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RISK MANAGEMENT
                  Topic:
Managing Quality Risk through control chart




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Is Risk a symbol of danger
             or
   symbol of opportunity

Answer: Both
Risk Management

Risk
Uncertainty of outcome

Terminologies

•Pure Risk- Always leads to loss
•Speculative Risk- May Result in loss or Gain
•Static Risk- Results in loss
•Dynamic Risk- May Result in loss or Gain
•Acceptable Risk
•Non Acceptable Risk
Control Charts for
    Variables
Types of Risks
1. Material Risk- Building,Plant & Machinery,
   Furniture,Fixtures,fittings,Stocks.
2. Consequential Risk- Loss of production,Loss of
   profit,Loss of market,Good will.
3. Social Risk
4. Legal Risk- Product liability,Public liability.
5. Political Risk- Subsidies,Sanctions etc.
Best Practice Risk Management

• Framework for Risk Management
  can be benchmarked in terms of:




   » Policies
   » Methodologies
   » Resources



                                    6
Risk Evaluation
•   Arrange them in order of priority
•   Provide information for deciding the most
    appropriate way of handling.

      Ranking risks according to :
      1. Frequency of loss
      2. Potential severity of loss.
Risk Analysis

  Risk and Human behavior looks into
  psychology of risk.
 How others look at the risk?
 How they behave in the face of risk?
 How they behave in groups?
  Perception of Risk.
Risk analysis is to be carried out with proper
perception of risk of risk and cost involved in
Analysis.
Not to stick to one method
Understand company and industry
Should be financially reasonable
Accurate record keeping
Amount of imagination of required
Risk Reduction / Loss Prevention
1.   Reduce probability of loss and its severity.
2.   Most important for PM process.
3.   Risk Reduction / Prevention can be from –

•Loss prevention
•Ensuring Safety
•Fire protection / Detection
•Environmental protection
Variation
• It is the measure of deviation from
  mean/average value
• Variation may be quite large or very small.
• If variation is very small, it may appear that
  items are identical, but precision instruments
  will show differences.
Categories of variation
• Within-piece variation
  • One portion of surface is rougher than another
    portion.
• A piece-to-piece variation
  • Variation among pieces produced at the same
    time.
• Time-to-time variation
  • Service given early would be different from that
    given later in the day.
Source of variation
• Equipment
  • Tool wear, machine vibration, …
• Material
  • Raw material quality
• Environment
  • Temperature, pressure, humadity
• Operator
  • Operator performs- physical & emotional
Control Chart Viewpoint

   Variation due to
      Common or chance causes
      Assignable causes




   Control chart may be used to discover
    “assignable causes”
   Run chart - without any upper/lower
    limits
   Specification/tolerance limits
   Control limits - statistical
Control chart functions
• Control charts are powerful aids to understanding the
  performance of a process over time.

                                       Noise

    Input                                                 Output

                       PROCESS

               What’s causing variability?
Control charts identify
variation
• Chance causes - “common cause”
 • inherent to the process or random and not
   controllable
 • if only common cause present, the process is
   considered stable or “in control”
• Assignable causes - “special cause”
 • variation due to outside influences
 • if present, the process is “out of control”
Types of Data

• Continuous data
    • Product characteristic that can be measured
       • Length, size, weight, height, time, velocity


• Discrete data
       Product characteristic evaluated with a discrete choice
        • Good/bad, yes/no
Control chart for variables
• Variables are the measurable characteristics
  of a product or service.

