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Quality ManagementQuality ManagementQuality ManagementQuality Management
QualityQuality
• PMI’s quality philosophy summarized by– Definition of quality
– No gold-plating
– Prevention over inspection
• PMI’s quality philosophy summarized by– Definition of quality
– No gold-plating
– Prevention over inspection
PMI Quality DefinitionPMI Quality Definition
• QUALITY IS CONFORMANCE TO REQUIREMENTS AND FITNESS OF USE
• QUALITY IS CONFORMANCE TO REQUIREMENTS AND FITNESS OF USE
No Gold-platingNo Gold-plating
• Don’t give the customer extras• Adds no-value to the project because
– it is beyond the scope – Could cost more– May be based on impressions not
requests
• Don’t give the customer extras• Adds no-value to the project because
– it is beyond the scope – Could cost more– May be based on impressions not
requests
Prevention over inspectionPrevention over inspection
– Quality must be planned NOT inspected– Quality must be planned NOT inspected
Six SigmaSix Sigma
Originally developed by Motorola, Originally developed by Motorola, Six Sigma refers to an extremely Six Sigma refers to an extremely high measure of process capabilityhigh measure of process capability
A Six Sigma capable process will A Six Sigma capable process will return no more than 3.4 defects per return no more than 3.4 defects per million operations (DPMO)million operations (DPMO)
Highly structured approach to Highly structured approach to process improvementprocess improvement
Six sigmaSix sigma
Six SigmaSix Sigma
1.1. Define critical outputs Define critical outputs and identify gaps for and identify gaps for improvementimprovement
2.2. Measure the work and Measure the work and collect process datacollect process data
3.3. Analyze the dataAnalyze the data
4.4. Improve the processImprove the process
5.5. Control the new process to Control the new process to make sure new performance make sure new performance is maintainedis maintained
DMAIC ApproachDMAIC Approach
Tools Of TQMTools Of TQM Check SheetsCheck Sheets
Scatter Diagrams Scatter Diagrams
Cause-and-Effect DiagramCause-and-Effect Diagram
Pareto ChartsPareto Charts
Flow ChartsFlow Charts
HistogramsHistograms
Statistical Process Control (SPC)Statistical Process Control (SPC)
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Defect 1 2 3 4 5 6 7 8
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Seven Tools for TQMSeven Tools for TQM
(a)(a) Check Sheet: An organized method of Check Sheet: An organized method of recording datarecording data
Figure 6.5Figure 6.5
Seven Tools for TQMSeven Tools for TQM
(b)(b) Scatter Diagram: A graph of the value Scatter Diagram: A graph of the value of one variable vs. another variableof one variable vs. another variable
AbsenteeismAbsenteeism
Pro
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Figure 6.5Figure 6.5
Seven Tools for TQMSeven Tools for TQM
(c)(c) Cause and Effect Diagram: A tool that Cause and Effect Diagram: A tool that identifies process elements (causes) that identifies process elements (causes) that might effect an outcomemight effect an outcome
Figure 6.5Figure 6.5
CauseCause
MaterialsMaterials MethodsMethods
ManpowerManpower MachineryMachinery
EffectEffect
Seven Tools for TQMSeven Tools for TQM
(d)(d) Pareto Charts: A graph to identify and plot Pareto Charts: A graph to identify and plot problems or defects in descending order of problems or defects in descending order of frequencyfrequency
Figure 6.5Figure 6.5
Fre
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Pe
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AA BB CC DD EE
Pareto ChartPareto Chart
Seven Tools for TQMSeven Tools for TQM
(e)(e) Flow Charts (Process Diagrams): A chart Flow Charts (Process Diagrams): A chart that describes the steps in a processthat describes the steps in a process
Figure 6.5Figure 6.5
Operator takes phoneorder.
Orders waitto be pickedup.
Supervisorinspectsorders.
Order isfulfilled.
Order waitsfor sales rep.
Is ordercomplete?
Yes
No
Orders aremoved tosupervisor’sin-box.
Orderswait forsupervisor.
