Predictive Analytics for Demand Forecasting: A deep Learning-based Decision Support System
Demand forecasting is a critical component of supply chain management and business operations, enabling organizations to make informed decisions about production, inventory management, and resource allocation. In recent years, predictive analytics has emerged as a powerful tool for enhancing the accuracy and efficiency of demand forecasting. This review paper explores the transformative role of predictive analytics and deep learning in demand forecasting. It examines how these advanced techniques have evolved from traditional models based on past sales data, offering nuanced predictions through sophisticated statistical and machine learning methods. Deep learning, with its neural network structures, brings automatic feature learning, complex pattern handling, and scalability, enhancing forecasting in sectors like retail, manufacturing, and healthcare. The paper reviews various deep learning models, compares them with traditional methods, and discusses their impact on business operations and decision-making. It concludes by looking at future trends in predictive analytics and deep learning in demand forecasting.
Geurts, M. D., & Kelly, J. P. (1986). Forecasting retail sales using alternative models. International Journal of Forecasting, 2(3), 261-272.
Choi, T. M., Li, D., & Yan, H. (2003). Optimal two- stage ordering policy with Bayesian information updating. Journal of the operational research society, 54(8), 846-859.
Zhang, G. P. (2003). Time series forecasting using a h y b r i d A R I M A a n d n e u r a l n e t w o r k model. Neurocomputing, 50, 159-175.
Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505.
Au, K. F., Choi, T. M., & Yu, Y. (2008). Fashion retail f o r e c a s t i n g b y e v o l u t i o n a r y n e u r a l networks. International Journal of Production Economics, 114(2), 615-630.
Thomassey, S. (2010). Sales forecasts in clothing industry: The key success factor of the supply chain management. International Journal of Production Economics, 128(2), 470-483.
Kumar, M., & Patel, N. R. (2010). Using clustering to i m p r o v e s a l e s f o r e c a s t s i n r e t a i l merchandising. Annals of Operations Research, 174, 33-46.
Murray, K. B., Di Muro, F., Finn, A., & Leszczyc, P.
P. (2010). The effect of weather on consumer spending. Journal of Retailing and Consumer Services, 17(6), 512-520.
Guo, Z. X., Wong, W. K., & Li, M. (2013). A
multivariate intelligent decision-making model for retail sales forecasting. Decision Support Systems, 55(1), 247-255.
Hu, Z., Bao, Y., Chiong, R., & Xiong, T. (2015). Mid- term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection. Energy, 84, 419-431Choi, T. M., Li, D., & Yan, H. (2003). Optimal two-stage ordering policy with Bayesian information updating. Journal of the operational research society, 54(8), 846-859.
Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & service operations management, 18(1), 69-88.
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60-74.
Wang, J., Liu, G. Q., & Liu, L. (2019, March). A
selection of advanced technologies for demand forecasting in the retail industry. In 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA) (pp. 317-320). IEEE.
Chen, C., Wang, Y., Huang, G., & Xiong, H. (2019, December). Hierarchical demand forecasting for factory production of perishable goods. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 188-193). IEEE.
Punia, S., Nikolopoulos, K., Singh, S. P., Madaan, J. K., & Litsiou, K. (2020). Deep learning with long short-term memory networks and random forests for d e m a n d f o r e c a s t i n g i n m u l t i - c h a n n e l retail. International journal of production research, 58(16), 4964-4979.
Badorf, F., & Hoberg, K. (2020). The impact of daily weather on retail sales: An empirical study in brick- and-mortar stores. Journal of Retailing and Consumer Services, 52, 101921.
Parizad, A., & Hatziadoniu, C. (2021). Deep learning algorithms and parallel distributed computing techniques for high-resolution load forecasting applying hyperparameter optimization. IEEE Systems Journal, 16(3), 3758-3769.
Shankar, S., Punia, S., & Ilavarasan, P. V. (2021). Deep learning- based container throughput forecasting: A triple bottom line approach. Industrial Management & Data Systems, 121(10), 2100-2117.
Shukla, A., & Gupta, A. K. (2022, November). Electric Load Forecasting through CNN: A Deep Learning Approach Considering Weather data. In 2022 IEEE 10th Power India International Conference (PIICON) (pp. 1-6). IEEE.
