A Bayesian Network Based Approach for Risk Modeling to Aid in Development of Sustainable Biomass Supply Chains

Amundson, Joseph; Faulkner, William; Sukumara, Sumesh; Seay, Jeffrey; Badurdeen, Fazleena

doi:10.1016/b978-0-444-59519-5.50031-9

Cited by 16 publications

(5 citation statements)

References 3 publications

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“…The impact of risk propagation was analysed in the GeNIe Modeller using sensitivity analysis and what-if scenario analysis. Sensitivity analysis was used to create a critical path of influence in the risk network map for the backward-looking traceability of risk root causes (Amundson et al, 2012), while what-if scenario analysis was undertaken to examine the impact specific risk events have on the key performance measures (Amundson et al, 2012) and to predict forward-looking risk propagation from one risk event to another, eventually to the performance measures. Colour coding and line width were utilised to indicate the impact of risk influence generated from the sensitivity analysis as shown in Figures 6 and 7.…”

Section: Discussionmentioning

confidence: 99%

An Ontology-based Bayesian network modelling for supply chain risk propagation

Cao

Bryceson

Hine

2019

IMDS

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Purpose Supply chain risks (SCRs) do not work in isolation and have impact both on each member of a chain and the performance of the entire supply chain. The purpose of this paper is to quantitatively assess the impact of dynamic risk propagation within and between integrated firms in global fresh produce supply chains. Design/methodology/approach A risk propagation ontology-based Bayesian network (BN) model was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower and exporter and a downstream Chinese integrated importer and online retailer. Findings An ontology-based BN can be generated to accurately represent the risk domain of interest using the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses revealed that supply discontinuity, product inconsistency and/or delivery delay originating in the upstream firm can propagate to increase the downstream firm’s customer value risk and business performance risk. Research limitations/implications The work was conducted in an Australian-China table grape supply chain, so results are only product chain-specific in nature. Additionally, only two state values were considered for all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain. Practical implications This study supports the backward-looking traceability of risk root causes through the ACTGSC and the forward-looking prediction of risk propagation to key risk performance measures. Social implications The methodology used in this paper provides an evidence-based decision-making capability as part of a system-wide risk management approach and fosters collaborative SCR management, which can yield numerous societal benefits. Originality/value The proposed methodology addresses the challenges in using a knowledge-based approach to develop a BN model, particularly with a large-scale model and integrates risk and performance for a holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.

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Section: Discussionmentioning

confidence: 99%

An Ontology-based Bayesian network modelling for supply chain risk propagation

Cao

Bryceson

Hine

2019

IMDS

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“…In the supplier selection BN model developed by [35], the resilience capacity was quantified in aspects of withstanding, adapting to, and recovering from a disruption. Moreover, [36] established a supply chain BN to forecast how risk factors affect the performance of the biomass supply chain network. Recently, in many studies, researchers have gradually explored the application of BNs in measuring system resilience.…”

Section: Literature Reviewmentioning

confidence: 99%

Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network

Qiao

Liu

et al. 2021

Applied Sciences

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The safety level of the northern sea route (NSR) is a common concern for the related stakeholders. To address the risks triggered by disruptions initiating from the harsh environment and human factors, a comprehensive framework is proposed based on the perspective of resilience. Notably, the resilience of NSR is decomposed into three capacities, namely, the absorptive capacity, adaptive capacity, and restorative capacity. Moreover, the disruptions to the resilience are identified. Then, a Bayesian network (BN) model is established to quantify resilience, and the prior probabilities of parent nodes and conditional probability table for the network are obtained by fuzzy theory and expert elicitation. Finally, the developed Bayesian networkBN model is simulated to analyze the resilience level of the NSR by back propagation, sensitivity analysis, and information entropy analysis. The general interpretation of these analyses indicates that the emergency response, ice-breaking capacity, and rescue and anti-pollution facilities are the critical factors that contribute to the resilience of the NSR. Good knowledge of the absorptive capacity is the most effective way to reduce the uncertainty of NSR resilience. The present study provides a resilience perspective to understand the safety issues associated with the NSR, which can be seen as the main innovation of this work.

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“…Douglas made a theoretical and conceptual comparative analysis on the policies and measures of coal-dependent employment, determined the measures to successfully improve the social and economic well-being of coal-dependent communities, and put forward the framework to successfully achieve a just transition [6]. Amundson proposed a quantitative risk analysis method of biomass supply chain based on Bayesian belief network (BBN) [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Supply Chain Investment in Carbon Emission‐Reducing Technology Based on Stochasticity and Low‐Carbon Preferences

Shan

Hou

2021

Complexity

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Due to excessive greenhouse gas emissions, carbon emission-reducing measures are urgently needed. Important emission-reduction measures mainly include carbon trading and low-carbon cost subsidies. Comprehensive consideration of these two policies is a research hotspot in the field of low-carbon technology investment. Based on this background, this paper considers the impact of consumer low-carbon preferences on market demand and the impact of uncertainty in carbon emission-reduction behaviour. We construct a stochastic differential game model with upstream and downstream enterprises based on cost-sharing coordination under a cost subsidy. From a dynamic perspective, this paper researches the optimal equilibrium strategy and evolution characteristics of the joint emission-reduction mechanism in a supply chain. This paper discusses the sensitivity of the parameters and uses numerical simulation to verify the impact of each parameter on the emission-reduction decision-making activities of stakeholders after introducing the cost subsidy. The results show that a cost subsidy policy can promote carbon emission-reduction investment and supply chain profit. Thus, it is important to strengthen technical cooperation and exchange among enterprises.

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A Bayesian Network Based Approach for Risk Modeling to Aid in Development of Sustainable Biomass Supply Chains

Cited by 16 publications

References 3 publications

An Ontology-based Bayesian network modelling for supply chain risk propagation

An Ontology-based Bayesian network modelling for supply chain risk propagation

Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network

Supply Chain Investment in Carbon Emission‐Reducing Technology Based on Stochasticity and Low‐Carbon Preferences

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