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7qc_tools_173.ppt for 7 QC tools implementation of the quality

1. The document discusses various quality control tools used to identify, analyze, and resolve problems in a systematic manner. It describes tools like check sheets, histograms, Pareto charts, cause-and-effect diagrams, scatter plots, defect concentration diagrams, and control charts. 2. These 7 QC tools are simple but powerful for solving day-to-day work problems by finding solutions systematically. They are widely used by quality circle members. 3. The document explains each tool in detail - their purpose, how they are constructed and interpreted, and how each can help identify issues, root causes, and ensure process control.

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1 7 QC TOOLS
2 Quality A subjective term for which each person has his or her own definition. In technical usage, quality can have two meanings: 1. The characteristics of a product or service that bear on its ability to satisfy stated or implied needs. 2. A product or service free from deficiencies.
3 Can also be termed as ‘A measure of excellence’ Quality
4  Quality - an essential and distinguishing attribute of something.  Attribute - an abstraction belonging to or characteristic of an entity  Appearance, visual aspect - outward or visible aspect of a thing  Attractiveness, attraction - the quality of arousing interest; being attractive or something that attracts;  Uncloudedness, clarity, clearness - the quality of clear water;  Ease, easiness, simplicity - freedom from difficulty or hardship or effort.  Suitability, suitableness - the quality of having the properties that are right for a specific purpose.  Excellence - the quality of excelling.  Characteristic - a distinguishing quality  Simpleness, simplicity - the quality of being simple or uncompounded Meaning of “Quality”
5 Meaning of “Quality” Q = P E P = Performance or result E = Expectations
6 Many people think that quality costs money and adversely effects profits. But these costs are the costs of doing it wrong first time . Quality in the long run results in increased profitability. Quality, Cost & Profit relationship Cost
7 Cost Quality and Profit : Traditional thinking
8 Quality and Profit : Paradigm shift Cost
9 1.Higher production due to improved cycle time and reduced errors and defects 2.Increased use of machine and resources. 3.Improved material use from reduced scrap and rejects 4.Increased use of personnel resources 5.Lower level of asset investments required to support operations. 6.Lower service and support costs for eliminated waste, rework and non value added activities. QUALITY Higher productivity Increased profitability due to : •Larger sales •Lower production costs •Faster turnover Quality and Profit
10 Quality and Profit If the organization does not offer high quality product or service , it will soon go out of business . But just having high quality will not be enough , because your competitors will also have the high quality. To win , companies will need to offer high quality for a lower price than their competitors.This requires organizations to identify and reduce their quality costs High Quality Lower price C2A2C
11  Offer high quality for a lower price than their competitors.  Reduce quality costs  Stop producing defective thru’  Process up-gradation  Improving quality of analysis to identify and eliminate root causes  Taking necessary countermeasure as when required  Usage of right analytical tools  Designing robust problem solving process CHELLANGES
12 PROBLEM SOLVING PROCESS Evaluating solution (6) Implementing solution (5) Selecting & planning solution (4) Generating potential solutions (3) Analysing problem causes (2) Identifying & selecting problem (1) PROBLEM SOLVING PROCESS
13 IDENTIFYING AND SELCTING PROBLEM  Write Statement of the problem(s)  Define Gap Between Actual & target  Prioritize
14 ANALYSIS PROBLEM AND CAUSES  Collect Data  Sort symptoms & Causes (effects)  Brain Storm  Fishbone - cause & effect analysis  Prioritize
15 GENERATING POTENTIAL SOLUTIONS  Brainstorm  Build on each other’s ideas  Analysis potential helps & hinders
16 SELECTING AND PLANNING SOLUTION  Prioritize solutions  Clarify tasks / Action plan  Resource / Costs  Present proposals
17 IMPLEMENTING SOLUTION  Establish controls  Maintain Commitments  Plan Contingencies
18 EVALUATING SOLUTION  Monitor results  Restart Process if necessary
19 7 QC TOOLS Used to identify,analyze and resolve problems Simple but very powerful tools to solve day to day work related problems Find solutions in a systematic manner Widely used by Quality Circle members world over
