ASQ - Team and Workplace Excellence Forum

January 1999

Emergency Quality Management

Mission Impossible: The Ultimate Facilitation Challenge

Do You Believe In Magic?

Remembering Root Cause Analysis

Conversations For A Change

by Peter Block

Fox Shows Employees It Has Heart
by Lynn L. Franzoi

Brief Cases
Business News Briefs

Views for a Change

Sites Unseen

The Quality Tool I Never Use

Book Review


The Quality Tool I Never Use

Larry Holpp
Wilton, Conn.

Larry Holpp has spent the last 25 years implementing team-based strategies in a wide variety of organizations to support total quality, worker involvement, union management cooperation efforts and manufacturing innovations. His clients include Alberta Pacific Forest Product, Lowes, Allied-Signal, and General Electric Capital Corporation. He is the author of “Managing Teams.” a textbook on team management published by McGraw-Hill in 1998 and “Team Turbo Training”, a series of tactical training modules that will be published this month.

What is the tool that didn't work for you?
Scatter Diagrams are used when you need to display what happens to one variable when another variable changes in order to test a theory that the two variables are related. The problem is that there are two ways that data could be organized to show possible relationships between variables.

Why didn't it work or why is it useless?
Scatter diagrams demand that we look for examples of continuous data. Continuous data is data that can be divided infinitely. If you are in certain service areas the amount of continuous data might be limited to only cycle time for example. Scatter diagrams demand continuous data like time periods, money and percentages. In businesses dealing in discrete events such as whether or not a loan closed, the number of meetings that took place prior to a decision being made or the results of attitude surveys, scatter diagrams are ineffective.

How would you fix the tool?
For instance, instead of asking whether a deal took too long, ask instead what the cycle time was from one event to another. Another way to get to continuous data is to use percentiles or percentages to reflect the results of the many surveys we conduct. For example, five percent answered in such a manner on question number three and 10 percent answered in a similar manner on question eight. Generally this type of analysis does not provide meaningful data. A third, and somewhat more technical approach to data that is not continuous is made by using logistic regression. This statistical procedure allows us to show relationships between discrete variables such as the relationship between common distribution problems and branch office locations. Logistic regression analyzes the relationship between different variables that don't have to be continuous. You can compare one continuous variable like total sales against regions or individuals. These become verifiable relationships not just correlations.

What words of counsel/warning would you give to someone else before they used the tool?
Make sure you are using continuous data or have transformed discrete data to continuous data. And make certain the client or internal customer considers the data important.

January '99 News for a Change | Email Editor
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