一、 中文文獻
王郁淳. (2007). 結合粒子群演算法及類神經網路技術於塑膠射出成形產品之成本預估 國立臺北科技大學]. https://ndltd.ncl.edu.tw/handle/w8zq7x
吳信宏. (2014). 類神經網路模式推測侵台颱風路徑之研究 國立屏東科技大學]. https://ndltd.ncl.edu.tw/id/102NPUS5015013
近期經貿與稅收情勢分析. (2022). https://service.mof.gov.tw/public/Data/statistic/bulletin/111/%E8%BF%91%E6%9C%9F%E7%B6%93%E8%B2%BF%E8%88%87%E7%A8%85%E6%94%B6%E6%83%85%E5%8B%A2_1111025.pdf
邱智仁. (2015). 運用類神經網路建立CNC銑床加工精度最佳化預測模式 義守大學]. https://ndltd.ncl.edu.tw/r/96135661736612417158
施振榮. (1996). 再造宏碁：開創、成長與挑戰. 天下文化出版股份有限公司.
原物料價格波動. (2025). https://en.macromicro.me/shared_chart/list?p=88&page=614&sort=viewed&tag=3
許恆. (2023). 從需求到任務模型：以Transformer為基礎的機器學習方法 國立臺灣大學]. https://ndltd.ncl.edu.tw/id/111NTU05392099
陳奕丞. (2023). 以SARIMA模型和LSTM模型預測客戶訂單量 國立中興大學]. https://ndltd.ncl.edu.tw/id/111NCHU5507010
陳起偉. (2007). 應用類神經網路模擬預測電子迴旋共振式蝕刻機台蝕刻深度與蝕刻良率系統 中原大學]. 桃園.
陳雅昀. (2020). 基於RNN、LSTM、GRU模型之比較－以電力需求預測為例 元智大學]. https://hdl.handle.net/11296/k623m8
黃之柔. (2023). 用LSTM神經網路預測銷售額—以小型企業為例 國立成功大學]. https://ndltd.ncl.edu.tw/id/111NCKU5028096
關智峰. (2009). 維修零件最終訂購問題之需求預測模式 臺中技術學院]. 臺中.
蘇韋仲. (2007). 考慮供應不確定下定期選擇補貨模式─以汽車維修零件為例 國立臺灣大學]. https://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/095NTU05318038
二、 英文文獻
Aaker, D. A. (1996). Building Strong Brands. Free Press.
Adadi, A., & Berrada, M. (2018). Peeking inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052
Ahmadov, Y., & Helo, P. (2023). Deep Learning-Based Approach for Forecasting Intermittent Online Sales. Discover Artificial Intelligence, 3(1). https://doi.org/10.1007/s44163-023-00085-1
Amiti, M., Redding, S. J., & Weinstein, D. E. (2019). The Impact of the 2018 Trade War on U.S. Prices and Welfare (NBER Working Paper, Issue. https://www.nber.org/system/files/working_papers/w25672/w25672.pdf
Armstrong, J. S. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Breque, M., De Nul, A., & Petridis, J. (2021). Industry 5.0: A Transformative Vision for Europe. https://research-and-innovation.ec.europa.eu/knowledge-publications-tools-and-data/publications/all-publications/industry-50-towards-sustainable-human-centric-and-resilient-european-industry_en
Campa, J. M., & Goldberg, L. S. (2005). Exchange Rate Pass-Through into Import Prices. Review of Economics and Statistics, 87(4), 679-690. https://doi.org/10.1162/003465305775098189
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154. https://doi.org/10.1016/j.ejor.2006.12.004
Chandrashekar, G., & Sahin, F. (2014). A Survey on Feature Selection Methods. Computers & Electrical Engineering, 40(1), 16-28. https://doi.org/10.1016/j.compeleceng.2013.11.024
Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information. Management Science, 46(3), 436-443. https://doi.org/10.1287/mnsc.46.3.436.12069
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623
Christopher, M., & Holweg, M. (2011). Supply Chain 2.0: Managing supply chains in the era of turbulence. International Journal of Physical Distribution & Logistics Management, 41(1), 63-82. https://doi.org/10.1108/09600031111101439
Clements, M. P., & Hendry, D. F. (1998). Forecasting Economic Time Series. Cambridge University Press.