• Measurement data is taken and arrayed on
  charts.
Control charts for variables
• X-bar chart
 • In this chart the sample means are plotted in order to
   control the mean value of a variable (e.g., size of piston
   rings, strength of materials, etc.).
• R chart
 • In this chart, the sample ranges are plotted in order to
   control the variability of a variable.
• S chart
 • In this chart, the sample standard deviations are plotted
   in order to control the variability of a variable.
• S2 chart
 • In this chart, the sample variances are plotted in order
   to control the variability of a variable.
Control chart components
• Centerline
  • shows where the process average is
    centered or the central tendency of the
    data
• Upper control limit (UCL) and Lower
  control limit (LCL)
  • describes the process spread
The Control Chart Method
X bar Control Chart:
UCL = XDmean + A2 x Rmean
LCL = XDmean - A2 x Rmean
CL = XDmean
 R Control Chart:
 UCL = D4 x Rmean
 LCL = D3 x Rmean
 CL = Rmean
 Capability Study:
 PCR = (USL - LSL)/(6s); where s = Rmean /d2
Control Chart Examples


                               UCL
  Variations




                               Nominal
                               LCL

               Sample number
Determine trial centerline
• The centerline should be the population mean, 
• Since it is unknown, we use X Double bar, or the grand average of
  the subgroup averages.




                               m

                            X            i

                X           i 1
                                   m
UCL & LCL calculation

 UCL  X  3
LCL  X  3
  standard deviation
Determining an alternative value for
the standard deviation
           m

          R       i
   R     i 1

               m

   UCL  X  A 2 R

   LCL  X  A 2 R
Example: Control Charts for Variable Data
         Slip Ring Diameter (cm)
Sample     1       2      3        4      5      X      R
  1      5.02    5.01   4.94     4.99   4.96   4.98    0.08
  2      5.01    5.03   5.07     4.95   4.96   5.00    0.12
  3      4.99    5.00   4.93     4.92   4.99   4.97    0.08
  4      5.03    4.91   5.01     4.98   4.89   4.96    0.14
  5      4.95    4.92   5.03     5.05   5.01   4.99    0.13
  6      4.97    5.06   5.06     4.96   5.03   5.01    0.10
  7      5.05    5.01   5.10     4.96   4.99   5.02    0.14
  8      5.09    5.10   5.00     4.99   5.08   5.05    0.11
  9      5.14    5.10   4.99     5.08   5.09   5.08    0.15
 10      5.01    4.98   5.08     5.07   4.99   5.03    0.10
                                               50.09   1.15
Calculation
   From Table above:
   • Sigma X-bar = 50.09
   • Sigma R = 1.15
   • m = 10
   Thus;
   • X-Double bar = 50.09/10 = 5.009 cm
   • R-bar = 1.15/10 = 0.115 cm

Note: The control limits are only preliminary with 10 samples.
It is desirable to have at least 25 samples.
3-Sigma Control Chart Factors


Sample size     X-chart          R-chart
    n           A2         D3               D4
    2          1.88         0              3.27
    3          1.02         0              2.57
    4          0.73         0              2.28
    5          0.58         0              2.11
    6          0.48         0              2.00
    7          0.42       0.08             1.92
    8          0.37       0.14             1.86
Trial control limit
X-bar chart

• UCLx-bar = X-D bar + A2 R-bar
           = 5.009 + (0.577)(0.115) = 5.075 cm

• LCLx-bar = X-D bar - A2 R-bar
           = 5.009 - (0.577)(0.115) = 4.943 cm
R-chart

• UCLR = D4R-bar = (2.114)(0.115) = 0.243 cm
• LCLR = D3R-bar = (0)(0.115) = 0 cm
X-bar Chart

           5.10
                                                             UCL
           5.08
           5.06
           5.04
   X bar




           5.02
           5.00                                              CL
           4.98
           4.96                                              LCL
           4.94
                  0   1   2   3   4    5   6     7   8   9   10    11

                                      Subgroup
R Chart
        0.25                                              UCL


        0.20
Range




        0.15
                                                           CL

        0.10

        0.05
                                                          LCL
        0.00
               0   1   2   3   4    5   6     7   8   9   10    11
                                   Subgroup
6.70
                                             6.65

     Run Chart                               6.60




                               Mean, X-bar
                                             6.55
                                             6.50
                                             6.45
                                             6.40
                                             6.35
                                             6.30
                                                    0   5    10        15      20   25
                                                             Subgroup number
           0.35
            0.3
           0.25
Range, R




