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研究生: 塞萊登
CELEDON GODOY, JOSE JULIAN
論文名稱: 易腐貨品銷量預測之比較分析:綜合與分散方法配合調和技術
Comparative Analysis of Sales Volume Forecasting for Perishable Goods: Aggregate vs. Disaggregate Approaches with Reconciliation Techniques
指導教授: 雷松亞
RAY, SOUMYA
口試委員: 徐茉莉
SHMUELI, GALIT
葛凌偉
GREENE, TRAVIS
學位類別: 碩士
Master
系所名稱: 科技管理學院 - 國際專業管理碩士班
International Master of Business Administration(IMBA)
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 62
中文關鍵詞: 預測易腐貨物和解零售聚合分解
外文關鍵詞: Forecasting, Perishable Goods, Reconciliation, Retail, Aggregated, Disaggregated
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    In the rapidly evolving retail landscape, accurate forecasting of sales volume for perishable goods is crucial. This thesis presents a comparative analysis of forecasting methods focusing on channel-specific and weekday/weekend segmented data versus Common Use aggregated approaches. Employing advanced reconciliation techniques, the study seeks to determine the most effective forecasting method. Results indicate that segmentation provides a more accurate prediction of sales volume, offering significant insights for retailers in managing perishable goods inventory. Using a unique dataset on sales by a large US retailer, we compare the different approaches for forecasting weekly sales volume series of different items. This research contributes to a deeper understanding of retail dynamics and enhances
    forecasting practices in the context of perishable goods. This thesis introduces a novel cost function to enhance the accuracy and financial relevance of sales volume forecasting for perishable goods. By innovatively blending traditional forecasting methods with this unique cost function, the study provides valuable insights into optimizing inventory management and reducing financial risks in the retail sector.

    Abstract ..................................................................................................................................................... 1 Acknowledgements................................................................................................................................... 2 List of Tables ............................................................................................................................................ 5 List of Figures........................................................................................................................................... 6 Chapter 1: Introduction ............................................................................................................................. 8 1.1 Background and Rationale ......................................................................................................... 8 1.2 Research Objectives................................................................................................................... 9 1.3 Scope ........................................................................................................................................ 10 1.4 Structure of the Thesis.............................................................................................................. 10 Chapter 2: Literature Review.................................................................................................................. 11 2.1 Overview of Retail Forecasting................................................................................................ 11 2.2 Challenges in Perishable Goods Forecasting ........................................................................... 13 2.3 Aggregated vs. Disaggregated Forecasting Approaches.......................................................... 14 2.4 Cost Implications of Forecast Accuracy .................................................................................. 15 2.5 Reconciliation Methods in Forecasting within Retail .............................................................. 17 Chapter 3: Data & Methods.................................................................................................................... 20 3.1 Data Description....................................................................................................................... 20 3.2 Forecasting Methods ................................................................................................................ 21 3.3 Development of the Cost Function........................................................................................... 23 4 3.4 Reconciliation Techniques....................................................................................................... 26 3.5 Analytical Tools and Software (R and the fable package)....................................................... 27 Chapter 4: Data Analysis ........................................................................................................................ 29 4.1 Data Preprocessing and Exploration ........................................................................................ 29 4.2 Forecasting Models Applied .................................................................................................... 34 4.3 Cost Function Application........................................................................................................ 37 4.4 Reconciliation Process ............................................................................................................. 39 Chapter 5: Results and Discussion.......................................................................................................... 41 5.1 Comparison of Forecasting Methods and Financial Implications of Forecast Accuracy......... 41 5.2 Effectiveness of Reconciliation in Forecasting ........................................................................ 47 5.3 Interpretation of Results........................................................................................................... 48 5.4 Implications for Retail Industry ............................................................................................... 50 Chapter 6: Conclusion and Recommendations....................................................................................... 52 6.1 Summary of Findings............................................................................................................... 52 6.2 Practical Implications............................................................................................................... 53 6.3 Limitations of the Study........................................................................................................... 55 6.4 Recommendations for Future Research ................................................................................... 56 References............................................................................................................................................... 58 Appendix................................................................................................................................................. 60