20 Check sheets  Histograms  Pareto charts  Cause & effect diagram (Ishikawa diagram)  Scatter plot  Defect concentration diagram  Control charts 7 QC TOOLS
21 Check sheets are formats used to collect and organize data Data can be collected easily and concisely Data data is collected on the characteristic of interest. The right data could be captured with all necessary facts included e.g. as when it happened ? how many ? what customer ? CHECK SHEETS
22 Check sheets for production process distribution Defective item check sheet Defect cause check sheet Check sheet for work station evaluation Check sheet for design information accuracy Check sheet for vendor reliability TYPES CHECK SHEETS
23 CHECK SHEETS Type of defects Check Sub-Total Scratch 3 Dent 7 Flow mark 11 Short Shot 2 Total 23 Component name : ABC Date of Production:22-Aug-03
24 Histogram is the “Frequency data” obtained from measurements displaying a peak around a certain value and represented in form of polls The variation of quality characteristics is called “Distribution” Purpose of drawing a Histogram is to understand the “Population” HISTOGRAM Population Sample
25 12 23 43 27 9 0 50 1 160-170 170-180 180-190 190-200 200-210 Histogram for distribution of Center Distance (mm) HISTOGRAM
26 HISTOGRAM A HISTORY OF PROCESS OUT PUT 0 2 4 8 10 12 14 16 6 Frequency 47 48 49 50 51 52 53 54 kg Distribution
27 Based on “80/20” rule (or ABC analysis) Pareto(V.Pareto,an Italian economist) discovered this universal law-80% of anything is attributed to 20% of its causes 80% of the wealth is held by 20% of the population. • 80% of our income goes into 20% of our needs. • 80% of road accidents occur on 20% of the road. • 80% of the absenteeism in a company is due to 20% of workmen “Significant few & in-significant many” PARETO CHART
28 PARETO CHART Pareto analysis begins by ranking problems from highest to lowest in order to fix priority  The cumulative number of problems is plotted on the vertical axis of the graph against the cause/phenomenon Pareto by Causes e.g. Man,Machine,Method etc Pareto by Phenomenon e.g.Quality,Cost,Delivery Tells about the relative sizes of problems indicates an important message about biggest few problems, if corrected, a large % of total problems will be solved
29 63.8 81.4 96.2 100.0 0 500 1000 1500 2000 2500 3000 No of peices 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Cum. Percentage DEFECT QTY 2064.0 567.0 480.0 122.0 CUM % 63.8 81.4 96.2 100.0 SHORT SHOT SILVER SINK MARK FLASH PARETO ANALYSIS
30 CAUSE n EFFECT (FISH BONE) DIAGRAM This diagram (resembles skeleton of a fish) helps to separate out causes from effects and to see problem in its totality It’s a systematic arrangement of all possible causes,generated thru’ brain storming This can be used to : Assist individual / group to see full picture.  Serve as a recording device for ideas generated.  Reveal undetected relationships between causes.  Discover the origin/root cause of a problem  Create a document or a map of a problem which can be posted in the work area.
31 The problem categories considered are : Man, Machine, Method, Materials, Equipments & Environmental. EFFECT MACHINE METHOD ENVIRONMENT MAN MATERIAL EQUIPMENT CAUSE n EFFECT (FISH BONE) DIAGRAM
32 SCATTER DIAGRAM The scatter diagram is used for identifying the relationships and performing preliminary analysis of relationship between any two quality characteristics. Clustering of points indicate that the two characteristics may be related e.g. Increasing in component weight with increase in hold time during plastic injection molding ( + ve co-relation) Increase in toughness components with decreasing injection pressure (-ve co-relation) during molding
33 SCATTER DIAGRAM (POSITIVE CORRELATION) 0 10 20 30 40 50 60 70 80 90 100 0 5 10 XY (Scatter) 1
34 SCATTER DIAGRAM (NEGATIVE CORERLATION) 0 10 20 30 40 50 60 70 80 0 5 10 XY (Scatter) 1
35 SCATTER DIAGRAM (NO CORERLATION) 0 10 20 30 40 50 60 70 80 0 5 10 XY (Scatter) 1
36 DEFECT CONCENTRATION DIAGRAM  This is used to understand the potential defect prone area of the parts produced  The “Concentration Diagram” check sheet carries the diagram of the problematic part,defects whenever observed to be updated in the same using tally marks  Based on the distribution of defects countermeasures are taken at process/system level  This tool is very useful to solve problems like Scratch, Dent,Breakage thru’ handling improvement  For plastic molded parts this tool is used to identify stress points,weak joints,effect of gate shape/position on the quality of parts etc.
