Impact of risks in foodgrains transportation system: a system dynamics approach

Rathore, Rishabh; Thakkar, Jitesh J.; Jha, J.K.

doi:10.1080/00207543.2020.1725683

Cited by 30 publications

(14 citation statements)

References 36 publications

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“…Some of the most visible implications of the pandemic in the food retail industry have included panic buying, changes in food purchasing patterns, food deliveries and digital services, frontline hygiene and preventive measures, logistics and organization of distribution in stores, and supply-side issues due to labour shortages and disruptions to transportation and supply networks ( ILO, 2020 , PwC, 2020 , Rathore et al, 2020 , Ivanov, 2021b , Paul and Chowdhury, 2021 ). Limits on people’s mobility have reduced seasonal workers’ availability for planting and harvesting in many countries.…”

Section: Introductionmentioning

confidence: 99%

Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions

Burgos

Ivanov

2021

Transportation Research Part E: Logistics and Transportation Re

299

112

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Section: Introductionmentioning

confidence: 99%

Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions

Burgos

Ivanov

2021

Transportation Research Part E: Logistics and Transportation Re

299

112

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“…Te study of system dynamics in medical risk management is one of its key areas of application. System dynamics models outperform other traditional methods in efectively identifying the interrelationships between risk factors [43]. Tey help to visualize the internal structure of the risk system by means of a chain of loops, thus refecting the specifc transmission process of risk [44].…”

Section: Risk Control Meansmentioning

confidence: 99%

Medical Big Data Risk Management: A Systematic Management Approach Based on Bayesian Belief Networks

Zhang

Liu²,

Zhou

et al. 2023

Mobile Information Systems

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The purpose of this paper is to identify the medical risk of applying big data technology and to build a medical big data risk (MBDR) control process and manage medical big data risk (MBDR) from a systematic perspective. In this process, we firstly used systematic literature reviews (SLRs) method to systematically search 322 papers in web of science with the topics of “medical risk” and “big data risk” to build a dimensional system of medical big data risk (MBDR) from the theoretical level. Based on a case study of a hospital in Shanghai, we explored the formation mechanism and interaction effect of medical big data risk (MBDR) by using Bayesian belief networks (BBNs) method, and built a systematic risk control process. This paper finally finds that: the dimensional system of medical big data risk (MBDR) includes 24 subdimensions and 5 major categories of dimensions, which helps to explore the medical application of big data technology from a risk perspective. In addition, the medical big data risk (MBDR) control process constructed in this paper includes: risk prediction, reverse reasoning, risk control, and risk prevention in 4 aspects, which is important for hospitals to actually carry out medical big data risk (MBDR) control.

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“…Mathematical equations and values are defined for input/output variables in this stage. After verification and validation of equations in the stock-and-flow diagram, the model is prepared for the simulation run with pre-defined input parameters (Rathore et al, 2020; Olivares-Aguila and ElMaraghy, 2020).…”

Section: Sc Performance Indicatorsmentioning

confidence: 99%

Modelling the impact of climate change risk on supply chain performance

Kara

Ghadge

Bititci

2020

International Journal of Production Research

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Climate change is among the top global risks due to its growing adverse impact on businesses.However, few empirical studies address this imminent risk from a supply chain perspective.Due to a lack of established approaches for capturing complex interaction between climate change risk and supply chain performance, a three-phase mixed methodology approach was attempted. A cognitive map first captured the inter-relationships based on a mental model established by a group of experts. Later, a survey gathered from industry practitioners assessing causal relationships identified key climate change factors and most influenced supply chain performance dimensions. Finally, a system dynamics model supported by multiple case scenarios assessed the implications of climate change on supply chain performance. The results indicated a significant reduction in the availability of natural resources/raw material and capacity, leading to increase in stock-outs, inventory costs and bottlenecks disrupting procurement, manufacturing and logistics functions. Supply chain performance captured through efficiency and effectiveness shows a negative trend with increasing climate change consequences. The systems approach followed in this paper contributes by providing a quantitative model for assessing the impact of climate change risk on supply chain performance.

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Impact of risks in foodgrains transportation system: a system dynamics approach

Cited by 30 publications

References 36 publications

Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions

Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions

Medical Big Data Risk Management: A Systematic Management Approach Based on Bayesian Belief Networks

Modelling the impact of climate change risk on supply chain performance

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