Encyclopedia of Leadership

7.7

TOTAL QUALITY LEADERSHIP OVERVIEW

Inspired by William Deming, Joseph Juran, Peter Scholtes, William Conway, and Amoco Progress program designers.

The quality movement has had great impact on leadership and organizations. Arising out of

programs that focused on keeping production processes within control limits (such as

Statistical Quality Control or SQC), the movement has expanded. It now includes improving

systems, as well as designing leadership processes, to support Total Quality organizations.

Quality offshoots have included quality circles, the International Organization for

Standardization (ISO), and benchmarking. The purpose of this tool is to provide an overview

of some key elements of leadership in these quality areas.

A quality measure defines a critical measure of success. Examples are satisfied customers,

shipments made within 24 hours, money saved, and calls answered within one minute. A qual-

ity measure can also define problems (e.g., errors, complaints, downtime, rejects), or it can

define variables (e.g., time, cost, frequency, type, counts, response time). The creative piece of

Statistical Quality leadership is deciding which quality measures to use to analyze a process.

Peter Scholtes has broadly summarized the key concepts of quality improvement.

The table that follows includes brief descriptions of commonly used specific quality mea-

sures. We caution against isolating use of these measures from the principles in the application

section of this tool, because those principles form the underpinning for the measures. Please

keep in mind that the principles need to come first!

SECTION 7 TOOLS FORPROBLEMSOLVING, DECISIONMAKING, ANDQUALITY 223

KEYQUALITYIMPROVEMENTCONCEPTS

• Processes and Systems


• Customers and Suppliers


• Quality
  
  
  • of target values and features
  
  
  • of execution
  
  
  
  


• Teams and Teamwork


• Scientific Approach


• Complexity
  
  
  1. Mistakes or defects
  
  
  2. Breakdowns or delays
  
  
  3. Inefficiencies
  
  
  4. Excessive variation
  
  
  
  


• Variation
  
  
  • common causes
  
  
  • special causes
  
  
  
  


• Statistically Designed Experiments

Reprinted with permission from Scholtes, Peter. The Team Handbook.Joiner Associates, 1988, page 2-2.