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A substantial number of production assets in the offshore oil and gas industry are facing operation beyond their anticipated design life, thus necessitating a service life extension program in the future. Selection of the most suitable strategy among a wide range of potential options to extend the lifetime of equipment (e.g. re-using, reconditioning, remanufacturing, refurbishing and adding on safety/process control measures) remains a challenging task that involves several technical, economic and organizational complexities. In order to tackle this challenge, it is crucial to develop analytical tools and methods capable of evaluating and prioritizing end-of-life strategies with respect to their associated costs and quantifiable benefits. The paper aims to discuss these issues.

This paper presents a life-cycle cost-benefit analysis approach to identify the most suitable life extension strategy for ageing offshore assets by taking into account all the capital, installation, operational, maintenance and risk expenditures during the extended phase of operation. The potential of the proposed methodology is demonstrated through a case study involving a three-phase separator vessel which was constructed in the mid-1970s.

The results from the application case indicate that the capital expenditure (CapEx) accounts for the largest portion of life cycle cost for the replacement strategy, while risk expenditure (RiskEx) is the major contributor to costs associated with life extension. A sensitivity analysis is also conducted to identify factors having the greatest impact on the optimum life extension solution, including oil price, production rate and money interest rate.

In the past, the decisions about life extension or replacement of in-service equipment were often made in a qualitative way based on experience and judgment of engineers and inspectors. This study presents a “quantitative” framework to evaluate and compare the costs, benefits and risks associated with life extension strategies and subsequently to select the best strategy based on benefit/cost ratios.

To the best of authors’ knowledge, no studies before have applied life cycle assessment and cost-benefit analysis methods to prioritize the potential life extension strategies in the oil and gas industry sector. The proposed approach not only assists decision makers in selecting the most suitable life extension strategy but also helps duty holders reduce the costs corresponding to life extension execution.

The modern helicopters are designed with maximum serviceability and long life expectancy to ensure minimum life cycle cost. The purpose of this paper is to present a framework to incorporate the customer requirements on reliability and maintainability (R&M) parameters into the design and development phase of a contemporary helicopter, and to discuss the way to capture operational data to establish and improve the R&M parameters to reduce life cycle cost.

From the analysis, it is established that the reliability and maintainability cost is the major contributor to the life cost. The significant reliability and maintainability parameters which influence R&M cost are identified from analysis. The operational and design data of a contemporary helicopter are collected, compiled and analyzed to establish and improve the reliability and maintainability parameters.

The process depicted in the paper is followed for a contemporary helicopter and substantial amount of life cycle cost reduction is observed with improvement of R&M parameters.

The benefits of this methodology not only reduce life cycle cost but also improve the availability/serviceability through less failure and less time for scheduled maintenance. The methodologies also provide the reliability trends indicating potential area for design improvement.

The proposed approach assists asset managers to reduce the life cycle costs through improvement of R&M parameters.

In a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but is also dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case.

In order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem.

The results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.

To the best of the authors’ knowledge, there are few papers focusing on quality-oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches.

Maintenance strategy selection (MSS) is considered as a complex multiple-criteria decision-making (MCDM) problem. The purpose of this paper is to present a comprehensive review on the use and application of MCDM approach and its associated case studies in the field of MSS.

The paper systematically classifies the published literature of both researchers and practitioners and then analyzes and reviews it methodically.

This paper outlines the important issues relevant to the subject, including the techniques used for data collection, the quantitative and qualitative criteria taken into account in decision making, the maintenance strategies considered for evaluation, the methods applied to find the solution, and the type of industries being studied. In each category, the gaps are identified along with recommendations for the future research work.

Literature on classification of the MCDM models used to select the most appropriate maintenance strategy is very limited. The proposed classification scheme not only will be useful to researchers, but also assists maintenance professionals to find the models that fit their specific needs.

The paper provides many references in the field, including the articles published in academic journals, conference papers, master and doctoral dissertations, text books, and industrial reports, and suggests a classification scheme according to various attributes.

The purpose of this paper is to review and develop stochastic models for the evaluation of the expected warranty cost, from dealer's viewpoint, for second hand items sold under different warranty policies.

This study makes a useful contribution to the warranty literature by developing a framework to study one dimensional warranty policies for second‐hand products from dealer's viewpoint. Also, numerical examples are provided to illustrate the application of these models.

Despite the fact that warranties for second‐hand products are commonly used, the accurate pricing of warranties in many situations remains an unsolved problem, for both the dealer and customer. The proposed framework allows the dealer to analyze the cost of alternative warranty policies before deciding on the policy to be offered with the sale of a second‐hand item.

The policies and models developed in this paper can be useful in managerial decisions making related to second‐hand products such as automobiles, home appliances, helicopters, electronic equipment and electronics.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

The purpose of this paper is to empirically investigate the impact of organizational intellectual capital (IC) components on absorptive capacity (ACAP) such as potential absorptive capacity (PACAP) and realized absorptive capacity (RACAP). Furthermore, it attempts to investigate the mechanism through which PACAP and RACAP jointly influence innovation strategies (i.e.) exploitative and exploratory innovations.

