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.
Refrences
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.
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.