37 DEFECT CONCENTRATION DIAGRAM Component name : XYZ Concentration diagram for Scratches produced ion 21-Aug-03 Total no of defective produced is 11 Nos Area of concern
38 Control Chart Quality control charts, are graphs on which the quality of the product is plotted as manufacturing or servicing is actually proceeding. It graphically, represents the output of the process and uses statistical limits and patterns of plot, for decision making  Enables corrective actions to be taken at the earliest possible moment and avoiding unnecessary corrections. The charts help to ensure the manufacture of uniform product or providing consistent services which complies with the specification.
39 Elements of Typical Control Chart 1. Horizontal axis for sample number 2. Vertical axis for sample statistics e.g. mean, range, standard deviation of sample. 3. Target Line 4. Upper control line 5. Upper warning line 6. Lower control line 7. Lower warning line 8. Plotting of sample statistics 9. Line connecting the plotted statistics
40 1 2 3 4 5 Target Lower control line Upper warning line Lower warning line Sample Number Upper control line Lower control line Sample Statistics Elements of Typical Control Chart
41 Interpreting Control Chart The control chart gets divided in three zones. Zone - 1 If the plotted point falls in this zone, do not make any adjustment, continue with the process. Zone - 2 If the plotted point falls in this zone then special cause may be present. Be careful watch for plotting of another sample(s). Zone - 3 If the plotted point falls in this zone then special cause has crept into the system, and corrective action is required.
42 Zones for Mean Control Chart 1 2 3 4 5 6 7 Sample Number UCL Target LCL UWL LWL Zone - 3 Sample Mean Zone - 2 Zone - 3 Zone - 2 Zone - 1 Action Action Warning Warning Continue Continue Zone - 1
43 UCL 1 2 3 4 5 6 7 8 Sample Number Statistics UWL LCL Target LWL Interpreting Control Chart Point outside the Control limit
44 Control Chart Views Process in Real Time Time Intervals Range Mean LCLx Output of the process in real time Target Target UCLx UCLr
45 Change in Location of Process Mean 43 48 49 50 51 52 53 44 45 46 47 Process with mean at Target Process with mean at more than target Process with mean at less than target
46 Case When Process Mean is at Target 43 48 49 50 51 52 53 44 45 46 47 Target Process Mean Chances of getting a reading beyond U & L is almost nil 42 U L - 3 s +3 s U - L = 6 s
47 Case - Small Shift of the Process Mean 43 48 49 50 51 52 53 44 45 46 47 Target Process Mean Chances of getting a reading outside U is small Small shift in process 42 Shaded area shows the probability of getting a reading beyond U U L U-L = 6 s
48 Process Mean Case - Large Shift of the Process Mean 43 48 49 50 51 52 53 44 45 46 47 Target Chances of getting a reading outside U is large Large shift in process 42 Shaded area shows the probability of getting a reading beyond U U L U-L = 6 s
49 Change in Spread of Process 43 48 49 50 51 52 53 44 45 46 47 Larger spread due to special causes Spread due to common causes
50 Special cause & Common cause  Special / Assignable cause : Causes due to negligence in following work instructions, problem in machines etc.This types of causes are avoidable and cannot be neglected. Common cause : Causes which are unavoidable and in-evitable in a process.It is not practical to eliminate the Chance cause technically and economically.
