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|Authors:||Amalia Vanacore: University of Naples "Federico II", Italy Department of Aerospace Engineering, Napoli, Italy|
|Publication title:||QIC Analysis: A Tool to Manage the Continuous Quality Improvement Process|
|Conference:||10th QMOD Conference. Quality Management and Organiqatinal Development. Our Dreams of Excellence, 18-20 June, 2007 in Helsingborg, Sweden|
|Publication type:||Full text not available|
|Abstract:||Continuous quality improvement (CQI) has emerged as an imperative for businesses’ growth in today’s fiercely competitive environment. CQI is a never-ending process that seeks to achieve defect-free, high quality products or services. Different approaches to quality management, like TQM, Business Excellence models and ISO standards, agree on the critical importance of the “continuous improvement” principle for the success of any strategy focused on quality. Notwithstanding this acknowledgement, a review of the extensive literature on quality management reveals that scarce effort is addressed to develop quantitative methods to analyze the quality improvement (QI) process. In fact, the literature on CQI consists mainly of case studies, anecdotal evidence and the prescriptive measures attributed to the recognized experts in the field of quality. Some popular prescriptions of the quality experts are the “14 points” of Deming (1982), the “10 steps” of Juran (1962) and the “14 steps” of Crosby (1979).|
There is no consensus as to which prescriptive framework should form the basis for CQI and the result is a lack of a practical model that is useful for the monitoring of a firm’s CQI program (Prybutok and Ramasesh, 2005).
Since CQI is an ongoing process, it is imperative that firms monitor the CQI program on a regular basis to ensure that it is working well and to continually identify areas for QI. In order to effectively monitor CQI reliable and valid instruments are necessary.
The original approach proposed in this paper aims at providing the organization with diagnostic tools enabling it to understand if it is on the right track to the planned QI and to evaluate the walk already covered as well as the residual distance to go up in its journey for QI. The proposed approach relies on the modelling of the quality growth which is obtained by 1) choosing a proper quality index to monitor over time and 2) identifying the Quality Improvement Curve (QIC) that describes the quality growth due to the CQI process.
The QIC analysis can be usefully applied to monitor a single QI process over time or to compare several QI processes by means of a suitable indicator.
The methodology is illustrated through a case study concerning the QI process of a teaching course in Probability and Statistics held at the Faculty of Engineering of the University of Naples Federico II. Starting from experimental data collected, during the second semester of the academic year 2005-06, by interviewing the attending students about the quality of the course of Probability and Statistics, three QI programs have been hypothesized. The values of the quality index for future semesters (in which QI programs are assumed to be adopted) have been obtained by Monte Carlo simulation.
|Keywords:||Quality Improvement Curve, quality growth data analysis, diagnostic tools|
|No. of pages:||9|
|Series:||Linköping Electronic Conference Proceedings|
|Publisher:||Linköping University Electronic Press, Linköpings universitet|
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