From Noise to Insights: Enhancing Supply Chain Decision Support through AI-Based Survey Integrity Analytics

TL;DR

This paper introduces an AI-based framework that effectively filters unreliable survey responses in supply chain research, enhancing data quality for decision-making during critical phases like product launches.

Contribution

It presents a supervised machine learning approach with a larger dataset to accurately identify fake survey responses, improving upon previous pilot results.

Findings

Best model achieved 92% accuracy in detecting fake responses

AI filtering improves survey data integrity in supply chain contexts

Scalable solution for enhancing decision support during product launches

Abstract

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.