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題名 以成本效益為基礎的需求預測研究–個案分析
Cost-based Demand Forecasting Analysis–A Case Study
作者 劉哲銘
Liu, Jhe-Ming
貢獻者 唐揆<br>洪叔民
Tang, Kwei<br>Horng, Shwu-Min
劉哲銘
Liu, Jhe-Ming
關鍵詞 需求預測
成本效益
供應鏈
存貨
日期 2020
上傳時間 3-八月-2020 18:44:26 (UTC+8)
摘要 本研究探討主題為電子業品牌商於海外電子商務平台進行銷售時，肇因於前置時 間長而導致的高昂成本極需降低的管理議題。根據 B 公司負責供應鏈管理資深 主管表示當前多數電子產業的銷售預測方法，大多採取「銷售人員意見法」，由 負責該產品的銷售人員以競爭者概況、對通路端的了解加上專業知識，以主觀推 測客戶之預估需求量。而這種預估模式因為充滿人為因素，而產生高變動性以及 精準度不足的問題。
因此本研究首先預測平台銷售業務所需要的出貨量，並根據此預測建立庫存模型， 進而假設庫存持有成本進行分析。本研究採用 ARIMA、VAR 以及 ANN 三種不同模型，以驗證出貨量與其餘內生變數間的相關性以及探討不同模型的預測精準度。為了證明立論的代表性，本研究另模擬 270 組數據以分析產品的集群分佈特性，發現符合不同產品的變異係數關係。最後依照變異係數的高低，挑選出兩項產品進行成本效益分析。
本研究進一步進行獨立樣本的 T 檢定，嘗試比較各樣本平均數是否有顯著差異。 發現高變異係數的 ARIMA 與 VAR、ANN 有顯著差異，而 VAR 與 ANN 則無。 而在低變異係數方面，則是三者皆無顯著差異。推測若分析樣本數增加，可以在統計上有更好的顯著性差異。因此本研究建議未來當分析實務上需要尋找產品所對應的最適預測模型時，可透過不同變異係數進行分類，尋找最適的預測模型，以達到降低成本的目的。
The topic of this study is the management issue that the case company is facing a high cost caused by the long lead time and inaccuracy forecast when selling on overseas e- commerce platforms. According to the senior director in charge of supply chain management of case company, most of the current sales forecasting methods for the electronics industry mostly adopt the "salesperson opinion method". And this forecasting model is full of human factors, resulting in high variability and insufficient accuracy.
Therefore, this study first predicts the shipments required by the platform`s sales, and builds an inventory model based on this forecast, and then assumes inventory holding costs for analysis. This study uses three different models including ARIMA, VAR, and ANN to verify the correlation between shipments and other variables and discuss the prediction accuracy of different models. In order to prove the representativeness of the argument, this study also simulated 270 sets of data to analyze the cluster distribution characteristics of the products and found that they corresponded to the coefficient of variation relationship of different products. Finally, according to the coefficient of variation, two products were selected for cost-benefit analysis.
Therefore, this study suggests that in the future, when analyzing the practical need to find the optimal prediction model corresponding to the product, it can be classified by different coefficients of variation to find the optimal prediction model to reduce costs.
參考文獻 中文文獻
1. 朱大奇, & 史慧 (2006)，人工神經網路原理及應用，北京：科學出版社。
2. 陳旭昇(2013)，時間序列分析：總體經濟與財務金融之應用，第二版，台北：東華書局。

英文文獻
3. Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & Industrial Engineering,065 .
4. Alpaydin, E. (2020). Introduction to machine learning. MIT press.
5. Amini, M. H., Kargarian, A., & Karabasoglu, O. (2016). ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electric Power Systems Research, 140, 378-390.
6. Barak, S., & Sadegh, S. S. (2016). Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. International Journal of Electrical Power & Energy Systems, 82, 92-104.
7. Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in rief, 105340.
8. Box, G. E, & Jenkins, G. M. (1970) Time Series Analysis, Forecasting, and Control. Francisco Holden-Day.
9. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
10. Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9(2), 109.
11. Chase Jr, C. W. (1993). Ways to improve sales forecasts. The Journal of Business. Forecasting, 12(3), 15.
12. Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458-467.
13.Gao, Q. (2016). Stock market forecasting using recurrent neural network (Doctoral dissertation, University of Missouri--Columbia).
14. Granger, C. W. (1969). Investigating causal relations by econometric models and. cross-spectral methods. Econometrica: ournal of the Econometric Society, 424-438.
