services (QoS) issues, the asset performance is not meeting the agreed key performance indicators (KPIs) on power availability (PA), a critical KPI which is affected by asset maintenance activities. Therefore, this paper focuses on the technical and human factors of asset management and maintenance practices. The methodology used in this paper is the quantitative and qualitative approaches with a systematic review of related literature on the research context. The primary data sources are through a structured survey questionnaire and semi-structured interviews. The secondary data source is the systematic literature review on related journal articles to the research subject matter. The paper used the statistical package for the social sciences software (SPSS 29) and Nvivo software for the data analysis. The research results and findings indicate critical maintenance strategic differences in existing asset maintenance activities and operations, cost pressure, and complex operating environments and markets that could be explained through intelligent and digitalised asset management and maintenance strategies. The systematic review results indicate the advancement of asset maintenance strategies to support maintenance planning, asset real-time monitoring and management, as the existing maintenance practice did not match the intelligent-based approach drawn from the concept of Industry 4.0R.

Industry 4.0 is usually presented as usage of technologies. Some of these play an important role in the development of smart maintenance technologies. However, although the subject of smart maintenance has been discussed for more than 10 years, the manufacturing industry still finds it challenging to implement smart maintenance technologies to add benefits to maintenance organizations in line with company’s goals. This study presents a conceptual process for implementing smart maintenance technologies, challenges and enablers to consider when implementing, and benefits. This article is based on an analysis of empirical findings from seven large manufacturing companies in Sweden, previous maintenance research, and authors’ three previous smart maintenance research articles. In the first article, the authors explored perspectives on smart maintenance technologies from 11 large companies within the manufacturing industry, while in the second one, perspectives on smart maintenance technologies from 15 manufacturing Small and medium-sized enterprises (SMEs) were presented. In the third and final one, the authors developed and presented a testbed for smart maintenance technologies.

The management of risks in the context of Industry 4.0 is currently lacking accurate and efficient systematic approaches and tools, leading to a potential underestimation or unrealistic perception of risks in various domains where effective risk management is crucial. Traditional methods, while valuable, have limitations and may not adequately capture all the factors that influence system safety. To address the challenges posed by conventional industry issues, emerging risks, and the complexities of socio-technical systems, there is a need for comprehensive Asset Management and Decision Support approaches. These approaches should encompass both conventional and emerging risk safety management, providing innovative and efficient solutions to support practitioners in navigating these complex environments. Based on the rationale provided, this paper is dedicated to the identification and analysis of risk management components, particularly pertaining to emerging safety risks in the context of Industry 4.0. It also examines the challenges posed by extreme, rare, and disruptive events that have the potential to severely impact organizational performance. The research focuses on relatively new methods grounded in system theories, specifically the Functional Resonance Analysis Method (FRAM) and the System-Theoretic Accident Model and Processes (STAMP). These approaches are considered the most suitable for investigating and addressing the research objectives. To validate the efficiency and practicality of the adopted methods, further research initiatives will be focused on conducting case studies. These case studies will aim to gather more accurate data and insights related to the application of FRAM and STAMP in real-world scenarios.

In the current era of Industry 4.0, we find ourselves in the midst of a profound transformation in the industrial landscape. This new era brings with it a host of challenges and problems, particularly in relation to the effective capture and processing of data. The success of this revolution hinges on our ability to harness data in a meaningful way, but achieving this goal is no small feat.

At the core of this data-driven revolution lies the critical importance of capturing data accurately. However, in many companies, this proves to be an incredibly complex problem. It is not simply a matter of capturing as much data as possible from the moment an asset or system is initiated. Rather, the focus is on acquiring a minimum amount of data that is sufficient to enable proper processing and analysis. This requirement presents a unique challenge in itself, as it often necessitates estimating this minimum data requirement based on a solid and reliable foundation of existing information.

The consequences of lacking adequate information can be far-reaching. Insufficient data availability inevitably leads to deviations in the processing and analysis of the captured data. However, this limitation also offers an opportunity for comparison. By examining assets of the same type that face similar challenges in data capture and processing, valuable insights can be gained. For instance, consider the scenario of comparing the health index of multiple transformers located in different electrical substations and operating under diverse conditions. If the data capture relating to the operational and maintenance variables is equally deficient across these transformers, and similar estimation techniques are employed, it becomes possible to compare the overall health of these equipment units.

To delve deeper into this topic, let us explore the specific example of calculating the Health Index for different pumps. In this particular case, the challenge arises from the fact that the start-up of these pumps predates the availability of operation and maintenance data. Consequently, due to this lack of information, a different approach must be taken. The estimation of various fundamental variables becomes necessary to facilitate the calculation of the Health Index and derive meaningful insights into the condition and performance of the pumps.

