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題名 利用無監督遍歷偵測供應鏈流程變異性
Tracking Supply Chain Process Variability with Unsupervised Cluster Traversal作者 林登庸
Lin, Teng-Yung貢獻者 郁方
Yu, Fang
林登庸
Lin, Teng-Yung關鍵詞 非監督分群
供應鏈管理
流程變異性
Unsupervised clustering
Supply chain management
Process variability日期 2018 上傳時間 3-Sep-2018 15:47:36 (UTC+8) 摘要 Supply chain processes need stability and predictability for the supply to better match demand at the right time with the right quantity. Reaching stable operations under uncertainty, however, is challenging as fluctuating demand patterns in the downstream are so common and make inventory control at the upstream a daunting task. Working with one of the leading semiconductor distributors in the world, who piles up stock that hampers profitability for the sake of satisfying lumpy/erratic demand in the downstream production plants, we help the distributor track process variability in its operations. Specifically, we integrate unsupervised clustering with the recurrent neural network for tracking supply chain process variability without pre-assumptions on demand patterns. We first apply unsupervised learning techniques to characterize weekly process performance of a wide variety of electronic items, where item-week pairs that have relatively-high similarity on values of demand and stock attributes are clustered together. The operational variability of each item can then be measured with the trajectory of the item on its clusters ordered by time. To predict the trajectory of how each item moves from week to week, we propose a new cluster sequence encoding and employ the recurrent neural network structure for sequence prediction. We show that with a training loss function tailored to our encoding scheme, the presented approach can achieve high accuracy on variability prediction for real-world data. Since any upstream supply operations are driven by downstream demand patterns, the prediction on items’ operational variability may help suppliers to better prepare for demand irregularities by dynamically adjusting their operations strategies, e.g., altering throughput rates, rescheduling deliveries, increasing/decreasing fulfillment frequencies, etc. 參考文獻 [1] H. Sak, A. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling,” in Fifteenth annual conference of the international speech communication association, 2014.[2] D. McFadden et al., “Conditional logit analysis of qualitative choice behavior,” 1973.[3] M. Ben-Akiva and S. R. Lerman, “Discrete choice analysis mit press cambridge,” MA Google Scholar, 1985.[4] D. M. Beyer, F. Safai, and F. AitSalia, “Profile-based product demand forecasting,” Dec. 20 2005, uS Patent 6,978,249.[5] N. Singh, S. J. Olasky, K. S. Clu, and W. F. Welch Jr, “Supply chain demand forecasting and planning,” Jul. 18 2006, uS Patent 7,080,026.[6] T. Efendigil, S. O ̈nu ̈t, and C. Kahraman, “A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis,” Expert Systems with Applications, vol. 36, no. 3, pp. 6697–6707, 2009.[7] R. Carbonneau, K. Laframboise, and R. Vahidov, “Application of machine learning techniques for supply chain demand forecasting,” European Journal of Operational Research, vol. 184, no. 3, pp. 1140–1154, 2008.[8] F. Murtagh and P. Legendre, “Ward’s hierarchical agglomerative clustering method: Which algorithms implement ward’s criterion?” Journal of Classification, vol. 31, no. 3, pp. 274–295, Oct 2014. [Online]. Available: https://doi.org/10.1007/ s00357-014-9161-z[9] J. Vesanto and E. Alhoniemi, “Clustering of the self-organizing map,” IEEE Transactions on neural networks, vol. 11, no. 3, pp. 586–600, 2000.[10] A. Rauber, D. Merkl, and M. Dittenbach, “The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1331–1341, 2002.[11] C.-H. Chiu, J.-J. Chen, and F. Yu, “An e↵ective distributed ghsom algorithm for unsupervised clustering on big data,” in Big Data (BigData Congress), 2017 IEEE International Congress on. IEEE, 2017, pp. 297–304.[12] M. J. Brusco, R. Singh, J. D. Cradit, and D. Steinley, “Cluster analysis in empirical om research: survey and recommendations,” International Journal of Operations & Production Management, vol. 37, no. 3, pp. 300–320, 2017.[13] N. Mladenovi ́c and P. Hansen, “Variable neighborhood search,” Computers & operations research, vol. 24, no. 11, pp. 1097–1100, 1997.