            0.2
           0.15
            0.1
           0.05
             0
                  0   5   10                    15      20   25
                          Subgroup number
X-bar Chart
R Chart
Trial Control Limits & Revised Control Limit
                6.65

                6.60                                  Revised control limits
                6.55
  Mean, X-bar




                                                                    UCL = 6.46
                6.50

                6.45

                6.40
                                                                    CL = 6.40
                6.35

                6.30                                                LCL = 6.34
                       0   2      4       6   8
                               Subgroup




                0.20
                                                                     UCL = 0.18

                0.15
  Range, R




                0.10                                                  CL = 0.08

                0.05


                0.00
                       0   2       4      6       8                   LCL = 0
                               Subgroup
Revise the charts
In certain cases, control limits are revised
because:
  1. out-of-control points were included in
     the calculation of the control limits.
  2. the process is in-control but the within
     subgroup variation significantly
     improves.
The Normal
      Distribution
       = Standard deviation

                                            Mean
                                -3 -2 -1     +1 +2 +3
                                           68.26%
                                           95.44%
LSL                                   USL 99.74%




        -3                    +3
                  CL
•   34.13% of data lie between  and 1 above the mean ().
•   34.13% between  and 1 below the mean.
•   Approximately two-thirds (68.28 %) within 1 of the mean.
•   13.59% of the data lie between one and two standard deviations
•   Finally, almost all of the data (99.74%) are within 3 of the mean.
Normal Distribution Review

   Define the 3-sigma limits for sample means as follows:
                      3          3(0.05)
    Upper Limit        5.01           5.077
                       n              5
                      3          3(0.05)
    Lower Limit        5.01           4.943
                       n              5
   What is the probability that the sample means will lie
    outside 3-sigma limits?
   Note that the 3-sigma limits for sample means are
    different from natural tolerances which are at
         3
Common Causes
Process Out of Control
• The term out of control is a change in the process due to an
  assignable cause.
• When a point (subgroup value) falls outside its control limits,
  the process is out of control.
Assignable Causes
     Average        (a) Mean




           Grams
Assignable Causes
          Average
                    (b) Spread




          Grams
Assignable Causes
          Average
                    (c) Shape




          Grams
Improvement
Chart zones
• Based on our knowledge of the normal curve, a
  control chart exhibits a state of control when:

  ♥Two thirds of all points are near the center value.
  ♥The points appear to float back and forth across
   the centerline.
  ♥The points are balanced on both sides of the
   centerline.
  ♥No points beyond the control limits.
  ♥No patterns or trends.
         What Is Six Sigma?

Sigma is a letter   • Degree of variation;
  in the Greek      • Level of performance in terms of defects;
    Alphabet
                    • Statistical measurement of process
                      capability;
                    • Benchmark for comparison;
                    • Process improvement methodology;
                    • It is a Goal;
                    • Strategy for change;
                    • A commitment to customers to achieve
                      an acceptable level of performance


                                                                  48
Six Sigma Definitions

• Business Definition
   A break through strategy to significantly improve
    customer satisfaction and shareholder value by
    reducing variability in every aspect of business.


• Technical Definition
   A statistical term signifying 3.4 defects per million
    opportunities.




                                                            49
Sigma Defects Per Million Rate of
Level Opportunities       Improveme
                          nt
  1        690,000
 2         308,000        2 times
 3         66,800         5 times
 4          6,210         11 times
 5          230          27 times
 6           3.4         68 times
                                      50
Six Sigma Project Methodology
Project Phases

  Define           Measure            Analyze           Improve           Control


 Identify,        Collect data     Analyze data,    Improvement  Establish
  evaluate and      on size of the    establish and     strategy          standards to
  select projects   selected          confirm the “    Develop ideas     maintain
  for               problem,          vital few “       to remove root    process;
  improvement  identify key          determinants      causes           Design the
 Set goals         customer          of the           Design and        controls,
 Form teams.       requirements,     performance.      carry out         implement and
                   Determine key  Validate            experiments,      monitor.
                    product and       hypothesis       Optimize the     Evaluate
                    process                             process.          financial
                    characteristic.                    Final solutions   impact of the
                                                                          project


