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Preventive Maintenance: Some Operations and Technology Related Practices at a Malaysian Oil and Gas Firm
Abstract
The effectiveness of a maintenance management system can be measured by the way a function has achieved its intended objectives and generally evaluated based on the quality of the product or service provided, from the perspective of the user. The issues of high casualties in terms of personnel, millions of dollars in losses due to the unexpected shutdown of operations, environmental threats and the impact on the country's income should be addressed. Malaysian Oil and Gas Firm’s maintenance system efficiency practices, technology availability, the selection of suppliers, the influence of preventive maintenance on company performance and the rationale for choosing PM for downstream activities were considered in the interest of the nation. The primary purpose of this study is to use a qualitative methodology to examine PM procedures at a Malaysian oil and gas firm. The oil and gas sector is commonly linked with high-risk, cutting-edge technology projects that can have devastating economic and ecological effects if anything goes wrong. The study utilized the semi-structured interview with 10 identified oil and gas maintenance management personnels such as Managers, Supervisors and Staffs who are attached to Maintenance Department as the ‘gatekeepers’ as they are the one who are directly involved directly in maintenance management activities as well as preparing the reports and data with regards oil and gas companies maintenance management system used for the study. The result from the study stated that the personnel involved in maintenance activities appreciate the practices utilized however with the new methods introduced, the study also proposed, appropriate maintenance implementation as well as the involvement of several parties in aspects of planning and formulating maintenance policy up to the implementation stage in the oil and gas operators’ operation. The study can provide significant implications for theoretical, methodology and management involvement in terms of preventive maintenance contribution to organizational performance. This study analysed the issue raised in the implementation of preventative maintenance, specifically in Turnaround Maintenance in oil and gas company.
... Additionally, the findings from Mohamed et al., [22] provide valuable insights into maintaining the structural integrity of offshore platforms during the decommissioning process [22]. Applying these strategies may reduce the risk of unexpected failures during operations, thus ensuring smoother decommissioning processes. ...
... Additionally, the findings from Mohamed et al., [22] provide valuable insights into maintaining the structural integrity of offshore platforms during the decommissioning process [22]. Applying these strategies may reduce the risk of unexpected failures during operations, thus ensuring smoother decommissioning processes. ...
The majority of Southeast Asian (SEA) nations rely on the rules set forth by the Association of Southeast Asian Nations Council on Petroleum (ASCOPE), which strives to manage regional decommissioning concerns, in the absence of comprehensive decommissioning legislation. To further enable efficient and secure offshore and decommissioning execution, even with the ASCOPE guideline, the existing decommissioning legislation must be improved. The originality of this study is in the provision of suggested improvement plans for the SEA region's present decommissioning execution strategy. This approach will also lessen the danger to HSE in offshore and decommissioning operations. To analyse the risk associated with current decommissioning removal techniques, Delphi method was conducted, bringing together experts from the SEA area, including Malaysia, Indonesia, Brunei, Thailand, and Indonesia. These experts came from a variety of backgrounds, including industry, academia, and regulator. We considered three (3) risk factors: financial/economical, environmental, and occupational safety. Based on the Delphi method conducted, we were able to identify the best removal method for decommissioning of fixed jacket structure based on the risk factors listed.
... In which a systematic method of managing design process through integration of multiple stakeholders in a construction project. In addition, preventive maintenance implemented in oil and gas company contribute to organizational performance [6]. ...
COVID-19 pandemic has affected the activities related to operations management in the manufacturing industry under the scope of production interruption of raw materials and spare parts, labour, logistics, finance as well as supply and demand fluctuation. Manufacturing industry has faced limitation in the import of raw materials and spare parts which cause delay in manufacturing projects and activities. Due to the pandemic, standard operating procedures are implemented to ensure the labour can work in a safer environment without having the risk of getting infected. Logistics are also adapted with a new practice to ensure contactless delivery of products and items are disinfected before delivering. The pandemic has caused companies to face many financial issues where they are forced to shut down or take loans to sustain their business. Moreover, the supply and customer demand for the products fluctuate as the lifestyle of people changes to adapt themselves to the pandemic. The effects have showed the importance of implementing countermeasures to prepare the manufacturing industry for the post-pandemic world.
Environment quality is essential aspects of life on earth, any changes in the quality have a significant impact on human beings. The implementation unprecedented Movement Control Order (MCO), which halted social, economic, and other activities except essential services contributed to opportunities for the earth to rejuvenate itself and build a better blue sky, peaceful wildlife, controlled noise pollution, and improved environmental features. Although the unprecedented Movement Control Order (MCO) due to COVID-19 pandemic posed some adverse effects on the environment, but there are also some benefits on the environment. One of the significant positive effects by the MCO is that the environment had rejuvenated during this period, especially significant water quality improvement. Many reports worldwide, including Malaysia, had reported that the environment including water quality had shown signification improvement. With controlled activities, MCO recognized contributed to river water quality rejuvenation amidst one of the main essential service sewage treatment plant operating at its fullest capacity. Amidst of various river pollution sources, Sewage Treatment Plant (STP) effluent discharge loading was identified as one of the main water resource polluters, and the MCO phenomenon turn raises the question of whether the STP effluent discharge loading onto river resources is the main culprit to the river pollution. This is a new challenge for water resources management to examine the impact of sewage treatment effluent discharge loading on water resource pollution loading in Malaysia. This study investigates the influence of sewerage treatment plant effluent loading from the Kuala Lumpur sewerage catchment to the Klang River basin within the Kuala Lumpur City Centre. The river’s natural self-carrying loading was investigated to evaluate the influence of sewerage effluent loading on the selected river basin. The STPs within the study area were identified and segregated based on its tributary river basin. The individual STP’s Biological Oxygen Demand (BOD) and Suspended Solid (SS) average effluent discharge loading of the year 2020 analyzed against the study River’s BOD and SS average self-loading of the year 2020. The STP and River loading were analyzed to investigate the fraction of STP effluent discharge loading against River loading in the study river basin during MCO.