Croson, R., & Donohue, K. (2006). Behavioral causes of the bullwhip effect and the observed value of inventory information. Management Science, 52(3), 323-336. https://doi.org/10.1287/mnsc.1050.0436
Cutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random Forests for Classification in Ecology. Ecology, 88(11), 2783-2792. https://doi.org/10.1890/07-0539.1
Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56(1-2), 437-460. https://doi.org/10.1080/00207543.2017.1331530
European, C. (2021). Industry 5.0: Towards a Human-Centric, Sustainable and Resilient European Industry. https://op.europa.eu/en/publication-detail/-/publication/468a892a-5097-11eb-b59f-01aa75ed71a1/
Fildes, R., Goodwin, P., & Lawrence, M. (2006). The design features of forecasting support systems and their effectiveness. Decision Support Systems, 42(1), 351-361. https://doi.org/10.1016/j.dss.2005.01.003
Fildes, R., & Petropoulos, F. (2015). Improving forecast quality in practice. Foresight: The International Journal of Applied Forecasting(36), 5-12. https://researchportal.bath.ac.uk/en/publications/improving-forecast-quality-in-practice
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. https://doi.org/10.1016/j.ejor.2017.11.054
Forrester, J. W. (1961). Industrial Dynamics. MIT Press.
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
Giant Manufacturing Co, L. (2025). Giant Group – Financial Information & Reports (2019–2024). https://www.giantgroup-cycling.com/en/ir-financial
Glick, R., & Rose, A. (2002). Does a currency union affect trade? The time series evidence. European Economic Review, 46(6), 1125-1151. https://doi.org/10.1016/S0014-2921(01)00202-1
Goldratt, E. M. (1984). The Goal: A Process of Ongoing Improvement. North River Press.
Goldratt, E. M., & Cox, J. (2016). The Goal: A Process of Ongoing Improvement (30th Anniversary Edition). Routledge.
Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015). Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. Journal of Computational and Graphical Statistics, 24(1), 44-65. https://doi.org/10.1080/10618600.2014.907095
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
Granger, C. W. J., & Teräsvirta, T. (1993). Modelling Nonlinear Economic Relationships. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199587148.001.0001
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Pedreschi, D., & Giannotti, F. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51(5), 93. https://doi.org/10.1145/3236009
Gunning, D., & Aha, D. W. (2019). DARPA's Explainable Artificial Intelligence (XAI) Program. AI Magazine, 40(2), 44-58. https://doi.org/10.1609/aimag.v40i2.2850
Gupta, M., & Boyd, L. (2008). Theory of constraints: a theory for operations management. International Journal of Operations & Production Management, 28(10), 991-1012. https://doi.org/10.1108/01443570810903122
Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157-1182. https://doi.org/10.5555/944919.944968
Hangl, J., Behrens, V. J., & Krause, S. (2022). Barriers, Drivers, and Social Considerations for AI Adoption in Supply Chain Management: A Tertiary Study. Logistics, 6(3), 63. https://doi.org/10.3390/logistics6030063
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. https://link.springer.com/book/10.1007/978-0-387-84858-7
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hohenstein, N., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90-117. https://doi.org/10.1108/IJPDLM-05-2013-0128
Hummels, D. (2007). Transportation Costs and International Trade in the Second Era of Globalization. Journal of Economic Perspectives, 21(3), 131-154. https://doi.org/10.1257/jep.21.3.131
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts. https://otexts.com/fpp2/
Ireland, R., & Bruce, M. (2000). CPFR: only the beginning of collaboration. Supply Chain Management: An International Journal, 5(5), 239-242. https://doi.org/10.1108/13598540010334973
