DocumentCode
1306254
Title
Comparison of manufacturing performance of three team structures in semiconductor plants
Author
Bailey, Diane E.
Author_Institution
Dept. of Ind. & Syst. Eng., Univ. of Southern California, Los Angeles, CA, USA
Volume
45
Issue
1
fYear
1998
fDate
2/1/1998 12:00:00 AM
Firstpage
20
Lastpage
32
Abstract
Manufacturing programs aimed at improving performance often feature employee teams that address production problems at the shop-floor level. According to cognitive models of participation, performance under such programs is improved via the better utilization of skills and knowledge that occurs as employees are allowed greater decision making in their tasks. The authors examine the cognitive-model premise in a high-technology industry where improvement-team programs are on the rise. The study three types of improvement-team programs among a sample of eight manufacturing sites. The programs feature continuous improvement teams (CITs), quality circles (QCs) or self-directed work teams (SDWTs) and vary in the amount of decision-making power, skill attainment via training and skill use granted to employees. A quantitative analysis of performance reveals that CIT programs were associated with the highest direct and indirect productivity, two metrics that were available for each firm. QC and SDWT programs should not be dismissed, however, as they may lead to improvements in quality metrics, as the authors note in suggestions for future research. Qualitative data gathered in site visits suggest that poor implementation and failure to integrate production programs with engineering departments are two factors that inhibit program success
Keywords
electronics industry; human resource management; personnel; quality control; cognitive-model; continuous improvement teams; decision making; employee team structures; engineering departments; high-technology industry; improvement-team programs; manufacturing performance; quality circles; self-directed work teams; semiconductor plants; Continuous improvement; Data engineering; Decision making; Industrial training; Manufacturing industries; Performance analysis; Production; Productivity; Quality assurance; Semiconductor device manufacture;
fLanguage
English
Journal_Title
Engineering Management, IEEE Transactions on
Publisher
ieee
ISSN
0018-9391
Type
jour
DOI
10.1109/17.658658
Filename
658658
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