Search results

The purpose of this paper is to propose a decision support framework that can be used by decision-makers to identify the most convenient disruption analysis (DA) methods for megaprojects and their stakeholders.

The framework was initially developed by conducting a comprehensive literature review to obtain extensive knowledge about disruption management and megaprojects. Focus group discussion (FGD) sessions with the participation of the construction practitioners were then organized to validate and strengthen the findings of the literature review. Consequently, 17 selection factors were identified and categorized as requirement, ability and outcome. Lastly, the most convenient DA methods for megaprojects were identified by performing integrated fuzzy analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) analysis. Additionally, consistency analysis was also conducted to verify the reliability of the results.

The results revealed that the measured mile method is the most appropriate DA method for megaprojects. In case the measured mile method cannot be adopted due to various technical and contractual reasons, the decision-makers are proposed to consider program analysis, work or trade sampling, earned value analysis and control chart method, respectively. Second, the selection factors such as “Comprehensible analysis procedure,” “Existing knowledge and experience about a particular DA method,” “Ability to resolve greater number of disruption events,” “Ability to resolve complex disruption events,” “Ability to exclude factors that are not under the owner's responsibility” and “General acceptance by practitioners, courts, and arbitration, etc.” were given the top priority by the experts, highlighting the critical aspects of the DA methods.

Disruption claims in megaprojects are very critical for the contractors to compensate for the losses stemming from disruption events. Although the effective use of DA methods maximizes the accuracy and reliability of disruption claims, decision-makers can barely implement these methods adequately since past studies neglect to present extensive knowledge about the most convenient DA methods for megaprojects. Thus, developing a decision support framework for the selection of DA methods, this study is the earliest attempt that examines the mechanisms and inherent differences of DA methods. Additionally, owing to the robustness and versatility of this research approach, the research approach could be replicated also for future studies focusing on other project-based industries since disruption is also a challenging issue for many other industries.

This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era. This study also aims to categorize and rank the identified enablers using expert panel input.

A review of the extant literature was conducted to investigate and identify the factors that contribute to SCR. The relative ranking of the enablers was carried out by a group of industry and academic experts. The expert panel was convened to compare the main categories and each enabler in pairs and to score the enablers using triangular fuzzy numbers.

This study identified 16 critical SCR enablers. Using the fuzzy analytic hierarchy process (AHP), these enablers were divided into three groups and analyzed. The results show that financial enablers, technology enablers and then social enablers are prioritized when it comes to SCR in emerging markets. The robustness of the ranking of enablers is tested through sensitivity analysis.

The results shall be helpful for policymakers and managers to understand the important enablers and also help allocate resources to important enablers. Managers will be able to formulate strategies to achieve SCR in an uncertain environment.

This is one of the first attempts to identify and rank the enablers of SCR in an emerging economy context.