A Model for Optimizing the Life Cycle of Physical Assets based on a Circular Economy Approach
Abstract
In the past years, Asset Management (AM) was normally described as maintenance
management; their focus was on programs, procedures, and other tasks to optimize the organization’s assets uptime. Nowadays, when considering asset management, an active life cycle management of the major assets must be taken in consideration and components considered from the cradle to the grave.
On the other side, some other points must be taken into consideration when performing asset management. In the past, only a few assets were considered to be managed; this old approach brings a few weaknesses. A new approach must consider all the assets as part of the organization. Even if their role after analyses is not pivotal, it is important to know them, and plans must be made for those assets, even if they are not complex or detailed.
When considering AM, there is a need to apply a Life Cycle Assessment (LCA). While doing so, many factors must be taken into consideration, focusing on reducing expenses and increasing sustainability in several aspects. Equipment with less environmental impact must always be considered and developed, while improving sustainability broadly.
The principal function of asset management is to realize value from assets, but where to start? To obtain their major value, the ISO 55001 presents a set of plans. One of them is the Strategic Asset Management Plan (SAMP), which details the asset management objectives and explains their relationship with the organizational objectives and the framework required to achieve the asset management objectives. The SAMP needs to have a set of processes to realize the expected value. Asset management deals with different areas. It must have a clear way to communicate inside and outside the organization because every sector plays a vital role in the whole. On the other hand, the SAMP must provide tools to
manage physical assets' life cycle. Those tools need to be quantitative to be easily measured.
On the other side, the SAMP must be aligned with the organization's objectives. This alignment is communicated to ensure that external and internal stakeholders, at all levels of the organization, understand why asset life cycle activities and asset management activities are implemented. While building a SAMP, it is fundamental that it can be measured with the use of tools such as Balanced Scorecard (BSC) with adequate Key Performance Indicators (KPIs) that can bring a quantitative approach that can easily measure the quality of the SAMP. While using BSC there are four perspectives: Financial, Customer, Internal Process, and Learning and Growth, which perfectly fit the SAMP requirements.
ResearchGate has not been able to resolve any citations for this publication.
ISO 55001 defines a set of requirements that when implemented and maintained
guarantee the good performance of an organization's asset management, responding to stakeholders needs and expectations and ensuring value creation and maintenance as well as a global vision of assets in a circular economy.
Organizations where physical asset management is of major importance include all those that involves: facilities, machinery, buildings, roads and bridges, utilities, transportation industries; oil and gas extraction and processing; mining and mining processing; chemicals, manufacturing, distribution, aeronautics and defence.
However, since ISO 55001 is a new standard in the global market, because it is
intrinsically difficult to implement, a diagnostic model on the state of organizations can greatly help on the implementation.
Before beginning to implement the ISO 55001 standard, it is necessary to verify whether the organization is ready to begin this task. It is usually necessary to fine-tune many aspects before starting a great task like this. But where to start? What aspects do I need to correct before starting the default implementation?
This thesis proposes a diagnostic model to evaluate the state of organizations in relation to their potential to implement the ISO 55001. The diagnosis allows to identify the aspects of the organization that are ready to receive the new standard, the critical, the fragile and the weak points of the company that must be corrected.
The diagnostic model is based on surveys, with several questions and with five possible answers. Each possibility of response has a quantification and a critical classification.
The final result is a global positioning of the company with the identification of the
various aspects to be corrected in order to be possible to implement ISO 55001. A radar chart provides a global "radiography" of the company diagnosis.
The diagnostic template has been validated and the results are presented in the document.
Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification.
With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life cycle management (ALCM) has been gaining increasing attention in recent years. This study explores the evolution of Industry 4.0 technology applications to sustainable ALCM from 2002 to 2021. This study is based on keywords collected from 3896 ALCM-related scientific articles published in the Web of Science, IEEE Xplore and Engineering Village between 2002 and 2021. We conducted a review analysis of these keywords using a network science-based methodology, which unlike the tedious traditional literature review methods, gives the capability to analyze a huge number of scientific articles efficiently. We built keyword co-occurrence networks (KCNs) from the keywords and explored the network characteristics to uncover meaningful knowledge patterns, knowledge components, knowledge structure, and research trends in the body of literature at the intersection of ALCM and Industry 4.0. The network modeling and data analysis results identify the emerging Industry 4.0-related keywords in ALCM literature and indicate the recent explosion of connectivity among keywords. We found IoT, predictive maintenance and big data to be the top three most popular Industry 4.0-related keywords in ALCM literature. Furthermore, this study maps relevant ALCM keywords in contemporary literature to the nine pillars of Industry 4.0 to help the responsible manufacturing community identify research trends and emerging technologies for sustainability.
Recent activities in the oil and gas industry have shown an increasing need for monitoring engagements, such as in shipping, logistics, exploration, drilling, or production. Hence, there is a need to have asset management of these offshore assets (or facilities). Much of the offshore infrastructure is currently approaching or past its operational life expectancy. The study presents an overview on asset management of offshore facilities towards monitoring, safe practices, maintenance, and sustainability. This study outlines the major considerations and the steps to take when evaluating asset life extensions for an aging offshore structure (or asset). The design and construction of offshore structures require some materials that are used to make the structural units, such as offshore platform rigs, ships, and boats. Maintaining existing assets in the field and developing new platforms that are capable of extracting future oil and gas resources are the two key issues facing the offshore sector. This paper also discusses fault diagnosis using sensors in the offshore facilities. The ocean environment is constantly corrosive, and the production activities demand extremely high levels of safety and reliability. Due to the limited space and remote location of most offshore operations, producing cost-effective, efficient, and long-lasting equipment necessitates a high level of competence. This paper presents the guidelines on asset monitoring, sustainable maintenance, and safety practices for offshore structures. In this study, the management of offshore structures were also presented with some discussions on fault monitoring using sensors. It also proposes sustainable asset management approaches as guidelines that are advised, with policy implications.