15. Guha, B., & Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2).
16. Holland, J. H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
17. Hopfield, J. J. (1982). Neural networks and physical systems with emergent. collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
18. Hrnjica, B., & Mehr, A. D. (2020). Energy demand forecasting using deep learning. In Smart Cities Performability, Cognition, & Security (pp. 71-104). Springer, Cham.
19. Jordan, M. I. (1986). Serial order: A parallel distributed processing approach, Institute for Cognitive Science Report 8604, UC San Diego.
20. Krenker, A., Bešter, J., & Kos, A. (2011). Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1-18.
21. Kristjanpoller, W., & Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems with Applications, 65, 233-241.
22. Kumaresan, K., & Ganeshkumar, P. (2020). Software reliability prediction model with realistic assumption using time series (S) ARIMA model. Journal of Ambient Intelligence and Humanized Computing.
23. Lewis, E. B. (1982). Control of body segment differentiation in Drosophila by the bithorax gene complex. In Genes, Development and Cancer (pp. 239-253). Springer, Boston, MA.
24. Liu, Y., Roberts, M. C., & Sioshansi, R. (2018). A vector autoregression weather model for electricity supply and demand modeling. Journal of Modern Power Systems and Clean Energy, 6(4), 763-776.
25. Liu, Y. H., Chang, W. S., & Chen, W. Y. (2019). Health progress and economic growth in the United States: the mixed frequency VAR analyses. Quality & Quantity, 53(4), 1895-1911.
26. Maciel, L. (2018). Technical analysis based on high and low stock prices forecasts: Evidence for Brazil using a fractionally cointegrated VAR model. Empirical Economics, 1-28.
27. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent. in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
28. Mentzer, J. T., & Moon, M. A. (2004). Sales forecasting management: a demand management approach. Sage Publications.
29. Naccarato, A., Falorsi, S., Loriga, S., & Pierini, A. (2018). Combining official and Google Trends data to forecast the Italian youth unemployment rate. Technological Forecasting and Social Change, 130, 114-122.
30. Nerlove, M., & Diebold, F. X. (1990). Autoregressive and Moving-average Time-series Processes. In Time Series and Statistics (pp. 25-35). Palgrave Macmillan, London.
31. Nyoni, T. (2018). Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe.
32. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.
33. Rogel-Salazar, J. (2020). Advanced Data Science and Analytics with Python. CRC Press.
34. Santhosh, M., Venkaiah, C., & Kumar, D. V. (2018). Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction. Energy conversion and management, 168, 482-493.
35. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
36. Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: Two. decades of research. Applied Soft Computing, 38, 788-804.
37. Walker, G. T. (1931). On Periodicity in Series of Related Terms. Proceedings of. the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 131(818), 518-532
38. Weng, T., Liu, W., & Xiao, J. (2019). Supply chain sales forecasting based on lightGBM and LSTM combination model. Industrial Management & Data Systems, 120(2).
39. Yule, G. U. (1926). Why do we sometimes get nonsense-correlations between Time-Series?--a study in sampling and the nature of time-series. Journal of the royal statistical society, 89(1), 1-63.
40. Zhang, Y., Zhong, M., Geng, N., & Jiang, Y. (2017). Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China. PloS one, 12(5), e0176729.
