RISK MANAGEMENT                  Topic:Managing Quality Risk through control chart                          NewGate India ...
Is Risk a symbol of danger             or   symbol of opportunityAnswer: Both
Risk ManagementRiskUncertainty of outcomeTerminologies•Pure Risk- Always leads to loss•Speculative Risk- May Result in los...
Control Charts for    Variables
Types of Risks1. Material Risk- Building,Plant & Machinery,   Furniture,Fixtures,fittings,Stocks.2. Consequential Risk- Lo...
Best Practice Risk Management• Framework for Risk Management  can be benchmarked in terms of:   » Policies   » Methodologi...
Risk Evaluation•   Arrange them in order of priority•   Provide information for deciding the most    appropriate way of ha...
Risk Analysis  Risk and Human behavior looks into  psychology of risk. How others look at the risk? How they behave in t...
Risk analysis is to be carried out with properperception of risk of risk and cost involved inAnalysis.Not to stick to one...
Risk Reduction / Loss Prevention1.   Reduce probability of loss and its severity.2.   Most important for PM process.3.   R...
Variation• It is the measure of deviation from  mean/average value• Variation may be quite large or very small.• If variat...
Categories of variation• Within-piece variation  • One portion of surface is rougher than another    portion.• A piece-to-...
Source of variation• Equipment  • Tool wear, machine vibration, …• Material  • Raw material quality• Environment  • Temper...
Control Chart Viewpoint   Variation due to      Common or chance causes      Assignable causes   Control chart may be ...
   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.       ...
Control charts identifyvariation• Chance causes - “common cause” • inherent to the process or random and not   controllabl...
Types of Data• Continuous data    • Product characteristic that can be measured       • Length, size, weight, height, time...
Control chart for variables• Variables are the measurable characteristics  of a product or service.• Measurement data is t...
Control charts for variables• X-bar chart • In this chart the sample means are plotted in order to   control the mean valu...
Control chart components• Centerline  • shows where the process average is    centered or the central tendency of the    d...
The Control Chart MethodX bar Control Chart:UCL = XDmean + A2 x RmeanLCL = XDmean - A2 x RmeanCL = XDmean R Control Chart:...
Control Chart Examples                               UCL  Variations                               Nominal                ...
Determine trial centerline• The centerline should be the population mean, • Since it is unknown, we use X Double bar, or ...
UCL & LCL calculation UCL  X  3LCL  X  3  standard deviation
Determining an alternative value forthe standard deviation           m          R       i   R     i 1               m  ...
Example: Control Charts for Variable Data         Slip Ring Diameter (cm)Sample     1       2      3        4      5      ...
Calculation   From Table above:   • Sigma X-bar = 50.09   • Sigma R = 1.15   • m = 10   Thus;   • X-Double bar = 50.09/10 ...
3-Sigma Control Chart FactorsSample size     X-chart          R-chart    n           A2         D3               D4    2  ...
Trial control limitX-bar chart• UCLx-bar = X-D bar + A2 R-bar           = 5.009 + (0.577)(0.115) = 5.075 cm• LCLx-bar = X-...
X-bar Chart           5.10                                                             UCL           5.08           5.06  ...
R Chart        0.25                                              UCL        0.20Range        0.15                         ...
6.70                                             6.65     Run Chart                               6.60                    ...
X-bar Chart
R Chart
Trial Control Limits & Revised Control Limit                6.65                6.60                                  Revi...
Revise the chartsIn certain cases, control limits are revisedbecause:  1. out-of-control points were included in     the c...
The Normal      Distribution       = Standard deviation                                            Mean                  ...
•   34.13% of data lie between  and 1 above the mean ().•   34.13% between  and 1 below the mean.•   Approximately tw...
Normal Distribution Review   Define the 3-sigma limits for sample means as follows:                      3          3(0....
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 (sub...
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 o...
         What Is Six Sigma?Sigma is a letter   • Degree of variation;  in the Greek      • Level of performance in terms ...
Six Sigma Definitions• Business Definition   A break through strategy to significantly improve    customer satisfaction a...
Sigma Defects Per Million Rate ofLevel Opportunities       Improveme                          nt  1        690,000 2    ...
Six Sigma Project MethodologyProject Phases  Define           Measure            Analyze           Improve           Contr...
Learning Outcome1. Risk can fixed only when it is scalable2. More than one form of risk can be present in a   project3. 10...
PSGIM, Coimbatore   E-procurement system of Honeywell & Vedanta                                                           ...
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Risk management Report

