標題: Performance assessment of processing and delivery times for very large scale integration using process capability indices
作者: Chen, KS
Chen, HT
Tong, LI
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: delivery time;normal distribution;process capability index;processing time;uniformly minimum;variance unbiased estimator;very large scale integration
公開日期: 2002
摘要: Process quality and delivery time have received increasing attention in the highly competitive electronics industry. Many studies have proposed process capability indices (PCIs) to assess process effectiveness. However, methods to assess the performance in terms of processing and delivery times of products have seldom been discussed. The conventional PCIs can no longer assess the processing time (PT) and delivery time (DT) performance objectively or identify the relationship between PCIs and the non-conformance rate of PT or the conformance rate of DT. Lacking an effective performance index or an objective testing procedure to assess process/product performance will lead to inefficiency or a high manufacturing management overhead cost. Therefore, this study offers effective performance indices (i.e., PCIs) to assess the PT and DT performance for very large scale integration (VLSI). The uniformly minimum variance unbiased (UMVU) estimators of the proposed PCIs are derived under the assumption of a normal process distribution. The PCI estimators are then employed to construct a one-to-one relationship between the PCIs and the conformance rate of DT or non-conformance rate of PT, respectively. Finally, hypothesis testing procedures for the proposed PCIs are also developed. The testing procedure can be used to determine whether DT or PT can satisfy a customer's requirements.
URI: http://hdl.handle.net/11536/29111
http://dx.doi.org/10.1007/s001700200186
ISSN: 0268-3768
DOI: 10.1007/s001700200186
期刊: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume: 20
Issue: 7
起始頁: 526
結束頁: 531
Appears in Collections:Articles


Files in This Item:

  1. 000179157300007.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.