Blockchain technology identifies and categorises product waste in supply chains, helps identify food contamination risks, and improves transit security by decreasing food degradation. This study aims to understand blockchain's adoption in the food supply chain. It is an attempt to determine causal relationship between the factors. The research includes a case study of a food processing unit, and the authors, drawing on a review of the relevant literature and the insights of subject-matter experts, have identified ten distinct factors. Fuzzy Interpretive Structure Modelling (F-ISM) is used to gain an understanding of the linkages among the factors and develop a hierarchical digraph. Furthermore, to understand the causal relationship, Fuzzy Decision-making trial and evaluation laboratory (F-DEMATEL) was applied. The outcomes of Fuzzy Matrice d'impacts croisés multiplication appliquée á un classment (F-MICMAC) and F-DEMATEL analysis appeared to be almost identical, confirming the cause-and-effect relationship among factors. The factor's association was validated using sensitivity analysis. Altering the experts' input weights affected the factor's causal analysis, and the results were robust. The findings of the study depict a) Decentralisation (FII), Data Sovereignty (FIX), Interoperability (FVIII)) in the independent region and two factor (Infrastructure (FX), Smart Systems (FIII)) in the linkage region; representing causes and b) Data Management (FI), Operation Responsiveness (FIV), Data Documentation (FV), Third Party Involvement (FVI), and Cost (FVII in independent region representing effects. Further sensitivity in the inputs revealed very less change in outputs thereby representing robustness of the results. The nodding of the experts from case organisation further validated the findings. The research assists policy makers in assessment of existing systems, creation of laws and frameworks pertaining to system and can create social awareness about health hazards particularly in the food supply chain. The manuscript assists managers and decision-makers in evaluating their existing supply-chain practices and develop efficient and effective blockchain system that is not only transparent but is also robust.

区块链技术可以识别供应链中的产品浪费并对其进行分类，帮助识别食品污染风险，并通过减少食品降解来提高运输安全。本研究旨在了解区块链在食品供应链中的采用。它试图确定因素之间的因果关系。该研究包括一个食品加工单位的案例研究，作者根据相关文献的回顾和主题专家的见解，确定了十个不同的因素。模糊解释结构建模 (F-ISM) 用于了解因素之间的联系并开发层次图。此外，为了了解因果关系，应用了模糊决策试验和评估实验室（F-DEMATEL）。模糊矩阵影响乘法应用程序分类 (F-MICMAC) 和 F-DEMATEL 分析的结果似乎几乎相同，证实了因素之间的因果关系。使用敏感性分析验证了该因素的关联性。改变专家的输入权重会影响因素的因果分析，并且结果是稳健的。研究结果描述了a）独立区域的去中心化（FII）、数据主权（FIX）、互操作性（FVIII））和链接区域的两个因素（基础设施（FX）、智能系统（FIII））；b) 数据管理 (FI)、操作响应性 (FIV)、数据文档 (FV)、第三方参与 (FVI) 和成本（独立区域中的 FVII 代表效果。输入的进一步敏感性表明输出的变化非常小，从而代表结果的稳健性。案例组织专家的点头进一步验证了这一结论。该研究帮助政策制定者评估现有系统，制定与系统有关的法律和框架，并可以提高社会对健康危害的认识，特别是在食品供应链中。该手稿帮助管理者和决策者评估他们现有的供应链实践，并开发高效且有效的区块链系统，该系统不仅透明而且稳健。制定与系统有关的法律和框架，可以提高社会对健康危害的认识，特别是在食品供应链中。该手稿帮助管理者和决策者评估他们现有的供应链实践，并开发高效且有效的区块链系统，该系统不仅透明而且稳健。制定与系统有关的法律和框架，可以提高社会对健康危害的认识，特别是在食品供应链中。该手稿帮助管理者和决策者评估他们现有的供应链实践，并开发高效且有效的区块链系统，该系统不仅透明而且稳健。