                                                                                          51
Learning Outcome
1. Risk can fixed only when it is scalable
2. More than one form of risk can be present in a
   project
3. 100% assurance on risk control can be guranteed
4. Reduction in Risk automatically enhances the
   quality of product
PSGIM, Coimbatore   E-procurement system of Honeywell & Vedanta




                                                            BENCHMARK – 2 0 1 1
     Questions Please ????
                                                               53
                                                              Fri
                                                            25 Feb

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Risk management Report

  • 1. RISK MANAGEMENT Topic: Managing Quality Risk through control chart NewGate India Hyderabad, Andhra Pradesh- 500038 Website: www.newgate.in Email: contact@newgate.in Slideshare URL : 1 http://www.slideshare.net/newgateindia
  • 2. Is Risk a symbol of danger or symbol of opportunity Answer: Both
  • 3. Risk Management Risk Uncertainty of outcome Terminologies •Pure Risk- Always leads to loss •Speculative Risk- May Result in loss or Gain •Static Risk- Results in loss •Dynamic Risk- May Result in loss or Gain •Acceptable Risk •Non Acceptable Risk
  • 4. Control Charts for Variables
  • 5. Types of Risks 1. Material Risk- Building,Plant & Machinery, Furniture,Fixtures,fittings,Stocks. 2. Consequential Risk- Loss of production,Loss of profit,Loss of market,Good will. 3. Social Risk 4. Legal Risk- Product liability,Public liability. 5. Political Risk- Subsidies,Sanctions etc.
  • 6. Best Practice Risk Management • Framework for Risk Management can be benchmarked in terms of: » Policies » Methodologies » Resources 6
  • 7. Risk Evaluation • Arrange them in order of priority • Provide information for deciding the most appropriate way of handling. Ranking risks according to : 1. Frequency of loss 2. Potential severity of loss.
  • 8. Risk Analysis Risk and Human behavior looks into psychology of risk.  How others look at the risk?  How they behave in the face of risk?  How they behave in groups? Perception of Risk.
  • 9. Risk analysis is to be carried out with proper perception of risk of risk and cost involved in Analysis. Not to stick to one method Understand company and industry Should be financially reasonable Accurate record keeping Amount of imagination of required
  • 10. Risk Reduction / Loss Prevention 1. Reduce probability of loss and its severity. 2. Most important for PM process. 3. Risk Reduction / Prevention can be from – •Loss prevention •Ensuring Safety •Fire protection / Detection •Environmental protection
  • 11. Variation • It is the measure of deviation from mean/average value • Variation may be quite large or very small. • If variation is very small, it may appear that items are identical, but precision instruments will show differences.
  • 12. Categories of variation • Within-piece variation • One portion of surface is rougher than another portion. • A piece-to-piece variation • Variation among pieces produced at the same time. • Time-to-time variation • Service given early would be different from that given later in the day.
  • 13. Source of variation • Equipment • Tool wear, machine vibration, … • Material • Raw material quality • Environment • Temperature, pressure, humadity • Operator • Operator performs- physical & emotional
  • 14. Control Chart Viewpoint  Variation due to  Common or chance causes  Assignable causes  Control chart may be used to discover “assignable causes”
  • 15. Run chart - without any upper/lower limits  Specification/tolerance limits  Control limits - statistical
  • 16. Control chart functions • Control charts are powerful aids to understanding the performance of a process over time. Noise Input Output PROCESS What’s causing variability?