Flow ChartsFlow Charts
Seven Tools for TQMSeven Tools for TQM
(f)(f) Histogram: A distribution showing the Histogram: A distribution showing the frequency of occurrence of a variablefrequency of occurrence of a variable
Figure 6.5Figure 6.5
DistributionDistribution
Repair time (minutes)Repair time (minutes)
Fre
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Seven Tools for TQMSeven Tools for TQM
(g)(g) Statistical Process Control Chart: A chart with Statistical Process Control Chart: A chart with time on the horizontal axis to plot values of a time on the horizontal axis to plot values of a statisticstatistic
Figure 6.5Figure 6.5
Upper control limitUpper control limit
Target valueTarget value
Lower control limitLower control limit
TimeTime
Variability is inherent in every processNatural or common causesSpecial or assignable causes
Provides a statistical signal when assignable causes are present
Detect and eliminate assignable causes of variation
Variability is inherent in every processNatural or common causesSpecial or assignable causes
Provides a statistical signal when assignable causes are present
Detect and eliminate assignable causes of variation
Statistical Process Control (SPC)Statistical Process Control (SPC)
Natural VariationsNatural Variations
Also called common causesAlso called common causes
Affect virtually all production processesAffect virtually all production processes
Expected amount of variationExpected amount of variation
Output measures follow a probability distributionOutput measures follow a probability distribution
For any distribution there is a measure of central For any distribution there is a measure of central
tendency and dispersiontendency and dispersion
If the distribution of outputs falls within acceptable If the distribution of outputs falls within acceptable
limits, the process is said to be “in control”limits, the process is said to be “in control”
Assignable VariationsAssignable Variations
Also called special causes of variationAlso called special causes of variation
Generally there is some change in the processGenerally there is some change in the process
Variations that can be traced to a specific reasonVariations that can be traced to a specific reason
The objective is to discover when assignable causes The objective is to discover when assignable causes
are presentare present
Eliminate the bad causesEliminate the bad causes
Incorporate the good causesIncorporate the good causes
SamplesSamples
To measure the process, we take To measure the process, we take samples and analyze the sample samples and analyze the sample statistics following these stepsstatistics following these steps
(a)(a) Samples of the Samples of the product, say five product, say five boxes of cereal boxes of cereal taken off the filling taken off the filling machine line, vary machine line, vary from each other in from each other in weightweight Fre
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WeightWeight
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Each of these Each of these represents one represents one sample of five sample of five
boxes of cerealboxes of cereal
Figure S6.1Figure S6.1
SamplesSamples
(b)(b) After enough After enough samples are samples are taken from a taken from a stable process, stable process, they form a they form a pattern called a pattern called a distributiondistribution
The solid line The solid line represents the represents the
distributiondistribution
Fre
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Fre
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WeightWeightFigure S6.1Figure S6.1
SamplesSamples
(c)(c) There are many types of distributions, including There are many types of distributions, including the normal (bell-shaped) distribution, but the normal (bell-shaped) distribution, but distributions do differ in terms of central distributions do differ in terms of central tendency (mean), standard deviation or tendency (mean), standard deviation or variance, and shapevariance, and shape
WeightWeight
Central tendencyCentral tendency
WeightWeight
VariationVariation
WeightWeight
ShapeShape
Fre
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Fre
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Figure S6.1Figure S6.1
SamplesSamples
(d)(d) If only natural If only natural causes of causes of variation are variation are present, the present, the output of a output of a process forms a process forms a distribution that distribution that is stable over is stable over time and is time and is predictablepredictable
WeightWeightTimeTimeF
req
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req
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Figure S6.1Figure S6.1
SamplesSamples
(e)(e) If assignable If assignable causes are causes are present, the present, the process output is process output is not stable over not stable over time and is not time and is not predicablepredicable
WeightWeightTimeTimeF
req
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Figure S6.1Figure S6.1
Control ChartsControl Charts
Constructed from historical data, the Constructed from historical data, the purpose of control charts is to help purpose of control charts is to help distinguish between natural variations and distinguish between natural variations and variations due to assignable causesvariations due to assignable causes
Types of DataTypes of Data
Characteristics that can
take any real value
May be in whole or in
fractional numbers
Continuous random
variables
VariablesVariables AttributesAttributes
Defect-related Defect-related
characteristics characteristics
Classify products as Classify products as
either good or bad or either good or bad or
count defectscount defects
Categorical or discrete Categorical or discrete
random variablesrandom variables
Control Charts for VariablesControl Charts for Variables
For variables that have continuous For variables that have continuous dimensionsdimensions Weight, speed, length, strength, etc.Weight, speed, length, strength, etc.
x-charts are to control the central x-charts are to control the central tendency of the processtendency of the process
R-charts are to control the dispersion of R-charts are to control the dispersion of the processthe process
These two charts must be used togetherThese two charts must be used together
Control ChartControl Chart
Patterns in Control ChartsPatterns in Control Charts
Normal behavior. Normal behavior. Process is “in control.”Process is “in control.”
Upper control limitUpper control limit
TargetTarget
Lower control limitLower control limit
Figure S6.7Figure S6.7
Upper control limitUpper control limit
TargetTarget
Lower control limitLower control limit
Patterns in Control ChartsPatterns in Control Charts
One plot out above (or One plot out above (or below). Investigate for below). Investigate for cause. Process is “out cause. Process is “out of control.”of control.”
Figure S6.7Figure S6.7
Upper control limitUpper control limit
TargetTarget
Lower control limitLower control limit
Patterns in Control ChartsPatterns in Control Charts
Trends in either Trends in either direction, 5 plots. direction, 5 plots. Investigate for cause of Investigate for cause of progressive change.progressive change.
Figure S6.7Figure S6.7
Upper control limitUpper control limit
TargetTarget
Lower control limitLower control limit
Patterns in Control ChartsPatterns in Control Charts
Two plots very near Two plots very near lower (or upper) lower (or upper) control. Investigate for control. Investigate for cause.cause.
Figure S6.7Figure S6.7
Upper control limitUpper control limit
TargetTarget
Lower control limitLower control limit
Patterns in Control ChartsPatterns in Control Charts
Run of 5 above (or Run of 5 above (or below) central line. below) central line. Investigate for cause. Investigate for cause. Figure S6.7Figure S6.7
Upper control limitUpper control limit
TargetTarget
Lower control limitLower control limit
Patterns in Control ChartsPatterns in Control Charts
Erratic behavior. Erratic behavior. Investigate.Investigate.
Figure S6.7Figure S6.7