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Total Quality Management TQM

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Total Quality Management ­ MGT510
VU
Lecture # 40
STATISTICAL PROCESS CONTROL....CONTD.
SPC learning through examples:
Example # 1 Shooting for Quality:
Mr. Khan observed that in basketball games, his son Ali's free throw percentage averaged between 45
and 50 percent.
Ali's process was simple: Go to the free throw line, bounce the ball four times, aim, and shoot.
To confirm these observations, Ali shot five sets of 10 free throws with an average of 42 percent,
showing little variation among the five sets.
Mr. Khan developed a Cause-and-Effect Diagram to identify the principal cause/s.
After analyzing the diagram and observing his son's process, he believed that the main cause was not
standing in the same place on the free-throw line every time and having an inconsistent focal point.
They developed a new process in which Ali stood at the centre of the line and focused on the middle of
the front part of the rim. The new process resulted in a 36 percent improvement in practice.
Toward the end of the 2004 season, he improved his average to 69 percent in the last three games.
During the 2005 season, Ali averaged 60 percent.
A control chart showed that the process was quite stable.
In the end of 2005, Ali attended a basketball camp where he was advised to change his shooting
technique. This process reduced his shooting percentage during the 2006 season to 50 percent.
However, his father helped him to reinstall his old process, and his percentage returned to its former
level, also improving his confidence SP charting followed by drawing a fishbone helped to find the
special cause of variation and corrected it improved the performance and hence making playing process
under control limits.
Variation in the Outputs is a function of the variation in the Inputs
Y = f (X)
This idea provides some initial guidance for breaking down and understanding sources of variation.
Changes in desired characteristics of the output are a direct result of changes or variation within inputs
to that process. If there are inherent flaws or shortcomings within the process, they will result in
variation in the outputs of the process. We examine all variation within the important input parameters
in order to determine which factor plays the greatest role on variation within the output.
There is a distinct difference between the inherent failures of the system which are random. We will
describe these as common cause based on the fact that we can predict the general area of the outcomes.
However, these types of errors are very different than special cause problems which we can tie into
specific events of conditions. These errors are special cause problems.
Variation in Product (Y) = Function of Process (X) or (Variation in Process)
Y = f (X)
How do we find Variation?
Typically we collect data and use basic TQ tools to view it:
Run chart
Histogram
Pareto ...................OR
We can also summarize this data using more advanced statistical methods:
Averages of smaller groups
Ranges, or dispersions, within these groups ....... and
By combining summary statistics like averages and ranges with Run Charts to create a
very powerful tool -----
164
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Total Quality Management ­ MGT510
VU
Control Charts
Control charts examine information and data typically already collected as a metric or measurement of
process output. We will examine averages and the differences in groups over a period of time in order
to determine what is normal, what is expected, and what is predictable.
Here are some questions that might start the process: How do we currently analyze the problem area?
Could we make more effective use of control charts to learn about the process by looking at the same
information? Can we make the same pieces of data tell us more about the problem than they currently
do? In short, are we getting our money's worth out of our current analysis?
We are motivated to improve the outputs of the process. The big Y's However, we know that variation
in the outputs is a function of the variation in the inputs. As a result, we are draw to focus on these
outputs. This often causes us to concentrate solely on the changes in the outputs without looking at the
changes in the inputs and "adjusting" the process to manipulate the output instead of making real
improvements. We concentrate primarily on the goals and not on how the system can truly perform. If
we instead examine where we expect to perform and driven changes to support where we want the
process to develop, the end result is improvement in the output.
When currently measure our process performance according to some standard. How will we quantify
improvement? How will we know when we are done? Are we operating within our normally expected
process limits? How do these relate to our specification limits? How do we measure the voice of the
customer compared to the voice of the process? These are important questions to ask and answer.
Special vs. Common Causes:
There are distinct differences between actions designed to eliminate special cause and common
cause variation:
Special cause action eliminates a specific isolated event; does not involve a major
process change
Common cause action makes a change in the process that results in a measurable
change in the normal process performance
Common cause variation is inherent to the system. It is the normal variation built into the process.
There is a distinct difference between the inherent failures of the system and those caused by specific
assignable events. We will describe these as common cause based on the fact that we can predict the
general area of the outcomes. However, these types of errors are very different than special cause
problems which we can tie into specific assignable events. These errors are special cause problems.
Common cause variation is inherent to the system. It is the normal variation built into the process.
There is a distinct difference between the inherent failures of the system and those caused by specific
assignable events. We will describe these as common cause based on the fact that we can predict the
general area of the outcomes. However, these types of errors are very different than special cause
problems which we can tie into specific assignable events. These errors are special cause problems.