This is an explanatory research using a deductive approach. This study uses survey data from 184 manufacturing export firms analyzed through partial least squares structural equation modelling.

The results have found that the cognitive and social capital of a firm positively affects PACAP and RACAP, whereas relational capital has a significant effect on RACAP. Moreover, the study reveals that both potential and realized absorptive capacities considerably lead to the development of organizational exploitative and exploratory innovation strategies.

The research focused on two driving factors, i.e. IC components and ACAP dimensions, and overlooked how each component of IC and ACAP influences ambidextrous innovative strategy.

Providing managers with insights about the critical role of developing IC to facilitate the transfer and exchange of crucial absorptive capacity necessary for ambidextrous innovative strategy.

This study makes a significant contribution to the existing literature by highlighting the importance of ACAP and provides useful insights for firms in developing economies to improve their exploitative and exploratory innovation capability. This study likewise reveals the significance of the four dimensions of IC, which can facilitate bringing in knowledge from developing economies.

The study aims to test the framework that proposes the role of resources (intellectual capital) in mobilizing entrepreneurial orientation that influences the competitiveness improvement of micro-small-medium enterprises (MSMEs) under the lens of resource orchestration theory.

In this study, 347 respondents from the MSMEs participated through a structured questionnaire. For the data analysis purpose, the structural equation modeling technique was employed using SmartPLS software.

The results suggest human, structural, and relational capital are significant antecedents of entrepreneurial orientation, which leads to competitiveness improvement. The findings also indicate the mediation role of entrepreneurial orientation between intellectual capital and competitiveness improvement.

The current study presumably will supplement the promising research effort to progress the research orchestration theory and also could be a strategic guideline for the managers/owners of the MSMEs.

This study is possibly a novel attempt to divulge the association between intellectual capital (tripartite model) and competitiveness improvement of firms under the lens of resource orchestration theory.

This study aims to identify how perceived destination social responsibility (DSR) drives destination brand loyalty through a jointly and independently mediated mechanism of cognitive and affective components (e.g. tourist-destination identification, cognitive image, affective image and tourist satisfaction) and to examine the moderating role of individual-level collectivist values in linking perceived DSR and tourist behaviors.

An online survey is conducted to collect the data of 351 domestic tourists visiting an urban tourism destination (e.g. Danang City) in Vietnam. A serial multiple mediation model and moderation model were examined by applying covariance-based structural equation modeling.

This research’s results highlight the leading factors of perceived DSR in the process of forming destination brand loyalty and confirm the vital role of the intermediary mechanism of tourists' cognition and affect during this process. The chain of causal relationships DSR → TDI → CI → AI → TS → DBL confirms the role of perceived DSR as an essential prerequisite factor of DBL, creating a close connection to tourists' cognition and affect and contributing to improving destination brand loyalty. Individual-level collectivist values were found to positively moderate the links between perceived DSR and tourist-destination identification, affective image and destination brand loyalty.

Future research would provide insights into the links between perceived DSR and tourist behaviors by considering moderating variables (e.g. cultural distance and tourist types) and uncovering specific insights into each destination stakeholder's DSR activities.

A new integrated model of destination brand loyalty development is proposed to explore a new path for destination brand loyalty formation through cognitive, affective and cognitive-affective pathways. This moderating stream of examining individual-level collectivist values can make a significant contribution to the extant tourism literature by promoting a more positive tourist perception of DSR, thereby increasing tourists' knowledge, beliefs and emotions and enhancing destination brand loyalty.

This paper seeks to understand the interrelationships between destination attractiveness, satisfaction, attachment and visitors' loyalty and to determine the potential moderating effect of generational cohorts. Using expectancy disconfirmation and generational cohort theories, the paper explains how visitors' cognitive knowledge and motivation factors influence international visitors' attraction and maintenance and their behavioral/attitudinal loyalty.

Data were collected from 390 international visitors to South Africa during the Southern Hemisphere's summertime via a quantitative research method. A two-stage approach of structural equation modeling was used to analyze the hypothesized model.

There was no relationship between visitors' loyalty and destination image or destination image and destination trust. However, destination image has a significant effect on visitors' satisfaction. Additionally, visitors' experience had a positive effect on satisfaction and loyalty, while destination trust and destination attachment were positively related.

The findings show that the tourism sector could reap greater benefits from international tourists if a deeper understanding of different generational cohorts was obtained. There was a varying effect of destination marketing organizations' efforts on different generational cohorts' attraction, trust, satisfaction, feelings and expectations. This study proposes that direct marketing organizations should focus on building brand trust, as it is key to attracting different generational cohorts. Lastly, the study found that generational cohort segmentation accrues many benefits and creates and enhances superior advantages.

The paper provides evidence that more than a destination's attractiveness is required to achieve loyalty; segmenting and understanding different generational cohorts' cognitive, affective and motivation aids in generating better response actions to tourists' changing needs and meeting their demands.