Statistical Process Control (SPC) application in a manufacturing firm to improve cost effectiveness: Case study

I. Madanhire, C. Mbohwa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This study investigated the use of the statistical process control (SPC) as a tool in a manufacturing firm to improve quality and cost effectiveness. With emphasis on early detection and prevention of the problems, SPC was shown to have a distinct advantage over other quality methods such as inspection of final products. There was need to check gauges and machines, and determine need for some maintenance or overhaul work to be carried out as faulty machines could not produce expected quality products. There was need for operators to be trained, new documents to be produced and actions for the future to be agreed on. There was a need for reviewing the operations and monitoring key equipment for re-tooling or adjustment to achieve required levels of plant performance.

Original languageEnglish
Title of host publication6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
PublisherIEOM Society
Pages2298-2305
Number of pages8
ISBN (Print)9780985549749
Publication statusPublished - 2016
Event6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016 - Kuala Lumpur, Malaysia
Duration: 8 Mar 201610 Mar 2016

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
Volume8-10 March 2016
ISSN (Electronic)2169-8767

Conference

Conference6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
Country/TerritoryMalaysia
CityKuala Lumpur
Period8/03/1610/03/16

Keywords

  • Cost effectiveness
  • Inspection
  • Monitoring
  • Performance
  • Quality
  • SPC

ASJC Scopus subject areas

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

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