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The purpose of this study is to identify and evaluate supply chain strategies (SCSs) that drive financial performance to guide practitioners, especially in liquefied natural gas (LNG) networks, to review and adopt SCSs that drive competitiveness and value creation for investors.

Analytical hierarchy process (AHP) was deployed to prioritise SCSs according to their relative impact on financial performance in LNG networks. Interviews with experts were analysed using template analysis to establish latent drivers of financial performance specific to LNG networks.

Results support the significant role of SCSs in improving financial performance. Although findings prioritised collaborative strategy as the most important driver of financial performance in LNG networks, to fully optimise financial outcomes, all the SCSs should be implemented across LNG networks as no single strategy in isolation is a standalone driver of financial performance.

The AHP model provides a novel ranking for SCSs and measures to guide decision-makers. LNG practitioners may exploit the results to make informed decisions.

The study extends previous literature by proposing a framework and a new LNG empirical model that facilitates understanding of how SCSs contribute positively to financial performance and support practitioners in making strategic supply chain decisions.

The current study addresses how blockchain can deal with the challenges that the midstream liquefied natural gas (LNG) supply chain poses combined from a management standpoint. Such challenges are: the volume of transactions, communication hurdles and the lack of contemporary management tools. The paper proposes a comprehensive framework to assess the impact of blockchain implementation in the midstream LNG supply chain in order to tackle those barriers.

The basis of the research is the business process modelling (BPM), through which entities, roles, tasks, resources and transactions can be modelled and simulated. The modelling of the midstream LNG supply chain, via BPM, is based on guidelines of the Society of International Gas Tanker and Terminal Operators (SIGGTO) and common industry business models. A quantitative analysis is employed to support the motivation and the potential impact of blockchain implementation. The methodology is used to identify (1) inefficiencies related to large volume of transactions between stakeholders and (2) critical areas of an LNG shipping company, where blockchain can be implemented.

Process repeatability, numerous shared documentation forms, excessive paperwork and communication imbroglios are mapped from the modelling section. Up to 327 processes are repeated during a typical vessel voyage, and up to 122 shared documentation forms are exchanged. Excessive paperwork and communication imbroglios are tracked through, which correspond to 25 severe errors as detected. By implementing the methodology, stakeholders can quantify the possible impact of blockchain on the operational performance of each stakeholder's operations separately and the supply chain as a whole in terms of real-time monitoring, transparency and paperwork reduction, time and cost savings.

The research has certain limitations deriving from its conceptual nature. The business processes' modelling is based on standard procedures described in the guidelines by SIGGTO and may need further adjustment for specific use cases. A structured case study has not been realisable as corporate data for an LNG shipping company regarding processes and other commercial sensitive information are required.

Potential practitioners may exploit the proposed framework as a low cost and seamless tool to evaluate how blockchain could disrupt their operations. Thus, the blockchain implementation's improvements or weaknesses can be pinpointed, and enabling the interested stakeholder of the LNG supply chain with specific feedback, it can guide them towards informed decisions on their operations.

The research has a novel approach as it combines the creation of practical management framework, with a comprehensive visualization of the midstream LNG supply chain. Thus, the reader can identify in which parts of the midstream LNG supply chain can blockchain be implemented, and what impact it could have in terms of supply chain operations.

The purpose of this paper is to improve the tactical planning of the stakeholders of the midstream liquefied natural gas (LNG) supply chain, using an optimisation approach. The results can contribute to enhance the proactivity on significant investment decisions.

A decision support tool (DST) is proposed to minimise the operational cost of a fleet of vessels. Mixed integer linear programming (MILP) used to perform contract assignment combined with a genetic algorithm solution are the foundations of the DST. The aforementioned methods present a formulation of the maritime transportation problem from the scope of tramp shipping companies.

The validation of the DST through a realistic case study illustrates its potential in generating quantitative data about the cost of the midstream LNG supply chain and the annual operations schedule for a fleet of LNG vessels.

The LNG transportation scenarios included assumptions, which were required for resource reasons, such as omission of stochasticity. Notwithstanding the assumptions made, it is to the authors’ belief that the paper meets its objectives as described above.

Potential practitioners may exploit the results to make informed decisions on the operation of LNG vessels, charter rate quotes and/or redeployment of existing fleet.

The research has a novel approach as it combines the creation of practical management tool, with a comprehensive mathematical modelling, for the midstream LNG supply chain. Quantifying future fleet costs is an alternative approach, which may improve the planning procedure of a tramp shipping company.

This paper aims to address how to systematically address vulnerability in a maritime transportation system using a formal vulnerability assessment approach, create quantitative measures of disruption risk and test the effect of mitigating measures. These quantitative data are prerequisites for cost efficiency calculations, and may be obtained without requiring excessive resources.

Supply chain simulation using heuristics‐based planning tools offers an approach to quantify the impact of disruption scenarios and mitigating measures. This is used to enrich a risk‐based approach to maritime supply chain vulnerability assessment. Monte Carlo simulation is used to simulate a stochastic nature of disruptions.

The exemplary assessment of a maritime liquefied natural gas (LNG) transportation system illustrates the potential for providing quantitative data about the cost of disruptions and the effects of mitigating measures, which are foundations for more precise cost efficiency estimates.

This simulation was done on a simplified version of a real transportation system. For resource reasons, several simplifications were made, both with regards to modeling the transportation system and with the implementation of the formal vulnerability assessment framework. Nevertheless, the authors believe the paper serves to illustrate the approach and potential outcome.

Practitioners are provided with an approach to get more precise quantitative data on disruption costs and cost/efficiency of mitigating measures, providing background data for decisions on investing in reduction of supply chain vulnerability.

The combination of risk assessment methods and inventory routing simulation of maritime supply chain problems is a novelty. Quantifying vulnerability, effects of disruptions and effects of mitigating measures in maritime transportation systems contributes to a little‐researched area.