As the increase in manufacturing competitiveness forces organizations to use more sophisticated and complex software, system performance depends on clever systems design, efficient planning and scheduling of the related processes. For these advanced manufacturing systems the dependence on human competence is greater. However, previous studies indicate that the human aspects for successfully implementing such systems have been neglected. The objective here is to test the hypotheses that system complexity is inversely related to performance, and that training of system operators, and the quality of the man/machine interface reduces the negative impact of system complexity. A sample of discreet manufacturing systems from 128 organizations was used to test these hypotheses empirically. Moderated multivariate regression indicates that man/machine interfaces are significant contributors to reducing the negative effect of systems complexity. With a lower level of significance, operator training has a similar impact. For complex manufacturing systems software, it behoves managers to insure that the man/machine interface provides the desirable features outlined in this study.
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1 December 1999
Research Article|
December 01 1999
Empirically testing the impact of manufacturing system complexity on performance Available to Purchase
Tor Guimaraes;
Tor Guimaraes
Tennessee Technological University, Cookeville, Tennessee, USA
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Nils Martensson;
Nils Martensson
Chalmers University of Technology, Gothenburg, Sweden, and
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Johan Stahre;
Johan Stahre
Chalmers University of Technology, Gothenburg, Sweden, and
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Magid Igbaria
Magid Igbaria
Claremont Graduate School, Claremont, California, USA
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Publisher: Emerald Publishing
Online ISSN: 1758-6593
Print ISSN: 0144-3577
© MCB UP Limited
1999
International Journal of Operations & Production Management (1999) 19 (12): 1254–1269.
Citation
Guimaraes T, Martensson N, Stahre J, Igbaria M (1999), "Empirically testing the impact of manufacturing system complexity on performance". International Journal of Operations & Production Management, Vol. 19 No. 12 pp. 1254–1269, doi: https://doi.org/10.1108/01443579910294228
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