[14] F. E. H. Tay and L. J. Cao, “Improved financial time series forecasting by combining support vector machines with self-organizing feature map,” Intelligent Data Analysis, vol. 5, no. 4, pp. 339–354, 2001.[15] C. Hung and C.-F. Tsai, “Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand,” Expert systems with applications, vol. 34, no. 1, pp. 780–787, 2008.[16] C.-J. Lu and Y.-W. Wang, “Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting,” International Journal of Production Economics, vol. 128, no. 2, pp. 603–613, 2010.[17] S.-Y. Huang, R.-H. Tsaih, and F. Yu, “Topological pattern discovery and feature extraction for fraudulent financial reporting,” Expert Systems with Applications, vol. 41, no. 9, pp. 4360–4372, 2014.[18] Z.-R. Fang, S.-W. Huang, and F. Yu, “Appreco: Behavior-aware recommendation for ios mobile applications,” in Web Services (ICWS), 2016 IEEE International Conference on. IEEE, 2016, pp. 492–499.[19] M. Sundermeyer, R. Schlu ̈ter, and H. Ney, “Lstm neural networks for language modeling,” in Thirteenth Annual Conference of the International Speech Communication Association, 2012.[20] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.[21] N. S. Chaudhari and X.-M. Yuan, “Demand forecasting of short life span products: Issues, challenges, and use of soft computing techniques,” in Handbook of Computational Intelligence in Manufacturing and Production Management. IGI Global, 2008, pp. 124–143.[22] C. L. Giles, S. Lawrence, and A. C. Tsoi, “Noisy time series prediction using recurrent neural networks and grammatical inference,” Machine learning, vol. 44, no. 1-2, pp. 161–183, 2001.[23] G. A. Darbellay and M. Slama, “Forecasting the short-term demand for electricity: Do neural networks stand a better chance?” International Journal of Forecasting, vol. 16, no. 1, pp. 71–83, 2000.[24] K. Mupparaju, A. Soni, P. Gujela, and M. A. Lanham, “A comparative study of machine learning frameworks for demand forecasting.”[25] A. Rauber, E. Pampalk, and D. Merkl, Using psycho-acoustic models and selforganizing maps to create a hierarchical structuring of music by sound similarity. na, 2002.[26] S.-H. Hsu, J. P.-A. Hsieh, T.-C. Chih, and K.-C. Hsu, “A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression,” Expert Systems with Applications, vol. 36, no. 4, pp. 7947–7951, 2009.[27] S. Liu, L. Lu, G. Liao, and J. Xuan, “Pattern discovery from time series using growing hierarchical self-organizing map,” in International Conference on Neural Information Processing. Springer, 2006, pp. 1030–1037.[28] M. A. Hern ́andez and S. J. Stolfo, “Real-world data is dirty: Data cleansing and the merge/purge problem,” Data mining and knowledge discovery, vol. 2, no. 1, pp. 9–37, 1998.[29] M. C. Wilson, “The impact of transportation disruptions on supply chain performance,” Transportation Research Part E: Logistics and Transportation Review, vol. 43, no. 4, pp. 295–320, 2007.[30] S. M. Homayouni, T. S. Hong, and N. Ismail, “Development of genetic fuzzy logic controllers for complex production systems,” Computers & Industrial Engineering, vol. 57, no. 4, pp. 1247–1257, 2009.[31] P. J. Huber, “Robust statistics,” in International Encyclopedia of Statistical Science. Springer, 2011, pp. 1248–1251.[32] L. B. Sheiner and S. L. Beal, “Some suggestions for measuring predictive performance,” Journal of pharmacokinetics and biopharmaceutics, vol. 9, no. 4, pp. 503– 512, 1981.[33] A. Tato and R. Nkambou, “Improving adam optimizer,” 2018. 描述 碩士
國立政治大學
資訊管理學系
105356006資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356006 資料類型 thesis dc.contributor.advisor 郁方 zh_TW dc.contributor.advisor Yu, Fang en_US dc.contributor.author (Authors) 林登庸 zh_TW dc.contributor.author (Authors) Lin, Teng-Yung en_US dc.creator (作者) 林登庸 zh_TW dc.creator (作者) Lin, Teng-Yung en_US dc.date (日期) 2018 en_US dc.date.accessioned 3-Sep-2018 15:47:36 (UTC+8) - dc.date.available 3-Sep-2018 15:47:36 (UTC+8) - dc.date.issued (上傳時間) 3-Sep-2018 15:47:36 (UTC+8) - dc.identifier (Other Identifiers) G0105356006 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/119880 - 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) 105356006 zh_TW dc.description.abstract (摘要) Supply chain processes need stability and predictability