In this article, we begin with characterization of technological disasters, emphasizing that human errors in one or more phases of the system life-cycle set the stage for disaster. To counter the unexpected, designers include multiple independent safety barriers capable of preventing the occurrence or mitigating the consequences of such unexpected events. The integrity of the barriers depends on adequate levels of maintenance. Maintenance actions sometimes cause technological disasters, but are shown in large part to prevent malfunctions in technology control systems and safety barriers. We argue that well-planned and executed maintenance actions are key in the reduction of risk of technological disasters. In our research, we reviewed well-documented technological disasters in a variety of organizations such as commercial aviation, nuclear power generation, and petroleum and chemical processing. Using a three-factor cause analysis scheme (human error, equipment or process failure, safety barrier failure) we analyzed twelve disasters and found these factors present in each disaster description. In each analysis, we paid particular attention to the role of maintenance managers and technicians in reducing the risk of disaster. However, maintenance was also a direct causal factor in six of the twelve (50%) disasters analyzed. In addition, we identified the phase of the technological system life-cycle when the disaster occurred.
As it is predicted that there will be a decrease in production at the oil and gas facilities that are currently operating, it becomes necessary to start developing new oil and gas fields. This results in changes to the state’s policy regarding the participation of private companies in the development and implementation of oil and gas offshore exploration and production new projects. Access to unique fields can be provided to the most socially responsible companies. The purpose of this study is to present the author’s methodology for assessing the dynamics of corporate sustainability. The methodology is based on the assessment of individual, well-founded indicators of sustainable development of companies. The proposed methodology takes into account factors in areas such as occupational health and safety, environmental protection and economic efficiency and identifies two performance indicators. The first indicator is an aggregated index for three groups of factors to assess company ratings relative to the performance of the best company. The second indicator is an assessment of the dynamics within the company relative to the previous values of indicators of corporate social responsibility. The research results obtained using the proposed methodology show that oil and gas companies differ significantly in terms of corporate sustainability. The developed methodology for assessing corporate sustainability is of practical importance and can be used by companies in the analysis and planning of operating and investment activities that ensure the achievement of goals of corporate social responsibility.
Complexities of crises force supply chains managers to formulate crisis-induced strategies, which contrast with the conventional strategies that give precedence to competitive priorities. Recent crises, such as the coronavirus outbreaks, large-scale product recalls, and financial crises, underscore the increasing regularity and severity of crises with imperatives for introspective and retrospective socio-economic insights on the contexts, priorities, and themes of supply chain management in times of crisis. The purpose of this article is to review the literature on supply chain management in times of crisis, systematically coalescing the related body of scholarly work; outlining current methods applied by researchers; capturing strategic priorities and themes of complexities in research studies; and highlighting potentials for future studies. Using a systematic review of 250 journal articles published between 1996 and 2021, the review finds four dimensions for restorative priorities that reflect operations strategy during crisis: (i) critical supplies with essential services, (ii) timely response with recovery, (iii) safety with security, and (iv) traceability with transparency. The review also finds that operational complexities during crises originate from network configurations and business cycle complexities, optimal selections and provisioning system complexes, and complex learning processes and demand predictions. Insights from the review aid in the proposal of build-to-cycle, organic capabilities, and operational mindfulness framings for supply chain management in times of crisis. The article concludes by recommending future research studies on supply chain upgrades, diagnosis, solidarity, mapping, temporariness, and thresholds, as well as optimal selection problems on linking crisis systems investments with liabilities and on linking crisis network allotments with cross-functionalities.
Sustainable business practices are those that allow companies to increase their profit while still considering the triple bottom line of sustainability, which involves economic, environmental, and social aspects. There are a lot of studies exploring various aspects of supply chain practices. However, there remains a gap for the proposal of a complete framework concerning various industries. This research fills this gap by studying existing empirical and review studies. Based on a content analysis of 86 studies, 789 practices are derived and categorized, leading to a comprehensive classification of sustainable practices in supply chains. Moreover, the employed methods to analyze the data are investigated. The practices are cross-checked versus the studied industries showing the current sustainable industries. The details of the studied papers are presented in a comprehensive table. The sustainable framework showing the industrial solutions toward sustainable supply chains is divided into 38 minor practices classified into 11 main categories. This paper provides a novel interpretation of the sustainable solutions addressed by different industries and presents a new and updated classification of the literature identifying future directions. This offers many advantages for practitioners and researchers to transform a supply chain into an improved version in the bigger picture.
Purpose – The aim of this study is to develop a framework for the implementation of a Supplier Kanban System.
Design/methodology/approach – The research was conducted within a factory focusing on the warehousing and supplier-related operations using an action research methodology.
Findings – The study period spanning over six years was aimed to reduce the inventory and the manpower required to manage this inventory. This initiative led to a substantial average savings of more than 500,000 Malaysian ringgit annually.
Research limitations/implications – The focus of the study is on a framework for implementing a Supplier Kanban System in a small or medium-sized company within the automotive industry.
Practical implications – This framework is designed to be self-sufficient such that the members of the organisation can use it without the help of external experts or consultants.
Social implications – Reduction in inventory stored in the warehouse, the amount of redundant work carried out by the employees and other non-monetary improvements which are difficult to monetise resulted in a much better work environment and happier employees.
Originality/value – The change in the paradigm of the manufacturing value chain affects the Tier 1 and 2 suppliers in Malaysia. The lines and value chains posing challenges to the suppliers have brought the idea of the Supplier Kanban System. The Supplier Kanban System adopted the action research cycles and continuous
Over many decades, the electric power industry has evolved from a single low-power generator serving a small area to highly interconnected networks serving a large number of countries, or even continents. Nowadays, an electric power system is one of the largest man-made systems ever created, consisting of an enormous number of components ranging from small electric appliances to very large turbo-generators. Running such a large system is a significant challenge. It has necessitated the resolution of numerous issues by educational and industrial institutions. The main issues of the power system can be categorized into planning, operation, and control issues which are analyzed in this chapter, separately. Machine learning, deep learning, and a variety of regression, classification, and clustering algorithms are all extremely effective tools for addressing these issues. These procedures can be used to resolve a variety of power system issues and concerns, including planning, operation, fault detection and protection, power system analysis and control, and cyber security.