Ivanov, D. (2020). Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 11, 1-21. https://doi.org/10.1007/s10479-020-03640-6
Ivanov, D., & Dolgui, A. (2021). After-shock risks and avoidance of disruption tails in supply chains. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04047-7
Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard: Translating Strategy into Action. Harvard Business School Press.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems 30,
Kukar, M. (2001). Making Reliable Diagnoses with Machine Learning: A Case Study. In S. Quaglini, P. Barahona, & S. Andreassen (Eds.), Artificial Intelligence in Medicine. AIME 2001, Lecture Notes in Computer Science (Vol. 2101, pp. 88-98). Springer. https://doi.org/10.1007/3-540-48229-6_12
Kumar, M. (2024). Role of AI Predictive Analytics in Supply Chain Management. https://www.datasciencecentral.com/role-of-ai-predictive-analytics-in-supply-chain-management/
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The Bullwhip Effect in Supply Chains. MIT Sloan Management Review, 38(3), 93-102.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56-67. https://doi.org/10.1038/s42256-019-0138-9
Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30,
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. https://link.springer.com/book/10.1007/978-3-540-27752-1
Makridakis, S., & Hibon, M. (1979). Accuracy of Forecasting: An Empirical Investigation. Journal of the Royal Statistical Society: Series A (General), 142(2), 97-145. https://doi.org/10.2307/2345077
Makridakis, S., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16(4), 451-476. https://doi.org/10.1016/S0169-2070(00)00057-1
Makridakis, S. G., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications. John Wiley & Sons.
Marston, R. C. (1990). Pricing to Market in Japanese Manufacturing. Journal of International Economics, 29(3-4), 217-236. https://doi.org/10.1016/0022-1996(90)90005-H
Meenakshi, Maurya, R. K., Salangai Nayagi, D., Rambabu, Srilakshmi, V., Reddi, N. V. K., & Surjeet. (2024). Enhanced Retail Sales Forecasting Using Optimized CNN–LSTM Hybrid Model: A Time Series-Based Analysis. Communications on Applied Nonlinear Analysis, 32(2s). https://doi.org/10.52783/cana.v32.2485
Metters, R. (1997). Quantifying the bullwhip effect in supply chains. Journal of Operations Management, 15(2), 89-100. https://doi.org/10.1016/S0272-6963(96)00098-8
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
Molnar, C. (2020). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Leanpub. https://christophmolnar.com/books/interpretable-machine-learning
Moroff, N. U., Kurt, E., & Kamphues, J. (2021). Machine Learning and Statistics: A Study for Assessing Innovative Demand Forecasting Models. Procedia Computer Science, 180, 40-49. https://doi.org/10.1016/j.procs.2021.01.127
Nikolopoulos, K., Syntetos, A. A., Boylan, J. E., Petropoulos, F., & Assimakopoulos, V. (2011). An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society, 62(3), 544-554. https://doi.org/10.1057/jors.2010.32
Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production. Productivity Press.
Onkal, D., Goodwin, P., Thomson, M., Gönül, S., & Pollock, A. (2011). The relative influence of advice from human experts and statistical methods on forecast adjustments. Journal of Behavioral Decision Making, 22(4), 390-409. https://doi.org/10.1002/bdm.637
Pauwels, K., Currim, I., Dekimpe, M. G., Ghysels, E., Hanssens, D. M., Mizik, N., & Naik, P. (2004). Modeling Marketing Dynamics by Time Series Econometrics. Marketing Letters, 15(4), 167-183. https://doi.org/10.1007/s11002-005-0455-0
Pesaran, M. H., Smith, R. P., & Akiyama, T. (1998). Energy Demand in Asian Developing Economies. Oxford Institute for Energy Studies. https://www.oxfordenergy.org/shop/energy-demand-in-asian-developing-economies/
Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143. https://doi.org/10.1108/09574090910954873
Porter, M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press.
