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題名 從黑箱和白箱觀點探討供應鏈上的顧客需求預測
Black Box or White Box? A Hybrid Approach for Predicting and Interpreting Customer Demands in SCM作者 莊焄沂
Zhuang, Xun-Yi貢獻者 張欣綠<br>杜雨儒
Chang, Hsin-Lu<br>Tu, Yu-Ju
莊焄沂
Zhuang, Xun-Yi關鍵詞 人工智慧
預測
決策樹
神經網絡
擴增智慧
供應鏈管理
Artificial intelligence
prediction
decision tree
neural network
augmented intelligence
supply chain management日期 2019 上傳時間 7-Aug-2019 16:07:40 (UTC+8) 摘要 隨著人工智慧和機器學習的蓬勃發展，運用人工智慧改善決策可使企業提高自身競爭力，因此精準的預測對企業極其重要。在過去的研究中，運用人工智慧來進行預測的方法有很多種，然而這些方法可大致分為黑箱及白箱兩種方法，許多研究也曾比較兩種方法的優缺點，但未曾證明兩種方法是否有排他性或互補性。因此，本研究旨在提供一個混和方法，結合黑箱和白箱的優點，以改善供應鏈上的顧客需求預測。本研究結合黑箱精準預測的特性和白箱的解釋性，期望藉由白箱為黑箱提供解釋性，並藉由有意義的解釋來改善預測模型。為了檢驗本研究的混合方法，本研究採用亞太區最大半導體零組件通路商(簡稱W公司)的顧客實際取貨資料，並運用神經網路呈現黑箱方法和決策樹呈現白箱方法來驗證模型。研究結果顯示，混和方法的預測表現確實優於W公司原有的預測方法，且白箱確實提供具有意義的解釋來呈現黑箱的預測準則，例如：來自WT COWINSZ子庫存的產品適用於以週為單位的神經網絡預測。本研究對於供應鏈上的顧客需求預測有一定的貢獻，期望透過本研究可以為科技產業鏈帶來一些新的觀點。
The black-box prediction method has proved to be efficient for its predictions, while the white-box method provides an effective interpretation of outputs. Many studies have identified and compared the merits and demerits of the two methods, yet it still remains unclear whether the two methods are exclusive or complementary. In this study, we propose and develop a hybrid approach that can successfully combine the merits of two such methods in order to improve customer demand prediction in Supply Chain Management. Our novel hybrid approach combines the black-box prediction method with the white-box classification method with the aim of obtaining the accurate performance of the former and the notable interpretation of the latter. To examine the performance of the proposed approach, we use a real-world data set collected from a top distributor of semiconductors and electronics in Asia. We conclude that the novel hybrid approach is beneficial for interpreting and improving customer demand prediction. We identify several product items from a specialized sub-inventory as unsuitable for neural network prediction methods, and other low frequency product items as suitable. For example, some product items from WT COWINSZ sub-inventory are suitable for neural network prediction methods, and some low frequency product items are suitable for neural network prediction methods with integrated zero prediction model.參考文獻 Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2016). TRANSFORMATIONAL ISSUES OF BIG DATA AND ANALYTICS IN NETWORKED BUSINESS. MIS quarterly, 40(4).Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626-4636.Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22.Espinoza, M., Joye, C., Belmans, R., & De Moor, B. (2005). Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series. IEEE Transactions on Power Systems, 20(3), 1622-1630.Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.Friedl, M. A., & Brodley, C. E. (1997). Decision tree classification of land cover from remotely sensed data. Remote sensing of environment, 61(3), 399-409.Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., et al. (2006).Modeling customer lifetime value. Journal of Service Research, 9(2),139–155.Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design (Vol. 20). Boston: Pws Pub..Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.Jain, H., Padmanabhan, B., Pavlou, P. A., & Santanam, R. T. (2018). Call for Papers—Special Issue of Information Systems Research—Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society. Information Systems Research, 29(1), 250-251.Kalchschmidt, M., Verganti, R., & Zotteri, G. (2006). Forecasting demand from heterogeneous customers. International Journal of operations & Production management, 26(6), 619-638.Kim, Y., Street, W. N., Russell, G. J., & Menczer, F. (2005). Customer targeting: A neural network approach guided by genetic algorithms. Management Science, 51(2), 264-276.Li, J., Mei, X., Prokhorov, D., & Tao, D. (2016). Deep neural network for structural prediction and lane detection in traffic scene. IEEE transactions on neural networks and learning systems, 28(3), 690-703.Li, X., & Chen, H. (2013). Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach. Decision Support Systems, 54(2), 880-890.Liu, J., Liu, B., Liu, Y., Chen, H., Feng, L., Xiong, H., & Huang, Y. (2018). Personalized Air Travel Prediction: A Multi-factor Perspective. ACM Transactions on Intelligent Systems and Technology (TIST), 9(3), 30.Loureiro, A. L. D., Miguéis, V. L., & da Silva, L. F. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93Ma, L., Zhao, X., Zhou, Z., & Liu, Y. (2018). A new aspect on P2P online lending default prediction using meta-level phone usage data in China. Decision Support Systems, 111, 60-71.Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187-197.Miguéis, V. L., Freitas, A., Garcia, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36-51.Murray, P. W., Agard, B., & Barajas, M. A. (2015). Forecasting supply chain demand by clustering customers. IFAC-PapersOnLine, 48(3), 1834-1839.Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569.Noh, H., Hongsuck Seo, P., & Han, B. (2016). Image question answering using convolutional neural network with dynamic parameter prediction. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 30-38).Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological modelling, 154(1-2), 135-150.Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464-473.Pandya, R., & Pandya, J. (2015). C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. International Journal of Computer Applications, 117(16), 18-21.Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS quarterly, 225-246.SAS Institute Inc., (2017) a. Neural Network Node: Reference. Retrieved August 30, 2017, from https://documentation.sas.com/?docsetId=emref&docsetTarget=p0zbgj1tu3h1uhn1x6regixbdg7v.htm&docsetVersion=14.3&locale=enSAS Institute Inc., (2017) b. Decision Tree Node. Retrieved August 30, 2017, from https://go.documentation.sas.com/?docsetId=emref&docsetTarget=n0cx4ud03paymdn1kargegadueml.htm&docsetVersion=14.3&locale=enSchmitz, G. P., Aldrich, C., & Gouws, F. S. (1999). ANN-DT: an algorithm for extraction of decision trees from artificial neural networks. IEEE Transactions on Neural Networks, 10(6), 1392-1401.Sell, S. P. D. (1999). Introduction to supply chain management.Szolovits, P., Patil, R. S., & Schwartz, W. B. (1988). Artificial intelligence in medical diagnosis. Annals of internal medicine, 108(1), 80-87.Veganzones, D., & Séverin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111-124.von Ahn, L. (2013). Augmented intelligence: the Web and human intelligence. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987), 20120383.Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2011). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.WPG Holdings (2019) About WPG Holdings, from https://www.wpgholdings.comYeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.Zhang, D., & Zhou, L. (2004). Discovering golden nuggets: data mining in financial application. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(4), 513-522.Zheng, N. N., Liu, Z. Y., Ren, P. J., Ma, Y. Q., Chen, S. T., Yu, S. Y., ... & Wang, F. Y. (2017). Hybrid-augmented intelligence: collaboration and cognition. Frontiers of Information Technology & Electronic Engineering, 18(2), 153-179. 描述 碩士
國立政治大學
資訊管理學系
106356029資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106356029 資料類型 thesis dc.contributor.advisor 張欣綠<br>杜雨儒 zh_TW dc.contributor.advisor Chang, Hsin-Lu<br>Tu, Yu-Ju en_US dc.contributor.author (Authors) 莊焄沂 zh_TW dc.contributor.author (Authors) Zhuang, Xun-Yi en_US dc.creator (作者) 莊焄沂 zh_TW dc.creator (作者) Zhuang, Xun-Yi en_US dc.date (日期) 2019 en_US dc.date.accessioned 7-Aug-2019 16:07:40 (UTC+8) - dc.date.available 7-Aug-2019 16:07:40 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 16:07:40 (UTC+8) - dc.identifier (Other Identifiers) G0106356029 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124714 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 106356029 zh_TW dc.description.abstract (摘要) 隨著人工智慧和機器學習的蓬勃發展，運用人工智慧改善決策可使企業提高自身競爭力，因此精準的預測對企業極其重要。