Visual cognitive strategies in construction hazard recognition (CHR) signifies prominent value for the development of CHR computer vision techniques and safety training. Nonetheless, most studies are based on either sparse fixations or cross-sectional (accumulative) statistics, which lack consideration of temporality and yielding limited visual pattern information. This research aims to investigate the temporal visual search patterns for CHR and the cognitive strategies they imply. An experimental study was designed to simulate CHR and document participants’ visual behavior. Temporal qualitative comparative analysis (TQCA) was applied to analyze the CHR visual sequences. The results were triangulated based on post-event interviews and show that: (1) In the potential electrical contact hazards, the intersection of the energy-releasing source and wire that reflected their interaction is the cognitively driven visual area that participants tend to prioritize; (2) in the PPE-related hazards, two different visual strategies, i.e., “scene-related” and “norm-guided”, can usually be generalized according to the participants’ visual cognitive logic, corresponding to the bottom-up (experience oriented) and top-down (safety knowledge oriented) cognitive models. This paper extended recognition-by-components (RBC) model and gestalt model as well as providing feasible practical guide for safety trainings and theoretical foundations of computer vision techniques for CHR.
The management of offshore platforms at the end of their production phase is a complex issue for technological, socioeconomic, ecological and safety reasons. The decommissioning or reconversion of offshore platforms in the context of a circular economy (CE) will lead to new knowledge acquisition, changing values and changing behaviours towards sustainability consistent with the ‘new’ business objectives. Multiuse platforms at sea (MUPSs) represent an interesting solution for development of marine infrastructures, including areas in which to start and develop various creative economic activities that are in harmony with the needs of environmental protection including renewable energy, sea shellfish farming, decarbonisation plants, tourism, and recreation. Particularly, the research activity focused on a deep literature review of offshore platform decommissioning and sustainable business model (SBM) in a CE context. This allowed us to access the sustainable business model canvas (SBMC), a conceptual tool that represents a holistic view of the different managerial multiuse options and their social and environmental impacts. Besides, to test the SBMC, we adopt an empirical analysis by semi‐structured questionnaires given to a sample of stakeholders in the decommissioning industry. The methodology was enriched by interviews with key informants to better investigate the business ecosystem and the feasibility of decommissioning applied to the case of an Italian offshore platform located in the Adriatic Sea. This article aims to contribute to supporting SBMs development following a holistic approach in relationship with all stakeholders and propose a multi‐criteria decision‐making analysis for evaluating and comparing alternative decommissioning options.
Total Productive Maintenance (TPM) is a worldwide maintenance program for plant equipment and machinery. It is a proactive maintenance that optimizes Overall Equipment Effectiveness (OEE) to get rid of the six (6) big losses. The research was inspired by observations made over a six (6) months period at Company X, a Small and Medium Enterprise (SME). Observations made were that the SME produced a lot of defective items, some requiring reworks, the SME had poor maintenance plans, poor relationship between departments, resulting in low morale of workers, frequent breakdowns of machines, as well as low production rate. The main objective of the research was to design a framework that would identify and address the aforementioned problems, resulting in an optimized OEE rate. The research data was obtained through various methodologies, including observation, questionnaire as well as interviews among the company employees - technicians, operators, and maintenance engineers. The data was analysed using Microsoft excel performance dashboards as well as TPM templates. Based on the findings, a framework was designed and developed that sought to address the aforementioned problems at the company. The study improved the OEE of machines and processes through the implementation of TPM approach at Company X.
Social scientists are increasingly interested in methodological advances that can illuminate the distinct experiences and health outcomes produced by various systems of inequality (e.g., race, gender, religion, sexual orientation). However, innovative methodological strategies are needed to (a) capture the breadth, complexity, and dynamic nature of moments co-constructed by multiple axes of power and oppression (i.e., intersectional experiences) and (b) keep pace with the increasing interest in testing links between such events and health among underresearched groups. Mixed methods designs may be particularly well suited for these needs, but are seldom adopted. In light of this, we describe a new mixed methods experience sampling approach that can aid researchers in detecting and understanding intersectional experiences, as well as testing their day-to-day associations with aspects of health. Drawn from two separate experience sampling studies examining day-to-day links between intersectional experiences and psychological health—one focusing on Black American LGBQ individuals and another on Muslim American LGBQ individuals—we provide quantitative and qualitative data examples to illustrate how mixed methods investigations can advance the assessment, interpretation, and analysis of everyday experiences constructed by multiple systems of power. Limitations, possible future adaptations, implications for research, and relevance to the clinical context are discussed.
Purpose
Technological advancements are reshaping the manufacturing industry toward digitalized manufacturing. Despite the importance of top-class maintenance in such systems, many industrial companies lack a clear strategy for maintenance in digitalized manufacturing. The purpose of this paper is to facilitate the implementation of maintenance in digitalized manufacturing by proposing a strategy development process for the Smart Maintenance concept.
Design/methodology/approach
This study is designed as a multiple-case study, where the strategy development in three industrial cases is analyzed. Several methods were used to collect data on the case companies' development of smart maintenance strategies. The data were analyzed with an inductive approach.
Findings
A process of strategy development for smart maintenance is proposed, including six steps: benchmarking, setting clear goals, setting strategic priority, planning key activities, elevating implementation and follow-up.
Practical implications
The proposed process provides industry practitioners with a step-by-step guide for the development of a clear smart maintenance strategy, based on the current state of their maintenance organization. This creates employee engagement and is a new way of developing maintenance strategies.
