Significance of Quality Certification: The Case of the Software Industry in India (pp. 8–32).
George Issac, Chandrasekharan Rajendran, and R. N. Anantharaman, Indian Institute of Technology
Total quality management (TQM), ISO 9000, the Capability Maturity Model (CMM), and the consequent operational performance indicators of organizations are of interest to researchers as well as the business community. This article presents an empirical study addressing these issues. Specifically, the study explores the following questions:
- Do quality certifications such as ISO 9000 and CMM influence the implementation of quality management programs in the software industry?
- Do quality certifications such as ISO 9000 and CMM contribute to the improvement of operational performance of organizations in the case of the software industry?
- Is there a difference between ISO 9000 certified firms and CMM certified firms with respect to the TQM constructs?
The study, which may be the first of its kind in the software industry, was conducted in India. India, a country that has relentlessly pursued the goal of acquiring the highest standards of quality for offering world-class software products and services, has more than 200 companies being quality accredited and serving the needs of more than 255 Fortune 500 companies.
Theoretical, empirical, and practitioner literature was explored to determine the factors or constructs of quality management in the software industry. A total of 15 constructs were identified. These constructs include the following: top management commitment and leadership, organizational culture, client focus, process quality management, quality measures, human resource management, employee empowerment, continuous improvement, benchmarking, infrastructure and facilities, communication, employee commitment and attitude, risk management, product attributes, and return on quality. Four hypotheses were then formulated and tested.
As a result of the study, quality-certified firms were found to be significantly better than noncertified firms in the software industry with respect to TQM practices and operational performance indicators. Quality-certified firms were then divided into ISO certified and CMM certified based on their quality certification. It was found that there is no significant difference between noncertified firms and ISO certified firms; however, there are significant differences between noncertified firms and CMM certified firms with respect to TQM constructs and operational performance indicators. It was also observed that CMM (levels 4 and 5) firms are significantly better than the ISO certified firms.
Empirical Study of QS-9000 Using Principal Components Analysis and Robust Regression (pp. 33–46).
Dana M. Johnson, Michigan Technological University
The QS-9000 standards were developed as a way to ensure that automotive suppliers have a quality system in place to provide a quality product, delivered on time, and at a low cost. However, all suppliers that have attained QS-9000 certification have not consistently achieved the objectives of quality, delivery, and cost. This article presents a study that identified and evaluated the relationships between organizational variables and operational performance measures such as quality and delivery, and business performance measures such as cost, sales growth, and customer satisfaction, and whether performance was affected after QS-9000 certification. Key organizational variables and dimensions were identified by review of QS-9000 and related ISO 9001 empirical studies.
The following organizational variables associated with a quality management system were identified:
- Leadership for quality
- Quality strategy
- Structure for quality management
- Quality technology and tools
- Quality culture
- Quality rewards and recognition
Participants in the study were selected from the American Society for Quality’s database of QS-9000 registered companies. Only those located in the United States were included in the study. A random sample of 1000 companies was selected to participate in the study. These companies received a questionnaire. Of the 1000 questionnaires sent, 153 were returned. Respondents were asked about their agreement with a particular statement using a five-point Likert scale.
The descriptive statistics were analyzed to gain an initial understanding of the organizational and business variables affected by QS-9000 and to compare previous results for statistical significance. The results were compared to a previous study conducted in 1999. The means and standard deviations for the variables were determined, and principal component analysis was applied to simplify the development of a predictive model.
Final results of the study suggest that companies are focusing on a few organizational variables from a high-level perspective to predict operational and business performance. Management supports the QS-9000 change effort by empowering employees through the use of team-based problem-solving methodologies.
Residual Control Charts for Improving Changeover Productivity (pp. 47–60).
Qidong Cao, Winthrop University, J. Wayne Patterson, Clemson University,
Xue Bai, Virginia State University, and Thomas E. Griffin, Indiana Wesleyan University
Although statistical control charts have been popularly used in monitoring process quality, the concepts of statistical control are essentially suited to the improvement of productivity in almost any area of business. This article presents a new statistical control chart, the residual control chart, which was employed to improve machine changeover productivity in a textile manufacturing company.
Managers in one textile manufacturing company noticed that there were large variations in machine changeover times, which indicated the potential for improvement. The difficulty was that there are other factors besides technicians that affect the changeover times. Managers needed a way to effectively monitor performance and control changeover time. They determined that the following questions needed to be answered:
- How can one block out the effect of other factors so that the standard changeover times can be set and technicians can be evaluated as a common base?
- Is it possible to use control charts as a tool for improving the performance of technicians?
- Can the number of control charts be maintained at a low level while still blocking out the effect of extraneous factors?
- Based on preceding analyses, how can the resultant models be used to work on technician performance appraisal, to plan training programs, and to balance workloads?
The authors collected data during a 12-week period and included 233 changeovers by 15 technicians. In the first stage of the study, the authors performed regression analysis for the data of each technician. Next, they performed clustering analysis for different groups of technicians. In the third stage a regression model was determined based on a group of technicians selected by the clustering analysis in the second stage. In the fourth stage, the residuals served as the basis for constructing a new residual control chart.
The residual chart can be used to address several problems faced by management. It can be used to establish standard operating times, to help in human resource planning, to facilitate performance appraisal, and to investigate assignable causes. Ostensibly, the proposed method can help managers set
standard changeover times when assigning a specific changeover to a technician. Also, there is almost no extra cost for using this approach. The data used for the analysis were not collected specifically for residual control charts.
Critical Success Factors for Controlling and Managing Hospital Errors (pp. 61–74). Kathleen L. McFadden, Northern Illinois University, Elizabeth R. Towell, Carroll College, and Gregory N. Stock, Northern Illinois University
Reducing hospital errors is a critical issue facing hospitals today. The Institute of Medicine estimates that medical errors are linked to more than one million injuries and approximately 98,000 deaths each year. The total national cost of these errors is estimated at about $37.6 billion per year.
Many hospitals are developing error management systems to help in reducing medical errors and adverse events. Hospitals are at different stages in their development of such systems and could benefit from a road map or list of critical factors necessary for controlling and managing hospital errors. In this article, the authors develop and evaluate their framework for reducing hospital errors and improving patient safety. They use their previously published aviation safety framework, the PROCESS model, as a foundation for developing a similar framework for hospitals, since the aviation industry has been very successful in improving safety through the management of errors.
The authors have identified seven critical success factors for successfully managing and controlling hospital errors. These are:
- Partnership of all stakeholders
- Reporting errors without blame
- Open-ended focus groups
- Cultural shift
- Education and training programs
- Statistical analysis of error data
- System redesign
The common theme tying these factors together is the emphasis on the process over the individual as a solution to the problem of hospital errors.
The authors conducted an exploratory case study of hospitals in Illinois to evaluate their PROCESS framework. Their primary data collection method was the interview. They explored whether hospitals viewed each of their seven critical success factors as important and tried to identify other critical success factors. They also tried to identify barriers to the implementation of each factor.
The results of the study provide support for the PROCESS framework of reducing hospital errors. This research indicates that for hospitals to develop an effective error management system, they must first develop a partnership with all stakeholders and a culture within the hospital where information about errors is shared and flows freely in a nonpunitive environment.
