从噪声到洞察：通过基于AI的调查完整性分析增强供应链决策支持
From Noise to Insights: Enhancing Supply Chain Decision Support through AI-Based Survey Integrity Analytics
AI总结：
本文提出基于AI的调查完整性分析框架，通过机器学习识别虚假响应，提升供应链决策支持数据的可靠性。
摘要：
调查数据的可靠性对供应链决策至关重要，特别是在评估人工智能驱动工具（如安全库存优化系统）的准备情况时。然而，调查往往吸引低效或虚假响应，从而降低推导出的洞察的准确性。本研究提出了一种轻量级的基于AI的框架，利用监督机器学习方法过滤不可靠的调查输入。在扩展研究中，收集了99个行业响应的更大数据集，并通过人工标注来识别基于逻辑不一致性和响应模式的虚假响应。在预处理和标签编码后，随机森林和基准模型（逻辑回归、XGBoost）被训练以区分真实和虚假响应。表现最好的模型实现了92.0%的准确率，显示出比试点研究更高的检测能力。尽管存在局限性，结果突显了将AI整合到调查流程中的可行性，并为供应链研究中提高数据完整性提供了可扩展的解决方案，尤其是在产品发布和技术采纳阶段。
英文摘要：
The reliability of survey data is crucial in supply chain decision-making, particularly when evaluating readiness for AI-driven tools such as safety stock optimization systems. However, surveys often attract low-effort or fake responses that degrade the accuracy of derived insights. This study proposes a lightweight AI-based framework for filtering unreliable survey inputs using a supervised machine learning approach. In this expanded study, a larger dataset of 99 industry responses was collected, with manual labeling to identify fake responses based on logical inconsistencies and response patterns. After preprocessing and label encoding, both Random Forest and baseline models (Logistic Regression, XGBoost) were trained to distinguish genuine from fake responses. The best-performing model achieved an 92.0% accuracy rate, demonstrating improved detection compared to the pilot study. Despite limitations, the results highlight the viability of integrating AI into survey pipelines and provide a scalable solution for improving data integrity in supply chain research, especially during product launch and technology adoption phases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