165
Table of Contents:
  1. OVERVIEW OF QUALITY MANAGEMENT:PROFESSIONAL MANAGERIAL ERA (1950)
  2. TOTAL QUALITY MANAGEMENT AND TOTAL ORGANIZATION EXCELLENCE:Measurement
  3. INTEGRATING PEOPLE AND PERFORMANCE THROUGH QUALITY MANAGEMENT
  4. FUNDAMENTALS OF TOTAL QUALITY AND RATERS VIEW:The Concept of Quality
  5. TOTAL QUALITY MANAGEMENT AND GLOBAL COMPETITIVE ADVANTAGE:Customer Focus
  6. TOTAL QUALITY MANAGEMENT AND PLANNING FOR QUALITY AT OFFICE
  7. LEADERS IN QUALITY REVOLUTION AND DEFINING FOR QUALITY:User-Based
  8. TAGUCHI LOSS FUNCTION AND QUALITY MANAGEMENT
  9. WTO, SHIFTING FOCUS OF CORPORATE CULTURE AND ORGANIZATIONAL MODEL OF MANAGEMENT
  10. HISTORY OF QUALITY MANAGEMENT PARADIGMS
  11. DEFINING QUALITY, QUALITY MANAGEMENT AND LINKS WITH PROFITABILITY
  12. LEARNING ABOUT QUALITY AND APPROACHES FROM QUALITY PHILOSOPHIES
  13. TOTAL QUALITY MANAGEMENT THEORIES EDWARD DEMING’S SYSTEM OF PROFOUND KNOWLEDGE
  14. DEMING’S PHILOSOPHY AND 14 POINTS FOR MANAGEMENT:The cost of quality
  15. DEMING CYCLE AND QUALITY TRILOGY:Juran’s Three Basic Steps to Progress
  16. JURAN AND CROSBY ON QUALITY AND QUALITY IS FREE:Quality Planning
  17. CROSBY’S CONCEPT OF COST OF QUALITY:Cost of Quality Attitude
  18. COSTS OF QUALITY AND RETURN ON QUALITY:Total Quality Costs
  19. OVERVIEW OF TOTAL QUALITY APPROACHES:The Future of Quality Management
  20. BUSINESS EXCELLENCE MODELS:Excellence in all functions
  21. DESIGNING ORGANIZATIONS FOR QUALITY:Customer focus, Leadership
  22. DEVELOPING ISO QMS FOR CERTIFICATION:Process approach
  23. ISO 9001(2000) QMS MANAGEMENT RESPONSIBILITY:Issues to be Considered
  24. ISO 9001(2000) QMS (CLAUSE # 6) RESOURCES MANAGEMENT:Training and Awareness
  25. ISO 9001(2000) (CLAUSE # 7) PRODUCT REALIZATION AND CUSTOMER RELATED PROCESSES
  26. ISO 9001(2000) QMS (CLAUSE # 7) CONTROL OF PRODUCTION AND SERVICES
  27. ISO 9001(2000) QMS (CLAUSE # 8) MEASUREMENT, ANALYSIS, AND IMPROVEMENT
  28. QUALITY IN SOFTWARE SECTOR AND MATURITY LEVELS:Structure of CMM
  29. INSTALLING AN ISO -9001 QM SYSTEM:Implementation, Audit and Registration
  30. CREATING BUSINESS EXCELLENCE:Elements of a Total Quality Culture
  31. CREATING QUALITY AT STRATEGIC, TACTICAL AND OPERATIONAL LEVEL
  32. BIG Q AND SMALL q LEADERSHIP FOR QUALITY:The roles of a Quality Leader
  33. STRATEGIC PLANNING FOR QUALITY AND ADVANCED QUALITY MANAGEMENT TOOLS
  34. HOSHIN KANRI AND STRATEGIC POLICY DEPLOYMENT:Senior Management
  35. QUALITY FUNCTION DEPLOYMENT (QFD) AND OTHER TOOLS FOR IMPLEMENTATION
  36. BASIC SQC IMPROVEMENT TOOLS:TOTAL QUALITY TOOLS DEFINED
  37. HOW QUALITY IS IMPLEMENTED? A DIALOGUE WITH A QUALITY MANAGER!
  38. CAUSE AND EFFECT DIAGRAM AND OTHER TOOLS OF QUALITY:Control Charts
  39. STATISTICAL PROCESS CONTROL (SPC) FOR CONTINUAL QUALITY IMPROVEMENT
  40. STATISTICAL PROCESS CONTROL….CONTD:Control Charts
  41. BUILDING QUALITY THROUGH SPC:Types of Data, Defining Process Capability
  42. AN INTERVIEW SESSION WITH OFFICERS OF A CMMI LEVEL 5 QUALITY IT PAKISTANI COMPANY
  43. TEAMWORK CULTURE FOR TQM:Steering Committees, Natural Work Teams
  44. UNDERSTANDING EMPOWERMENT FOR TQ AND CUSTOMER-SUPPLIER RELATIONSHIP
  45. CSR, INNOVATION, KNOWLEDGE MANAGEMENT AND INTRODUCING LEARNING ORGANIZATION