51 Most Commonly Used Variable Control Charts  To track the accuracy of the process - Mean control chart or x-bar chart  To track the precision of the process - Range control chart
52 Control Chart PART NAME : GLASS RUN PART NO : MODEL : Page THICKNESS SPECS : MIN 1.10 TO 1.50 MAX REASON : PROCESS CAPABILITY STUDY AUDIT DATE 25/9/01 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 n d2 A2 D4 1 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.60 1.50 1.60 1.50 1.60 1.55 1.60 1.55 1.50 1.50 1 1.123 2.66 3.27 2 1.50 1.50 1.50 1.53 1.50 1.50 1.50 1.50 1.50 1.55 1.60 1.55 1.55 1.60 1.55 1.45 1.60 1.50 1.50 1.48 2 1.128 1.88 3.27 3 1.60 1.48 1.50 1.50 1.48 1.50 1.50 1.50 1.50 1.55 1.50 1.55 1.50 1.55 1.50 1.50 1.50 1.55 1.60 1.55 3 1.693 1.02 2.57 4 1.50 1.48 1.52 1.50 1.53 1.50 1.50 1.50 1.45 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.60 1.60 1.50 4 2.059 0.73 2.29 5 1.50 1.50 1.60 1.50 1.50 1.50 1.55 1.55 1.45 1.55 1.55 1.50 1.50 1.50 1.50 1.50 1.45 1.50 1.55 1.55 5 2.326 0.58 2.11 SUM X SUM X1+..+Xn 30.37 X 1.52 1.49 1.52 1.51 1.50 1.50 1.51 1.51 1.48 1.53 1.55 1.52 1.53 1.53 1.53 1.50 1.53 1.54 1.55 1.52 X SUM X1+..+Xn/n 1.519 R 0.10 0.02 0.10 0.03 0.05 0.00 0.05 0.05 0.05 0.05 0.10 0.05 0.10 0.10 0.10 0.10 0.15 0.10 0.10 0.07 R SUM R1+..+Rn/n 0.074 SIGMA R/d2 0.032 3 SIGMA 3 * R/d2 0.095 6 SIGMA 6 * R/d2 0.190 Cp = 2.11 Cpk= MIN OF -0.20 Cpu OR Cpl 4.41 Cpk = USL 1.500 LSL 1.100 FOR X UCL = X + A2.R 1.561 LCL = X - A2.R 1.476 FOR R (D3 = 0) UCL = D4.R 0.155 LCL = D3.R 0.000 PROCESS STATAUS CONTROLLED NOT CONTROLLED XYZ Ltd O -0.05 0.00 0.05 0.10 0.15 0.20 0.25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 R - CHART R UCL LCL CL 1.400 1.420 1.440 1.460 1.480 1.500 1.520 1.540 1.560 1.580 1.600 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 X - CHART X UCL LCL CL How to draw?
86 Summary of Effect of Process Shift  When there is no shift in the process nearly all the observations fall within -3 s and + 3 s.  When there is small shift in the mean of process some observations fall outside original -3 s and +3 s zone.  Chances of an observation falling outside original -3 s and + 3 s zone increases with the increase in the shift of process mean.
87 Our Conclusion from Normal Distribution  When an observation falls within original +3 s and -3 s zone of mean of a process, we conclude that there is no shift in the mean of process. This is so because falling of an observation between these limits is a chance.  When an observation falls beyond original +3 s and -3 s zone of process mean, we conclude that there is shift in location of the process
88 Interpreting Control Chart  Because the basis for control chart theory follows the normal distribution, the same rules that governs the normal distribution are used to interpret the control charts.  These rules include: - Randomness. - Symmetry about the centre of the distribution. - 99.73% of the population lies between - 3 s of and + 3 s the centre line. - 95.4% population lies between -2 s and + 2 s of the centre line.
89 Interpreting Control Chart If the process output follows these rules, the process is said to be stable or in control with only common causes of variation present. If it fails to follow these rules, it may be out of control with special causes of variation present.  These special causes must be found and corrected.
90 Interpreting Control Chart UCL 1 2 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL One point outside control limit
91 Interpreting Control Chart UCL 1 2 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Two points out of three consecutive points between warning limit and corresponding control limit
92 Interpreting Control Chart UCL 1 2 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Two consecutive points between warning limit and corresponding control limit
93 Interpreting Control Chart UCL 1 2 3 4 5 6 7 8 UWL LCL LWL Seven consecutive points on one side of the centre line Sample Number Statistics
94 Interpreting Control Chart UCL 1 2 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Seven consecutive points having upward trend
95 Interpreting Control Chart UCL 1 2 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Seven consecutive points having downward trend
96 Learning  Concept and definition of “Quality”  Importance of improving Quality as a tool for cost reduction  Importance of proper analysis of Quality problems  Usage of 7 QC tools to ensure “Defect free production”
97 Thank You

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7qc_tools_173.ppt for 7 QC tools implementation of the quality

  • 1.
  • 2.
    2 Quality A subjective termfor which each person has his or her own definition. In technical usage, quality can have two meanings: 1. The characteristics of a product or service that bear on its ability to satisfy stated or implied needs. 2. A product or service free from deficiencies.
  • 3.