Falatouri, T., Darbanian, F., Brandtner, P., & Udokwu,
C. (2022). Predictive analytics for demand forecasting–a comparison of SARIMA and LSTM in retail SCM. Procedia Computer Science, 200, 993-1003.
Ghazal, T. M. (2022). Energy demand forecasting using fused machine learning approaches. Intelligent Automation & Soft Computing, 31(1), 539-553.
Mostafa, N., Ramadan, H. S. M., & Elfarouk, O. (2022). Renewable energy management in smart grids by using big data analytics and machine learning. Machine Learning with Applications, 9, 100363.
Yi, Z., Liu, X. C., Wei, R., Chen, X., & Dai, J.
(2022). Electric vehicle charging demand forecasting using deep learning model. Journal of Intelligent Transportation Systems, 26(6), 690-703.
Kaya, K., Yılmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel c lustering f indings for hospitality i n d u s t r y . I n f o r m a t i o n P r o c e s s i n g & Management, 59(1), 102816.
Gehlot, A., Ansari, B. K., Arora, D., Anandaram, H., Singh, B., & Arias-Gonzáles, J. L. (2022, July). Application of neural network in the prediction models of machine learning based design. In 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-6). IEEE.
Rao, S. N. V. B., Yellapragada, V. P. K., Padma, K.,
Pradeep, D. J., Reddy, C. P., Amir, M., & Refaat, S. S. (2022). Day-ahead load demand forecasting in urban community cluster microgrids using machine learning methods. Energies, 15(17), 6124.
Zhu, M., Wang, J., Yang, X., Zhang, Y., Zhang, L., Ren, H., ... & Ye, L. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment & Health, 1(2), 107-116.
Rohini, P., Tripathi, S., Preeti, C. M., Renuka, A., Gonzales, J. L. A., & Gangodkar, D. (2022, April). A study on the adoption of Wireless Communication in Big Data Analytics Using Neural Networks and Deep Learning. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1071-1076). IEEE.
Velasquez, C. E., Zocatelli, M., Estanislau, F. B., & Castro, V. F. (2022). Analysis of time series models f o r B r a z i l i a n e l e c t r i c i t y d e m a n d forecasting. Energy, 247, 123483.
N. Ali et al., “Fusion-based supply chain collaboration using machine learning techniques,” Intell. Autom. Soft Comput., vol. 31, no. 3, pp. 1671– 1687, 2022, doi: 10.32604/IASC.2022.019892.
Pallonetto, F., Jin, C., & Mangina, E. (2022). Forecast electricity demand in commercial building with machine learning models to enable demand response programs. Energy and AI, 7, 100121.
Chung, W. H., Gu, Y. H., & Yoo, S. J. (2022). District heater load forecasting based on machine learning and parallel CNN-LSTM attention. Energy, 246, 123350.
Fu, G., Jin, Y., Sun, S., Yuan, Z., & Butler, D. (2022).
The role of deep learning in urban water management: A critical review. Water Research, 223, 118973.
Huang, F., Yi, P., Wang, J., Li, M., Peng, J., & Xiong,
X. (2022). A dynamical spatial-temporal graph neural network for traffic demand prediction. Information Sciences, 594, 286-304.
Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. (2022). Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review. Engineering Applications of Artificial Intelligence, 115, 105287.
Torres, J. F., Martínez-Álvarez, F., & Troncoso, A. (2022). A deep LSTM network for the Spanish electricity consumption forecasting. Neural Computing and Applications, 34(13), 10533-10545.
Mohammad, F., Kang, D. K., Ahmed, M. A., & Kim,
Y. C. (2023). Energy demand load forecasting for electric vehicle charging stations network based on ConvLSTM and BiConvLSTM architectures. IEEE Access.
Bharadiya, J. P. (2023). Machine learning and AI in b u s i n e s s i n t e l l i g e n c e : T r e n d s a n d opportunities. International Journal of Computer (IJC), 48(1), 123-134.
Tsolaki, K., Vafeiadis, T., Nizamis, A., Ioannidis, D., & Tzovaras, D. (2023). Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express, 9(3), 284-295.
Rao, C., Zhang, Y., Wen, J., Xiao, X., & Goh, M. (2023). Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model. Energy, 263, 125955.
Copyright (c) 2024 International Journal of Engineering and Computer Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.