Originality/value
Maintenance strategies are traditionally regarded as a selection of corrective/reactive and preventive maintenance actions using a top-down approach. By contrast, the proposed process is starting from the current state of the maintenance organization and allows a mixture of top-down and bottom-up approaches, supporting organizational development. This is a rare perspective of maintenance strategies and will make maintenance organizations ready for the demands of digitalized manufacturing.
The local authority in Malaysia is the giant holder of public assets and they are obligated in performing better management to ensure the assets are of prominent quality to serve the needs of the local people. The local authority should take initiatives in having the right systems in managing and maintaining their assets. Therefore, the purpose of this study is to explore the maintenance management practices and performance improvement techniques to be integrated into developing the continuous improvement model towards achieving a sustainable performance of public assets. The resource-based view (RBV) and performance improvement (PI) theory are integrated as the reference to develop the maintenance management framework. The RBV emphasizing the resources and capabilities to achieve the value of strategic management. While the PI provides a suitable tool which is the maturity model for improving the current practices. Nine key elements of maintenance management were identified which demonstrating the maintenance capabilities and resources. Grounded on the theory of RBV and PI, the conceptual framework was established as the foundation towards developing strategic maintenance practices for sustainable performance of public assets and continuous improvement in maintenance management by the local authority in Malaysia.
Many authors have highlighted the importance of physical assets maintenance management in relation to resilience engineering, especially for systems operating under significant uncertainty. Thus, the authors presented a new approach to system maintenance based on resilience concept implementation. They introduced Maintenance Support Potentials (MSP) as a measure of an organization's maintenance support capacity. Moreover, based on the MSP definition, they developed a fuzzy-based organization's maintenance support potential level assessment method. The proposed approach takes into account two main MSP parameters – potential readiness level and process regency. It followed four main steps, including organization's MSP identification/evaluation, MSP weights assessment, Maintenance Support Capacity assessment, and final reasoning. A case study of a global manufacturer from the automotive industry is presented to illustrate the method's applicability. The authors also indicated further research directions to optimize the maintenance strategy based on Resilience-Based Maintenance concept.
Pavement damages are always associated with its optimum function and considered among the main contributing factors to fatal accident. With significance growth of vehicular road traffic and of accompanying problems of overloading vehicles in Malaysia will indirectly contributed to worsen the condition. Unceasingly, road pavement will experience failures more rapidly than expected hence lead to negative socioeconomic effects. A proper and timely maintenance by road authorities must be made in order to sustain and maintain the roadway condition to keep them serviceable so that it will benefit not only safety but also improve economic growth and social development. Hence, this study was carried out to explore current practise of pavement maintenance strategies in Malaysia. To achieve that objective, a qualitative data of questionnaires distribution to industry practitioners that are involved in managing roads are analyzed to indicate the viability and practicability of pavement maintenance strategies in Malaysia. Other than that, specifics issue pertinent to pavement management also identified. The findings of this study are valuable in developing the realistic performance criteria for seamless road pavement maintenance strategies.
Previous literature has highlighted many opportunities for digital technologies, such as the Internet of Things (IoT) and data analytics, to enable circular strategies, i.e., strategies which support the transition to a circular economy (CE). As one of the key circular strategies for which the digital opportunities are apparent, maintenance is selected as the focus area for this study. In the field of maintenance, IoT and data analytics enable companies to implement condition-based maintenance (CBM), i.e., maintenance based on monitoring the actual condition of products in the field. CBM can lead to more timely and efficient maintenance, better performing products-in-use, reduced downtime in operations, and longer product lifetimes. Despite these benefits, CBM implementation in practice is still limited. The aim of this research is thus to understand the challenges related to CBM implementation in practice, and to extract solutions which companies have applied to address these challenges. Towards this aim, a multiple case study is conducted at three original equipment manufacturers (OEMs). A framework is derived which allows for a broad analysis of challenges and solutions in the cases. We identify 19 challenges and 16 solutions and translate these into a set of actionable recommendations. Our findings contribute to the field of CBM with a comprehensive view of challenges and solutions in practice, from the OEM’s point of view. Moreover, we contribute to CE literature with a concrete case study about IoT-enabled circular strategy implementation.
Purpose
This paper aims to assess the contributions of maintenance practices and extract various significant factors that influence the implementation of maintenance practices in northern Indian small and medium-sized enterprises’ (SMEs) business performance.
Design/methodology/approach
In the current study, 216 north Indian SMEs have been extensively surveyed to assess the contributions of different maintenance practices implementation dimensions and manufacturing performance attributes through different statistical techniques. Analysis of variance (ANOVA) was used to statistically validate the hypotheses, while Levene’s experiment and Wilk–Shapiro tests were used to confirm ANOVA’s assumptions. Finally, the discriminating validity test extracts highly successful and moderately successful organizations.
Findings
The present research aims to evaluate the contributions of different maintenance practices implementation dimensions on SMEs’ manufacturing performance attributes. The study highlights that strategic maintenance practices such as corrective maintenance (CM), general maintenance issues, preventive maintenance (PM) issues and predictive maintenance (Pd.M) initiatives have enhanced overall equipment effectiveness, overall business performance, quality, cost optimization, safety, delivery and morale in SMEs.
Originality/value
The study validates the capabilities of maintenance practices toward significant improvements of various implementation dimensions such as breakdown maintenance issues, general maintenance issues, PM issues, Pd.M issues, CM issues, computer maintenance management system issues, maintenance scheduling issues and total productive maintenance issues. The study reveals significant implications for SMEs for realizing significantly improved manufacturing performance through strategic maintenance initiatives.
