東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Visual Analysis of the Product Supply Chain in Smart Manufacturing and Industry 4.0.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Visual Analysis of the Product Supply Chain in Smart Manufacturing and Industry 4.0./
作者:	Sun, Dong.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	131 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Raw materials. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29057119
ISBN:	9798426864191

Visual Analysis of the Product Supply Chain in Smart Manufacturing and Industry 4.0.
Sun, Dong.

Visual Analysis of the Product Supply Chain in Smart Manufacturing and Industry 4.0. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 131 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.

This item must not be sold to any third party vendors.

The product supply chain often includes the purchase of raw materials, production, product distribution, and the sale of products. The exploration and analysis of the data in the product supply chain are important for evaluating the operation of the product supply chain, identifying potential problems, improving the supply chain, and adjusting the strategy for product supply in response to market changes. However, challenges exist due to the huge amount of data, the complicated relationship between different components of the product supply chain, the increasing number and complexity of the machine learning models used, and the uncertainty of the market. Prior studies on visualizing the data in the product supply chain lack a detailed comparison of different algorithm outputs and do not show the reason for the difference between the outputs. What's more, the uncertainty of the market is not considered in these studies, which makes it difficult for users to adjust the product supply chain in case of market changes. In this thesis, I propose interactive visual analytics approaches for exploring and diagnosing multiple stages of the product supply chain. Specifically, the approaches support the detailed comparison and selection of product demand forecasting models, enable the fast exploration and comparison of different production plans, allow a quick adjustment to a production plan in case of market changes, and help users to explore, inspect and diagnose the strategy for the multistage distribution of products. The effectiveness and usability of the proposed approaches are demonstrated by case studies with real-world datasets of the product supply chain and interviews with experts working on managing the product supply chain in large manufacturing companies.

ISBN: 9798426864191Subjects--Topical Terms:

648907
Raw materials.

Visual Analysis of the Product Supply Chain in Smart Manufacturing and Industry 4.0.
LDR:02921nmm a2200337 4500 001 2350697
005 20221020130421.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798426864191
035 $a (MiAaPQ)AAI29057119
035 $a (MiAaPQ)HongKongSciTech_991012986004103412
035 $a AAI29057119
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sun, Dong. $3 1259761
245 1 0 $a Visual Analysis of the Product Supply Chain in Smart Manufacturing and Industry 4.0.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 131 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Pong, Ting-Chuen;Qu, Huamin.
502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The product supply chain often includes the purchase of raw materials, production, product distribution, and the sale of products. The exploration and analysis of the data in the product supply chain are important for evaluating the operation of the product supply chain, identifying potential problems, improving the supply chain, and adjusting the strategy for product supply in response to market changes. However, challenges exist due to the huge amount of data, the complicated relationship between different components of the product supply chain, the increasing number and complexity of the machine learning models used, and the uncertainty of the market. Prior studies on visualizing the data in the product supply chain lack a detailed comparison of different algorithm outputs and do not show the reason for the difference between the outputs. What's more, the uncertainty of the market is not considered in these studies, which makes it difficult for users to adjust the product supply chain in case of market changes. In this thesis, I propose interactive visual analytics approaches for exploring and diagnosing multiple stages of the product supply chain. Specifically, the approaches support the detailed comparison and selection of product demand forecasting models, enable the fast exploration and comparison of different production plans, allow a quick adjustment to a production plan in case of market changes, and help users to explore, inspect and diagnose the strategy for the multistage distribution of products. The effectiveness and usability of the proposed approaches are demonstrated by case studies with real-world datasets of the product supply chain and interviews with experts working on managing the product supply chain in large manufacturing companies.
590 $a School code: 1223.
650 4 $a Raw materials. $3 648907
650 4 $a Usability. $3 3681540
650 4 $a Manufacturing. $3 3389707
650 4 $a Deep learning. $3 3554982
650 4 $a Forecasting. $3 547120
650 4 $a Optimization. $3 891104
650 4 $a Decision making. $3 517204
650 4 $a Data processing. $3 680224
650 4 $a Design. $3 518875
650 4 $a Subject specialists. $3 3690203
650 4 $a Algorithms. $3 536374
650 4 $a Time series. $3 3561811
650 4 $a Visualization. $3 586179
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Industrial engineering. $3 526216
690 $a 0389
690 $a 0800
690 $a 0984
690 $a 0546
710 2 $a Hong Kong University of Science and Technology (Hong Kong). $3 1022235
773 0 $t Dissertations Abstracts International $g 83-11B.
790 $a 1223
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29057119