41. Zurada, J. M. (1992). Introduction to artificial neural systems (Vol. 8). St. Paul: West.
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
107363105
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107363105
資料類型 thesis
dc.contributor.advisor 唐揆<br>洪叔民zh_TW
dc.contributor.advisor Tang, Kwei<br>Horng, Shwu-Minen_US
dc.contributor.author (作者) 劉哲銘zh_TW
dc.contributor.author (作者) Liu, Jhe-Mingen_US
dc.creator (作者) 劉哲銘zh_TW
dc.creator (作者) Liu, Jhe-Mingen_US
dc.date (日期) 2020en_US
dc.date.accessioned 3-八月-2020 18:44:26 (UTC+8)-
dc.date.available 3-八月-2020 18:44:26 (UTC+8)-
dc.date.issued (上傳時間) 3-八月-2020 18:44:26 (UTC+8)-
dc.identifier (其他 識別碼) G0107363105en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131361-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 107363105zh_TW
dc.description.abstract (摘要) 本研究探討主題為電子業品牌商於海外電子商務平台進行銷售時，肇因於前置時 間長而導致的高昂成本極需降低的管理議題。根據 B 公司負責供應鏈管理資深 主管表示當前多數電子產業的銷售預測方法，大多採取「銷售人員意見法」，由 負責該產品的銷售人員以競爭者概況、對通路端的了解加上專業知識，以主觀推 測客戶之預估需求量。而這種預估模式因為充滿人為因素，而產生高變動性以及 精準度不足的問題。
因此本研究首先預測平台銷售業務所需要的出貨量，並根據此預測建立庫存模型， 進而假設庫存持有成本進行分析。本研究採用 ARIMA、VAR 以及 ANN 三種不同模型，以驗證出貨量與其餘內生變數間的相關性以及探討不同模型的預測精準度。為了證明立論的代表性，本研究另模擬 270 組數據以分析產品的集群分佈特性，發現符合不同產品的變異係數關係。最後依照變異係數的高低，挑選出兩項產品進行成本效益分析。
本研究進一步進行獨立樣本的 T 檢定，嘗試比較各樣本平均數是否有顯著差異。 發現高變異係數的 ARIMA 與 VAR、ANN 有顯著差異，而 VAR 與 ANN 則無。 而在低變異係數方面，則是三者皆無顯著差異。推測若分析樣本數增加，可以在統計上有更好的顯著性差異。因此本研究建議未來當分析實務上需要尋找產品所對應的最適預測模型時，可透過不同變異係數進行分類，尋找最適的預測模型，以達到降低成本的目的。		zh_TW
dc.description.abstract (摘要) The topic of this study is the management issue that the case company is facing a high cost caused by the long lead time and inaccuracy forecast when selling on overseas e- commerce platforms. According to the senior director in charge of supply chain management of case company, most of the current sales forecasting methods for the electronics industry mostly adopt the "salesperson opinion method". And this forecasting model is full of human factors, resulting in high variability and insufficient accuracy.
Therefore, this study first predicts the shipments required by the platform`s sales, and builds an inventory model based on this forecast, and then assumes inventory holding costs for analysis. This study uses three different models including ARIMA, VAR, and ANN to verify the correlation between shipments and other variables and discuss the prediction accuracy of different models. In order to prove the representativeness of the argument, this study also simulated 270 sets of data to analyze the cluster distribution characteristics of the products and found that they corresponded to the coefficient of variation relationship of different products. Finally, according to the coefficient of variation, two products were selected for cost-benefit analysis.
Therefore, this study suggests that in the future, when analyzing the practical need to find the optimal prediction model corresponding to the product, it can be classified by different coefficients of variation to find the optimal prediction model to reduce costs.		en_US
dc.description.tableofcontents 誌謝 I
中文摘要 II
英文摘要 III
目錄 IV
圖次 V
表次 VI
第一章 緒論 p.1
第一節 研究背景與動機 p.1
第二節 研究問題與目的 p.2
第三節 研究流程 p.4
第四節 研究範圍 p.4
第二章 文獻探討 p.5
第一節 銷售預測方法 p.5
第二節 銷售預測影響因素 p.5
第三節 單變量時間序列預測方法 p.6
第四節 多變量時間序列預測方法 p.11
第五節 人工神經網路 p.14
第三章 研究方法 p.20
第一節 研究架構 p.20
第二節 研究資料說明及預處理 p.27
第三節 資料檢定 p.29
第四章 研究結果與分析 p.32
第一節 ARIMA 模型 p.32
第二節 VAR 模型 p.34
第三節 ANN 模型 p.37
第四節 預測結果分析 p.40
第五節 產品性質與成本分析 p.41
第五章 結論與建議 p.45
第一節 研究結論 p.45
第二節 研究建議 p.45
第三節 研究限制 p.47
第四節 後續研究建議 p.47
參考文獻 p.48		zh_TW
dc.format.extent 1919885 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107363105en_US
dc.subject (關鍵詞) 需求預測zh_TW
dc.subject (關鍵詞) 成本效益zh_TW
dc.subject (關鍵詞) 供應鏈zh_TW
dc.subject (關鍵詞) 存貨zh_TW
dc.title (題名) 以成本效益為基礎的需求預測研究–個案分析zh_TW
dc.title (題名) Cost-based Demand Forecasting Analysis–A Case Studyen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 中文文獻
1. 朱大奇, & 史慧 (2006)，人工神經網路原理及應用，北京：科學出版社。
2. 陳旭昇(2013)，時間序列分析：總體經濟與財務金融之應用，第二版，台北：東華書局。

英文文獻
3. Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & Industrial Engineering,065 .
