Leveraging variational autoencoders and recurrent neural networks for demand forecasting in supply chain management: A case study

Khaoula Khlie; Zoubida Benmamoun; Widad Fethallah; Ikhlef Jebbor

doi:10.24294/jipd.v8i8.6639

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Leveraging variational autoencoders and recurrent neural networks for demand forecasting in supply chain management: A case study

Khaoula Khlie

Zoubida Benmamoun

Widad Fethallah

Ikhlef Jebbor

Journal of Infrastructure Policy and Development 2024, 8(8), 6639; https://doi.org/10.24294/jipd.v8i8.6639

Submitted：24 May 2024

Accepted：12 Jun 2024

Published：26 Aug 2024

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Abstract

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

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References

Akkem, Y., Biswas, S. K., & Varanasi, A. (2024). A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Engineering Applications of Artificial Intelligence, 131, 107881. https://doi.org/10.1016/j.engappai.2024.107881

Almaleh, A., Almushabb, R., & Ogran, R. (2023). Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model. Applied Sciences, 13(9), 5439. https://doi.org/10.3390/app13095439

Baskin, I. I. (2020). The power of deep learning to ligand-based novel drug discovery. Expert Opinion on Drug Discovery, 15(7), 755–764. https://doi.org/10.1080/17460441.2020.1745183

Bassiouni, M. M., Chakrabortty, R. K., Hussain, O. K., et al. (2023). Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions. Expert Systems with Applications, 211, 118604. https://doi.org/10.1016/j.eswa.2022.118604

Benmamoun, Z., Fethallah, W., Ahlaqqach, M., et al. (2023). Butterfly Algorithm for Sustainable Lot Size Optimization. Sustainability, 15(15), 11761. https://doi.org/10.3390/su151511761

Benmamoun, Z., Khlie, K., Dehghani, M., & Gherabi, Y. (2024). WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems. Mathematics, 12(7), 1059.

Chen, Z., Ma, M., Li, T., et al. (2023). Long sequence time-series forecasting with deep learning: A survey. Information Fusion, 97, 101819. https://doi.org/10.1016/j.inffus.2023.101819

Cheng, F., He, Q. P., & Zhao, J. (2019). A novel process monitoring approach based on variational recurrent autoencoder. Computers & Chemical Engineering, 129, 106515. https://doi.org/10.1016/j.compchemeng.2019.106515

Cho, K., van Merrienboer, B., Bahdanau, D., & Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. Available online: http://arxiv.org/abs/1409.1259 (accessed on 4 May 2024).

de Castro Moraes, T., Qin, J., Yuan, X. M., et al. (2023). Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions. Logistics, 7(4), 79. https://doi.org/10.3390/logistics7040079

de Bruijne, M., Cattin, P. C., Cotin, S., et al. (2021). Medical Image Computing and Computer Assisted Intervention—MICCAI. In: Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-030-87199-4

El Filali, A., Lahmer, E. H. B., El Filali, S., et al. (2022). Machine Learning Applications in Supply Chain Management: A Deep Learning Model Using an Optimized LSTM Network for Demand Forecasting. Available online: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=2185310X&AN=155479850&h=QNfWAvzGl6HNvFi%2FLjb2GUtZRfjGDzA15IGsntYvOkKEF7OvyNJJGmtU%2BgdYRrOTviGX%2B2cFyWSwjN%2BHkPboMQ%3D%3D&crl=c (accessed on 5 May 2024).

Germain, M., Gregor, K., Murray, I., & Larochelle, H. (2015). Made: Masked autoencoder for distribution estimation. Available online: http://proceedings.mlr.press/v37/germain15.html (accessed on 4 May 2024).

Giannakopoulou, K. M., Roussaki, I., & Demestichas, K. (2022). Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review. Sensors, 22(5), 1799. https://doi.org/10.3390/s22051799

Hangl, J., Behrens, V. J., & Krause, S. (2022). Barriers, Drivers, and Social Considerations for AI Adoption in Supply Chain Management: A Tertiary Study. Logistics, 6(3), 63. https://doi.org/10.3390/logistics6030063

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Jackson, I., Ivanov, D., Dolgui, A., et al. (2024). Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. International Journal of Production Research, 1–26. https://doi.org/10.1080/00207543.2024.2309309

Jebbor, I., Benmamoun, Z., & Hachimi, H. (2023). Optimizing Manufacturing Cycles to Improve Production: Application in the Traditional Shipyard Industry. Processes, 11(11), 3136. https://doi.org/10.3390/pr11113136

Khlie, K. (2024). Optimizing Inventory Control in Supply Chain Management. In: Proceedings of the 2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA). pp. 53–56.

