政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119880

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/119880

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题名:	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
贡献者:	郁方 Yu, Fang 林登庸 Lin, Teng-Yung
关键词:	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
上传时间:	2018-09-03 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.
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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
数据类型:	thesis
DOI:	10.6814/THE.NCCU.MIS.023.2018.A05
显示于类别:	[資訊管理學系] 學位論文

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