تحلیل احساسات مشتریان با هوش مصنوعی برای بهبود زنجیره تأمین هوشمند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی صنایع، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران

2 گروه آینده‌پژوهی، دانشگاه شمال، آمل، ایران

3 گروه مهندسی صنایع، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران

4 کارشناس ارشد مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران

5 گروه مهندسی صنایع، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران.

10.22091/jemsc.2025.3654.1260

چکیده

درک احساسات مشتریان و تحلیل آن‌ها با استفاده از هوش مصنوعی نقش مهمی در بهبود تصمیم‌گیری در زنجیره تأمین ایفا می‌کند. این پژوهش با هدف بررسی تأثیر تحلیل احساسات مشتریان مبتنی بر هوش مصنوعی بر پیش‌بینی تقاضا، مدیریت موجودی و طراحی محصول در زنجیره تأمین هوشمند انجام شده است. داده‌های متنی، صوتی و ویدئویی از توییتر، فیسبوک، آمازون و تماس‌های خدمات مشتریان جمع‌آوری و با مدل BERT پیش‌آموزش‌یافته برای تحلیل احساسات پردازش شدند. همچنین، مدل‌های Wav2Vec 2.0 و DeepFace برای تحلیل داده‌های صوتی و تصویری به کار گرفته شدند. یافته‌ها نشان داد که استفاده از تحلیل احساسات دقت پیش‌بینی تقاضا را ۱۸٪ افزایش داده، هزینه‌های مدیریت موجودی را ۲۰٪ کاهش داده و رضایت مشتریان از طراحی محصول را ۲۵٪ بهبود بخشیده است. نتایج نشان می‌دهد که ادغام تحلیل احساسات مشتریان با هوش مصنوعی می‌تواند موجب بهینه‌سازی فرآیندهای زنجیره تأمین و افزایش دقت تصمیم‌گیری شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Analyzing customer sentiment with AI to improve the smart supply chain

نویسندگان [English]

  • Asghar Hemmati 1
  • Seyed Hesamoddin Motevalli 2
  • Adel Pourghader Chobar 3
  • Ali Akhlaghpour 4
  • Leila Nazari 5
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
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Sentiment analysis
  • Deep learning
  • Intelligent supply chain
  • Product design
  • Digital transformation

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