The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Study on Quality Prediction Technology of Manufacturing Supply Chain
|
Author(s): Genbao Zhang (College of Mechanical Engineering, Chongqing University, Chongqing, China), Yan Ran (College of Mechanical Engineering, Chongqing University, Chongqing, China)and Dongmei Luo (College of Mechanical Engineering, Chongqing University, Chongqing, China)
Copyright: 2015
Volume: 8
Issue: 4
Pages: 19
Source title:
International Journal of Information Systems and Supply Chain Management (IJISSCM)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 10.4018/ijisscm.2015100104
Purchase
|
Abstract
Supply chain quality is the assurance of product quality in its full life-cycle. Although supply chain quality control is a hot topic among researchers, supply chain quality prediction is actually an important but unsolved problem in manufacturing industry. In this paper, an approach of manufacturing supply chain quality prediction based on quality satisfaction degree is proposed to control supply chain better, in order to help ensure product quality. Supply chain quality prediction 3D model and model based on customer satisfaction and process control are established firstly. And then technologies used in quality prediction are studied, including quality prediction index system established on Expert scoring -AHP and prediction workflow built on ABPM. Finally an example is given to illustrate this approach. The customer satisfaction prediction result of supply chain quality can help supply chain management, and the quality prediction software system can make it easier, which provides a new direction for the product quality control technology research.
Related Content
George Maramba, Hanlie Smuts, Marie Hattingh, Funmi Adebesin, Harry Moongela, Tendani Mawela, Rexwhite Enakrire.
© 2024.
24 pages.
|
Wenfeng Niu, Miaomiao Fan.
© 2024.
17 pages.
|
Airong Zhang.
© 2024.
20 pages.
|
Chunrong Ni, Katarzyna Dohn.
© 2024.
14 pages.
|
Ying Wang.
© 2024.
18 pages.
|
Yao Wang, Zhijie Kang.
© 2024.
16 pages.
|
Linran Sun, Nojun Kwak.
© 2024.
19 pages.
|
|
|