  • 17. Control charts identify variation • Chance causes - “common cause” • inherent to the process or random and not controllable • if only common cause present, the process is considered stable or “in control” • Assignable causes - “special cause” • variation due to outside influences • if present, the process is “out of control”
  • 18. Types of Data • Continuous data • Product characteristic that can be measured • Length, size, weight, height, time, velocity • Discrete data Product characteristic evaluated with a discrete choice • Good/bad, yes/no
  • 19. Control chart for variables • Variables are the measurable characteristics of a product or service. • Measurement data is taken and arrayed on charts.
  • 20. Control charts for variables • X-bar chart • In this chart the sample means are plotted in order to control the mean value of a variable (e.g., size of piston rings, strength of materials, etc.). • R chart • In this chart, the sample ranges are plotted in order to control the variability of a variable. • S chart • In this chart, the sample standard deviations are plotted in order to control the variability of a variable. • S2 chart • In this chart, the sample variances are plotted in order to control the variability of a variable.
  • 21. Control chart components • Centerline • shows where the process average is centered or the central tendency of the data • Upper control limit (UCL) and Lower control limit (LCL) • describes the process spread
  • 22. The Control Chart Method X bar Control Chart: UCL = XDmean + A2 x Rmean LCL = XDmean - A2 x Rmean CL = XDmean R Control Chart: UCL = D4 x Rmean LCL = D3 x Rmean CL = Rmean Capability Study: PCR = (USL - LSL)/(6s); where s = Rmean /d2
  • 23. Control Chart Examples UCL Variations Nominal LCL Sample number
  • 24. Determine trial centerline • The centerline should be the population mean,  • Since it is unknown, we use X Double bar, or the grand average of the subgroup averages. m X i X i 1 m
  • 25. UCL & LCL calculation UCL  X  3 LCL  X  3   standard deviation
  • 26. Determining an alternative value for the standard deviation m R i R  i 1 m UCL  X  A 2 R LCL  X  A 2 R
  • 27. Example: Control Charts for Variable Data Slip Ring Diameter (cm) Sample 1 2 3 4 5 X R 1 5.02 5.01 4.94 4.99 4.96 4.98 0.08 2 5.01 5.03 5.07 4.95 4.96 5.00 0.12 3 4.99 5.00 4.93 4.92 4.99 4.97 0.08 4 5.03 4.91 5.01 4.98 4.89 4.96 0.14 5 4.95 4.92 5.03 5.05 5.01 4.99 0.13 6 4.97 5.06 5.06 4.96 5.03 5.01 0.10 7 5.05 5.01 5.10 4.96 4.99 5.02 0.14 8 5.09 5.10 5.00 4.99 5.08 5.05 0.11 9 5.14 5.10 4.99 5.08 5.09 5.08 0.15 10 5.01 4.98 5.08 5.07 4.99 5.03 0.10 50.09 1.15
  • 28. Calculation From Table above: • Sigma X-bar = 50.09 • Sigma R = 1.15 • m = 10 Thus; • X-Double bar = 50.09/10 = 5.009 cm • R-bar = 1.15/10 = 0.115 cm Note: The control limits are only preliminary with 10 samples. It is desirable to have at least 25 samples.
  • 29. 3-Sigma Control Chart Factors Sample size X-chart R-chart n A2 D3 D4 2 1.88 0 3.27 3 1.02 0 2.57 4 0.73 0 2.28 5 0.58 0 2.11 6 0.48 0 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86
  • 30. Trial control limit X-bar chart • UCLx-bar = X-D bar + A2 R-bar = 5.009 + (0.577)(0.115) = 5.075 cm • LCLx-bar = X-D bar - A2 R-bar = 5.009 - (0.577)(0.115) = 4.943 cm R-chart • UCLR = D4R-bar = (2.114)(0.115) = 0.243 cm • LCLR = D3R-bar = (0)(0.115) = 0 cm
  • 31. X-bar Chart 5.10 UCL 5.08 5.06 5.04 X bar 5.02 5.00 CL 4.98 4.96 LCL 4.94 0 1 2 3 4 5 6 7 8 9 10 11 Subgroup
  • 32. R Chart 0.25 UCL 0.20 Range 0.15 CL 0.10 0.05 LCL 0.00 0 1 2 3 4 5 6 7 8 9 10 11 Subgroup
  • 33. 6.70 6.65 Run Chart 6.60 Mean, X-bar 6.55 6.50 6.45 6.40 6.35 6.30 0 5 10 15 20 25 Subgroup number 0.35 0.3 0.25 Range, R 0.2 0.15 0.1 0.05 0 0 5 10 15 20 25 Subgroup number