    Abolghasemi, M., Hyndman, R. J., Spiliotis, E., & Bergmeir, C. (2022). Model selection in reconciling
    hierarchical time series. Machine Learning, 111, 739–789. 10.1007/s10994-021-06126-z
    Andrade, L. A. C. G., & Cunha, C. B. (2023). Disaggregated retail forecasting: A gradient boosting
    approach. Applied Soft Computing, 141, 110283. https://doi.org/10.1016/j.asoc.2023.110283
    Bradlow, E. T., et al. (2017). The Role of Big Data and Predictive Analytics in Retailing. Journal of
    Retailing. https://doi.org/10.1016/j.jretai.2016.12.004
    Chen, C., Wang, Y., Huang, G., & Xiong, H. (2019). Hierarchical Demand Forecasting for Factory
    Production of Perishable Goods. 2019 IEEE International Conference on Big Data (Big Data), Los
    Angeles, CA, USA, 188-193. https://doi.org/10.1109/BigData47090.2019.9006161
    Corani, G., Azzimonti, D., Augusto, J. P., & Zaffalon, M. (2021). Probabilistic reconciliation of
    hierarchical forecast via Bayes’ rule. In Machine Learning and Knowledge Discovery in Databases:
    European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part
    III (pp. 211-226). Springer International Publishing.
    Espasa, A., & Mayo-Burgos, I. (2013). Forecasting aggregates and disaggregates with common features.
    International Journal of Forecasting, 29(4), 718-732. https://doi.org/10.1016/j.ijforecast.2012.10.004
    Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal
    of Forecasting, 38(4), 1283-1318. https://doi.org/10.1016/j.ijforecast.2019.06.004
    Hollyman, R., Petropoulos, F., & Tipping, M. E. (2021). Understanding forecast reconciliation.
    European Journal of Operational Research, 294(1), 149-160. https://doi.org/10.1016/j.ejor.2021.01.017.
    Hyndman, R.J., & Athanasopoulos, G. (2021). Forecasting: principles and practice, 3rd edition, OTexts:
    Melbourne, Australia. OTexts.com/fpp3
    Hyndman, R. J., & O’Hara-Wild, M. (2021). Fable: Forecasting models for tidy time series. R package
    version 0.3.1. Retrieved from https://cran.r-project.org/web/packages/fable/index.html
    Jiang, H., Ruan, J., & Sun, J. (2021). Application of Machine Learning Model and Hybrid Model in Retail
    Sales Forecast. 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA).
    http://dx.doi.org/10.1109/ICBDA51983.2021.9403224
    Kadam, A., Warekar, D., & Kamble, N. (2015). Forecasting the Daily Sales of Perishable Food to
    Reduce Spoilage in Hypermarket. International Journal for Scientific Research and Development, 3,
    264-267.
    Kolassa, S. (2019). Forecasting the Future of Retail Forecasting. Foresight: The International Journal
    of Applied Forecasting, (52), 11-19.
    Kolassa, S. (2019). Forecasting the Future of Retail Forecasting. Foresight: The International Journal
    of Applied Forecasting, International Institute of Forecasters, issue 52, pp. 11-19, Winter.
    Makkar, G. (2019.). Real-Time Football Prediction Using Weather Data: A Case on Retail Analytics. In
    Advances in Intelligent Systems and Computing (pp. 395–404). Springer. https://doi.org/10.1007/978-
    981-32-9949-8_37
    59
    Manjili, H. K., & Tabar, M. M. (2011). Postponement a Speculation in Electronics Retailing: Case
    Studies on Swedish Retailers.
    O'Hara-Wild, M., Hyndman, R. J., et al. (2021). Fable: Forecasting Models for Tidy Time Series. R
    package version 0.3.3. Retrieved from https://cran.r-project.org/web/packages/fable/index.html
    Oliveira, J. M., & Ramos, P. (2019). Assessing the Performance of Hierarchical Forecasting Methods
    on the Retail Sector. Entropy, 21(4), 436. https://doi.org/10.3390/e21040436
    Ou, T. Y., Chen, Y. J., & Tsai, W. L. (2020). Sales forecasting of perishable foods with multiple stores
    and communities-an empirical study of convenience stores in Taiwan. International Journal of Intelligent
    Technologies and Applied Statistics, 13(4), 385-409. https://doi.org/10.6148/IJITAS.202012_13(4).0005
    Ozkale, B. (2013). Forecasting and Modelling of Cash Payments at Retail Stores: The Case of SAR and
    Kappe.
    Pang, S. (2022). Retail Sales Forecast Based on Machine Learning Methods. 2022 6th Annual
    International Conference on Data Science and Business Analytics (ICDSBA).
    https://doi.org/10.1109/ICDSBA57203.2022.00030
    Petropoulos, Fotios & Grushka-Cockayne, Yael. (2021). Fast and frugal time series forecasting.
    Savan, E.-E., Gica, O., & Sofică, A. (2020). Retail Demand Forecasting for Small-Medium Enterprises
    During COVID-19 Pandemic: Case Studies Based on Romanian Convenience Stores. In Lecture Notes
    in Networks and Systems (pp. 57–70). Springer. https://doi.org/10.1007/978-3-030-82755-7_7
    Shmueli, G., & Lichtendahl, K. C., Jr. (2016). Practical time series forecasting with R: A hands-on guide
    (2nd ed.). Axelrod Schnall Publishers.
    Yang, C. -L., & Sutrisno, H. (2018). Short-Term Sales Forecast of Perishable Goods for Franchise
    Business. 2018 10th International Conference on Knowledge and Smart Technology (KST), Chiang Mai,
    Thailand, 101-105. http://dx.doi.org/10.1109/KST.2018.8426091

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