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研究生： 卡西歐
Cassio C. Finotti
論文名稱： 提高銷售預測準確性：判斷式、Prophet及混合預測方法之比較分析
Enhancing sales forecasting accuracy: a comparative analysis of judgmental, prophet, and hybrid forecasting approaches
指導教授： 莊皓鈞
Chuang, Howard
口試委員: 許嘉霖
Hsu, Chia-Lin
周彥君
Chou,Yen-Chun
學位類別： 碩士
Master
系所名稱： 商學院 - 國際經營管理英語碩士學位學程(IMBA)
International MBA Program College of Commerce(IMBA)
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 51
中文關鍵詞： 預測 、預測模型 、判斷性預測 、銷售數據 、時間序列分析
外文關鍵詞： Forecasting, Prophet model, Judgmental forecasts, Sales data, Time-series analysis
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    This thesis explores how Prophet, a time-series model by Meta, can enhance judgmental forecasts for predicting the monthly demand of ten products from a single customer of a B2B manufacturing company. The dataset spans from 2018-2022, providing monthly sales data with 2022 as the focus. Forecast accuracy is assessed using the Cumulative Forecast Error (CFE) method. Results show that the Prophet model excels judgmental forecasts in 6 out of 10 products, and a Hybrid approach of incorporating judgmental forecasts as regressors improve performance, outperforming in 8 out of 10 products. The findings show the benefits of integrating advanced statistical models like Prophet into business forecasting processes to mitigate over and underforecasting and boost accuracy. The study outlines limitations and future research opportunities, such as expanding datasets, exploring new comparison metrics, and periodically updating the Prophet model. Practical implications discuss challenges and benefits of statistical forecasting models, Prophet’s accessibility, and the need to counter underforecasting and overforecasting. By harnessing new technologies, businesses can enhance operations and improve demand forecasting accuracy. This thesis highlights the potential of merging statistical models like Prophet with judgmental forecasts and proposes areas for further exploration to refine these models’ effectiveness in business contexts.

    TABLE OF CONTENTS
    1. Introduction: Traditional Approaches to Sales Forecasting 1
    1.1. Limitations of Statistical and Judgmental Forecasts 3
    1.2. Company background and Forecasting Process 4
    1.3. The importance of Managing Overforecasting and Underforecasting in Sales Forecasting 7
    1.4. The Potential of Prophet Model in Improving Forecasting Accuracy and Efficiency 8
    2. Literature Review 10
    2.1. Judgmental Forecasting Techniques 10
    2.2. Statistical Forecasting Techniques 12
    2.3. Comparison between Prophet, ARIMA and Neural Network Models 14
    2.4. Combining Judgmental Forecasting with Statistical Forecasting 15
    2.5. Overforecasting and Underforecasting 16
    2.6. Conclusion of the Literature Review 16
    3. Methodology 18
    3.1. Data Collection and Preprocessing 18
    3.2. Forecasting Models 19
    3.3. Model Comparison 21
    4. Results and Discussion 24
    4.1. Comparison of Performance for Judgmental, Prophet, and Hybrid Forecasts 24
    4.2. Analysis and Comparison of Forecasting Methods 35
    4.3. Implications 42
    5. Conclusion 44
    5.1. Summary of Findings 44
    5.2. Limitations and Future Research 46
    5.3. Practical Implications for Businesses 47
    Reference 50
     
    List of Figures and Tables
    Figure 1: Sales Forecasting and Production Process Flowchart 6
    Figure 2: Comparison of results for Part # 1 25
    Figure 3: Comparison of results for Part # 2 26
    Figure 4: Comparison of results for Part # 3 27
    Figure 5: Comparison of Results for Part #4 28
    Figure 6: Comparison of results for Part #5 29
    Figure 7: Comparison of results for Part #6 30
    Figure 8: Comparison of results for Part #7 31
    Figure 9: Comparison of results for Part #8 32
    Figure 10: Comparison of results for Part #9 33
    Figure 11: Comparison of results for Part #10 34
    Figure 12: Comparison of Cumulative Forecast Error for Products using Judgmental, Prophet, and Hybrid Models 41
    Table 1: Comparison of Cumulative Forecast Errors (CFE) for Judgmental Forecast, Prophet Forecast, and Prophet Forecast with Judgmental Forecast as Regressor………………………………………………………………………………………….…33

    Reference
    1. Chen, H., Frank, M. Z., & Wu, O. Q. (2005). What Actually Happened to the Inventories of American Companies Between 1981 and 2000? Management Science, 51(7), 1015-1031.
    2. De Livera, A., Hyndman, R. J., & Snyder, R. (2010). Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106, 1513-1527.
    3. Facebook. (n.d.). Quick start — Prophet 1.0 documentation. Retrieved March 31, 2023, from https://facebook.github.io/prophet/docs/quick_start.html
    4. Fildes, R., & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576. http://www.jstor.org/stable/20141547
    5. Goodwin, P. (2002). Integrating management judgment and statistical methods to improve short-term forecasts. Omega, 30(2), 127-135.
    6. Helmer, O. (1983). Looking forward: a guide to future research. Sage Publications, Inc.
    7. Hsu, C. C., & Sandford, B. A. (2007). The Delphi technique: making sense of consensus. Practical Assessment, Research, and Evaluation, 12(1), 10.
    8. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
    9. Kutzkov, K. (2023, April 19). ARIMA vs Prophet vs LSTM for Time Series Prediction [web log]. Retrieved April 22, 2023, from https://neptune.ai/blog/arima-vs-prophet-vs-lstm.
    10. Lawrence, M., Goodwin, P., O'Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3).
    11. Menculini, L., Marini, A., Proietti, M., Garinei, A., Bozza, A., Moretti, C., & Marconi, M. (2021). Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices. Forecasting, 3, 644–662.
    12. Moon, M. A., Mentzer, J. T., & Smith, C. D. (2003). Researching Sales Forecasting Practice: Commentaries and authors' response on "Conducting a Sales Forecasting Audit." International Journal of Forecasting, 19, 27-42.
    13. Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: issues and analysis. International Journal of Forecasting, 15(4), 353-375.
    14. Smolic, H. (2022, September 1). Sales Forecasting: How to Apply Machine Learning. Graphite Note. Retrieved May 10, 2023, from https://graphite-note.com/machine-learning-sales-forecasting
    15. Taylor, S. J., & Letham, B. (2017). Forecasting at Scale. The American Statistician, 72(1).
    16. Van der Heijden, K. (2005). Scenarios: The Art of Strategic Conversation. John Wiley & Sons.
    17. Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53, 5929–5955.
    18. Wright, G., & Goodwin, P. (2009). Decision making and planning under low levels of predictability: Enhancing the scenario method. International Journal of Forecasting, 25(4), 813-825.

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