for the supply to better match demand at the right time with the right quantity. Reaching stable operations under uncertainty, however, is challenging as fluctuating demand patterns in the downstream are so common and make inventory control at the upstream a daunting task. Working with one of the leading semiconductor distributors in the world, who piles up stock that hampers profitability for the sake of satisfying lumpy/erratic demand in the downstream production plants, we help the distributor track process variability in its operations. Specifically, we integrate unsupervised clustering with the recurrent neural network for tracking supply chain process variability without pre-assumptions on demand patterns. We first apply unsupervised learning techniques to characterize weekly process performance of a wide variety of electronic items, where item-week pairs that have relatively-high similarity on values of demand and stock attributes are clustered together. The operational variability of each item can then be measured with the trajectory of the item on its clusters ordered by time. To predict the trajectory of how each item moves from week to week, we propose a new cluster sequence encoding and employ the recurrent neural network structure for sequence prediction. We show that with a training loss function tailored to our encoding scheme, the presented approach can achieve high accuracy on variability prediction for real-world data. Since any upstream supply operations are driven by downstream demand patterns, the prediction on items’ operational variability may help suppliers to better prepare for demand irregularities by dynamically adjusting their operations strategies, e.g., altering throughput rates, rescheduling deliveries, increasing/decreasing fulfillment frequencies, etc. en_US dc.description.tableofcontents 1 Introduction 12 Related Works 4 2.1 Supply Chain Demand Forecasting 4 2.2 Growing Hierarchical SOM( GHSOM) 42.3 RecurrentNeuralNetwork(RNN) 52.4 Time Series Data Prediction with Clustering 5 3 Methodology 7 3.1 SystemOverview 83.2 SCM process variability 83.3 Unsupervised clustering with GHSOM 113.4 Clusteritemswithtrajectories 163.5 Clustersequenceprediction 163.6 ModelAccuracyandPerformance 21 4 Case Study 234.1 System Settings 23 4.2 Comparison on lMSE and wMSE in fluctuating and stable items 26 4.3 Trajectory prediction 26 5 Conclusion 29 References 30 zh_TW dc.format.extent 1176521 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356006 en_US dc.subject (關鍵詞) 非監督分群 zh_TW dc.subject (關鍵詞) 供應鏈管理 zh_TW dc.subject (關鍵詞) 流程變異性 zh_TW dc.subject (關鍵詞) Unsupervised clustering en_US dc.subject (關鍵詞) Supply chain management en_US dc.subject (關鍵詞) Process variability en_US dc.title (題名) 利用無監督遍歷偵測供應鏈流程變異性 zh_TW dc.title (題名) Tracking Supply Chain Process Variability with Unsupervised Cluster Traversal en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] H. Sak, A. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling,” in Fifteenth annual conference of the international speech communication association, 2014.[2] D. McFadden et al., “Conditional logit analysis of qualitative choice behavior,” 1973.[3] M. Ben-Akiva and S. R. Lerman, “Discrete choice analysis mit press cambridge,” MA Google Scholar, 1985.[4] D. M. Beyer, F. Safai, and F. AitSalia, “Profile-based product demand forecasting,” Dec. 20 2005, uS Patent 6,978,249.[5] N. Singh, S. J. Olasky, K. S. Clu, and W. F. Welch Jr, “Supply chain demand forecasting and planning,” Jul. 18 2006, uS Patent 7,080,026.[6] T. Efendigil, S. O ̈nu ̈t, and C. Kahraman, “A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis,” Expert Systems with Applications, vol. 36, no. 3, pp. 6697–6707, 2009.[7] R. Carbonneau, K. Laframboise, and R. Vahidov, “Application of machine learning techniques for supply chain demand forecasting,” European Journal of Operational Research, vol. 184, no. 3, pp. 1140–1154, 2008.[8] F. Murtagh and P. Legendre, “Ward’s hierarchical agglomerative clustering method: Which algorithms implement ward’s criterion?” Journal of Classification, vol. 31, no. 3, pp. 274–295, Oct 2014. [Online]. Available: https://doi.org/10.1007/ s00357-014-9161-z[9] J. Vesanto and E. Alhoniemi, “Clustering of the self-organizing map,” IEEE Transactions on neural networks, vol. 11, no. 3, pp. 586–600, 2000.