在過去的研究中，運用人工智慧來進行預測的方法有很多種，然而這些方法可大致分為黑箱及白箱兩種方法，許多研究也曾比較兩種方法的優缺點，但未曾證明兩種方法是否有排他性或互補性。因此，本研究旨在提供一個混和方法，結合黑箱和白箱的優點，以改善供應鏈上的顧客需求預測。本研究結合黑箱精準預測的特性和白箱的解釋性，期望藉由白箱為黑箱提供解釋性，並藉由有意義的解釋來改善預測模型。為了檢驗本研究的混合方法，本研究採用亞太區最大半導體零組件通路商(簡稱W公司)的顧客實際取貨資料，並運用神經網路呈現黑箱方法和決策樹呈現白箱方法來驗證模型。研究結果顯示，混和方法的預測表現確實優於W公司原有的預測方法，且白箱確實提供具有意義的解釋來呈現黑箱的預測準則，例如：來自WT COWINSZ子庫存的產品適用於以週為單位的神經網絡預測。本研究對於供應鏈上的顧客需求預測有一定的貢獻，期望透過本研究可以為科技產業鏈帶來一些新的觀點。 zh_TW dc.description.abstract (摘要) The black-box prediction method has proved to be efficient for its predictions, while the white-box method provides an effective interpretation of outputs. Many studies have identified and compared the merits and demerits of the two methods, yet it still remains unclear whether the two methods are exclusive or complementary. In this study, we propose and develop a hybrid approach that can successfully combine the merits of two such methods in order to improve customer demand prediction in Supply Chain Management. Our novel hybrid approach combines the black-box prediction method with the white-box classification method with the aim of obtaining the accurate performance of the former and the notable interpretation of the latter. To examine the performance of the proposed approach, we use a real-world data set collected from a top distributor of semiconductors and electronics in Asia. We conclude that the novel hybrid approach is beneficial for interpreting and improving customer demand prediction. We identify several product items from a specialized sub-inventory as unsuitable for neural network prediction methods, and other low frequency product items as suitable. For example, some product items from WT COWINSZ sub-inventory are suitable for neural network prediction methods, and some low frequency product items are suitable for neural network prediction methods with integrated zero prediction model. en_US dc.description.tableofcontents Table of Contents ivList of Figures vList of Tables viCHAPTER 1: INTRODUCTION 1CHAPTER 2: LITERATURE REVIEW 5CHAPTER 3: METHODOLOGY 11CHAPTER 4: PREDICTION RESULTS 25CHAPTER 5: DISCUSSION 39CHAPTER 6: CONCLUSION AND FUTURE WORK 42REFERENCE 44APPENDIX A 49APPENDIX B 51 zh_TW dc.format.extent 1035047 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106356029 en_US dc.subject (關鍵詞) 人工智慧 zh_TW dc.subject (關鍵詞) 預測 zh_TW dc.subject (關鍵詞) 決策樹 zh_TW dc.subject (關鍵詞) 神經網絡 zh_TW dc.subject (關鍵詞) 擴增智慧 zh_TW dc.subject (關鍵詞) 供應鏈管理 zh_TW dc.subject (關鍵詞) Artificial intelligence en_US dc.subject (關鍵詞) prediction en_US dc.subject (關鍵詞) decision tree en_US dc.subject (關鍵詞) neural network en_US dc.subject (關鍵詞) augmented intelligence en_US dc.subject (關鍵詞) supply chain management en_US dc.title (題名) 從黑箱和白箱觀點探討供應鏈上的顧客需求預測 zh_TW dc.title (題名) Black Box or White Box? A Hybrid Approach for Predicting and Interpreting Customer Demands in SCM en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2016). TRANSFORMATIONAL ISSUES OF BIG DATA AND ANALYTICS IN NETWORKED BUSINESS. MIS quarterly, 40(4).Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626-4636.Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22.Espinoza, M., Joye, C., Belmans, R., & De Moor, B. (2005). Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series. IEEE Transactions on Power Systems, 20(3), 1622-1630.Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.Friedl, M. A., & Brodley, C. E. (1997). Decision tree classification of land cover from remotely sensed data. Remote sensing of environment, 61(3), 399-409.Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., et al. (2006).Modeling customer lifetime value. Journal of Service Research, 9(2),139–155.Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design (Vol. 20). Boston: Pws Pub..Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.Jain, H., Padmanabhan, B., Pavlou, P. A., & Santanam, R. T. (2018). Call for Papers—Special Issue of Information Systems Research—Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society. Information Systems Research, 29(1), 250-251.Kalchschmidt, M., Verganti, R., & Zotteri, G. (2006). Forecasting demand from heterogeneous customers. International Journal of operations & Production management, 26(6), 619-638.Kim, Y., Street, W. N., Russell, G. J., & Menczer, F. (2005). Customer targeting: A neural network approach guided by genetic algorithms. Management Science, 51(2), 264-276.Li, J., Mei, X., Prokhorov, D., & Tao, D. (2016). Deep neural network for structural prediction and lane detection in traffic scene. IEEE transactions on neural networks and learning systems, 28(3), 690-703.Li, X., & Chen, H. (2013). Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach. Decision Support Systems, 54(2), 880-890.Liu, J., Liu, B., Liu, Y., Chen, H., Feng, L., Xiong, H., & Huang, Y. (2018). Personalized Air Travel Prediction: A Multi-factor Perspective. ACM Transactions on Intelligent Systems and Technology (TIST), 9(3), 30.Loureiro, A. L. D., Miguéis, V. L., & da Silva, L. F. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93Ma, L., Zhao, X., Zhou, Z., & Liu, Y. (2018). A new aspect on P2P online lending default prediction using meta-level phone usage data in China. Decision Support Systems, 111, 60-71.Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187-197.Miguéis, V. L., Freitas, A., Garcia, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36-51.Murray, P. W., Agard, B., & Barajas, M. A. (2015). Forecasting supply chain demand by clustering customers. IFAC-PapersOnLine, 48(3), 1834-1839.Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569.Noh, H., Hongsuck Seo, P., & Han, B. (2016). Image question answering using convolutional neural network with dynamic parameter prediction. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 30-38).Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological modelling, 154(1-2), 135-150.Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464-473.Pandya, R., & Pandya, J. (2015). C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. International Journal of Computer Applications, 117(16), 18-21.Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81-106.Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS quarterly, 225-246.SAS Institute Inc., (2017) a. Neural Network Node: Reference. Retrieved August 30, 2017, from https://documentation.sas.com/?docsetId=emref&docsetTarget=p0zbgj1tu3h1uhn1x6regixbdg7v.htm&docsetVersion=14.3&locale=enSAS Institute Inc., (2017) b. Decision Tree Node. Retrieved August 30, 2017, from https://go.documentation.sas.com/?docsetId=emref&docsetTarget=n0cx4ud03paymdn1kargegadueml.htm&docsetVersion=14.3&locale=enSchmitz, G. P., Aldrich, C., & Gouws, F. S. (1999). ANN-DT: an algorithm for extraction of decision trees from artificial neural networks. IEEE Transactions on Neural Networks, 10(6), 1392-1401.Sell, S. P. D. (1999). Introduction to supply chain management.Szolovits, P., Patil, R. S., & Schwartz, W. B. (1988). Artificial intelligence in medical diagnosis. Annals of internal medicine, 108(1), 80-87.Veganzones, D., & Séverin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111-124.von Ahn, L. (2013). Augmented intelligence: the Web and human intelligence. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987), 20120383.Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2011). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.WPG Holdings (2019) About WPG Holdings, from https://www.wpgholdings.comYeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.Zhang, D., & Zhou, L. (2004). Discovering golden nuggets: data mining in financial application. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(4), 513-522.Zheng, N. N., Liu, Z. Y., Ren, P. J., Ma, Y. Q., Chen, S. T., Yu, S. Y., ... & Wang, F. Y. (2017). Hybrid-augmented intelligence: collaboration and cognition. Frontiers of Information Technology & Electronic Engineering, 18(2), 153-179. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900561 en_US
