Analyzing customer sentiment with AI to improve the smart supply chain

Document Type : Original Article

Authors

1 Department of Industrial Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran

2 Department of Future Studies, Shomal University, Amol, Iran

3 Assistant Professor, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

4 Master of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

5 Department of Industrial Engineering, Abhar branch, Islamic Azad University, Abhar, Iran

10.22091/jemsc.2025.3654.1260

Abstract

Understanding customer sentiment and analyzing it using artificial intelligence plays an important role in improving decision-making in the supply chain. This study aimed to investigate the impact of AI-based customer sentiment analysis on demand forecasting, inventory management, and product design in smart supply chains.Text, audio, and video data from Twitter, Facebook, Amazon, and customer service calls were collected and processed with a pre-trained BERT model for sentiment analysis. Also, Wav2Vec 2.0 and DeepFace models were used to analyze audio and video data. The findings showed that using sentiment analysis increased the accuracy of demand forecasting by 18%, reduced inventory management costs by 20%, and improved customer satisfaction with product design by 25%. The results show that integrating customer sentiment analysis with AI can optimize supply chain processes and increase decision-making accuracy.

Keywords

Main Subjects

References

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