Infrastructural assets such as roads, bridges, and buildings make a considerable contribution to national economies. These assets deteriorate due to aging, environmental conditions, and other external factors. Maintaining the performance of an asset in line with rational repair strategies represents a considerable challenge for decision-makers, who may not pay attention to developing adequate maintenance plans or leave the assets unmaintained. Worldwide, organizations are under pressure to ensure the sustainability of their assets. Such organizations may burden their treasury with random maintenance operations, especially with a limited budget. This research aims to develop a generalized condition assessment approach to monitor and evaluate existing facility elements. The proposed approach represents a methodology to determine the element condition index (CI). The methodology is reinforced with an artificial neural network (ANN) model to predict the element deterioration. The performance of this model was evaluated by comparing the obtained predicted CIs with ordinary least squares (OLS) regression model results to choose the most accurate prediction technique. A case study was applied to a group of wooden doors. The ANN model showed reliable results with R2 values of 0.99, 0.98, and 0.99 for training, cross-validation, and testing sets, respectively. In contrast, the OLS model R2-squared value was 1.00. These results show the high prediction capability of both models with an advantage to the OLS model. Applying this approach to different elements can help decision-makers develop a preventive maintenance schedule and provide the necessary funds.
The Indian transport sector powered by fossil fuels is primarily responsible for creating severe issues like greenhouse gas emissions, foreign fuel dependency, economic burden, and chronic health effects. To mitigate these severe issues, electric vehicles (EVs) are positioned as an alternate green and clean technology, which can potentially enable the efficient transition to a sustainable low-carbon emission transportation system and preservation of natural scarce resources. Despite announcing favorable policy measures to encourage EV adoption, the multiplicity of potential factors with mutual interaction has resisted its penetration in several countries. Though researchers have identified the critical factors, the question "How do these factors mutually interact among themselves?" has remained largely unanswered in empirical research. Unpacking the relationship between the factors will empower manufacturers and policymakers in strategic planning, and devising suitable measures in controlling the factors. The primary goal of this research is to undertake a thorough assessment and give a brief understanding of the various factors responsible for sustainable growth of EVs market in India. Factors are identified from past academic literature and experts' interviews. Study further empirically validated the identified factors. Then integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-Inter-pretive Structural Modeling (ISM) approach has been used to demonstrate the interrelationship and hierarchal structure of the factors. The present study will be useful to the manufacturers, policymakers to focus on the gray area so that they can expedite the growth of EVs deployment in India.
Green growth plays a vital role in achieving ecological sustainability in the presence of eco-innovation. This study explores the relationship between technological innovation, the knowledge economy, and sustainability using panel 1996–2017 data from 30 Organization for Economic Cooperation and Development countries. The knowledge economy variables are presented as intangible assets from the EU KLEMS Release 2019 database, and panel vector autoregression estimation, the Granger causality tests impulse response functions, and the forecast-error variance decomposition technique are used to test the link. The findings demonstrate that the knowledge economy is good for sustainability and that technological innovation results in green growth. Intangible asset is positively associated with green growth. Green growth has a positive effect on purchased organizational capital, vocational training, and own-account organizational capital. Granger causality test results indicate a causal relationship between intangible asset and green growth. This work contributes to the current literature on sustainable development and highlights five aspects that affect performance. Our study provides a novel insight on knowledge intensive growth impact on environment and economy. Policymakers and practitioners can use this study quantitative knowledge to design efficient policy and management guidelines for promoting green growth.
The rapid transformation to renewable energy will require an equal uptake of energy storage technologies. Therefore, green hydrogen production via water electrolysis has been touted as a practical solution for...
Predictive maintenance is fundamental for modern industries, in order to improve the physical assets availability, decision making and rationalize costs. That requires deployment of sensor networks, data storage and development of data treatment methods that can satisfy the quality required in the forecasting models. The present paper describes a case study where data collected in an industrial pulp paper press was pre-processed and used to predict future behavior, aiming to anticipate potential failures, optimize predictive maintenance and physical assets availability. The data were processed and analyzed, outliers identified and treated. Time series models were used to predict short-term future behavior. The results show that it is possible to predict future values up to ten days in advance with good accuracy.KeywordsData analysisAutoregressive modelsARIMADeep LearningTime series forecastingPredictive maintenance
The economic sustainability of any industry is directly linked to the management and efficiency of its physical assets. The maintenance of these assets is one of the key elements for the success of a company since it represents a relevant part of its Capital and Operational Expenses (CAPEX and OPEX). Due to the importance of maintenance, a lot of research has been done to improve the methodologies aiming to maximize physical assets’ availability at the most rational costs. The introduction of Artificial Intelligence in the world of maintenance increased the quality of prediction on equipment failures, namely when associated to continuous equipment monitoring. This paper presents a case study where a neural network is proposed to predict the future values of various sensors installed on a paper pulp press. Data from the following variables is processed: electric current; pressure; temperature; torque; and speed.KeywordsPredictive maintenanceCondition monitoringNeural networksForecastingArtificial intelligence