Modelling and assessing sustainability of a supply chain network leveraging multi Echelon Bayesian Network

Amrani, Safae El; Hossain, Niamat Ullah Ibne; Karam, Sofia; Jaradat, Raed; Nur, Farjana; Hamilton, Michael A.; Ma, Junfeng

doi:10.1016/j.jclepro.2021.126855

Cited by 34 publications

(34 citation statements)

References 88 publications

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“…Current literature has provided an insight into SSC management across different areas and dimensions (Di Vaio et al, 2020; Nilsson & Göransson, 2021; Sanders et al, 2019). In addition, previous literature has investigated the role of AI in creating SSC models to integrate different dimensions of sustainability such as social (Klumpp & Zijm, 2019), environmental (Son et al, 2021) as well as social, environmental and economic altogether (El Amrani et al, 2021).…”

Section: Discussion and Proposed Research Modelmentioning

confidence: 99%

Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions

et al. 2022

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The sustainable supply chain (SSC) has received significant attention worldwide in the last few years because it integrates sustainability dimensions into its process.Recent artificial intelligence (AI)-based advancements in technology make it possible to overcome many problems associated with supply chain (SC) networks. The current study was performed to explore the role of AI in establishing an SSC. The research contribution in the field of AI and SSC was examined through a systematic literature review. A total of 353 articles were gathered from the Scopus database in the selected research field. A big data-based approach, namely, structural topic modelling (STM), was applied to generate emerging thematic topics from shortlisted articles of AI in SSC. Moreover, using R package, a bibliometric analysis was conducted to examine the research trends in the field of AI in SSC. The research trends were then analysed, with generated thematic topics discussed. The proposed research framework can help researchers and practitioners to develop an SSC model using AI-based techniques. Propositions for future research are given and implications are suggested.

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Section: Discussion and Proposed Research Modelmentioning

confidence: 99%

Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions

et al. 2022

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“…They summarized that the approach could help the supply chain managers identify the risk factors early. El Amrani et al [25] studied the sustainability of the supply chain network. These methods were successful because they focus on predicted outcomes and contextual explanations.…”

Section: B Ascs Management Approachmentioning

confidence: 99%

The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains

2022

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The balancing of demand and supply in the market is a complex problem because of the dynamic supply chain and environment. It causes uncertain situations and is a limitation in decisions making systems that cannot produce reasonable descriptions to help decision makers eliminate uncertainties. This study proposes the design and development of a Causal Bayesian Networks (CBNs) model for market understanding, which encodes a human-like approach to explain demand and supply events for decision makers. A framework for generating reasonable descriptions in Agricultural Supply Chains (ASCs) management is proposed. The qualitative and quantitative design of the CBNs model is developed and proved that the CBNs model can reasonably explain events using predictive performance measurement and sensitivity analysis for producing reasonable descriptions. The results illustrate that the CBNs model is suitable for ASCs situation explanation involving uncertain situations, and is ready to apply to real-world applications to support decision making systems.

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“…The supply chain resilience receives significant attentions recently ( Amrani et al, 2021 ). The investigation of supply chain resilience can be categorized depending on whether the crisis/uncertainty involved (e.g., Park et al, 2018 , Alizadeh et al, 2019a , Alizadeh et al, 2019b ).…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-period multi-modal stochastic supply chain model under COVID pandemic: A poultry industry case study in Mississippi

Yazdekhasti¹,

Wang

Zhang

et al. 2021

Transportation Research Part E: Logistics and Transportation Re

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The poultry industry is one of the most important agricultural sectors, which constitutes a significant part of the per capita consumption of protein and meat. Integrating operations of poultry industry sections including production, distribution and consumption becomes vital. Although the proper poultry supply chain has been established and made plenty of benefits for a long time, the global outbreak of COVID-19 shows that operations under pandemic are still challenge for the poultry industry. In this paper, the impacts of pandemic on poultry industry is investigated by developing a multi-period multi-modal stochastic poultry supply chain. Two models are developed aiming to mitigate the negative effects of pandemic occurrence through product stocking policy. In the first model, distribution system is in accordance with a multi-component structure, while the second model allows direct connections between suppliers (farmers) and demanders (customers). In both models, poultry productions are negatively affected by COVID 19. Due to the complexity of the model, a hybrid solution approach based on Branch and Cut and Dynamic Programming is developed. To validate the performance of the proposed model and solution procedure, a case study on the broiler industry in the state of Mississippi is performed. The results show that storing poultry products in the pre-pandemic along with direct logistics during pandemic period can save the broiler supply chain cost up to 30%.

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Modelling and assessing sustainability of a supply chain network leveraging multi Echelon Bayesian Network

Cited by 34 publications

References 88 publications

Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions

Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions

The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains

A multi-period multi-modal stochastic supply chain model under COVID pandemic: A poultry industry case study in Mississippi

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