Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters

In harsh environments, offshore oil and gas support operations are subjected to frequent logistics and supply chain operational disruption, due to harsh environmental factors with their associated risks. To capture these stochastic influential factors and support related decision making, it is helpf...

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Autores principales:	Sidum Adumene, Modestus Okwu, Mohammad Yazdi, Mawuli Afenyo, Rabiul Islam, Charles Ugochukwu Orji, Francis Obeng, Floris Goerlandt
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Logistics disruption Bayesian network Harsh environment Cost aggregation technique Monte Carlo simulation Economic risks Shipment of goods. Delivery of goods HF5761-5780
Acceso en línea:	https://doaj.org/article/3293306e652c4d4fbbf81bb66f5a335a
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id	oai:doaj.org-article:3293306e652c4d4fbbf81bb66f5a335a
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spelling	oai:doaj.org-article:3293306e652c4d4fbbf81bb66f5a335a2021-11-12T04:49:07ZDynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters2666-822X10.1016/j.martra.2021.100039https://doaj.org/article/3293306e652c4d4fbbf81bb66f5a335a2021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666822X21000307https://doaj.org/toc/2666-822XIn harsh environments, offshore oil and gas support operations are subjected to frequent logistics and supply chain operational disruption, due to harsh environmental factors with their associated risks. To capture these stochastic influential factors and support related decision making, it is helpful to develop a robust and dynamic probabilistic model.The current study presents a proactive methodology that integrates the Pure-Birth Markovian process (PBMP) with the Bayesian network (BN) for the effective analysis of offshore logistics disruption risk. The PBMP captures the stochasticity in the failure characteristics of the engineering systems for estimating the time-evolution degradation probability. The BN explores the dynamic interactions among the most important offshore logistics influential factors to analyze the disruption risk in a harsh environment. The effects of influential factors’ non-linear dependencies are propagated and updated, given evidence on the degree of disruption. The level of logistics disruption is further assessed using cost aggregation-based expectation theory. The theory explores the incurred cost/economic risk under different operational scenarios. The proposed methodology is tested on an offshore supply vessel operation to estimate the likely operational disruption risk in terms of financial loss in a harsh operating environment. The most critical influential functions are assessed to establish their degree of impact on the logistics disruption. At the upper bound probability of disruption occurrence, an economic risk/additional incurred cost of US$2.38E+05 with a variance (σ2) of 3.05×109 was predicted. The result obtained suggests that the proposed methodology is adaptive and effective for dynamic logistics disruption risk analysis in harsh offshore environments.Sidum AdumeneModestus OkwuMohammad YazdiMawuli AfenyoRabiul IslamCharles Ugochukwu OrjiFrancis ObengFloris GoerlandtElsevierarticleLogistics disruptionBayesian networkHarsh environmentCost aggregation techniqueMonte Carlo simulationEconomic risksShipment of goods. Delivery of goodsHF5761-5780ENMaritime Transport Research, Vol 2, Iss , Pp 100039- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Logistics disruption Bayesian network Harsh environment Cost aggregation technique Monte Carlo simulation Economic risks Shipment of goods. Delivery of goods HF5761-5780
spellingShingle	Logistics disruption Bayesian network Harsh environment Cost aggregation technique Monte Carlo simulation Economic risks Shipment of goods. Delivery of goods HF5761-5780 Sidum Adumene Modestus Okwu Mohammad Yazdi Mawuli Afenyo Rabiul Islam Charles Ugochukwu Orji Francis Obeng Floris Goerlandt Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters
description	In harsh environments, offshore oil and gas support operations are subjected to frequent logistics and supply chain operational disruption, due to harsh environmental factors with their associated risks. To capture these stochastic influential factors and support related decision making, it is helpful to develop a robust and dynamic probabilistic model.The current study presents a proactive methodology that integrates the Pure-Birth Markovian process (PBMP) with the Bayesian network (BN) for the effective analysis of offshore logistics disruption risk. The PBMP captures the stochasticity in the failure characteristics of the engineering systems for estimating the time-evolution degradation probability. The BN explores the dynamic interactions among the most important offshore logistics influential factors to analyze the disruption risk in a harsh environment. The effects of influential factors’ non-linear dependencies are propagated and updated, given evidence on the degree of disruption. The level of logistics disruption is further assessed using cost aggregation-based expectation theory. The theory explores the incurred cost/economic risk under different operational scenarios. The proposed methodology is tested on an offshore supply vessel operation to estimate the likely operational disruption risk in terms of financial loss in a harsh operating environment. The most critical influential functions are assessed to establish their degree of impact on the logistics disruption. At the upper bound probability of disruption occurrence, an economic risk/additional incurred cost of US$2.38E+05 with a variance (σ2) of 3.05×109 was predicted. The result obtained suggests that the proposed methodology is adaptive and effective for dynamic logistics disruption risk analysis in harsh offshore environments.
format	article
author	Sidum Adumene Modestus Okwu Mohammad Yazdi Mawuli Afenyo Rabiul Islam Charles Ugochukwu Orji Francis Obeng Floris Goerlandt
author_facet	Sidum Adumene Modestus Okwu Mohammad Yazdi Mawuli Afenyo Rabiul Islam Charles Ugochukwu Orji Francis Obeng Floris Goerlandt
author_sort	Sidum Adumene
title	Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters
title_short	Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters
title_full	Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters
title_fullStr	Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters
title_full_unstemmed	Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters
title_sort	dynamic logistics disruption risk model for offshore supply vessel operations in arctic waters
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3293306e652c4d4fbbf81bb66f5a335a
work_keys_str_mv	AT sidumadumene dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters AT modestusokwu dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters AT mohammadyazdi dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters AT mawuliafenyo dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters AT rabiulislam dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters AT charlesugochukwuorji dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters AT francisobeng dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters AT florisgoerlandt dynamiclogisticsdisruptionriskmodelforoffshoresupplyvesseloperationsinarcticwaters
_version_	1718431160988598272

Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters

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