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Increased globalization and a growing world population have a significant impact on the sustainability of supply chains, especially within the food industry. In the food industry, transport logistic activities play an essential role in ensuring global food safety. Specifically, transport logistics is likely the most critical step throughout the food journey from farm to fork because of potential stress that affects the product, such as perishable nature and efficiency requirement, cost optimization, environmental impact, and sustainability. Hence, considering the volume of food transported and distributed globally and the range of participants involved in the process, there is a high complexity of risk factors that threaten the smooth flow and the food product's safety with severe consequences for global business. Although the assessment of the risk factors in an end-to-end food supply chain has emerged as a significant concern in research, Previous studies on the subject had only focused on the risk assessment of food supply chains from the production, post-harvest, and processing chain, there are very few studies on a whole food transport logistic (FTL) chain, particularly from quantitative assessment perspective due to imprecise and uncertainty of data information along with the networks, revealing a significant research gap to address. If the risk factors present in the FTL chain are left unaddressed, it will affect the whole supply chain link of the product and creates a considerable loss to the global economy. The study aims to bridge the knowledge gap by developing new uncertainty treatment models that facilitate the assessment and mitigation of risk associated with the safety of the agro-food product during the transportation and logistics network.
Methodology: To meet up the requirement of the research objectives, this research conducts empirical studies among the global Agro-food handling companies in Thailand, the Republic of China, and the Republic of Vietnam and follow the four steps of an effective risk management process, namely risk identification and classification, risk assessment, risk cause and effect assessment and risk mitigation strategies. To ensure the analysis is systematic and inclusive, all the various risk factors associated with the agro-food transport and logistic (AFTL) chain were identified through a careful review of the literature following a Delphi technique with industry experts, to verify the reliability of the identified risk factors. The assessment of the risk was conducted with the data collected via a two-stages of questionnaire surveys and evaluated through the Analytic Hierarchy Process (AHP) and fuzzy rule-based(FRB), and Bayesian network (BN). Thereafter, the risk cause and effect and risk mitigation strategies identified via literature review were validated through a set of empirical studies and evaluated through DEMATEL, Evidential reasoning and Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) technique.
Findings: The verified forty-six risk factors are classified into two main categories: Internal risk derived from the internal activities of the AFTL firm (i.e Finance, physical, information, organisation, infrastructure /technology, and supply chain risk) and external risk derived from external events or situation that negatively impact AFTL firm that either occurs naturally or caused by human error (i.e Environmental and security risk). The risk and their sub-criteria were identified through empirical studies after the risk assessment with a fuzzy rule base and Bayesian network. The top priority risks are “deterioration in service quality” “leadership in food safety management” “food supplier transparency” and “adaptation to food standard regulation.” Furthermore, thirty-five multi-criteria risk causal variables influencing the service quality were identified and classified into four main groups (i.e. Management, operation,resource and relational) such as “flexibility,” “completeness of order,” “the correctness of order,” “the safety of service delivery,” the “security of service delivery,” “availability of order information,” “a consistent procedure in the handling of orders” and “timeliness of shipment, pickup and delivery” among the indicators influencing the service quality of firm in the AFTL chain. The causal variable indicators (CVI’s) such as “openness in information exchange” “company ethical image” “social responsiveness” “equipment efficiency” “correctness of order” “the application of IT and electronic data interface” are the net causal variables which would positively influence the other causal risk variables. To mitigate these causal risk variables, the strategies such as “transformation leadership and top management commitment strategy,” “service culture, strategy,” “information and analysis strategy” and the “continuous improvement and innovation strategy” are identified through empirical studies. After applying the fuzzyTOPSIS technique, assessment results indicate that “service culture strategy” and “information and analysis strategy” are the most important with strong relevance to the service quality performances of the firm in the AFTL chain.
Research implication: This study is one of the earliest to recognize the need for a comprehensive risk assessment in the AFTL chain. It contributes to the AFTL risk analysis from different networks of stakeholders and applies an advanced uncertainty modelling technique to evaluate such diversified AFTL risks with high uncertainty in data in the same framework and provide mitigation strategies to manage the AFTL service quality causal risk variable in an uncertain environment
Practical implications- The profile of the risk sources, the risk priority weighting, cause and effect interdependency relationship of the causal variables, quantitative assessment of the CVI’s and the prioritization of the causal variables mitigation strategies can be beneficial to decision-makers in the food supply networks, transport logistic service provider, food risk assessor, the internal/external auditors in tackling uncertainty and vague information data to support safety-based decision making in the Agro-food transport logistic supply network in Thailand, the Republic of China and the Republic of Vietnam. The research findings can also be applied in other countries

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Transport; logistic; risk modelling; Evidential reasoning; DENMATEL Model; TOPSIS
Subjects: H Social Sciences > HF Commerce > HF5001 Business
H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
H Social Sciences > HE Transportation and Communications
Divisions: Business & Management (from Sep 19)
SWORD Depositor: A Symplectic
Date Deposited: 05 Jan 2023 11:38
Last Modified: 05 Jan 2024 00:50
DOI or ID number: 10.24377/LJMU.t.00018332
Supervisors: Zhuohua, QU and Yang, Zaili
URI: https://researchonline.ljmu.ac.uk/id/eprint/18332
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