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Risk management Report

  1. 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. 2. Is Risk a symbol of danger or symbol of opportunityAnswer: Both
  3. 3. Risk ManagementRiskUncertainty of outcomeTerminologies•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. 4. Control Charts for Variables
  5. 5. Types of Risks1. 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 Risk4. Legal Risk- Product liability,Public liability.5. Political Risk- Subsidies,Sanctions etc.
  6. 6. Best Practice Risk Management• Framework for Risk Management can be benchmarked in terms of: » Policies » Methodologies » Resources 6
  7. 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. 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. 9. Risk analysis is to be carried out with properperception of risk of risk and cost involved inAnalysis.Not to stick to one methodUnderstand company and industryShould be financially reasonableAccurate record keepingAmount of imagination of required
  10. 10. Risk Reduction / Loss Prevention1. 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. 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. 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. 13. Source of variation• Equipment • Tool wear, machine vibration, …• Material • Raw material quality• Environment • Temperature, pressure, humadity• Operator • Operator performs- physical & emotional
  14. 14. Control Chart Viewpoint Variation due to  Common or chance causes  Assignable causes Control chart may be used to discover “assignable causes”
  15. 15.  Run chart - without any upper/lower limits Specification/tolerance limits Control limits - statistical
  16. 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. 17. Control charts identifyvariation• 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. 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. 19. Control chart for variables• Variables are the measurable characteristics of a product or service.• Measurement data is taken and arrayed on charts.
  20. 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. 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. 22. The Control Chart MethodX bar Control Chart:UCL = XDmean + A2 x RmeanLCL = XDmean - A2 x RmeanCL = 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. 23. Control Chart Examples UCL Variations Nominal LCL Sample number
  24. 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. 25. UCL & LCL calculation UCL  X  3LCL  X  3  standard deviation
  26. 26. Determining an alternative value forthe standard deviation m R i R  i 1 m UCL  X  A 2 R LCL  X  A 2 R
  27. 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. 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 cmNote: The control limits are only preliminary with 10 samples.It is desirable to have at least 25 samples.
  29. 29. 3-Sigma Control Chart FactorsSample 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. 30. Trial control limitX-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 cmR-chart• UCLR = D4R-bar = (2.114)(0.115) = 0.243 cm• LCLR = D3R-bar = (0)(0.115) = 0 cm
  31. 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. 32. R Chart 0.25 UCL 0.20Range 0.15 CL 0.10 0.05 LCL 0.00 0 1 2 3 4 5 6 7 8 9 10 11 Subgroup
  33. 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 0.25Range, R 0.2 0.15 0.1 0.05 0 0 5 10 15 20 25 Subgroup number
  34. 34. X-bar Chart
  35. 35. R Chart
  36. 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. 37. Revise the chartsIn certain cases, control limits are revisedbecause: 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. 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. 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. 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
  41. 41. Common Causes
  42. 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. 43. Assignable Causes Average (a) Mean Grams
  44. 44. Assignable Causes Average (b) Spread Grams
  45. 45. Assignable Causes Average (c) Shape Grams
  46. 46. Improvement
  47. 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. 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. 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. 50. Sigma Defects Per Million Rate ofLevel 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. 51. Six Sigma Project MethodologyProject 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. 52. Learning Outcome1. Risk can fixed only when it is scalable2. More than one form of risk can be present in a project3. 100% assurance on risk control can be guranteed4. Reduction in Risk automatically enhances the quality of product
  53. 53. PSGIM, Coimbatore E-procurement system of Honeywell & Vedanta BENCHMARK – 2 0 1 1 Questions Please ???? 53 Fri 25 Feb

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