Punia, S., Nikolopoulos, K., Prakash Singh, S., Madaan, J. K., & Litsiou, K. (2020). Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. International Journal of Production Research, 58(16), 4964-4979. https://doi.org/10.1080/00207543.2020.1735666
Rajavelloo, S. K. (2023). Machine Learning Approaches for Aftermarket Demand Forecasting: Tackling Intermittent Time Series Challenges Concordia University]. https://spectrum.library.concordia.ca/993220/
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors: High-Precision Model-Agnostic Explanations. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18),
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215. https://doi.org/10.1038/s42256-019-0048-x
Seifert, D. (2003). Collaborative Planning, Forecasting, and Replenishment: How to Create a Supply Chain Advantage. AMACOM.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies. McGraw-Hill/Irwin.
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent research. Tourism Management, 29(2), 203-220. https://doi.org/10.1016/j.tourman.2007.02.017
Srikanth, M. L., & Umble, M. (1997). Synchronous Manufacturing: Principles for World Class Excellence. St. Lucie Press.
Stock, J. H., & Watson, M. W. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business & Economic Statistics, 20(2), 147-162. https://doi.org/10.1198/073500102317351921
Stock, J. H., & Watson, M. W. (2012). Disentangling the Channels of the 2007–2009 Recession (NBER Working Paper, Issue. https://www.nber.org/papers/w18094
Stock, J. R., & Boyer, S. L. (2009). Developing a Consensus Definition of Supply Chain Management: A Qualitative Study. International Journal of Physical Distribution & Logistics Management, 39(8), 690-711. https://doi.org/10.1108/09600030910996323
Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures. Bioinformatics, 23(7), 960-967. https://doi.org/10.1093/bioinformatics/btm087
Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting Big Data to Enhance Supply Chain Innovation Capabilities: An Analytic Infrastructure Based on Deduction Graph. International Journal of Production Economics, 165, 223-233. https://doi.org/10.1016/j.ijpe.2014.12.034
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https://doi.org/10.1016/j.jbusres.2020.09.009
TrueCommerce. (2022). Understanding the Supply Chain Bullwhip Effect. https://www.truecommerce.com/blog/bullwhip-effect-supply-chain/
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30, 5998-6008.
Verhagen, W. J. C., & Curran, R. (2014). Stochastic forecasting of lumpy distributed aircraft spare parts demand. Moving Integrated Product Development to Service Clouds in the Global Economy (IFAC), 706-714. https://doi.org/10.3233/978-1-61499-440-4-706
Vernon, R. (1966). International Investment and International Trade in the Product Cycle. The Quarterly Journal of Economics, 80(2), 190-207. https://doi.org/10.2307/1880689
Watson, K. J., Blackstone, J. H., & Gardiner, S. C. (2007). The Theory of Constraints and Its Applications. Routledge.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. https://www.elsevier.com/books/data-mining-practical-machine-learning-tools-and-techniques/witten/978-0-12-804291-5
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. https://doi.org/10.1109/4235.585893
Womack, J. P., Jones, D. T., & Roos, D. (1990). The Machine That Changed the World. Rawson Associates.
Yang, M., Zhang, Z., & Li, X. (2023). Supply Chain Risk Management with Machine Learning Technology: A Literature Review and Future Research Directions. Computers & Industrial Engineering, 175. https://doi.org/10.1016/j.cie.2022.108859
Yeh, W.-C., & Chuang, M.-C. (2011). Using Multi-Objective Genetic Algorithm for Partner Selection in Green Supply Chain Problems. Expert Systems with Applications, 38(4), 4244-4253. https://doi.org/10.1016/j.eswa.2010.09.091
Zhao, Z., Chen, W., Liu, J., & Chen, P. C. Y. (2017). LSTM Network: A Deep Learning Approach for Short-Term Traffic Forecast. IET Intelligent Transport Systems, 11(2), 68-75. https://doi.org/10.1049/iet-its.2016.0208