Author(s)

Sara Esposito

Affiliation(s)

Faculty of Management, Politecnico di Milano, Milano 20133, Italy. 

Corresponding Author

Sara Esposito

ABSTRACT

This paper investigates the application of deep learning techniques for product demand prediction in dynamic markets, addressing the growing need for accurate forecasting in supply chain management. As consumer preferences become increasingly volatile and market conditions fluctuate, traditional statistical methods, such as Autoregressive Integrated Moving Averageand exponential smoothing, often fail to adapt effectively, resulting in significant forecasting inaccuracies. This study highlights the limitations of these conventional approaches and explores the potential of deep learning models, particularly Long Short-Term Memory networks, to enhance demand forecasting accuracy. By leveraging historical sales data and integrating various external factors, including economic indicators and market trends, deep learning models can capture complex nonlinear relationships and temporal dependencies that traditional methods overlook. The methodology comprises a comprehensive data collection and preprocessing phase, followed by the selection and training of the LSTM model. The results demonstrate that deep learning architectures significantly outperform traditional forecasting techniques, providing organizations with improved insights into demand patterns and enabling proactive decision-making.

Furthermore, the paper emphasizes the necessity for models that can adapt to rapidly changing market conditions and suggests avenues for future research, including the exploration of hybrid models and the incorporation of additional data sources such as social media. The findings underscore the transformative potential of deep learning in demand forecasting, offering valuable implications for practitioners in supply chain management and contributing to the ongoing evolution of forecasting methodologies.

KEYWORDS

Demand forecasting; Deep learning; Supply chain management

CITE THIS PAPER

Sara Esposito. A deep learning approach for product demand prediction in dynamic markets. World Journal of Management Science. 2024, 2(4): 39-46. DOI: https://doi.org/10.61784/wms3048.

REFERENCES

[1] Chandriah K K, Naraganahalli R V. RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimedia Tools and Applications, 2021, 80(17): 26145-26159.

[2] Van Nguyen T, Zhou L, Chong A Y L, et al. Predicting customer demand for remanufactured products: A data-mining approach. European Journal of Operational Research, 2020, 281(3): 543-558.

[3] Andronie M, Lazaroiu G, Iatagan M, et al. Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 2021, 10(20): 2497.

[4] Qiu L. DEEP LEARNING APPROACHES FOR BUILDING ENERGY CONSUMPTION PREDICTION. Frontiers in Environmental Research, 2024, 2(3): 11-17.

[5] Punia S, Nikolopoulos K, Singh S P, et al. Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. International journal of production research, 2020, 58(16): 4964-4979.

[6] Oroojlooyjadid A, Snyder L V, Taká? M. Applying deep learning to the newsvendor problem. IISE Transactions, 2020, 52(4): 444-463.

[7] Li P, Ren S, Zhang Q, et al. Think4SCND: Reinforcement Learning with Thinking Model for Dynamic Supply Chain Network Design. IEEE Access, 2024.

[8] Kulshrestha A, Krishnaswamy V, Sharma M. Bayesian BILSTM approach for tourism demand forecasting. Annals of tourism research, 2020, 83, 102925.

[9] Lazaroiu G, Andronie M, Iatagan M, et al. Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the internet of manufacturing things. ISPRS International Journal of Geo-Information, 2022, 11(5): 277.

[10] Zhang X, Chen S, Shao Z, et al. Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning with Synthetic Pattern Generation. IEEE Open Journal of the Computer Society, 2024.

[11] Shakya M, Lee B S, Ng H Y. A Deep Reinforcement Learning Approach for Inventory Control under Stochastic Lead Time and Demand. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022: 760-766.

[12] Seyedan M, Mafakheri F. Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 2020, 7(1), 53.

[13] Wang, M. AI Technologies in Modern Taxation: Applications, Challenges, and Strategic Directions. International Journal of Finance and Investment, 2024, 1(1): 42-46.

[14] Kumar I, Rawat J, Mohd N, et al. Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021(1), 4535567.

[15] Agbulut U. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustainable Production and Consumption, 2022, 29: 141-157.

[16] Zhang X, Li P, Han X, et al. Enhancing Time Series Product Demand Forecasting with Hybrid Attention-Based Deep Learning Models. IEEE Access, 2024.

[17] Antonopoulos I, Robu V, Couraud B, et al. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 2020, 130: 109899.