Risk identification and evaluation research on F-AHP evaluation based supply chain

Guan, Gaofeng; Dong, Qianli; Li, Chunhua

doi:10.1109/icieem.2011.6035447

Cited by 14 publications

(3 citation statements)

References 3 publications

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“…AFSCs possess unique attributes that distinguish them from traditional supply chains and necessitate specialized managerial expertise. These distinct characteristics include hazards associated with supply and demand, weather-related instability, short life-cycle items, price volatility, and food safety and quality [52][53][54].…”

Section: Agri-food Supply Chain Definitionmentioning

confidence: 99%

Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection

Aungkulanon,

Atthirawong,

Luangpaiboon

et al. 2024

Mathematics

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Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management.

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Section: Agri-food Supply Chain Definitionmentioning

confidence: 99%

Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection

Aungkulanon,

Atthirawong,

Luangpaiboon

et al. 2024

Mathematics

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“…With the development of information technology, supply chains have changed from the traditional linear model to the network model, thus, increasing the complexity of supply chains, and potential supply chain risks are derived in turn (Sharma et al, 2020), including supply and sales risks (Guan et al, 2011;Assefa et al, 2017), equipment and quality risks (Shirani & Demichela, 2015), and environmental risks (Freise & Seuring, 2015). Thus, new methods or technologies, such as blockchain, IoT, and machine learning, have been applied to eliminate such risks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning-Based Anomaly Detection Method and Blockchain-Based Secure Protection Technology in Collaborative Food Supply Chain

Chen

2023

International Journal of E-Collaboration

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The complexity of the collaborative food supply chain has resulted in the frequent occurrence of food safety incidents, which harm people's health and life. Therefore, the maintenance of food safety has become a key value. This study expects to solve the food safety problem and bring more benefits to people using intelligent systems. To meet the safety needs of the collaborative food supply chain, this study designed a food safety protection system architecture which collects the supply and sales data of various suppliers, as well as the data of equipment used in production. The architecture can carry out anomaly detections with machine learning to make a preliminary judgement on whether a problem has occurred in this batch of food during the transaction, and then implement in-depth anomaly detections with the supplier's equipment to determine the stage at which this problem occurred. The proposed system can help food operators achieve effective food monitoring, prediction, prevention, and improvement, thereby improving food safety.

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“…Most importantly, food products are easily damaged, while logistically handling these products requires utmost care. Guan et al (2011) emphasizes the unique characteristics of the food supply chain compared to other types due to the following reasons: (1) longer and more complex factors, (2) stricter supervision of production time, storage, and transportation, and (3) its inherent strong commonality and sociality. Given the fragility and sensitivity of agricultural products, such unprecedented delays should not be present and adequately mitigated.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Risk Susceptibility of Agro-Industry Circular Supply Chain During Major Disruptions: A Life Cycle Perspective

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The agro-industry is one of the main contributors to the food supply worldwide. Thus, the importance of its supply chain activities (i.e., harvest to fork) is crucial and should not be affected by any delays. However, inevitable disruptions may have an adverse impact on the supply chain activities in the agro-industry, such as natural disasters, climate change impacts, health crises, and technological lags. These risks must be appropriately mitigated considering the fragility and sensitivity of agricultural products to various factors (e.g., temperature, time). However, an assessment for agro-industry risk susceptibility remains unexplored in the literature. Thus, this study intends to assess the aversion of the agro-industry to such disruptions through conditional value-at-risk (CVaR) based risk control. A life cycle perspective will also be utilized to obtain a systematic view of the circular supply chain. The methodology used will be in two folds: (1) calculate the CVaR variable in the agro-industry supply chain and integrate it to the (2) life cycle assessment of agro-industry circular supply chain using the data collected from existing literature. The integrated methodology is utilized in the cassava starch production. The findings of the study show that the average loss at worst 10% volatile price is 15.22%, at worst 5% volatile price is 22.62%, and at worst 1% volatile price is 33.94%. Furthermore, the environmental impact assessment conducted revealed that the acidification has the highest kg-equivalent in terms of the environmental impact of the cassava starch production. Machineries and equipment utilized in the current production process of cassava starch has contributed immensely to the adverse impact on the environment, specifically in the acidification due to emissions.

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Risk identification and evaluation research on F-AHP evaluation based supply chain

Cited by 14 publications

References 3 publications

Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection

Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection

A Machine Learning-Based Anomaly Detection Method and Blockchain-Based Secure Protection Technology in Collaborative Food Supply Chain

Modelling the Risk Susceptibility of Agro-Industry Circular Supply Chain During Major Disruptions: A Life Cycle Perspective

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