In conclusion, the advent of Industry 4.0 has brought forth a range of challenges and problems in the realm of data capture and processing. The ability to obtain and process data accurately is a critical factor in the success of this revolution. However, the complexity of the task lies not only in capturing a substantial amount of data but also in determining the minimum data requirements for meaningful analysis. Despite the difficulties posed by limited information, the comparison of similar assets facing data capture challenges can provide valuable insights. Through a specific example involving pump health index calculations, we can further understand the importance of addressing data estimation and processing in the context of Industry 4.0. Throughout this paper, the example of calculating the Health Index of different pumps will be developed in which the start-up of these goes back to times prior to the date of capture of the operation and maintenance data. Due to this lack of information, it will be necessary to start from the estimation of different fundamental variables for the processing of the data to be calculated.

Defining the existence of a digital asset, integrating multiple platforms that represent its entities digitally, and simultaneously meeting the specific demands of the operational context of railway infrastructure systems represents an unresolved challenge for this industry. This study focuses on the search for commonalities, complementing the perspectives of the scientific community and research centers with real-world applications. From there, the development of a framework presented in our research emerges, capturing both the state of the art and practice, providing a starting point for the development of scientific discussions and the search for future models that offer an effective solution to the problem. The integration of maintenance management models with architectures for the development of digital twins in Industry 4.0, and the applied study of the railway industry itself, are part of the foundation of this study. Seeking to adhere to the principles already proposed for Industry 4.0, the scheme introduces new relationship factors that will be prototyped in the industry, especially in railway infrastructures, allowing for scalability and the digitization of processes as crucial as the criticality assessment for asset prioritization.

In most industrial assets, determining the preventive interval is a task carried out by the maintenance engineer. In non-critical assets, the optimization process of the interval must consider the costs of operation and maintenance, as well as the income generated by its operation. The result is the economic determination optimal moment to perform preventive intervention (PM). Mathematically, an expression can be found that relates these variables to the failure occurrence process. However, when the equipment is critical to the business, it is necessary to avoid the occurrence of failure. For this purpose, investment is made in techniques that determine asset degradation (CBM). In this case, not only must the failure occurrence process be controlled, but the degradation of the asset must also be analyzed. To determine the economically optimal moment for the preventive replacement of a component subject to CBM, a semi-Markovian model has been developed. The model considers degradation as a Wiener process and integrates it with the failure occurrence process, adjusted to a Weibull distribution. The result is two mathematical formulas to determine the optimal degradation threshold and the interval for preventive replacement, optimizing costs, income, degradation, and failure distribution.

Currently, the timely identification of improvements, shortcomings, and potential failures applied to maintenance has taken relevant attention from the scientific community in recent years. In order to carry out appropriate diagnosis, the employment of methods to properly measure the reliability of industrial processes has been a trend. In this work, AMORMS and AMS-ISO 55001 are applied to a seawater desalination plant aiming for carrying out a fitted measurement, generating suited improvement plans. In this context, AMORMS is a model based on 8 phases, which focuses on assets management. On the other hand, AMS-ISO 55001 focuses on the asset management norm ISO55001. The results yielded include the design and generation of actions to tackle the 20% more deficient categories needed to achieve a competitive industrial performance.

Currently, the optimization process in the maintenance management has been treated as a critical issue by the industry. The proposed work focuses on maintenance model diagnosis, the process aims to detect positive practices and highly possible future improvements in the models. In order to carry out the diagnostic, a systematic process is performed over the maintenance model employed through the usage of AMORMS (Asset Management, Operational Reliability & Maintenance Survey). The study case presented in this work was carried out over a pulp mill from Chile, which has an annual production over 1 million tons. Regarding the overall analysis output, several issues were illustrated in order to reach a world level performance. Thus, the employment of such instruments aims to detect key issues in urgent need to be fixed, helping in successfully designing a fitted model to be competitive and reach higher productivity.

This study investigates the relationship between drivers’ electrodermal activity (EDA) and their eco-driving behaviours through real-time monitoring. Electrodermal activity, a physiological marker of sympathetic nervous system arousal, reflects emotional and cognitive states, providing a valuable window into drivers’ internal experiences. EDA and driving data were collected for 48 trips from 10 different drivers. Cluster analysis and the Pearson correlation coefficient was used to uncover potential patterns between driver EDA and their driving behaviour as measured using a driving score. The results follow the Yerkes-Dodson Law. Driving performance increase with EDA arousal, but only to a point. The investigation has implications for enhancing road safety, as it contributes to our understanding of how drivers’ emotional states influence their on-road performance. Additionally, it holds promise for developing innovative in-car systems that can adapt to drivers’ changing emotional states, promoting safer and more comfortable driving experiences. Ultimately, this study bridges the gap between psychophysiology and transportation, shedding light on the often-overlooked emotional aspects of driving behaviour.