3 G’ s3 G’ s

GENCHI : ACTUAL PLACEGENBUTSU: ACTUAL THING

GENJITSU: ACTUALLY

GENCHI : ACTUAL PLACEGENBUTSU: ACTUAL THING

GENJITSU: ACTUALLY

QC TOOLSQC TOOLS

 • QC Tools are based on statistical

methods and are effective for improving the production process and reducing its defects.

•  The first step in finding the true cause is careful observation of the phenomenon of the defect. After such careful observation, the true cause becomes apparent.

 • QC Tools are based on statistical

methods and are effective for improving the production process and reducing its defects.

•  The first step in finding the true cause is careful observation of the phenomenon of the defect. After such careful observation, the true cause becomes apparent.

Analyzing Problems Using DataAnalyzing Problems Using Data

• Objective of Collecting Data• Data is a guide for our actions. From

data we learn pertinent facts which are useful for problem solving.

• Data is required for controlling and monitoring the process

• Data is required for analysis of non-conformance

• Objective of Collecting Data• Data is a guide for our actions. From

data we learn pertinent facts which are useful for problem solving.

• Data is required for controlling and monitoring the process

• Data is required for analysis of non-conformance

QC TOOLSQC TOOLS

Whenever a problem occurs:   Urgently proceed to the workplace where the fault has occurred.   Ascertain the fault visually.   Observe well the conditions leading to the fault.Listen attentively to the opinion of the operator.

Whenever a problem occurs:   Urgently proceed to the workplace where the fault has occurred.   Ascertain the fault visually.   Observe well the conditions leading to the fault.Listen attentively to the opinion of the operator.

QC TOOLSQC TOOLS

QC tools lend objectivity and accuracy to observation. The principles of statistical way of thinking are:    Give greater importance to facts.   Do not express facts in terms of senses or ideas. Use figures derived from specific observed results.   Go to the actual site where problem is occurring, observe the actual object, and make measurements on that object actually by yourself.    

QC tools lend objectivity and accuracy to observation. The principles of statistical way of thinking are:    Give greater importance to facts.   Do not express facts in terms of senses or ideas. Use figures derived from specific observed results.   Go to the actual site where problem is occurring, observe the actual object, and make measurements on that object actually by yourself.    

QC TOOLSQC TOOLS

Observational results, accompanied as they are by error and variation, are part of a hidden whole. Finding that hidden whole is observation’s ultimate goal.Accept regular tendency, which appears in a large number of observational results as a reliable information.       

Observational results, accompanied as they are by error and variation, are part of a hidden whole. Finding that hidden whole is observation’s ultimate goal.Accept regular tendency, which appears in a large number of observational results as a reliable information.       

QC TOOLSQC TOOLS

Seven QC Tools are useful for analyzing problems using data. These seven tools are: Check Sheet HistogramScatter Diagram GraphsPareto DiagramCause and Effect DiagramControl Charts       

Seven QC Tools are useful for analyzing problems using data. These seven tools are: Check Sheet HistogramScatter Diagram GraphsPareto DiagramCause and Effect DiagramControl Charts       

CHECK SHEETSCHECK SHEETS

•SIMPLIFYING DATA COLLECTION•ENSURES THAT NO ITEMS ARE OMITTED WHEN INSPECTING

•STARTING POINT FOR PROBLEM SOLVING

•USEFUL FOR PROCESS CONTROL

•SIMPLIFYING DATA COLLECTION•ENSURES THAT NO ITEMS ARE OMITTED WHEN INSPECTING

•STARTING POINT FOR PROBLEM SOLVING

•USEFUL FOR PROCESS CONTROL

CHECK SHEETSCHECK SHEETS•Check Sheet•Check Sheet

Product: Date:No. Inspected Lot no.Inspector’s name

Type of defect Number of Total

CracksScratchBlowholesDimensionSurface finish

7

HISTOGRAMSHISTOGRAMS

•PLOTTING THE SHAPE OF A DISTRIBUTION

•COMPARING THE DISTRIBUTION WITH SPECIFICATIONS

•USEFUL FOR QUALITY, COST AND DELIVERY IMPROVEMENT

•USEFUL FOR PROCESS CONTROL

•PLOTTING THE SHAPE OF A DISTRIBUTION

•COMPARING THE DISTRIBUTION WITH SPECIFICATIONS

•USEFUL FOR QUALITY, COST AND DELIVERY IMPROVEMENT

•USEFUL FOR PROCESS CONTROL

HistogramsHistograms

• Histogram• Histogram

LSLUSL

Diameter

Frequency

SCATTER DIAGRAMSSCATTER DIAGRAMS

EXAMPLES:POSITIVE CORRELATION -OVERTIME VS ERRORSHEIGHT VS WEIGHTNEGATIVE CORRELATION-EXTERNAL TEMPERATURE VS GAS BILLQUALITY VS CUSTOMER COMPLAINTSNO CORRELATIONHEIGHT VS MARKS IN BOARD EXAMS

EXAMPLES:POSITIVE CORRELATION -OVERTIME VS ERRORSHEIGHT VS WEIGHTNEGATIVE CORRELATION-EXTERNAL TEMPERATURE VS GAS BILLQUALITY VS CUSTOMER COMPLAINTSNO CORRELATIONHEIGHT VS MARKS IN BOARD EXAMS

Scatter DiagramsScatter Diagrams

• Scatter Diagram• Scatter Diagram

Temperature

% defective

. .

…

..

…

.

. .

. .

.. .. .. .. ..

.. .. .. .. .. . . .. ....