    3 Can also betermed as ‘A measure of excellence’ Quality
  • 4.
    4  Quality -an essential and distinguishing attribute of something.  Attribute - an abstraction belonging to or characteristic of an entity  Appearance, visual aspect - outward or visible aspect of a thing  Attractiveness, attraction - the quality of arousing interest; being attractive or something that attracts;  Uncloudedness, clarity, clearness - the quality of clear water;  Ease, easiness, simplicity - freedom from difficulty or hardship or effort.  Suitability, suitableness - the quality of having the properties that are right for a specific purpose.  Excellence - the quality of excelling.  Characteristic - a distinguishing quality  Simpleness, simplicity - the quality of being simple or uncompounded Meaning of “Quality”
  • 5.
    5 Meaning of “Quality” Q= P E P = Performance or result E = Expectations
  • 6.
    6 Many people thinkthat quality costs money and adversely effects profits. But these costs are the costs of doing it wrong first time . Quality in the long run results in increased profitability. Quality, Cost & Profit relationship Cost
  • 7.
    7 Cost Quality and Profit: Traditional thinking
  • 8.
    8 Quality and Profit: Paradigm shift Cost
  • 9.
    9 1.Higher production dueto improved cycle time and reduced errors and defects 2.Increased use of machine and resources. 3.Improved material use from reduced scrap and rejects 4.Increased use of personnel resources 5.Lower level of asset investments required to support operations. 6.Lower service and support costs for eliminated waste, rework and non value added activities. QUALITY Higher productivity Increased profitability due to : •Larger sales •Lower production costs •Faster turnover Quality and Profit
  • 10.
    10 Quality and Profit Ifthe organization does not offer high quality product or service , it will soon go out of business . But just having high quality will not be enough , because your competitors will also have the high quality. To win , companies will need to offer high quality for a lower price than their competitors.This requires organizations to identify and reduce their quality costs High Quality Lower price C2A2C
  • 11.
    11  Offer highquality for a lower price than their competitors.  Reduce quality costs  Stop producing defective thru’  Process up-gradation  Improving quality of analysis to identify and eliminate root causes  Taking necessary countermeasure as when required  Usage of right analytical tools  Designing robust problem solving process CHELLANGES
  • 12.
    12 PROBLEM SOLVING PROCESS Evaluatingsolution (6) Implementing solution (5) Selecting & planning solution (4) Generating potential solutions (3) Analysing problem causes (2) Identifying & selecting problem (1) PROBLEM SOLVING PROCESS
  • 13.
    13 IDENTIFYING AND SELCTINGPROBLEM  Write Statement of the problem(s)  Define Gap Between Actual & target  Prioritize
  • 14.
    14 ANALYSIS PROBLEM ANDCAUSES  Collect Data  Sort symptoms & Causes (effects)  Brain Storm  Fishbone - cause & effect analysis  Prioritize
  • 15.
    15 GENERATING POTENTIAL SOLUTIONS Brainstorm  Build on each other’s ideas  Analysis potential helps & hinders
  • 16.
    16 SELECTING AND PLANNINGSOLUTION  Prioritize solutions  Clarify tasks / Action plan  Resource / Costs  Present proposals
  • 17.
    17 IMPLEMENTING SOLUTION  Establishcontrols  Maintain Commitments  Plan Contingencies
  • 18.
    18 EVALUATING SOLUTION  Monitorresults  Restart Process if necessary
  • 19.
    19 7 QC TOOLS Usedto identify,analyze and resolve problems Simple but very powerful tools to solve day to day work related problems Find solutions in a systematic manner Widely used by Quality Circle members world over
  • 20.
    20 Check sheets  Histograms Pareto charts  Cause & effect diagram (Ishikawa diagram)  Scatter plot  Defect concentration diagram  Control charts 7 QC TOOLS
  • 21.
    21 Check sheets areformats used to collect and organize data Data can be collected easily and concisely Data data is collected on the characteristic of interest. The right data could be captured with all necessary facts included e.g. as when it happened ? how many ? what customer ? CHECK SHEETS
  • 22.
    22 Check sheets forproduction process distribution Defective item check sheet Defect cause check sheet Check sheet for work station evaluation Check sheet for design information accuracy Check sheet for vendor reliability TYPES CHECK SHEETS
  • 23.
