AI-Powered Demand Forecasting for Supply Chain Management
Abstract
Accurate demand forecasting is critical for effective supply chain management. This chapter delves into the application of AI technologies, such as neural networks and deep learning, to improve demand forecasting accuracy. It highlights the benefits of AI-powered forecasting over traditional methods, including increased responsiveness to market changes and reduced inventory holding costs. Real-world examples and practical guidelines for implementing AI-based demand forecasting are provided.
References
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883. https://doi.org/10.1111/poms.12838
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341-361. https://doi.org/10.1111/1467-8551.12355
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 31(10), 775-788. https://doi.org/10.1080/09537287.2020.1768450
Koushik, P. OPTIMIZING FULFILLMENT: A MULTI-FACETED APPROACH INTEGRATING LINEAR PROGRAMMING, BRANCH AND BOUND TECHNIQUES, AND REINFORCEMENT LEARNING. International Journal of Computer Engineering and Technology (IJCET) Volume 15, Issue 3, May-June 2024, pp. 134-149
Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. https://doi.org/10.1016/j.jbusres.2016.08.007
Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. https://doi.org/10.1016/j.ijinfomgt.2018.11.021
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014
Koushik, P. Data-Driven Simulation: Integrating Sensitivity Analysis into Supply Chain Optimization, International Journal of Science and Research (IJSR), Volume 13 Issue 5, May 2024
