Analysis and Optimization Strategies for Demand Forecasting Issues at MIXUE
Keywords:
Analysis, Optimization Strategies, Demand, Forecasting, Issues, MIXUE
Abstract
This study addresses demand forecasting challenges at MIXUE, a beverage chain, focusing on issues like high subjectivity and significant prediction errors that lead to market volatility and operational inefficiencies. Despite the critical role of the food and beverage industry in economic growth, MIXUE faces intense competition, emphasizing the need for accurate demand forecasting. This research aims to analyze historical sales data using time series analysis to identify patterns, optimize demand forecasting methods, and reduce data volatility. The study develops a demand forecasting model to align supply and demand, improve revenue management, and mitigate risks associated with uncertainty. Results highlight the importance of precise demand forecasting in enhancing operational efficiency, reducing costs, and maximizing profits. This research provides actionable insights for MIXUE to better anticipate market trends and strengthen its competitive position.
References
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[15] Y. Barlas and B. Gunduz, "Demand Forecasting and Sharing Strategies to Reduce Fluctuations and the Bullwhip Effect in Supply Chains," Journal of the Operational Research Society, vol. 62, no. 3, pp. 458–473, 2011.
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[18] K. Li, et al., "A Data-Driven Strategy to Forecast Next-Day Electricity Usage and Peak Electricity Demand of a Building Portfolio Using Cluster Analysis, Cubist Regression Models, and Particle Swarm Optimization," Journal of Cleaner Production, vol. 273, Article ID 123115, 2020.
[19] J. Pascual, et al., "Energy Management Strategy for a Renewable-Based Residential Microgrid with Generation and Demand Forecasting," Applied Energy, vol. 158, pp. 12–25, 2015.
[20] S. R. Gayam, "AI for Supply Chain Visibility in E-Commerce: Techniques for Real-Time Tracking, Inventory Management, and Demand Forecasting," Distributed Learning and Broad Applications in Scientific Research, vol. 5, pp. 218–251, 2019.
[2] Q. Deng, "Research on Demand Forecasting in the Supply Chain of Company L," M.S. thesis, Univ. of International Business and Economics, Beijing, China, 2021.
[3] X. Xie, "Research on Finished Goods Inventory Management of Y Clothing Company," M.S. thesis, Xiamen Univ., Xiamen, China, 2017.
[4] J. Ruan, "Optimization of the Distribution System of Z Cold Chain Logistics Company in Zhoukou City," M.S. thesis, Hebei Univ. of Science and Technology, Hebei, China, 2021.
[5] Research Group of the Gannan Prefecture Central Branch of the People's Bank of China and C. Guo, "Comparative Study on Forecasting Methods for Time Series Indicators of Deposits and Loans: An Empirical Forecast Based on Data from Gannan Prefecture," Gansu Finance, no. 3, pp. 52–57, 2016.
[6] M. Seyedan and F. Mafakheri, "Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities," Journal of Big Data, vol. 7, no. 1, p. 53, 2020.
[7] N. Li, et al., "From Demand Forecasting to Inventory Ordering Decisions for Red Blood Cells Through Integrating Machine Learning, Statistical Modeling, and Inventory Optimization," Transfusion, vol. 62, no. 1, pp. 87–99, 2022.
[8] Z. H. Kilimci, et al., "An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain," Complexity, vol. 2019, no. 1, Article ID 9067367, 2019.
[9] H. Abbasimehr, M. Shabani, and M. Yousefi, "An Optimized Model Using LSTM Network for Demand Forecasting," Computers & Industrial Engineering, vol. 143, Article ID 106435, 2020.
[10] L. Suganthi and A. A. Samuel, "Energy Models for Demand Forecasting—A Review," Renewable and Sustainable Energy Reviews, vol. 16, no. 2, pp. 1223–1240, 2012.
[11] M. Seyedan, F. Mafakheri, and C. Wang, "Order-Up-to-Level Inventory Optimization Model Using Time-Series Demand Forecasting with Ensemble Deep Learning," Supply Chain Analytics, vol. 3, Article ID 100024, 2023.
[12] J. M. Chandran and M. R. B. Khan, "A Strategic Demand Forecasting: Assessing Methodologies, Market Volatility, and Operational Efficiency," Malaysian Journal of Business, Economics and Management, vol. 2024, pp. 150–167, 2024.
[13] F. Olaoye, K. Potter, and B. Klinton, "Big Data Analytics in Retail: Optimizing Inventory and Demand Forecasting," unpublished.
[14] M. Q. Raza and A. Khosravi, "A Review on Artificial Intelligence-Based Load Demand Forecasting Techniques for Smart Grid and Buildings," Renewable and Sustainable Energy Reviews, vol. 50, pp. 1352–1372, 2015.
[15] Y. Barlas and B. Gunduz, "Demand Forecasting and Sharing Strategies to Reduce Fluctuations and the Bullwhip Effect in Supply Chains," Journal of the Operational Research Society, vol. 62, no. 3, pp. 458–473, 2011.
[16] S. Gattinoni, "Supply Chain Planning: Inventory Optimization Through the Demand Forecasting Tools and Algorithm," unpublished, 2022.
[17] H. Hu, S. Gong, and B. Taheri, "Energy Demand Forecasting Using Convolutional Neural Network and Modified War Strategy Optimization Algorithm," Heliyon, vol. 10, no. 6, Article ID e13010, 2024.
[18] K. Li, et al., "A Data-Driven Strategy to Forecast Next-Day Electricity Usage and Peak Electricity Demand of a Building Portfolio Using Cluster Analysis, Cubist Regression Models, and Particle Swarm Optimization," Journal of Cleaner Production, vol. 273, Article ID 123115, 2020.
[19] J. Pascual, et al., "Energy Management Strategy for a Renewable-Based Residential Microgrid with Generation and Demand Forecasting," Applied Energy, vol. 158, pp. 12–25, 2015.
[20] S. R. Gayam, "AI for Supply Chain Visibility in E-Commerce: Techniques for Real-Time Tracking, Inventory Management, and Demand Forecasting," Distributed Learning and Broad Applications in Scientific Research, vol. 5, pp. 218–251, 2019.
Published
2024-12-24
How to Cite
Shihua, C., Xin, M., & Wen, L. (2024). Analysis and Optimization Strategies for Demand Forecasting Issues at MIXUE. International Journal on Economics, Finance and Sustainable Development, 6(12), 383-396. https://doi.org/10.31149/ijefsd.v6i12.5346
Section
Articles
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