NegativeCorrelation

Zero correlation

PositiveCorrelation

PARETO CHARTPARETO CHART•BAR CHART•RANKS PROBLEMS IN DESCENDING ORDER OF COST OF FREQUENCY

•SEPARATES “VITAL FEW” FROM THE “USEFUL MANY”

•SINGLES OUT THE REALLY SERIOUS PROBLEMS FROM AMONG ALL THE LESSER ONES

•HIGHLIGHTS WHERE YOUR PROBLEM SOLVING ACTIVITY WILL MAKE THE GREATEST IMPACT.

•BAR CHART•RANKS PROBLEMS IN DESCENDING ORDER OF COST OF FREQUENCY

•SEPARATES “VITAL FEW” FROM THE “USEFUL MANY”

•SINGLES OUT THE REALLY SERIOUS PROBLEMS FROM AMONG ALL THE LESSER ONES

•HIGHLIGHTS WHERE YOUR PROBLEM SOLVING ACTIVITY WILL MAKE THE GREATEST IMPACT.

PARETO CHARTPARETO CHART•Pareto Diagram •Pareto Diagram

No.Of

ProblemItems

Problem Type

CumulativePercentage

CAUSE AND EFFECT DIAGRAMS

CAUSE AND EFFECT DIAGRAMS

•DESIGNED TO REPRESENT A MEANINGFUL RELATIONSHIP BETWEEN AN EFFECT AND ALL ITS POSSIBLE CAUSES.

•PICKING UP AND ARRANGING ALL POSSIBLE CAUSES WITHOUT ANY OMISSIONS

•USED TO INVESTIGATE DEFECT CAUSE AS WELL AS TO INVESTIGATE AN IN-CONTROL PROCESS

•ALSO KNOWN AS FISH BONE OR ISHIKAWA DIAGRAM.

•DESIGNED TO REPRESENT A MEANINGFUL RELATIONSHIP BETWEEN AN EFFECT AND ALL ITS POSSIBLE CAUSES.

•PICKING UP AND ARRANGING ALL POSSIBLE CAUSES WITHOUT ANY OMISSIONS

•USED TO INVESTIGATE DEFECT CAUSE AS WELL AS TO INVESTIGATE AN IN-CONTROL PROCESS

•ALSO KNOWN AS FISH BONE OR ISHIKAWA DIAGRAM.

CAUSE AND EFFECT DIAGRAMS

CAUSE AND EFFECT DIAGRAMS

•Main bone•Main bone

Problem or

Effect

Man Machine

Material MethodEnvironment

CONTROL CHARTS AND GRAPHS

CONTROL CHARTS AND GRAPHS

CONTROL CHARTSCHECKING WHETHER OR NOT A PROCESS IS IN CONTROL.

FACILITATING PROACTIVE ACTIONS FOR QUALITY IMPROVEMENT.GRAPHSMAKING DATA VISUAL

EASY FOR UNDERSTANDING AT THE WORK PLACE.

CONTROL CHARTSCHECKING WHETHER OR NOT A PROCESS IS IN CONTROL.

FACILITATING PROACTIVE ACTIONS FOR QUALITY IMPROVEMENT.GRAPHSMAKING DATA VISUAL

EASY FOR UNDERSTANDING AT THE WORK PLACE.

GraphsGraphs

• Line Graph• Bar graph

• Line Graph• Bar graph

Defect %

Production

Mon Tue Wed Thu

GraphsGraphs

• Pie Chart• Pie ChartScratch

Size error

hardness

Blowholes

Control ChartControl Chart

• Control chart• Control chart

CL

LCL

UCL

Control ChartControl Chart

• SPC is defined as the application of statistical methods to the measurement and analysis of variation in any process.

• Process variations are traceable to two kinds of causes:

• Chance (or random) causes which are inherent in the process

• Assignable (or special) causes which cause excessive variation.

• SPC is defined as the application of statistical methods to the measurement and analysis of variation in any process.

• Process variations are traceable to two kinds of causes:

• Chance (or random) causes which are inherent in the process

• Assignable (or special) causes which cause excessive variation.

Control ChartControl Chart

• A process that is operating without assignable causes of variation is said to be “in a state of statistical control”.

• X bar and R chart• UCL = X ± A2R• LCL = X ± A2R• CL = X

• A process that is operating without assignable causes of variation is said to be “in a state of statistical control”.

• X bar and R chart• UCL = X ± A2R• LCL = X ± A2R• CL = X

Control ChartControl Chart

• p chart (control chart for defectives)

• UCL = p ± 3 * SQRT ( p(1-p)/n)• LCL = p ± 3 * SQRT ( p(1-p)/n)• CL = p

• p chart (control chart for defectives)

• UCL = p ± 3 * SQRT ( p(1-p)/n)• LCL = p ± 3 * SQRT ( p(1-p)/n)• CL = p

Control ChartControl Chart

• c chart (control chart for defects)• UCL = c + 3 * SQRT (c)• LCL = c - 3 * SQRT ( c)• CL = c

• c chart (control chart for defects)• UCL = c + 3 * SQRT (c)• LCL = c - 3 * SQRT ( c)• CL = c

Process CapabilityProcess Capability

• Cp = (USL – LSL)/6• Cpk =

Min (( USL-Mean)/3s, (Mean – LSL)/3s)

• For traditional quality company, Cp ≥ 1.33

• For six sigma quality company, Cp = 2 and Cpk = 1.5

• Cp = (USL – LSL)/6• Cpk =

Min (( USL-Mean)/3s, (Mean – LSL)/3s)

• For traditional quality company, Cp ≥ 1.33

• For six sigma quality company, Cp = 2 and Cpk = 1.5