For many social science scholars, the COVID-19 pandemic has forced us to re-think our approaches to research. As a result of new social distancing measures, those of us who conduct in-person qualitative and ethnographic research have faced significant challenges in accessing the populations and fields we study. Technology served as an incredibly useful tool for social interaction and research prior to the pandemic, and it has since become even more important as a way to engage with others. Although not all types of social research, or even all projects, lend themselves to online activities, digital communication platforms like Zoom, Skype, and Facebook have allowed many of us to continue our studies from a distance—in some cases, significant temporal and spatial distances away from our research sites. As such, it is important to consider how these different methodological approaches challenge our understandings of fieldwork. While the disadvantages of not physically accessing the places we study are clear, can mediated approaches offer (any) hope of the immersion we experienced with in-person fieldwork? If many of us are able to continue ethnographic research (in some form) without co-locating with our participants in our field sites, how are our studies fundamentally affected, as well as the ways we conceptualize the ‘field’ more largely? This paper explores these methodological and epistemological questions through reflections on conducting online research during the beginning of the COVID-19 pandemic.
The most commonly used probabilistic model in reliability studies is the Perfect Renewal Process (PRP), which is characterized by the condition or type of maintenance represented: once the maintenance activities are executed, the equipment is restored to its original condition, leaving it “as good as new.” It is widely used since it represents an optimistic state when an item is replaced, assuming a perfect operational condition of the item after the maintenance. Some models have been developed for determining optimum preventive maintenance (PM) based on different criteria, and almost all aimed at PRP reliability modeling. The contribution of this paper is to analyze a model for determining the optimal preventive maintenance policy for a long time run under PRP and developing a general and chart-based tool for the problem, making it easier to solve the day-to-day practice and operation of equipment. As a result, a generalized chart was developed to support maintenance decisions through the elaboration of an original isometric table and complemented with a step-by-step methodology to determine the optimum time in which the preventive maintenance activities must be implemented. In most cases, these types of maintenance activities will consider a replacement activity.
Safety Improvement of engineering processes, especially Oil & Gas operations, has gained a lot of attention during the last decades. This fundamental vision results in risk remediation programs, minimizing the risks of failure, and reducing the associated costs for operation and maintenance. As failures may represent serious threats for both humans and the environment, a comprehensive tool is required to employ maintenance and avoid immoderate dangerous consequences. Traditional risk frameworks mainly include estimation approaches, such as Fault Tree (FT) and Event Tree (ET), producing more simplified models than other tools, such as Bayesian inference. The present work aimed at developing an advanced Risk-Based Maintenance (RBM) methodology for prioritizing maintenance operations, by addressing associated uncertainties through the accident modelling of the process. For this purpose, a Hierarchical Bayesian Approach (HBA) is applied to estimate the failure probabilities of each component while a Failure Mode, Effects and Criticality Analysis is performed to assess the severity. With Markov Chain Monte Carlo simulation from likelihood function and prior distribution, the HBA is capable of incorporating the fluctuations and uncertainties associated with operational data including the variability between the source of data and the correlation of observations. Lastly, to make a meaningful difference between different kinds of risk consequences, whether the risk has a direct or indirect loss, the cost of failures of components is accounted for. To demonstrate the application of the methodology, a Natural Gas Reduction and Measuring Station (NGRMS) is taken into account as a case study. The outcome of the case study proofed that PTG and pump are the most failures sensitive components among other if they being left unattended in the operation with an average number of failure occurrences of 67 and 45; While the THT pipelines and THT tank are less sensitive for being considered for major maintenance request with almost average of 5 times in their lifetime. The proposed method can be exploited by maintenance engineers , asset managers, and policymakers to reduce the downtime periods as well as the risks of ongoing operations.
The long-run effect of the synergy between natural resource consumption and environmental sustainability varies across countries depending on the economic structure. However, the transboundary effect of natural resource capital underscores the importance of environmental convergence. Here, we map ecological performance, biocapacity, and carbon footprint of nations. We assess the socio-economic drivers of environmental performance and convergence using novel cross-country time series techniques. We find that the expansion of biocapacity of nations has an ameliorating effect on ecological performance. The hotspot countries of environmental performance include Australia, Brazil, China, Germany, India, Japan, Russia, and the US. We confirm the existence of environmental convergence across nations — implying that the disparity in carbon and ecological footprint between higher-income and lower-income countries will converge in the long-run. This accentuates the need for global partnership towards achieving environmental sustainability.
In this article, we examine how scholars innovate research methods. Based on a review of published qualitative strategy and management research, we identify highly innovative academic papers, that is, papers that demonstrate substantial novelty in every part of the research process. We work through these papers in detail to demonstrate their novelty, highlighting concrete ways in which scholars have innovated three interconnected parts of the research process: data generation, data analysis, and presentation of findings. Based on our analysis, we develop a two-layered “iceberg” model of method innovation, which shows that important principles guide the innovative use of organizational research tools. These principles are engaging in holistic innovation, being excruciatingly clear in the presentation of methods, developing theory and method together, and being reflexive in innovating methods. Our model demystifies the largely implicit process of innovating research methods. We hope it serves to orient and encourage a more creative use of methods in future studies.
Preventive maintenance (PM), which is performed periodically on the system to lessen its failing probability, can effectively decrease the loss caused by the system breakdown or the performance degradation. The optimal PM interval has been well studied for both binary-state systems (BSSs) and discrete multistate systems (MSSs). However, in reality, the performance of many systems can change continuously, ranging from complete failure to perfect functioning. Considering such characteristics of systems, two types of performance-based measures, performance availability and probabilistic resilience, are addressed to quantify the system’s behaviour for continuous MSS. A Monte Carlo-based method is given to analyse the performance change process of the system, and an optimization framework is proposed to find the optimal PM interval with the considerations of per-unit-time cost, system breakdown rate, performance availability, and probabilistic resilience. A computer cluster is used as an example to illustrate the effectiveness of our proposed method.
To this day, continuous developments of technical systems and increasing reliance on equipment have resulted in a growing importance of effective maintenance activities. During the last couple of decades, a substantial amount of research has been carried out on this topic. In this study we review more than two hundred papers on maintenance modeling and optimization that have appeared in the period 2001 to 2018. We begin by describing terms commonly used in the modeling process. Then, in our classification, we first distinguish single-unit and multi-unit systems. Further sub-classification follows, based on the state space of the deterioration process modeled. Other features that we discuss in this review are discrete and continuous condition monitoring, inspection, replacement, repair, and the various types of dependencies that may exist between units within systems. We end with the main developments during the review period and with potential future research directions.
