Organizational and quality performance change following achievement of QS-9000 registration: An event study

QS-9000 is a technical and managerial quality standard mandated by the Big 3 automotive original equipment manufacturers. There are major changes to company's organization as a result of implementing this system. The QS-9000 standard is prescriptive in nature and more comprehensive than the international quality standard, ISO 9000.;A study was conducted to look at the impact of organizational variables on QS-9000 registration outcome and results---actual and perceived. The target population of the survey was quality or plant operational personnel who were primarily responsible or actively involved with QS-9000 registration activities. In addition to the responses from the Tier 2 suppliers, Johnson Controls provided performance information for quality and delivery PPMs. This allowed for a comparison of the perceived improvements achieved by the suppliers (QS-9000 registration perceived outcome and results) and the actual results measured by Johnson Controls. In this event study, a comparison (of the quality and delivery performance six months prior to the event, QS-9000 registration, and six months after yielded interesting results. Percentages of change were calculated, and as expected, there was deterioration in performance that had occurred from six months prior to the event date. This also could be a result of other "noise" issues that could have resulted in a decline in performance. There was negative correlation between the quality and delivery performance for suppliers who participated in the survey.;Factor and principal component analysis were used to reduce the dimensionality of the variables. These methods did not yield any predictive models. PCA was conducted on the perceived outcome and result variables to arrive at two factors. One factor, identified as Quality Management Integration is included in a predictive model to predict the one year (January 1998 to January 1999) percentage change quality PPM. Robust regression yielded a model that included both technical and human resource variables in a predictive model that is statistically significant. A major limitation was the small sample size and population.