    23 CHECK SHEETS Type of defects CheckSub-Total Scratch 3 Dent 7 Flow mark 11 Short Shot 2 Total 23 Component name : ABC Date of Production:22-Aug-03
  • 24.
    24 Histogram is the“Frequency data” obtained from measurements displaying a peak around a certain value and represented in form of polls The variation of quality characteristics is called “Distribution” Purpose of drawing a Histogram is to understand the “Population” HISTOGRAM Population Sample
  • 25.
    25 12 23 43 27 9 0 50 1 160-170 170-180 180-190190-200 200-210 Histogram for distribution of Center Distance (mm) HISTOGRAM
  • 26.
    26 HISTOGRAM A HISTORYOF PROCESS OUT PUT 0 2 4 8 10 12 14 16 6 Frequency 47 48 49 50 51 52 53 54 kg Distribution
  • 27.
    27 Based on “80/20”rule (or ABC analysis) Pareto(V.Pareto,an Italian economist) discovered this universal law-80% of anything is attributed to 20% of its causes 80% of the wealth is held by 20% of the population. • 80% of our income goes into 20% of our needs. • 80% of road accidents occur on 20% of the road. • 80% of the absenteeism in a company is due to 20% of workmen “Significant few & in-significant many” PARETO CHART
  • 28.
    28 PARETO CHART Pareto analysisbegins by ranking problems from highest to lowest in order to fix priority  The cumulative number of problems is plotted on the vertical axis of the graph against the cause/phenomenon Pareto by Causes e.g. Man,Machine,Method etc Pareto by Phenomenon e.g.Quality,Cost,Delivery Tells about the relative sizes of problems indicates an important message about biggest few problems, if corrected, a large % of total problems will be solved
  • 29.
    29 63.8 81.4 96.2 100.0 0 500 1000 1500 2000 2500 3000 No of peices 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Cum. Percentage DEFECT QTY2064.0 567.0 480.0 122.0 CUM % 63.8 81.4 96.2 100.0 SHORT SHOT SILVER SINK MARK FLASH PARETO ANALYSIS
  • 30.
    30 CAUSE n EFFECT(FISH BONE) DIAGRAM This diagram (resembles skeleton of a fish) helps to separate out causes from effects and to see problem in its totality It’s a systematic arrangement of all possible causes,generated thru’ brain storming This can be used to : Assist individual / group to see full picture.  Serve as a recording device for ideas generated.  Reveal undetected relationships between causes.  Discover the origin/root cause of a problem  Create a document or a map of a problem which can be posted in the work area.
  • 31.
    31 The problem categoriesconsidered are : Man, Machine, Method, Materials, Equipments & Environmental. EFFECT MACHINE METHOD ENVIRONMENT MAN MATERIAL EQUIPMENT CAUSE n EFFECT (FISH BONE) DIAGRAM
  • 32.
    32 SCATTER DIAGRAM The scatterdiagram is used for identifying the relationships and performing preliminary analysis of relationship between any two quality characteristics. Clustering of points indicate that the two characteristics may be related e.g. Increasing in component weight with increase in hold time during plastic injection molding ( + ve co-relation) Increase in toughness components with decreasing injection pressure (-ve co-relation) during molding
  • 33.
    33 SCATTER DIAGRAM (POSITIVECORRELATION) 0 10 20 30 40 50 60 70 80 90 100 0 5 10 XY (Scatter) 1
  • 34.
    34 SCATTER DIAGRAM (NEGATIVECORERLATION) 0 10 20 30 40 50 60 70 80 0 5 10 XY (Scatter) 1
  • 35.
    35 SCATTER DIAGRAM (NOCORERLATION) 0 10 20 30 40 50 60 70 80 0 5 10 XY (Scatter) 1
  • 36.
    36 DEFECT CONCENTRATION DIAGRAM This is used to understand the potential defect prone area of the parts produced  The “Concentration Diagram” check sheet carries the diagram of the problematic part,defects whenever observed to be updated in the same using tally marks  Based on the distribution of defects countermeasures are taken at process/system level  This tool is very useful to solve problems like Scratch, Dent,Breakage thru’ handling improvement  For plastic molded parts this tool is used to identify stress points,weak joints,effect of gate shape/position on the quality of parts etc.
  • 37.