Abstract
Purpose – Every year, hundreds of people have died and thousands have been injured because of insufficient management of well control at oil and gas drilling and production sites. Major causes which have been reported in previous studies included uncontrollable blowouts and failure of blowout preventers because of insufficient safety practices. These onshore and offshore blowout disasters not only harm the work force but also critically affect the environment and marine life. In this research paper, a detailed quantitative survey and qualitative risk assessments (RA) have been carried out for assessing the potentially hazardous activities associated with well control along with their appropriate controls and risk reduction factors and mitigating measures in Middle East and south East Asian countries.
Design/methodology/approach – The sequential explanatory research design has been adopted in this study. Whereas, descriptive statistical approach has been used for the quantitative data analysis of this study. While, in-depth interview approach has been used for qualitative data collection. Similarly, what-if analysis method has been adopted in this study for the identification of effective safety and health risk mitigating factors because it provides in-depth information from health and safety environment experts.
Findings – The cumulative quantitative results based on the response from Saudi Arabian drilling industry have indicated that the well control operation is highly hazardous then Malaysian and Pakistani oil and gas industries. Likewise, findings from what-if analysis approach demonstrate that the drilling crew members have repetitively faced life threatening hazards which occur (safety and chemical) during well control onshore
and offshore operation because of oil base mud, confined space at site, pinch points and falling during working on blow out preventers. According to the overall result, respondents have highly recommended engineering and administrative hazard controlling factors as most suitable for the elimination of safety and chemical hazards during well control activities.
Practical implications – Besides, the developed methodological framework for the identification of suitable hazard controls can also be effectively used for potential hazards reorganization and identification of suitable hazard controls for other drilling and production industries and regions for accident prevention and safety and health management.
Due to the particular characteristics of hospital environments, it is necessary to carefully select the maintenance management model to be implemented in a health institution. This model must guarantee not only the availability of medical technology but also residual risk management and patient safety. This article is focused on a general review about the maintenance process and its management models through time and analyses in a particular way its impact in the hospital sector. This review will classify the evolution of the maintenance function through time according to its historical context. It will summarize earlier literature review papers related to maintenance management. Moreover, it examines the impact of maintenance models within the literature regarding the clinic-hospital environment. We can find a number of papers about the application of a strategy for hospital maintenance management, independently of its orientation. Among the models reported in the literature for maintenance management, it was detected that only seven had risk orientation or criticality of the assets. These models could be the most adequate for clinical and hospital environment. Nevertheless, it was also identified that none of these models were aimed at maximizing the safety of physiologically impaired people. This highlights the need to develop a model that integrates the hospital maintenance management, the medical technology residual risk management and patient safety, which must nowadays be an aim to achieve by biomedical engineers. The paper provides an organized and structured literature review and identifies gaps from the perspective of research and practice. It is useful for biomedical engineers, maintenance managers or other professionals related directly or indirectly with hospital technology management or hospital facility management.
Human-Computer Interaction draws on the fields of computer science, psychology, cognitive science, and organisational and social sciences in order to understand how people use and experience interactive technology. Until now, researchers have been forced to return to the individual subjects to learn about research methods and how to adapt them to the particular challenges of HCI. This book provides a single resource through which a range of commonly used research methods in HCI are introduced. Chapters are authored by internationally leading HCI researchers who use examples from their own work to illustrate how the methods apply in an HCI context. Each chapter also contains key references to help researchers find out more about each method as it has been used in HCI. Topics covered include experimental design, use of eyetracking, qualitative research methods, cognitive modelling, how to develop new methodologies and writing up your research.
As an aftermath of the 4 th Industrial Revolution at the beginning of century XXI, augmented information integrated into a cyber-physical system has changed the information age. From this point in mankind's history (circa 2012), emerging technologies to allow operations of everything in a smarter way have led both industry and society to re-think well-established processes or scratch new ones for (re) executions of anything as much as autonomous and intelligent. It permitted unforeseen improvements in the economy and well-being towards high-level standards of efficiency, sustainability, and safety, to name a few. Within the so-called Industry 4.0 (I4) age, advances in information and computing technologies (ICT) coupled with new capabilities in modeling and solving algorithms (MSA) and mechatronics (MEC) have enabled improvements in both industry and society. In industry, it represents more innovative design, operation, and control in several fields of manufacturing, logistics and supply chains, agricultural productions, etc. In society, besides the widespread information and communication capabilities that transformed the previous states of society into a more interconnected world, we observe the rise of crowdsourcing that aims to meet a reduced operational cost at the shared phase of new business models. Additionally, in the wake of the global 1 st pandemic occurring within the era of high-tech communities, the push in the educational processes has modified how individuals share, connect, and communicate a collection of workloads, expertise, follow-ups, etc., where capabilities, readiness, and preparedness have changed in the named Society 5.0 (S5). In this work, we analyze the interplay of I4 pillars in industry and society to identify gaps and challenges in the research and development to be overcome towards an achievable deployment within a complete I4-S5 phase.
The oil and gas sector is a very specialised and focused industry, where deep knowledge and experience is the norm. Often there is a significant level of government involvement through either shared ownership and/or strict regulatory controls. Making genuine mistakes in some countries can lead to arrest and imprisonment. On the other hand, due to the level of government involvement and the size of contract awards, there can often be an unhealthy tension between trying to achieve the best outcome and satisfying senior partner expectations.
