An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction
In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2024-01-01
|
| Series: | Decision Science Letters |
| Online Access: | http://www.growingscience.com/dsl/Vol13/dsl_2023_46.pdf |
| _version_ | 1797388857469042688 |
|---|---|
| author | Issam Amellal Asmae Amellal Hamid Seghiouer Mohammed Rida Ech-Charrat |
| author_facet | Issam Amellal Asmae Amellal Hamid Seghiouer Mohammed Rida Ech-Charrat |
| author_sort | Issam Amellal |
| collection | DOAJ |
| description | In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management. |
| first_indexed | 2024-03-08T22:47:15Z |
| format | Article |
| id | doaj.art-12086e351e1d42eb8d5bbdb388230e4b |
| institution | Directory Open Access Journal |
| issn | 1929-5804 1929-5812 |
| language | English |
| last_indexed | 2024-03-08T22:47:15Z |
| publishDate | 2024-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Decision Science Letters |
| spelling | doaj.art-12086e351e1d42eb8d5bbdb388230e4b2023-12-17T06:28:13ZengGrowing ScienceDecision Science Letters1929-58041929-58122024-01-0113123724810.5267/j.dsl.2023.9.003An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price predictionIssam AmellalAsmae AmellalHamid SeghiouerMohammed Rida Ech-Charrat In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management.http://www.growingscience.com/dsl/Vol13/dsl_2023_46.pdf |
| spellingShingle | Issam Amellal Asmae Amellal Hamid Seghiouer Mohammed Rida Ech-Charrat An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction Decision Science Letters |
| title | An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction |
| title_full | An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction |
| title_fullStr | An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction |
| title_full_unstemmed | An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction |
| title_short | An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction |
| title_sort | integrated approach for modern supply chain management utilizing advanced machine learning models for sentiment analysis demand forecasting and probabilistic price prediction |
| url | http://www.growingscience.com/dsl/Vol13/dsl_2023_46.pdf |
| work_keys_str_mv | AT issamamellal anintegratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction AT asmaeamellal anintegratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction AT hamidseghiouer anintegratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction AT mohammedridaechcharrat anintegratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction AT issamamellal integratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction AT asmaeamellal integratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction AT hamidseghiouer integratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction AT mohammedridaechcharrat integratedapproachformodernsupplychainmanagementutilizingadvancedmachinelearningmodelsforsentimentanalysisdemandforecastingandprobabilisticpriceprediction |