    37 DEFECT CONCENTRATION DIAGRAM Componentname : XYZ Concentration diagram for Scratches produced ion 21-Aug-03 Total no of defective produced is 11 Nos Area of concern
  • 38.
    38 Control Chart Quality controlcharts, are graphs on which the quality of the product is plotted as manufacturing or servicing is actually proceeding. It graphically, represents the output of the process and uses statistical limits and patterns of plot, for decision making  Enables corrective actions to be taken at the earliest possible moment and avoiding unnecessary corrections. The charts help to ensure the manufacture of uniform product or providing consistent services which complies with the specification.
  • 39.
    39 Elements of TypicalControl Chart 1. Horizontal axis for sample number 2. Vertical axis for sample statistics e.g. mean, range, standard deviation of sample. 3. Target Line 4. Upper control line 5. Upper warning line 6. Lower control line 7. Lower warning line 8. Plotting of sample statistics 9. Line connecting the plotted statistics
  • 40.
    40 1 2 34 5 Target Lower control line Upper warning line Lower warning line Sample Number Upper control line Lower control line Sample Statistics Elements of Typical Control Chart
  • 41.
    41 Interpreting Control Chart Thecontrol chart gets divided in three zones. Zone - 1 If the plotted point falls in this zone, do not make any adjustment, continue with the process. Zone - 2 If the plotted point falls in this zone then special cause may be present. Be careful watch for plotting of another sample(s). Zone - 3 If the plotted point falls in this zone then special cause has crept into the system, and corrective action is required.
  • 42.
    42 Zones for MeanControl Chart 1 2 3 4 5 6 7 Sample Number UCL Target LCL UWL LWL Zone - 3 Sample Mean Zone - 2 Zone - 3 Zone - 2 Zone - 1 Action Action Warning Warning Continue Continue Zone - 1
  • 43.
    43 UCL 1 2 34 5 6 7 8 Sample Number Statistics UWL LCL Target LWL Interpreting Control Chart Point outside the Control limit
  • 44.
    44 Control Chart ViewsProcess in Real Time Time Intervals Range Mean LCLx Output of the process in real time Target Target UCLx UCLr
  • 45.
    45 Change in Locationof Process Mean 43 48 49 50 51 52 53 44 45 46 47 Process with mean at Target Process with mean at more than target Process with mean at less than target
  • 46.
    46 Case When ProcessMean is at Target 43 48 49 50 51 52 53 44 45 46 47 Target Process Mean Chances of getting a reading beyond U & L is almost nil 42 U L - 3 s +3 s U - L = 6 s
  • 47.
    47 Case - SmallShift of the Process Mean 43 48 49 50 51 52 53 44 45 46 47 Target Process Mean Chances of getting a reading outside U is small Small shift in process 42 Shaded area shows the probability of getting a reading beyond U U L U-L = 6 s
  • 48.
    48 Process Mean Case - LargeShift of the Process Mean 43 48 49 50 51 52 53 44 45 46 47 Target Chances of getting a reading outside U is large Large shift in process 42 Shaded area shows the probability of getting a reading beyond U U L U-L = 6 s
  • 49.
    49 Change in Spreadof Process 43 48 49 50 51 52 53 44 45 46 47 Larger spread due to special causes Spread due to common causes
  • 50.
    50 Special cause &Common cause  Special / Assignable cause : Causes due to negligence in following work instructions, problem in machines etc.This types of causes are avoidable and cannot be neglected. Common cause : Causes which are unavoidable and in-evitable in a process.It is not practical to eliminate the Chance cause technically and economically.
  • 51.
    51 Most Commonly UsedVariable Control Charts  To track the accuracy of the process - Mean control chart or x-bar chart  To track the precision of the process - Range control chart
  • 52.