4. Alpaydin, E. (2020). Introduction to machine learning. MIT press.
5. Amini, M. H., Kargarian, A., & Karabasoglu, O. (2016). ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electric Power Systems Research, 140, 378-390.
6. Barak, S., & Sadegh, S. S. (2016). Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. International Journal of Electrical Power & Energy Systems, 82, 92-104.
7. Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in rief, 105340.
8. Box, G. E, & Jenkins, G. M. (1970) Time Series Analysis, Forecasting, and Control. Francisco Holden-Day.
9. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
10. Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9(2), 109.
11. Chase Jr, C. W. (1993). Ways to improve sales forecasts. The Journal of Business. Forecasting, 12(3), 15.
12. Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458-467.
13.Gao, Q. (2016). Stock market forecasting using recurrent neural network (Doctoral dissertation, University of Missouri--Columbia).
14. Granger, C. W. (1969). Investigating causal relations by econometric models and. cross-spectral methods. Econometrica: ournal of the Econometric Society, 424-438.
15. Guha, B., & Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2).
16. Holland, J. H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
17. Hopfield, J. J. (1982). Neural networks and physical systems with emergent. collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
18. Hrnjica, B., & Mehr, A. D. (2020). Energy demand forecasting using deep learning. In Smart Cities Performability, Cognition, & Security (pp. 71-104). Springer, Cham.
19. Jordan, M. I. (1986). Serial order: A parallel distributed processing approach, Institute for Cognitive Science Report 8604, UC San Diego.
20. Krenker, A., Bešter, J., & Kos, A. (2011). Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1-18.
21. Kristjanpoller, W., & Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems with Applications, 65, 233-241.
22. Kumaresan, K., & Ganeshkumar, P. (2020). Software reliability prediction model with realistic assumption using time series (S) ARIMA model. Journal of Ambient Intelligence and Humanized Computing.
23. Lewis, E. B. (1982). Control of body segment differentiation in Drosophila by the bithorax gene complex. In Genes, Development and Cancer (pp. 239-253). Springer, Boston, MA.
24. Liu, Y., Roberts, M. C., & Sioshansi, R. (2018). A vector autoregression weather model for electricity supply and demand modeling. Journal of Modern Power Systems and Clean Energy, 6(4), 763-776.
25. Liu, Y. H., Chang, W. S., & Chen, W. Y. (2019). Health progress and economic growth in the United States: the mixed frequency VAR analyses. Quality & Quantity, 53(4), 1895-1911.
26. Maciel, L. (2018). Technical analysis based on high and low stock prices forecasts: Evidence for Brazil using a fractionally cointegrated VAR model. Empirical Economics, 1-28.
27. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent. in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
28. Mentzer, J. T., & Moon, M. A. (2004). Sales forecasting management: a demand management approach. Sage Publications.
29. Naccarato, A., Falorsi, S., Loriga, S., & Pierini, A. (2018). Combining official and Google Trends data to forecast the Italian youth unemployment rate. Technological Forecasting and Social Change, 130, 114-122.
30. Nerlove, M., & Diebold, F. X. (1990). Autoregressive and Moving-average Time-series Processes. In Time Series and Statistics (pp. 25-35). Palgrave Macmillan, London.
31. Nyoni, T. (2018). Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe.
32. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.
33. Rogel-Salazar, J. (2020). Advanced Data Science and Analytics with Python. CRC Press.
34. Santhosh, M., Venkaiah, C., & Kumar, D. V. (2018). Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction. Energy conversion and management, 168, 482-493.
35. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
36. Tkáč, M., & Verner, R. (2016). Artificial neural networks in business: Two. decades of research. Applied Soft Computing, 38, 788-804.
37. Walker, G. T. (1931). On Periodicity in Series of Related Terms. Proceedings of. the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 131(818), 518-532
38. Weng, T., Liu, W., & Xiao, J. (2019). Supply chain sales forecasting based on lightGBM and LSTM combination model. Industrial Management & Data Systems, 120(2).
39. Yule, G. U. (1926). Why do we sometimes get nonsense-correlations between Time-Series?--a study in sampling and the nature of time-series. Journal of the royal statistical society, 89(1), 1-63.
40. Zhang, Y., Zhong, M., Geng, N., & Jiang, Y. (2017). Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China. PloS one, 12(5), e0176729.
41. Zurada, J. M. (1992). Introduction to artificial neural systems (Vol. 8). St. Paul: West.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202000890en_US