  • 36. Trial Control Limits & Revised Control Limit 6.65 6.60 Revised control limits 6.55 Mean, X-bar UCL = 6.46 6.50 6.45 6.40 CL = 6.40 6.35 6.30 LCL = 6.34 0 2 4 6 8 Subgroup 0.20 UCL = 0.18 0.15 Range, R 0.10 CL = 0.08 0.05 0.00 0 2 4 6 8 LCL = 0 Subgroup
  • 37. Revise the charts In certain cases, control limits are revised because: 1. out-of-control points were included in the calculation of the control limits. 2. the process is in-control but the within subgroup variation significantly improves.
  • 38. The Normal Distribution  = Standard deviation Mean -3 -2 -1 +1 +2 +3 68.26% 95.44% LSL USL 99.74% -3 +3 CL
  • 39. 34.13% of data lie between  and 1 above the mean (). • 34.13% between  and 1 below the mean. • Approximately two-thirds (68.28 %) within 1 of the mean. • 13.59% of the data lie between one and two standard deviations • Finally, almost all of the data (99.74%) are within 3 of the mean.
  • 40. Normal Distribution Review  Define the 3-sigma limits for sample means as follows: 3 3(0.05) Upper Limit     5.01   5.077 n 5 3 3(0.05) Lower Limit     5.01   4.943 n 5  What is the probability that the sample means will lie outside 3-sigma limits?  Note that the 3-sigma limits for sample means are different from natural tolerances which are at   3
  • 42. Process Out of Control • The term out of control is a change in the process due to an assignable cause. • When a point (subgroup value) falls outside its control limits, the process is out of control.
  • 43. Assignable Causes Average (a) Mean Grams
  • 44. Assignable Causes Average (b) Spread Grams
  • 45. Assignable Causes Average (c) Shape Grams
  • 47. Chart zones • Based on our knowledge of the normal curve, a control chart exhibits a state of control when: ♥Two thirds of all points are near the center value. ♥The points appear to float back and forth across the centerline. ♥The points are balanced on both sides of the centerline. ♥No points beyond the control limits. ♥No patterns or trends.
  • 48. What Is Six Sigma? Sigma is a letter • Degree of variation; in the Greek • Level of performance in terms of defects; Alphabet • Statistical measurement of process capability; • Benchmark for comparison; • Process improvement methodology; • It is a Goal; • Strategy for change; • A commitment to customers to achieve an acceptable level of performance 48
  • 49. Six Sigma Definitions • Business Definition  A break through strategy to significantly improve customer satisfaction and shareholder value by reducing variability in every aspect of business. • Technical Definition  A statistical term signifying 3.4 defects per million opportunities. 49
  • 50. Sigma Defects Per Million Rate of Level Opportunities Improveme nt 1 690,000 2 308,000 2 times 3 66,800 5 times 4 6,210 11 times 5 230 27 times 6 3.4 68 times 50
  • 51. Six Sigma Project Methodology Project Phases Define Measure Analyze Improve Control  Identify,  Collect data  Analyze data,  Improvement  Establish evaluate and on size of the establish and strategy standards to select projects selected confirm the “  Develop ideas maintain for problem, vital few “ to remove root process; improvement  identify key determinants causes  Design the  Set goals customer of the  Design and controls,  Form teams. requirements, performance. carry out implement and  Determine key  Validate experiments, monitor. product and hypothesis  Optimize the  Evaluate process process. financial characteristic.  Final solutions impact of the project 51
  • 52. Learning Outcome 1. Risk can fixed only when it is scalable 2. More than one form of risk can be present in a project 3. 100% assurance on risk control can be guranteed 4. Reduction in Risk automatically enhances the quality of product
  • 53. PSGIM, Coimbatore E-procurement system of Honeywell & Vedanta BENCHMARK – 2 0 1 1 Questions Please ???? 53 Fri 25 Feb