    52 Control Chart PART NAME: GLASS RUN PART NO : MODEL : Page THICKNESS SPECS : MIN 1.10 TO 1.50 MAX REASON : PROCESS CAPABILITY STUDY AUDIT DATE 25/9/01 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 n d2 A2 D4 1 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.60 1.50 1.60 1.50 1.60 1.55 1.60 1.55 1.50 1.50 1 1.123 2.66 3.27 2 1.50 1.50 1.50 1.53 1.50 1.50 1.50 1.50 1.50 1.55 1.60 1.55 1.55 1.60 1.55 1.45 1.60 1.50 1.50 1.48 2 1.128 1.88 3.27 3 1.60 1.48 1.50 1.50 1.48 1.50 1.50 1.50 1.50 1.55 1.50 1.55 1.50 1.55 1.50 1.50 1.50 1.55 1.60 1.55 3 1.693 1.02 2.57 4 1.50 1.48 1.52 1.50 1.53 1.50 1.50 1.50 1.45 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.60 1.60 1.50 4 2.059 0.73 2.29 5 1.50 1.50 1.60 1.50 1.50 1.50 1.55 1.55 1.45 1.55 1.55 1.50 1.50 1.50 1.50 1.50 1.45 1.50 1.55 1.55 5 2.326 0.58 2.11 SUM X SUM X1+..+Xn 30.37 X 1.52 1.49 1.52 1.51 1.50 1.50 1.51 1.51 1.48 1.53 1.55 1.52 1.53 1.53 1.53 1.50 1.53 1.54 1.55 1.52 X SUM X1+..+Xn/n 1.519 R 0.10 0.02 0.10 0.03 0.05 0.00 0.05 0.05 0.05 0.05 0.10 0.05 0.10 0.10 0.10 0.10 0.15 0.10 0.10 0.07 R SUM R1+..+Rn/n 0.074 SIGMA R/d2 0.032 3 SIGMA 3 * R/d2 0.095 6 SIGMA 6 * R/d2 0.190 Cp = 2.11 Cpk= MIN OF -0.20 Cpu OR Cpl 4.41 Cpk = USL 1.500 LSL 1.100 FOR X UCL = X + A2.R 1.561 LCL = X - A2.R 1.476 FOR R (D3 = 0) UCL = D4.R 0.155 LCL = D3.R 0.000 PROCESS STATAUS CONTROLLED NOT CONTROLLED XYZ Ltd O -0.05 0.00 0.05 0.10 0.15 0.20 0.25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 R - CHART R UCL LCL CL 1.400 1.420 1.440 1.460 1.480 1.500 1.520 1.540 1.560 1.580 1.600 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 X - CHART X UCL LCL CL How to draw?
  • 53.
    86 Summary of Effectof Process Shift  When there is no shift in the process nearly all the observations fall within -3 s and + 3 s.  When there is small shift in the mean of process some observations fall outside original -3 s and +3 s zone.  Chances of an observation falling outside original -3 s and + 3 s zone increases with the increase in the shift of process mean.
  • 54.
    87 Our Conclusion fromNormal Distribution  When an observation falls within original +3 s and -3 s zone of mean of a process, we conclude that there is no shift in the mean of process. This is so because falling of an observation between these limits is a chance.  When an observation falls beyond original +3 s and -3 s zone of process mean, we conclude that there is shift in location of the process
  • 55.
    88 Interpreting Control Chart Because the basis for control chart theory follows the normal distribution, the same rules that governs the normal distribution are used to interpret the control charts.  These rules include: - Randomness. - Symmetry about the centre of the distribution. - 99.73% of the population lies between - 3 s of and + 3 s the centre line. - 95.4% population lies between -2 s and + 2 s of the centre line.
  • 56.
    89 Interpreting Control Chart Ifthe process output follows these rules, the process is said to be stable or in control with only common causes of variation present. If it fails to follow these rules, it may be out of control with special causes of variation present.  These special causes must be found and corrected.
  • 57.
    90 Interpreting Control Chart UCL 12 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL One point outside control limit
  • 58.
    91 Interpreting Control Chart UCL 12 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Two points out of three consecutive points between warning limit and corresponding control limit
  • 59.
    92 Interpreting Control Chart UCL 12 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Two consecutive points between warning limit and corresponding control limit
  • 60.
    93 Interpreting Control Chart UCL 12 3 4 5 6 7 8 UWL LCL LWL Seven consecutive points on one side of the centre line Sample Number Statistics
  • 61.
    94 Interpreting Control Chart UCL 12 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Seven consecutive points having upward trend
  • 62.
    95 Interpreting Control Chart UCL 12 3 4 5 6 7 8 Sample Number Statistics UWL LCL LWL Seven consecutive points having downward trend
  • 63.
    96 Learning  Concept anddefinition of “Quality”  Importance of improving Quality as a tool for cost reduction  Importance of proper analysis of Quality problems  Usage of 7 QC tools to ensure “Defect free production”
  • 64.