Quality Measurement

last edited by: Samer Alhmdan on Mar 29, 2018 2:43 AM login/register to edit this page

Contents
1 Applications
2 Procedures
3 Instructions
4 References

A set of techniques, used on a continual basis, that measures performance in terms of customer needs and satisfiers. Quality Measurement is concerned with the perception levels of customers and how to use those measurements in the design of a product or service and/or to track towards the achievement of activity goals.

Applications

  • To identify the indicators needed to measure customers' perception of the enterprise, in terms of quality or performance.
  • To quantify product-oriented indicators, in order to determine fitness for use characteristics (e.g., reliability).
  • To establish a measurement process that is consistent with measuring objectives, targets, and goals set out in the reengineered value stream or redesigned process.

Procedures

  1. Establish the framework for measurement.
  2. Select the appropriate indicators, consistent with the value stream, process, product or activity.
  3. Use the appropriate cross-referenced techniques and tools to collect and analyze the data.
  4. Make decisions based upon the analysis of the data.

Instructions

To enable continual improvement, it is important that the appropriate indicators are selected and that they can be measured. Activity profiles should be reviewed to identify the measures required for the activity. These measures may address customer needs, problems, error rates, or other satisfaction or performance indicators. All measures should be defined in such a way that facilitates repeatability and consistency. Sometimes referred to as "macro" indicators, the following list may be included:

  • number of sales
  • number of customers who are repeat buyers
  • number of customers who purchase our other products
  • number of complaints
  • response time
  • response effectiveness
  • number of customers who complained that remained as customers
"Micro" indicators can also be collected to measure service-oriented variables. A "service," in basic terminology, is nothing more than a simple action that becomes a customer's experience. This experience translates into a customer perception. Focus on the customer's perception of the business. The absolute measure of service quality is the customer's perception of that service. Create a subjective scale that measures this perception. In addition to this subjective measuring system, establish the means to measure your performance against satisfying or not satisfying these customer perceptions. (See Customer Needs Analysis and/or Customer Satisfier Analysis.) It is also important to measure against the achievement of the goals set for the new, redesigned activities. Appropriate indicators should be tracked to measure these goals. Prior to selecting an appropriate data collection technique, identify the "type" of measurement data. There are two basic types: attribute data and variable data.

Attribute data should be used, if you want to provide either/or or yes/no (binary decisions) information. For example, "Is a department below budget?" The answer, either yes or no, is an example of attribute data. Attribute data should be collected in large volumes only. Variable data should be used to understand trends or to provide a more detailed history. In the example above, the difference between budget and actual, and the trend over time, represents variable data. Variable data involves collecting numeric values that quantify measurement and, therefore, requires smaller samples. Other examples of variable data include costs, number of hours, etc. Quality function deployment, focus groups, surveys, questionnaires, or sampling techniques can be used to collect the needed data (see Quality Function Deployment, Focus Groups, Questionnaires / Customer Surveys). Pareto, or other analysis techniques, can be used to analyze the data collected (see Pareto Analysis). Lastly, to improve effective measurement over time, it will be important to test for measurement bias (see Measurement Bias Analysis).

To enable effective decisions regarding the results of the analysis, it is important to know the characteristics of the measurements themselves. For example, if there is a 90% fulfillment rate target for meeting a customer need, it is important to determine if this is good enough. Who are the people affected by the 10% failure rate? If they are customers, did they stay customers? What is the price of losing a customer? How were their needs satisfied? What is the cost to measure the characteristics of that 10%? If this were a scenario for a car dealership, the cost of losing a customer might be $140,000 over the lifetime of the car. Is that acceptable? Does this 10% failure rate translate to a compounded customer base over time? These are all questions that should be addressed during analysis whenever a satisfier or expectation is not met, and to seek continual improvement.

References

  1. Thomas Berry. Managing the Total Quality Transformation. McGraw-Hill, Inc., 1990.
  2. Richard C. Whiteley. The Customer Driven Company. Addison-Wesley Publishing Company, Inc., 1991.


last edited by: Samer Alhmdan on Mar 29, 2018 2:43 AM login/register to edit this page


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