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The purpose of this paper is to present a risk-based prioritization method (RBPM), using functional failure risk and strategic importance assessment, in order to support public managers' decision-making process for the planning and allocation of scarce resources.

The methodology incorporates two assessment matrices into a management group’s decision-making process for resource allocation. As part of an action research strategy, the authors present the developed RBPM and its application in one Norwegian police district.

The RBPM provides a processual approach to detect risk of failures in the organization, which can facilitate better planning and allocation of resources, to mitigate risks from materializing and enhance performance. The results also indicate that the proposed assessment models provide an easier approach to consolidating different perspectives and weighting the strategic importance of disciplinary functions, when prioritizing the planning and allocation of scarce resources is necessary.

The research is restricted in that it only considers one police district in Norway. Further limitation surrounds the issue of generalizability, as only one unit from the district is used. Further research should adapt and extend the developed RBPM in the district.

The RBPM may be of interest to public managers in the emergency sector operating with a large number of disciplinary functions in a highly dynamic and uncertain environment.

The RBPM applied in this study is based on approaches with advanced application in the oil and gas industry adapted to a contextually different environment which, in contrast, consists of functional hierarchies composed of human resources.

The purpose of this paper is to focus on developing a knowledge-based engineering (KBE) approach to recycle the knowledge accrued in an industrial organization for the mitigation of unwanted events due to human error. The recycling of the accrued knowledge is vital in mitigating the variance present at different levels of engineering applications, evaluations and assessments in assuring systems’ safety. The approach is illustrated in relation to subsea systems’ functional failure risk (FFR) analysis.

A fuzzy expert system (FES)-based approach has been proposed to facilitate FFR assessment and to make knowledge recycling possible via a rule base and membership functions (MFs). The MFs have been developed based on the experts’ knowledge, data, information, and on their insights into the selected subsea system. The rule base has been developed to fulfill requirements and guidelines specified in DNV standard DNV-RP-F116 and NORSOK standard Z-008.

It is possible to use the FES-based KBE approach to make FFR assessments of the equipment installed in a subsea system, focussing on potential functional failures and related consequences. It is possible to integrate the aforementioned approach in an engineering service provider’s existing structured information management system or in the computerized maintenance management system (CMMS) available in an asset owner’s industrial organization.

The FES-based KBE approach provides a consistent way to incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data and risk categories. It minimizes the variations present in the assessments.

The FES-based KBE approach has been demonstrated in relation to the requirements and guidelines specified in DNV standard DNV-RP-F116 and NORSOK standard Z-008. The suggested KBE-based FES that has been utilized for FFR assessment allows the relevant quantitative and qualitative data (or information) related to equipment installed in subsea systems to be employed in a coherent manner with less variability, while improving the quality of inspection and maintenance recommendations.

The purpose of this paper is to review the evolution of inspection and maintenance (I&M) practices used for aging and newly built oil and gas (O&G) facilities. It also proposes a framework and an approach for mechanizing inspection planning to perform preventive maintenance (PM) activities, taking technical condition (TC) and relative degradation (RD) into consideration.

The paper systematically collects, categorizes, and analyzes the published literature of both researchers and practitioners. It also utilizes industrial experience that has been accrued and utilized from inspection planning practices for static mechanical equipment on aging O&G production plants.

The paper defines significant issues in I&M of O&G assets related to: different philosophies; stakeholders’ requirements trade-off; dependability and asset deterioration challenges; items interacting with inspection planning mechanization processes and I&M optimization approaches. A framework is identified to mechanize the inspection planning process in order to reduce the effect arising from human involvement, while improving the effective utilization of data from different sources. The suggested approach improves the quality of an inspection program, while minimizing the variability and cost to the engineering contractors as well as to the owners of O&G facilities.

The mechanization of inspection planning (MIP) is vital to have inspection programs with uniform quality. The currently employed inspection practices face challenges in maintaining uniform quality from one inspection program to another due to the variability present in the planning process, especially among the different inspection planning engineers. The suggested fuzzy logic-based MIP supports the minimization of the variability and increases the quality of inspection programs.

The paper provides a comprehensive review of research contributions and industrial development efforts. These will be useful to the life cycle stakeholders in both academia and industry in understanding the inspection planning problem and solution space within the O&G asset I&M context.

While the petroleum industry remains to be the main source of energy in the world, it is responsible for a large amount of resource consumption, environmental emission and safety issues. In this industry, most of the refinery equipment are running out of their designed life cycle, leading to many challenges regarding equipment reliability, products quality, organizations’ profitability, human resources safety and job satisfaction, and environmental pollution, which affects not only the human resources of the refinery but also the people who live in the vicinity. This study aims to model and simulate the maintenance system of an oil refinery to reduce the rotating equipment’s downtime while simultaneously considering the three pillars of sustainability.

Considering the complexity of the system and its inherent dynamism, System Dynamics (SD) approach is applied to model and simulate the maintenance system of an oil refinery, aiming at reducing equipment’s downtime considering the three pillars of sustainability simultaneously. As a case study, the maintenance system of rotating equipment in the Abadan oil refinery of Iran is investigated.

Individual policies are investigated and categorized into three main groups: economic, social and environmental. The dynamic nature of the system demonstrates that applying combinations of the policies would be more effective than performing individual ones or even a combination of all policies at the same time. The findings show that to manage the maintenance and reliability issues in complex industries, only operational level maintenance strategies would not be helpful; rather, a holistic strategic solution counting different suppliers or even the government policies supporting the operational level maintenance decisions would be effective.

This study is the first which brings the perspective of sustainable policy-making in the SD modeling of a complex maintenance system like that of the petroleum industry. The developed model considers economic, environmental and social objectives simultaneously. Besides, it reflects the role of different stakeholders in the system. Furthermore, the policy-making attempt is not limited to the operational level corrective and maintenance solutions; instead, a comprehensive, holistic view is applied.

