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Risk management
Risk management
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Risk management
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Risk management

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  • PSG Institute of Management,Coimbatore E-procurement System at Honeywell & Vedanta
  • CIBC’s framework for MRM can be benchmarked in terms of Policies Methodologies and Infrastructure Risk wears many disguises. One can ensure maximum transparency of risk through implementing an independent first class proactive market risk management program. Click
  • 05/03/12 Statistics 30 And finally, this chart shows a process with two points actually outside the control limits, an easy indicator to detect but not the only one. These rules, there are a few more, are commonly referred to as the Western Electric Rules and can be found in any advanced quality reference.
  • 05/03/12 Statistics Example4.3
  • 05/03/12 Statistics 20 And at +/- 3 sigma, the most common choice of confidence/control limits in quality control application, the area is 99.97%.
  • 05/03/12 Statistics 2 The first set of slides presents the various aspects of common and assignable causes. This slide and the two following show a normal process distribution and how it allows for expected variability which is termed common causes.
  • 05/03/12 Statistics 7 The new distribution will look as shown in this slide.
  • 05/03/12 Statistics 9 The new distribution has a much greater spread (higher standard deviation).
  • 05/03/12 Statistics 11 A skewed (non-normal) distribution will result in a different pattern of variability.
  • Transcript

    • 1. RISK MANAGEMENTTopic: Managing Quality Risk through control chart Presented By: Ritesh Agarwal Sunam Pal 1
    • 2. Is Risk a symbol of danger or symbol of opportunityAnswer: Both
    • 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. Control Charts for Variables
    • 5. Types of Risks• Material Risk- Building,Plant & Machinery, Furniture,Fixtures,fittings,Stocks.• Consequential Risk- Loss of production,Loss of profit,Loss of market,Good will.• Social Risk• Legal Risk- Product liability,Public liability.• Political Risk- Subsidies,Sanctions etc.
    • 6. Best Practice Risk Management• Framework for Risk Management can be benchmarked in terms of: METHO DO LOGIE »Policies S S »Methodologies POLICIE »Resources 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 : 2. Frequency of loss 3. 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 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. 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. 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 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. 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 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. Control Chart Examples UCL Variations Nominal LCL Sample number
    • 24. Determine trial centerline• The centerline should be the population mean, µ• 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 + 3σLCL = X − 3σσ = standard deviation
    • 26. Determining an alternative value forthe standard deviation m ∑R i R = i= 1 m UCL = + 2 R X A LCL = − 2 R X A
    • 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 cmNote: The control limits are only preliminary with 10 samples.It is desirable to have at least 25 samples.
    • 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. 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. 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 0.15Range 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 0.25Range, R 0.2 0.15 0.1 0.05 0 0 5 10 15 20 25 Subgroup number
    • 34. X-bar Chart
    • 35. R Chart
    • 36. Trial Control Limits & Revised Control Limit 6.65 6.60 Revised control limits 6.55 Mean, X-bar 6.50 UCL = 6.46 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 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. The Normal Distribution σ = Standard deviation Mean -3σ -2σ -1σ +1σ +2σ +3σ 68.26% 95.44%LSL USL 99.74% -3σ +3σ CL
    • 39. • 34.13% of data lie between µ and 1σ above the mean (µ).• 34.13% between µ and 1σ below the mean.• Approximately two-thirds (68.28 %) within 1σ 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 3σ of the mean.
    • 40. Normal Distribution Review Define the 3-sigma limits for sample means as follows: 3σ 3(0.05) Upper Limit = µ + = 5.01 + = 5.077 n 5 3σ 3(0.05) Lower Limit = µ − = 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 µ ± 3σ
    • 41. Common Causes
    • 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
    • 46. Improvement
    • 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. σ 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 ofLevel Opportunities Improveme nt 1σ 690,000 2σ 308,000 2 times 3σ 66,800 5 times 4σ 6,210 11 times 5σ 230 27 times 6σ 3.4 68 times 50
    • 51. Six Sigma Project MethodologyProject Phases Define Measure Analyze Improve Control Identify,  Collect data  Analyze  Improvemen  Establish evaluate on size of data, t strategy standards to and select the selected establish  Develop maintain projects for problem, and confirm ideas to process; improvemen  identify key the “ vital remove root  Design the t customer few “ causes controls, Set goals requirement determinant  Design and implement Form teams. s, s of the carry out and  Determine performance experiments monitor. key product . ,  Evaluate and process  Validate  Optimize financial characteristi hypothesis the process. impact of c.  Final the project solutions 51
    • 52. Learning Outcome3.Risk can fixed only when it is scalable4.More than one form of risk can be present in aproject5.100% assurance on risk control can be guranteed6.Reduction in Risk automatically enhances thequality of product
    • 53. PSGIM, Coimbatore E-procurement system of Honeywell & Vedanta BENCHMARK – 2 0 1 1 Questions Please ???? 53 Fri 25 Feb

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