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. Available online: http://arxiv.org/abs/1312.6114 (accessed on 4 May 2024).

Liao, W., Ge, L., Bak-Jensen, B., et al. (2022). Scenario prediction for power loads using a pixel convolutional neural network and an optimization strategy. Energy Reports, 8, 6659–6671. https://doi.org/10.1016/j.egyr.2022.05.028

Lin, H., Liu, G., Wu, J., et al. (2024). Deterring the Gray Market: Product Diversion Detection via Learning Disentangled Representations of Multivariate Time Series. INFORMS Journal on Computing, 36(2), 571–586. https://doi.org/10.1287/ijoc.2022.0155

Luleci, F., & Catbas, F. N. (2023). A brief introductory review to deep generative models for civil structural health monitoring. AI in Civil Engineering, 2(1). https://doi.org/10.1007/s43503-023-00017-z

Ma, S. (2024). Retail store-SKU level replenishment planning with attribute-space graph recurrent neural networks. Expert Systems with Applications, 249, 123727. https://doi.org/10.1016/j.eswa.2024.123727

Molęda, M., Małysiak-Mrozek, B., Ding, W., et al. (2023). From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry. Sensors, 23(13), 5970. https://doi.org/10.3390/s23135970

Nguyen, N. T., Chbeir, R., Exposito, E., et al. (2019). Computational Collective Intelligence. In: Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-030-28377-3

Pan, Z., Wang, Y., Cao, Y., Gui, W. (2023). VAE-based interpretable latent variable model for process monitoring. Available online: https://ieeexplore.ieee.org/abstract/document/10149177/ (accessed on 5 May 2024).

Raja Santhi, A., & Muthuswamy, P. (2022). Influence of Blockchain Technology in Manufacturing Supply Chain and Logistics. Logistics, 6(1), 15. https://doi.org/10.3390/logistics6010015

Rimal, Y., Sharma, N., & Alsadoon, A. (2024). The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-18426-2

Salehinejad, H., Sankar, S., Barfett, J., et al. (2018). Recent Advances in Recurrent Neural Networks. Available online: http://arxiv.org/abs/1801.01078 (accessed on 4 May 2024).

Schroeder, M., & Lodemann, S. (2021). A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management. Logistics, 5(3), 62. https://doi.org/10.3390/logistics5030062

Sefati, S. S., Arasteh, B., Halunga, S., et al. (2023). Meet User’s Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm. Available online: https://ieeexplore.ieee.org/abstract/document/10210672/ (accessed on 5 May 2024).

Serrou, D., Khlie, K., & Abouabdellah, A. (2016). Improvement of the lean-maintenance by hospital logistics. In: Proceedings of the 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt). pp. 19–24.

Simian, D., & Husac, F. (2022). Challenges and Opportunities in Deep Learning Driven Fashion Design and Textiles Patterns Development. Available: https://link.springer.com/chapter/10.1007/978-3-031-27034-5_12 (accessed on 5 May 2024).

Singh, A., & Ogunfunmi, T. (2021). An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications. Entropy, 24(1), 55. https://doi.org/10.3390/e24010055

Talasila, V., Madhubabu, K., Mahadasyam, M., et al. (2020). The Prediction of Diseases using Rough Set Theory with Recurrent Neural Network in Big Data Analytics. International Journal of Intelligent Engineering and Systems, 13(5), 10–18. https://doi.org/10.22266/ijies2020.1031.02

Trivedi, S., & Negi, S. (2023). The metaverse in supply chain management: application and benefits. International Journal of Advanced Virtual Reality, 1(1), 36–43.

Wang, T., & Hong, L. J. (2023). Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach. INFORMS Journal on Computing, 35(1), 196–215. https://doi.org/10.1287/ijoc.2022.1253

Yilmaz, B., & Korn, R. (2022). Synthetic demand data generation for individual electricity consumers : Generative Adversarial Networks (GANs). Energy and AI, 9, 100161. https://doi.org/10.1016/j.egyai.2022.100161

Zietlow, D., Rolinek, M., & Martius, G. (2021). Demystifying inductive biases for (beta-) VAE based architectures. Available online: http://proceedings.mlr.press/v139/zietlow21a.html (accessed on 5 May 2024).

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