Quality Management Journal Executive Briefs - October 2000 - ASQ

Quality Management Journal Executive Briefs - October 2000

Contents

Improvement in Organizational Performance and Self-assessment Practices by Selected American Firms

Ton Van der Wiele, Erasmus University; Alan Brown, Edith Cowan University; Robert Millen, Northeastern University; and Daniel Whelan, Boston Section, ASQ

Quality award models, such as the Malcolm Baldrige National Quality Award, have stimulated considerable interest in quality management and provided guidelines for organizations seeking to introduce quality management. The notion of self-assessment has been adopted by companies throughout the world as a mechanism for guiding the development of such quality activities. This involves regular and systematic reviews of an organization’s activities and performance against a quality model, usually based on an award, culminating in planned improvement actions.

In this paper the authors discuss the self-assessment practices of U.S. companies based on a questionnaire survey. A range of issues is discussed including why self-assessment was introduced, how it is used, and outcomes–including the impact on organizational performance.

The survey data suggest that self-assessment is linked to better-performing companies. The data also highlight some of the self-assessment practices that are used by organizations in their quest to put meaning to quality.

Self-assessment is clearly defined in organizations as a management issue aimed at increasing quality awareness, driving the quality improvement activities, and improving business performance. Many survey respondents are familiar with the Baldrige Award model (and others) and the underlying criteria that help to assess the organization. Many organizations have adopted these and other criteria for their internal self-assessment process.

Specifically, this study found the benefits of self-assessment to include the following:

  • Providing strategic direction on the dimensions of quality
  • Helping to align quality processes and activities throughout an organization by defining quality in terms of principles that allows individual operating units of large organizations to use it as a means of setting goals and monitoring these
  • Developing short- to medium-term targets for the organization and various business units
  • Linking quality to the strategic planning process
  • Serving to focus attention on the means of achieving better organizational performance

Most interestingly, those utilizing self-assessment reported greater returns on sales than those firms not utilizing self-assessment.

Focus on the Classroom
Developing Control Charts and Illustrating Type I and Type II Errors

Elisabeth J. Umble, Texas A&M University, and M. Michael Umble, Baylor University

In this article, Umble and Umble describe an entertaining class exercise that effectively illustrates the development of control charts, the impact of various levels of significance, and the two types of errors that can be made when using control charts. Actual sample data drawn from a sampling bowl in an in-class simulation are used to construct control charts for proportion defective. Then, how subsequent draws from the sampling bowl can result in either a correct decision that the process remains in control or a Type I error is illustrated.

The proportion defective in the bowl is increased to simulate a process mean shift and illustrate how the new data can result in a correct decision that the process is out of control or a Type II error. This exercise also generates valuable insights about the tradeoffs between significance levels and Type I and Type II errors.

Specifying the control limits is one of the critical decisions that must be made in designing a control chart. Clearly, there is a tradeoff between committing a Type I and a Type II error. By moving the control limits further from the estimated process mean, the authors decrease the risk of a Type I error–the risk of a point falling beyond the control limits, indicating an out-of-control condition, when no assignable (or specific “findable”) cause is present. However, widening the control limits also increases the risk of a Type II error–the risk of a point falling between the control limits when the process is really out of control. If tighter control limits are used, the opposite effect occurs: The risk of Type I error increases while the risk of Type II error decreases.

