Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks
Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks
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DOI：
10.1080/00207543.2023.2270719
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发表时间：
2023-11
影响因子：
9.2
通讯作者：
Alexandra Brintrup;E. Kosasih;Philipp Schaeffer;Ge Zheng;G. Demirel;Bart L. MacCarthy
中科院分区：
文献类型：
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作者：
Alexandra Brintrup;E. Kosasih;Philipp Schaeffer;Ge Zheng;G. Demirel;Bart L. MacCarthy
数字供应链监控（DSCS）是对数字数据的主动监测和分析，允许企业在没有参与供应链的企业明确同意的情况下提取与供应网络相关的信息。人工智能使DSCS变得更容易和更大规模，为自动检测供应链中涉及的参与者和依赖关系提供了重要机会，这反过来又可以帮助公司检测风险、不道德和环境不可持续的做法。在这里，我们定义DSCS，审查优先领域使用在英国进行的一项调查。通过多种机器学习方法和预测算法，可见性、可持续性和弹性是DSCS可以支持的重要领域。尽管关于算法结果可解释性的重要性的轶事叙述，从业者往往更喜欢准确性而不是可解释性；然而，工业部门和应用领域之间存在显著差异。通过案例研究，我们强调了在DSCS中不受限制地使用人工智能的一些问题，例如导致错误结论的偏见或误解，这可能导致次优决策或关系损害。在此基础上，我们开发和讨论了一些说明性案例，以突出从业者应该意识到的风险，并提出了进一步研究的关键领域。
Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply network, without the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. Here, we define DSCS, review priority areas using a survey conducted in the UK. Visibility, sustainability, resilience are significant areas that DSCS can support, through a number of machine-learning approaches and predictive algorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability; however, there are significant differences between industrial sectors and application areas. Using a case study, we highlight a number of concerns on the unchecked use of AI in DSCS, such as bias or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, proposing key areas of further research.