A process targeting model for a product with two dependent quality characteristics using 100% inspection

Salih O. Duffuaa, Umar M. Al-Turki, Ahmet A. Kolus

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The purpose of this paper is to develop a process targeting model for a product with two quality characteristics produced by two processes in series. The first quality characteristic is determined by the setting of the first process, whereas the second quality characteristic depends on the setting of the two processes. The quality of the product is controlled by a 100% inspection plan, and inspection is assumed to be error free. The objective of the model is to determine the optimal target for both processes that maximizes the profit. A realistic case study has been used to demonstrate the utility of the model. The results have shown that the model results improve the profit per lot and aid the factory in conducting cost/benefit analysis for reducing the variances of the two processes. In addition, sensitivity analysis has been performed to study the effect of different parameters on the expected profit and optimal processes means. It has been shown that the results of the model are sensitive to the variances of the two processes, selling prices, and rework costs.

Original languageEnglish
Pages (from-to)1039-1053
Number of pages15
JournalInternational Journal of Production Research
Volume47
Issue number4
DOIs
StatePublished - Jan 2009

Bibliographical note

Funding Information:
The authors wish to acknowledge the support provided by the Department of Systems Engineering and the excellent facilities provided by King Fahd University of Petroleum and Minerals (KFUPM), Dhahran Saudi Arabia for conducting this research. The authors would also like to thank Professor M. A. Rahim for his valuable comments which have improved the presentation of the paper.

Keywords

  • 100% inspection
  • Process targeting
  • Quality characteristics

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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