[10] A. Rauber, D. Merkl, and M. Dittenbach, “The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1331–1341, 2002.[11] C.-H. Chiu, J.-J. Chen, and F. Yu, “An e↵ective distributed ghsom algorithm for unsupervised clustering on big data,” in Big Data (BigData Congress), 2017 IEEE International Congress on. IEEE, 2017, pp. 297–304.[12] M. J. Brusco, R. Singh, J. D. Cradit, and D. Steinley, “Cluster analysis in empirical om research: survey and recommendations,” International Journal of Operations & Production Management, vol. 37, no. 3, pp. 300–320, 2017.[13] N. Mladenovi ́c and P. Hansen, “Variable neighborhood search,” Computers & operations research, vol. 24, no. 11, pp. 1097–1100, 1997.[14] F. E. H. Tay and L. J. Cao, “Improved financial time series forecasting by combining support vector machines with self-organizing feature map,” Intelligent Data Analysis, vol. 5, no. 4, pp. 339–354, 2001.[15] C. Hung and C.-F. Tsai, “Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand,” Expert systems with applications, vol. 34, no. 1, pp. 780–787, 2008.[16] C.-J. Lu and Y.-W. Wang, “Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting,” International Journal of Production Economics, vol. 128, no. 2, pp. 603–613, 2010.[17] S.-Y. Huang, R.-H. Tsaih, and F. Yu, “Topological pattern discovery and feature extraction for fraudulent financial reporting,” Expert Systems with Applications, vol. 41, no. 9, pp. 4360–4372, 2014.[18] Z.-R. Fang, S.-W. Huang, and F. Yu, “Appreco: Behavior-aware recommendation for ios mobile applications,” in Web Services (ICWS), 2016 IEEE International Conference on. IEEE, 2016, pp. 492–499.[19] M. Sundermeyer, R. Schlu ̈ter, and H. Ney, “Lstm neural networks for language modeling,” in Thirteenth Annual Conference of the International Speech Communication Association, 2012.[20] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.[21] N. S. Chaudhari and X.-M. Yuan, “Demand forecasting of short life span products: Issues, challenges, and use of soft computing techniques,” in Handbook of Computational Intelligence in Manufacturing and Production Management. IGI Global, 2008, pp. 124–143.[22] C. L. Giles, S. Lawrence, and A. C. Tsoi, “Noisy time series prediction using recurrent neural networks and grammatical inference,” Machine learning, vol. 44, no. 1-2, pp. 161–183, 2001.[23] G. A. Darbellay and M. Slama, “Forecasting the short-term demand for electricity: Do neural networks stand a better chance?” International Journal of Forecasting, vol. 16, no. 1, pp. 71–83, 2000.[24] K. Mupparaju, A. Soni, P. Gujela, and M. A. Lanham, “A comparative study of machine learning frameworks for demand forecasting.”[25] A. Rauber, E. Pampalk, and D. Merkl, Using psycho-acoustic models and selforganizing maps to create a hierarchical structuring of music by sound similarity. na, 2002.[26] S.-H. Hsu, J. P.-A. Hsieh, T.-C. Chih, and K.-C. Hsu, “A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression,” Expert Systems with Applications, vol. 36, no. 4, pp. 7947–7951, 2009.[27] S. Liu, L. Lu, G. Liao, and J. Xuan, “Pattern discovery from time series using growing hierarchical self-organizing map,” in International Conference on Neural Information Processing. Springer, 2006, pp. 1030–1037.[28] M. A. Hern ́andez and S. J. Stolfo, “Real-world data is dirty: Data cleansing and the merge/purge problem,” Data mining and knowledge discovery, vol. 2, no. 1, pp. 9–37, 1998.[29] M. C. Wilson, “The impact of transportation disruptions on supply chain performance,” Transportation Research Part E: Logistics and Transportation Review, vol. 43, no. 4, pp. 295–320, 2007.[30] S. M. Homayouni, T. S. Hong, and N. Ismail, “Development of genetic fuzzy logic controllers for complex production systems,” Computers & Industrial Engineering, vol. 57, no. 4, pp. 1247–1257, 2009.[31] P. J. Huber, “Robust statistics,” in International Encyclopedia of Statistical Science. Springer, 2011, pp. 1248–1251.[32] L. B. Sheiner and S. L. Beal, “Some suggestions for measuring predictive performance,” Journal of pharmacokinetics and biopharmaceutics, vol. 9, no. 4, pp. 503– 512, 1981.[33] A. Tato and R. Nkambou, “Improving adam optimizer,” 2018. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.023.2018.A05 -
