Chapter 10 Quality Improvement

Traditional Economic Model of Quality of Conformance Total cost Cost due to nonconformance Cost of quality assurance 100% “optimal level” of quality

Modern Economic Model of Quality of Conformance Total cost Cost due to nonconformance Cost of quality assurance 100%

Problem Solving Problem: any deviation between what “should be” and what “is” that is important enough to need correcting Structured Semistructured Ill-structured Problem Solving: the activity associated with changing the state of what “is” to what “should be”

Quality Problem Types Conformance problems Unstructured performance problems Efficiency problems Product design problems Process design problems

Problem Solving Process Redefining and analyzing the problem Generating ideas Evaluating and selecting ideas Implementing ideas

The Deming Cycle Act Plan Study Do

Juran’s Improvement Program Proof of the need Project identification Organization for breakthrough Diagnostic journey Remedial journey Holding the gains

Bethesda Hospital Model Start Review current situation Describe process Explore cause theories Collect and analyze data Improvement? Generate solutions Plan Do Check Improvement? Act no no yes yes

Crosby Quality Improvement Program Management commitment Quality improvement team Quality measurement Cost of quality evaluation Quality awareness Corrective action Zero defect committee Supervisor training Zero defects day Goal setting Error cause removal Recognition Quality councils Do it over again

Creative Problem Solving Mess Finding – identify symptoms Fact Finding – gather data; operational definitions Problem Finding – find the root cause Idea Finding – brainstorming Solution Finding – evaluate ideas and proposals Implementation – make the solution work

Six-Sigma Quality Ensuring that process variation is half the design tolerance (Cp = 2.0) while allowing the mean to shift as much as 1.5 standard deviations.

Six-Sigma Metrics Defects per unit (DPU) = number of defects discovered  number of units produced Defects per million opportunities (dpmo) = DPU  1,000,000  opportunities for error

k-Sigma Quality Levels Six sigma results in at most 3.4 defects per million opportunities

Six-Sigma Implementation Emphasize dpmo as a standard metric Provide extensive training Focus on on corporate sponsor support Create qualified process improvement experts Ensure identification of appropriate metrics Set stretch objectives

GE’s Six-Sigma Problem Solving Approach Define Measure Analyze Improve Control DMAIC

Tools for Six-Sigma and Quality Improvement Elementary statistics Advanced statistics Product design and reliability Measurement Process control Process improvement Implementation and teamwork

The Seven QC Tools Flowcharts Check sheets Histograms Cause-and-effect diagrams Pareto diagrams Scatter diagrams Control charts

Flowcharts Shows unexpected complexity, problem areas, redundancy, unnecessary loops, and where simplification may be possible Compares and contrasts actual versus ideal flow of a process Allows a team to reach agreement on process steps and identify activities that may impact performance Serves as a training tool

Run Chart Monitors performance of one or more processes over time to detect trends, shifts, or cycles Allows a team to compare performance before and after implementation of a solution to measure its impact Focuses attention on truly vital changes in the process * * * * * *

Control Chart Focuses attention on detecting and monitoring process variation over time Distinguishes special from common causes of variation Serves as a tool for on-going control Provides a common language for discussion process performance * * * * * *

Check Sheet Creates easy-to-understand data Builds, with each observation, a clearer picture of the facts Forces agreement o the definition of each condition or event of interest Makes patterns in the data become obvious quickly xx xxxxxx x

Pareto Diagram Helps a team focus on causes that have the greatest impact Displays the relative importance of problems in a simple visual format Helps prevent “shifting the problem” where the solution removes some causes but worsens others

Histogram Displays large amounts of data that are difficult to interpret in tabular form Shows centering, variation, and shape Illustrates the underlying distribution of the data Provides useful information for predicting future performance Helps to answer the question “Is the process capable of meeting requirements?

Cause and Effect Diagram Enables a team to focus on the content of a problem, not on the history of the problem or differing personal interests of team members Creates a snapshot of collective knowledge and consensus of a team; builds support for solutions Focuses the team on causes, not symptoms Effect Cause

Scatter Diagram Supplies the data to confirm a hypothesis that two variables are related Provides both a visual and statistical means to test the strength of a relationship Provides a good follow-up to cause and effect diagrams * * * * *

Poka-Yoke (Mistake-Proofing) An approach for mistake-proofing processes using automatic devices or methods to avoid simple human or machine error, such as forgetfulness, misunderstanding, errors in identification, lack of experience, absentmindedness, delays, or malfunctions

Poka-Yoke Examples (from John Grout’s Poka-Yoke Page)