In this feature topic, we explore the burgeoning trend to employ templates in qualitative research. To understand authors’ motivations to use templates and perceptions regarding template use in the scholarly community, we conducted an interview study with 21 interviewees who had published qualitative research in one of nine premier management journals between 2014 and 2018. Our analysis identified four key tensions in template use: structure versus restriction, convention versus innovativeness, legitimization versus obscuration, and instrumentality versus craft. We discuss these tensions and their implications for the practice and evaluation of qualitative research. We then introduce the eight empirical papers included in this feature topic. Each of them addresses and explores specific aspects of the origins and limitations of template use, whilst providing insights and guidance for future qualitative research. We envisage this feature topic to be a catalyst for discourse amongst scholars, engaging with contemporary trends and tensions to pave new pathways that embrace the diversity and plurality of qualitative approaches.
The purpose of the paper is to review literatures related to total productive maintenance (TPM) in small and medium-sized enterprises (SMEs). The first part of paper presents the research background to underline the importance for SMEs to integrate holistic maintenance strategies in their productions. The second part of the paper briefly covers the history of equipment maintenance, followed by systematic discussion of TPM, including the definition of TPM, the eight pillars, the objective of TPM, and the benefits of TPM. Next, the paper summarizes the influencing factors and analyzes case studies to seek research gaps in TPM. As findings, the paper revealed that the suitability of TPM in SMEs and the need in research to pay attention to the implementation method by fully considering the changing characteristics of SMEs.
Maintenance is one of the main stages to deliver business outcomes from physical assets over their life cycles. However, as unexpected events and performance may occur in maintenance management, organizations shall be aware of how to address them as well as other opportunities for improvement. Accordingly, this chapter intends to present an improvement framework for maintenance management. The first two sections provide an introduction to maintenance management improvement and its interface with the ISO 55000 series for asset management and the maintenance management model (MMM). Then, the proposed framework and its activities for improvement in maintenance management are discussed in the third section. The fourth section addresses an overview of the main RCA techniques to support the framework implementation. Finally, a hydroelectric power plant case study is discussed to demonstrate the framework in a real operational context.
Digital transformation is one of the most discussed themes across the globe. The disruptive potential arising from the joint deployment of IoT, robotics, AI and other advanced technologies is projected to be over $300 trillion over the next decade. With the advances and implementation of these technologies, they have become more widely-used in all aspects of oil and gas industry in several processes. Yet, as it is a relatively new area in petroleum industry with promising features, the industry overall is still trying to adapt to IR 4.0. This paper examines the value that Industry 4.0 brings to the oil and gas upstream industry. It delineates key Industry 4.0 solutions and analyzes their impact within this segment. A comprehensive literature review has been carried out to investigate the IR 4.0 concept's development from the beginning, the technologies it utilizes, types of technologies transferred from other industries with a longer history of use, robustness and applicability of these methods in oil and gas industry under current conditions and the incremental benefits they provide depending on the type of the field are addressed. Real field applications are illustrated with applications indifferent parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of machine learning technologies.
Emotions have a significant impact on our decision-making, learning, awareness, social interactions, and mental and physical health. Even though large efforts have been put into quantifying human emotions, their subjectivity, context-dependence, and complexity render them as being almost unpredictable. However, while different streams of research in pervasive computing and psychology have made significant progress in the quantification of emotions, the most successful research results come out of controlled laboratory studies. In this article, we present a retrospective of a series of in-the-wild studies through the lens of human emotions. We are looking at the weaknesses and strengths of traditional research methods and present lessons learned. We furthermore call for a readjustment of research rigor and describe potential new research designs specific to out-of-the-lab studies.
Research in psychopathology is booming in an unprecedented way, at least, in terms of increasing number of publications. Yet, a few questions arise: Does quantity also give us quality? Is the collected data generally of sound quality? How is data typically collected in psychopathology? Are the applied methods of data collection appropriate for this particular field of study? This article explores three different methods of data collection in psychopathology, namely self-rating scales, structured interviews, and semi-structured, phenomenological interviews. To identify the most adequate methodological approach, we first establish the nature of the object of psychopathology and then we critically assess each method’s appropriateness to this field of study. We emphasize fundamental issues that make self-rating scales and structured interviews unfit for the task of adequately examining psychopathology. By contrast, we propose that a semi-structured, phenomenological interview presents a more appropriate method. Finally, we describe two types of semi-structured, phenomenological interviews that can be applied to assess and explore psychopathology, respectively.
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This paper presents a new bi-objective model to deal with an integrated production planning and reliability-based multi-level preventive maintenance (PM) scheduling problem. The production system includes a set of parallel deteriorating machines. The model aims to find the production lot sizes and the sequence and interval of PM activities over a multi-shift planning horizon. The PM activities are classified as the adjustment and replacement ones, each of which has a specific cost and effect on machines. The model serves to minimise the total cost while maximising customer satisfaction. Also, the most profitable customers are identified by applying a hybrid multi-attribute decision making (MADM) approach (BWM-TOPSIS) in order to fulfil their orders in their desirable time windows. Three efficient meta-heuristic algorithms are developed to solve the proposed model for large-scale problems. Besides, TOPSIS method is employed to select the most desirable solution among the obtained Pareto solutions. Finally, a case study is provided to show the applicability of the proposed approach.
The nonprofit sector may suffer financially from inconsistency in regulations and polices surrounding internal control implementation. To address this issue, our study explores how perceived internal control strength differs between nonprofit and for-profit organizations. Furthermore, we examine three components of the Committee of Sponsoring Organization framework to determine which components might significantly influence whistleblowing for nonprofit organizations. As expected, all three components appear to significantly influence whistleblowing for those in for-profit organizations. For those in nonprofit organizations, the perception of control activities and monitoring activities significantly mediates the relationship between organization type and whistleblowing intentions. Finally, the data indicate that the use of an anonymous website for whistleblowing at a nonprofit organization may require added attention and resources if employees at nonprofits are to use this outlet to the same extent as it is used at a for-profit organization.
