{"title":"释放人工智能在食品供应链中的潜力:克服障碍的新方法","authors":"","doi":"10.1016/j.jafr.2024.101349","DOIUrl":null,"url":null,"abstract":"<div><p>This paper delves into the challenges impeding the seamless integration of artificial intelligence (AI) within the food supply chain (FSC) and introduces a novel methodological framework that combines the NK Model with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. Through an exhaustive literature analysis and expert discussions, the research identifies and categorizes significant obstacles to AI deployment in the FSC. These hurdles include the imperative for a skilled labor force, financial limits, regulatory complexity and technological limitations. The unique DEMATEL-NK approach highlights the interconnected nature of these barriers, pinpointing the most critical impediments. 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引用次数: 0
摘要
本文深入探讨了阻碍在食品供应链(FSC)中无缝整合人工智能(AI)的挑战,并引入了一个新颖的方法框架,该框架将 NK 模型与决策试验和评估实验室(DEMATEL)技术相结合。通过详尽的文献分析和专家讨论,该研究确定并归类了在供应链中部署人工智能的重大障碍。这些障碍包括对熟练劳动力的需求、资金限制、监管复杂性和技术限制。独特的 DEMATEL-NK 方法强调了这些障碍的相互关联性,并指出了最关键的障碍。这项研究的意义延伸到农业和食品行业采用人工智能的更广泛领域,为政策制定者、行业利益相关者和研究人员提供了一个细致入微的视角。研究结果强调,要在食品安全委员会成功实施人工智能技术,必须克服这些障碍,从而有望提高效率、质量和可持续性。创新的方法不仅揭示了这些障碍之间的相互联系,还为确定优先次序和实施解决方案提供了系统方法。这项研究为障碍关系提供了一个全新的视角,指导决策者制定有效的战略和干预措施,推动人工智能在渔业安全委员会的整合。
Unlocking AI's potential in the food supply chain: A novel approach to overcoming barriers
This paper delves into the challenges impeding the seamless integration of artificial intelligence (AI) within the food supply chain (FSC) and introduces a novel methodological framework that combines the NK Model with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. Through an exhaustive literature analysis and expert discussions, the research identifies and categorizes significant obstacles to AI deployment in the FSC. These hurdles include the imperative for a skilled labor force, financial limits, regulatory complexity and technological limitations. The unique DEMATEL-NK approach highlights the interconnected nature of these barriers, pinpointing the most critical impediments. The study's implications extend to the broader domains of AI adoption in agriculture and the food industry, offering a nuanced perspective for policymakers, industry stakeholders, and researchers. The findings underscore the imperative of overcoming these barriers for the successful implementation of AI technologies in the FSC, promising advancements in efficiency, quality, and sustainability. The innovative methodology not only sheds light on the interconnectedness of these barriers but also provides a systematic approach for prioritizing and implementing solutions. This research offers a fresh viewpoint on barrier relationships, guiding decision-makers in crafting effective strategies and interventions to propel AI integration in the FSC forward.