An optimal network design model is formulated providing a set of link investment pattern for the most reliable network with highest network performance under uncertain conditions. The connectivity probability of a link is assumed to be improved by the investment to the link. The object function is represented as the expected performance measure. The formulated model is categorized in a group of stochastic network design problem in which the existence of a link is probabilistic. The characteristics of the gradient vector of the objective function are analyzed. The derivatives of the objective function can be approximately evaluated without enumerating all possible network state vectors. Numerical examples are calculated for analyzing the sensitivity of optimal investment policies.

This paper aims to propose a methodology to support public managers' adaptation of the Hoshin Kanri (HK) strategy deployment approach in the context of lean thinking (LT), considering strategic alignment and consensus reaching when prioritizing a vital few of the organization's continuous improvement (CI) projects.

The methodology incorporates the A3 problem-solving report into the HK approach to identify and outline CI projects. The priority and deployment of the projects are weighted by a composite score for impact and innovation using the Delphi method. The proposed methodology was applied in one Norwegian police district as part of action research.

The obtained results indicate that the proposed methodology provides an intuitive and systematic approach to weigh the importance and ensure alignment of CI projects with the organization's strategy and goals. Consequently, this minimizes the possibility of strategy deployment priorities being weighted by decision bias and siloed decision-making.

The literature on strategy deployment in the context of LT in police services is significantly limited and this study aids in fixing this gap. The adapted HK approach can support the implementation of LT as an integral part of a comprehensive strategic management system, thereby enabling knowledge sharing and exploration of the extendibility of implemented best practices and improvement ideas to problems arising across the organization.

The technical level of aircraft failure analysis plays a special role in ensuring the safety of civil aviation flight. Using appropriate methods for functional failures analysis can provide a reliable reference for aircraft safety. The purpose of this paper is to provide a new and comprehensive measure based on conventional functional hazard analysis (FHA) and grey system theory to analysis and evaluate the class that each failure belongs to.

This paper integrates multiple methods including the FHA, the fixed weight cluster, the Delphi method and the analytic hierarchy process (AHP). To begin with, use FHA method to sort out the corresponding failure states of a certain system from the perspective of function and determine the evaluation index. And then using group decision and AHP, determine the expert weight and index weight in the fixed weight cluster. The fixed weight cluster function is used to determine the grey class to which a certain functional failure belongs in the complex system.

In the past, the risk assessment of aircraft was mostly dominated by the subjective judgment of the experts, but it was not possible to give an objective observation score for each failure state. This paper addresses the problem efficiently as well as the feature of “little data, poor information.” The risk degree of each failure state can ultimately be replaced by a quantitative value.

This paper uses the idea of clustering in grey system theory to evaluate the risk of landing gear system. In the expert evaluation stage, different experts evaluated the impact degree of the aircraft's failure caused by its functions, so the final risk classification is subjective to some extent.

This study analyzed the different conditions of the landing gear, including the front wheel steering, front wheel damping, front wheel steering system, brake system fault information and so on. It can effectively divide the different failure states and their effects, which is helpful to improve the safety of aircraft landing gear system and provide some useful methods and ideas for studying the safety of aircraft systems.

Based on the FHA analysis process and the grey system theory, this paper determines various potential risks and their consequences of various functions according to the hierarchy, so as to carry out further detailed analysis on the risks that may occur under various functional conditions and take certain measures to prevent them. It is helpful to improve the risk management and control ability of aircraft in the actual flight process and to guarantee the safety of people's lives and property.

This paper is a pioneer in integrating the FHA method and the grey system theory, which exactly can be used to address the problem with the character of “little data, poor information.” The model established in this paper for the defects of FHA can effectively improve the accuracy of FHA, which is of great significance for the study of safety. In this paper, a case about landing gear system is given to illustrate the effectiveness of the model.

Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues.

In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization.

A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed.

A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.

The purpose of this paper is to present issues and challenges of scheduled maintenance task development within the maintenance review board (MRB) process, and to find potential areas of improvement in the application of the MSG‐3 methodology for aircraft systems.

The issues and challenges as well as potential areas of improvement have been identified through a constructive review that consists of two parts. The first part is a benchmarking between the Maintenance Steering Group (MSG‐3) methodology and other established and documented versions of reliability‐centred maintenance (RCM). This benchmarking focuses on the MSG‐3 methodology and compares it with some RCM standards to identify differences and thereby find ways to facilitate the application of MSG‐3. The second part includes a discussion about methodologies and tools that can support different steps of the MSG‐3 methodology within the framework of the MRB process.

The MSG‐3 methodology is closely related to the RCM methodology, in which the anticipated consequences of failure are considered for risk evaluation. However, MSG‐3 considers neither environmental effects of failures nor operational consequences of hidden failures. Furthermore, in MSG‐3, the operational check (failure‐finding inspection) is given priority before all other tasks, whereas in RCM it is considered as a default action, where there is no other applicable and effective option. While RCM allows cost‐effectiveness analysis for all failures that have no safety consequences, MSG‐3 just allows it for failures with economic consequences. A maintenance program that is established through the MRB process fulfils the requirements of continuous airworthiness, but there is no foundation to claim that it is the optimal or the most effective program from an operator's point‐of‐view. The major challenge when striving to achieve a more effective maintenance program within the MRB process is to acquire supporting methodologies and tools for adequate risk analysis, for optimal interval assignments, and for selection of the most effective maintenance task.

The paper presents a critical review of existing aircraft scheduled maintenance program development methodologies, and demonstrates the differences between MSG‐3 and other RCM methodologies.

Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level.

Three scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level.

Non-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level.

The findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.