用AI识别供应链调查中的无效回答，提升决策数据可靠性。
From Noise to Insights: Enhancing Supply Chain Decision Support through AI-Based Survey Integrity Analytics
- 用监督学习模型区分真实与虚假调查回复。
- 在99份行业问卷中达到92.0%的识别准确率。
- 适合产品发布和新技术推广阶段的数据质量把关。
调查数据的可靠性对供应链决策至关重要，尤其在评估企业对AI驱动工具（如安全库存优化系统）的准备程度时。然而，调查常遭遇低投入或伪造回复，影响分析结果准确性。本研究提出一种轻量级AI框架，通过监督学习方法过滤不可靠输入。在扩展研究中，收集了99份行业问卷，并基于逻辑矛盾和响应模式进行人工标注以识别虚假回复。经预处理与标签编码后，训练随机森林及基线模型（逻辑回归、XGBoost），最佳模型达到92.0%的准确率，优于前期试点研究。尽管存在局限性，结果表明将AI融入调查流程具有可行性，为供应链研究中的数据完整性提供可扩展解决方案，尤其适用于产品发布与技术采纳阶段。
原文摘要 · Abstract (English)
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.
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