Resilience is an emerging concept whose application has increased significantly in managing engineering systems. To have effective resilience management, first, an estimation of the system resilience should be undertaken. However, a lack of historical data and limited information are major challenges for system resilience estimation. The main reason is that most data collection systems are not designed for resilience assessment. Moreover, the available study dealing with resilience assessment have been used different indices to quantify the resilience of critical infrastructures including, robustness, recoverability, etc. These indices can be affected in a complex way by different factors such as operational conditions, protective practices, the recovery process, logistic process, etc. However, the available resilience studies are not so detailed regarding identifying and quantifying these influencing factors. Therefore, this paper aims to develop and apply a practical methodology to estimate resilience based on the combination of expert judgment and fuzzy set theory. By adopting this methodology, factors influencing resilience can be modeled effectively. Finally, the application of the proposed methodology is illustrated for the main fan system of an underground space.
One of the main aspects that must be considered while developing any industrial project is its cost. Design to Cost (DtC) processes allow the design team to manage any kind of project based on the costs of each of the phases and of the elements, by applying a number of methodologies, techniques, cost models and tools. In this paper, a DtC framework is proposed, taking other DtC centered research as references and focusing on a probabilistic approach, in order to lead the early design stages of large industrial products, where most of the information is unclear or is still being defined. A literature review about DtC methodologies and techniques is first carried out in order to analyze, compare and classify them and identify the strengths and weaknesses of each one.
With the increasing demand for complicated control and monitoring systems in chemical process plants based on tremendous improvements in the digital world, the number of devices for monitoring and controlling the plants has risen dramatically. In such huge networks of a myriad of instrument devices, determination of an optimum proactive maintenance interval (PMI) has become vital for enhancing plant safety and reducing maintenance costs. PMI optimization is a matter of considerable benefit variation, process reliability, personnel safety, and environmental concerns. In this paper, a comprehensive formula and a concise formula are developed and applied for obtaining the optimum PMI based on the expected utility theory. The proposed formulas are applicable for determining the most affordable and effective maintenance plan providing the lowest cost and acceptable reliability, availability, and safety in oil and gas industries in terms of an optimal decision-making problem. By considering variable failure rates and components' wear and tear, a comprehensive formula has been developed. Moreover, for constant failure rate and returning to the “as-new” condition after each proactive maintenance, a concise and “easy-to-use” formula has been obtained. Finally, it has been shown through practical implementation of the presented methodology in a typical gas refinery that about 41% benefit is achieved in one year, compared with the previous heuristic maintenance strategy in the same duration.
The essential aim of Industry 4.0 is to enable industries to be more productive, efficient, and flexible. A predictive maintenance strategy can make a positive contribution to all these things, as it uses industrial IoT technologies to monitor asset health, optimise maintenance schedules, provide real-time alerts about operational risks, and maximise uptime, and can provide digital services to customers based on data from its machines. It improves productivity, improves customer satisfaction, and therefore gives the company a competitive advantage. Nevertheless, decision making in relation to a predictive maintenance strategy is not systematised, and this may lead to some inappropriate decisions, which do not achieve the goal sought. This chapter describes a multicriteria model, designed with the analytic hierarchy process, to systematise decision making with respect to a predictive maintenance strategy.
In the present-day competitive environment, industries are facing with a new crisis of shrinking profit margins. Organizations’/companies cannot ill afford quality, safety, poor environment and productivity issues. There is thus the requirement of an integrated approach towards management of maintenance. The aim is to present a framework for a programme for an effective continuous improvement of issues related to maintenance. Maintenance undoubtedly plays a key role in an organization’s long-term profitability. In this article, there is a proposal for an integrated maintenance management. The suggested proposal is based on maintenance management, maintenance operation and equipment management (predictive maintenance, preventive maintenance, total productive maintenance). This article explores the benefits of integrated maintenance management compared with the traditional maintenance approach and discusses some of the latest tools in this area.
The information revolution leaves its mark on businesses, resulting in organizations looking for digital innovation (DI) to apply to their business processes and anticipate competitors. Since the interplay between business process management (BPM) and DI has been underdeveloped, this mixed-methods article investigates the strength and nature of the relationship. We supplement the findings of an international survey (stage 1) with explanations from an expert panel (stage 2) to generalize a positive yet moderate link because of manifold contextual factors affecting stra-tegic decision-making. We extend the technology–organization–environment (TOE) framework and profile organizations along their digital process innovation (DPI) mastery in a readiness ma-trix.
Modern manufacturing organizations have started giving paramount importance to sustainable aspects of the manufacturing processes, realizingnot only that the natural resources are dwindling rapidly but also that they bear significant responsibility to the society and surroundings for the overall future development. Catastrophic failures and the maintenance of complex equipment can generate a large amount of hazardous waste within the organization that can affect the overall production level, environment, along with impacting the health of workers in the long run. Failure mode and effect analysis (FMEA) is an efficient risk analysis tool for processes, products, designs or services and has been adopted by different types of organizations. In this paper, for the first time in the literature, the consequences of failure modes of industrial equipment are considered from the sustainable point of view, which is believed to be a requirement for the establishment of a successful sustainable manufacturing strategy. Severities of failure modes are considered from environmental, societal and economic points of view, along with the chances of occurrences and detections. However, due to lack of exact data, these risk factors are evaluated linguistically by cross-functional experts, which made the situation complex. To properly prioritize the failure modes according to their risk levels, a novel hybrid Multi-Criteria Group Decision Making (MCGDM) approach by integrating Interval Type-2 Fuzzy Decision-Making Trial and Evaluation Laboratory (IT2F-DEMATEL) and Modified Fuzzy Multi-Attribute Ideal Real Comparative Analysis (Modified FMAIRCA) methods is proposed. Calculating the causal dependencies among the risk factors and finding out their relative importance are the twofold benefits of the IT2F-DEMATEL approach. Defuzzified criteria weights are further utilized in the proposed modified FMAIRCA approach for risk ranking of failure modes. The effectiveness of the proposed hybrid approach is demonstrated by considering a case-study from a process plant gearbox. Next, the obtained ranking results are compared with the results obtained from other commonly applied fuzzy MCDM methods in the FMEA domain. Stability and robustness of the proposed approach is also highlighted by performing sensitivity analysis.