An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction

Amellal, I.; Amellal, A.; Seghiouer, H.; Ech-Charrat, M.

Abstract: 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.

How to cite this paper

 Amellal, I., Amellal, A., Seghiouer, H & Ech-Charrat, M. (2024). 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 , 13(1), 237-248.

References

Aamer, A., Eka Yani, L., & Alan Priyatna, I. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1-13.
Amellal, A., Amellal, I., & Seghiouer, H., & Ech-Charrat, M. (2023). Improving Lead Time Forecasting and Anomaly Detection for Automotive Spare Parts with A Combined CNN-LSTM Approach. Operations and Supply Chain Management: An International Journal, 16(2), 265-278.
Avilés, A., Célleri, R., Solera, A., & Paredes, J. (2016). Probabilistic forecasting of drought events using Markov chain-and Bayesian network-based models: A case study of an Andean regulated river basin. Water, 8(2), 37.
Aydinonat, N. E., & Köksal, E. (2019). Explanatory value in context: the curious case of Hotelling’s location model. The European Journal of the History of Economic Thought, 26(5), 879-910.
Balta, G. K., Dikmen, I., & Birgonul, M. T. (2021). Bayesian network based decision support for predicting and mitigating delay risk in TBM tunnel projects. Automation in Construction, 129, 103819.
Boutselis, P., & McNaught, K. (2019). Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context. International Journal of Production Economics, 209, 325-333.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Buntine, W. (1996). A guide to the literature on learning probabilistic networks from data. IEEE Transactions on knowledge and data engineering, 8(2), 195-210.
Chickering, D. M. (2002). Learning equivalence classes of Bayesian-network structures. The Journal of Machine Learning Research, 2, 445-498.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Chopra, S., Meindl, P., & Kalra, D. V. (2007). Supply Chain Management by Pearson. Pearson Education India.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Dou, Z. Y., Yu, K., & Anastasopoulos, A. (2019). Investigating meta-learning algorithms for low-resource natural language understanding tasks. arXiv preprint arXiv:1908.10423.
Durairaj, A. K., & Chinnalagu, A. (2021). Transformer based Contextual Model for Sentiment Analysis of Customer Reviews: A Fine-tuned BERT. International Journal of Advanced Computer Science and Applications, 12(11).
Esmaeili, P., Makui, A., Seyedhosseini, S., & Ghousi, R. (2022). The effect of probabilistic incentives to promote cooperation during the pandemics using simulation of multi-agent evolutionary game. International Journal of Industrial Engineering Computations, 13(3), 319-328.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119-142.
Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., & Miranda, S. (2017). The role of uncertainty in supply chains under dynamic modeling. International Journal of Industrial Engineering Computations, 8(1), 119-140.
Friedman, N., Goldszmidt, M., Heckerman, D., & Russell, S. (1997). Where is the impact of Bayesian networks in learning. In International Joint Conference on Artificial Intelligence.
Geetha, M. P., & Renuka, D. K. (2021). Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model. International Journal of Intelligent Networks, 2, 64-69.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Honjo, K., Zhou, X., & Shimizu, S. (2022, July). CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Huang, H., Zhang, B., Jing, L., Fu, X., Chen, X., & Shi, J. (2022). Logic tensor network with massive learned knowledge for aspect-based sentiment analysis. Knowledge-Based Systems, 257, 109943.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904-2915.
Kelleher, J. D., Mac Namee, B., & D'arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.
Ma, Z., Wang, C., & Zhang, Z. (2021, September). Deep Learning Algorithms for Automotive Spare Parts Demand Forecasting. In 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI) (pp. 358-361). IEEE.
Noorbeh, P., Roozbahani, A., & Kardan Moghaddam, H. (2020). Annual and monthly dam inflow prediction using Bayesian networks. Water Resources Management, 34, 2933-2951.
Noh, J., Park, H. J., Kim, J. S., & Hwang, S. J. (2020). Gated recurrent unit with genetic algorithm for product demand forecasting in supply chain management. Mathematics, 8(4), 565.
Pathak, U., Kant, R., & Shankar, R. (2020). Price and profit decisions in manufacturer-led dual-channel supply chain configurations. International Journal of Industrial Engineering Computations, 11(3), 377-400.
Pauwels, S., & Calders, T. (2020). Bayesian network based predictions of business processes. In Business Process Management Forum: BPM Forum 2020, Seville, Spain, September 13–18, 2020, Proceedings 18 (pp. 159-175). Springer International Publishing.
Pearl, J. (1985, August). Bayesian netwcrks: A model cf self-activated memory for evidential reasoning. In Proceedings of the 7th conference of the Cognitive Science Society, University of California, Irvine, CA, USA (pp. 15-17).
Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons.
Rychłowska-Musiał, E. (2020). Real options games between two competitors: the case of price war. International Journal of Computational Economics and Econometrics, 10(1), 92-110.
Saini, U., Kumar, R., Jain, V., & Krishnajith, M. U. (2020, July). Univariant Time Series forecasting of Agriculture load by using LSTM and GRU RNNs. In 2020 IEEE Students Conference on Engineering & Systems (SCES) (pp. 1-6). IEEE.
Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision support systems, 54(4), 1513-1520.
Sirusstara, J., Alexander, N., Alfarisy, A., Achmad, S., & Sutoyo, R. (2022, September). Clickbait Headline Detection in Indonesian News Sites using Robustly Optimized BERT Pre-training Approach (RoBERTa). In 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp. 1-6). IEEE.
Tao, X., & Yang, W. (2022, May). Prediction of machine production state based on Bayesian Network. In ISCTT 2022; 7th International Conference on Information Science, Computer Technology and Transportation (pp. 1-4). VDE.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Wang, X., Chaolu, T., Gao, Y., Wen, Y., & Liu, P. (2023). Marketplace channel encroachment under private brand introduction of online platform. International Journal of Industrial Engineering Computations, 14(2), 403-414.
Wu, S., Liu, Y., Zou, Z., & Weng, T. H. (2022). S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 34(1), 44-62.
Yarullin, R., & Serdyukov, P. (2021). Bert for sequence-to-sequence multi-label text classification. In Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers 9 (pp. 187-198). Springer International Publishing.