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

arXiv: https://arxiv.org/abs/2601.17005 · HTML: https://mlsanity.com/paper/2601.17005/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Detecting The Corruption Of Online Questionnaires By Artificial Intelligence](https://mlsanity.com/paper/2308.07499/) (2023)
- [Big Dataunicodex2013Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques](https://mlsanity.com/paper/2307.12971/) (2023)
- [AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis](https://mlsanity.com/paper/2401.10895/) (2023)

## Abstract (arXiv)

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.