HOMBSAT–An Instrument for Measuring Home-Buyer Satisfaction

Zeljko M. Torbica, Florida International University, and Robert C. Stroh, University of Florida

Improving customer satisfaction has been identified as one of the most important challenges facing businesses. Keeping customers satisfied is rapidly becoming the way companies differentiate themselves from competitors. In this article, Torbica and Stroh describe the development of a 51-item instrument, called HOMBSAT, for assessing home-buyer satisfaction. A model was proposed describing home-buyer satisfaction as a three-dimensional composite of satisfaction with design, house, and service. The instrument was found to be both valid and reliable. The paper concludes with a discussion of potential applications of the instrument. These include the following:

  • Home builders could use HOMBSAT scores to assess home-buyer satisfaction with each of the three satisfaction dimensions.
  • HOMBSAT can provide a total company home-buyer satisfaction in the form of an average score across all three dimensions (DESIGN, HOUSE, and SERVICE).
  • HOMBSAT is most valuable when it is used periodically to track home-buyer satisfaction trends.
  • Using HOMBSAT scores would allow home builders to track their improvement in delivering home-buyer satisfaction over the coming years.
  • HOMBSAT can be used by home builders to track and make comparisons among the levels of home-buyer satisfaction provided by different divisions, projects, or in different geographic locations.
  • HOMBSAT can be used to determine the relative importance of the three dimensions (house, design, and service) in influencing home buyers overall satisfaction.

Benchmarking the Postgraduate Admission Process

T. Fiekers, Research Institute Technology and Work, University of Kaiserslautern; B. G. Dale, Manchester School of Management, UMIST; D. A. Littler, Manchester School of Management, UMIST; and W. Voß, Research Institute Technology and Work, University of Kaiserslautern

This article reports the main findings of a benchmarking study on the postgraduate admission process of higher education institutions. The project has involved three departments at the University of Manchester Institute of Science and Technology (UMIST), eight specialist master’s programs within the Manchester School of Management (MSM), and five other universities in the United Kingdom, Germany, Hong Kong, and Spain. The work was undertaken as part of a European Union Leonardo da Vinci-founded project. The first four steps of the benchmarking process have been completed, namely the selection of the subject and the partners, and the collection and analysis of data. While the process is not yet complete it is felt that important lessons are worth reporting. These include the applicability and feasibility of benchmarking to higher education, and the suitability of the various types of benchmarking, in particular internal benchmarking to this environment.


From the application of the first four steps of Camp’s (1989) benchmarking concept to the postgraduate admission process the following lessons have been learned with regard to its applicability to higher education.

  • In general, the methodology of best practice benchmarking is applicable to higher education. The application is time-consuming and, even though the concept is simple, practice is needed to utilize it for maximum success. Completing these first four steps has taken six months.
  • The project has attracted attention from all levels of UMIST from the Graduate School Office staff and tutors to the dean of UMIST. However, a climate of continuous improvement–one of the success factors of best practice benchmarking–is not yet adequately established in higher education and this puts constraints on the implementation of findings. The drive to put into place changes to processes tends to be dependent on the motivation of individuals in a system in which people can have different and often conflicting objectives.
  • A set of indicators was introduced on the basis of which performance could be evaluated and compared. There are, however, other important areas of higher education, such as lecturing and research, where performance cannot be measured as easily as the postgraduate admission process. Therefore, other benchmarking studies focusing on different processes of a university might fail because of the lack of agreement, understanding, and acceptance of the selected key process indicators.
  • When performance indicators consist of subperformance indicators, it is necessary to select and use weightings, no matter how subjective. Otherwise, subconsciously, all subperformance indicators tend to be weighted equally. As this weighting has an impact on the outcome, it needs to be agreed to at the stage when the indicators are selected (that is, data gathering and analysis); thereby preventing any potential bias in weightings chosen.
  • There is a high diversity of practices within a university, which justifies internal benchmarking as a first source of performance improvement. This facilitated the identification of departments displaying best practices. However, considerable differences in departmental policy, structure, market conditions, and background have tended to limit the transfer and application of these practices. Nevertheless, internal benchmarking was crucial to staff becoming familiar with the methodology of benchmarking, the process under study, and identifying opportunities for improvement.
  • The findings that emerged from competitive benchmarking have been more beneficial than the internal analysis. However, increasing “friendly” competition in the UK higher education market, use of performance measures by the government to allocate resources to universities, and the lack of history in information sharing have the potential to undermine this type of benchmarking.

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