3G and 7 QC tools -pom
Transcript of 3G and 7 QC tools -pom
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
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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