risk management
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Topic: Managing Quality Risk through control chart
Presented By:
Ritesh AgarwalSunam Pal
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RISK MANAGEMENT
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Is Risk a symbol of danger or
symbol of opportunity
Answer: Both
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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
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Control Charts for Variables
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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 Risk4. Legal Risk- Product liability,Public liability.5. Political Risk- Subsidies,Sanctions etc.
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Best Practice Risk Management
• Framework for Risk Managementcan be benchmarked in terms of:
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POLICIES
METHODOLOGIES
RESOURCES
»Policies»Methodologies»Resources
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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.
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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.
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Risk analysis is to be carried out with proper perception of risk of risk and cost involved in Analysis.Not to stick to one methodUnderstand company and industryShould be financially reasonableAccurate record keepingAmount of imagination of required
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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
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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.
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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.
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Source of variation• Equipment
• Tool wear, machine vibration, …• Material
• Raw material quality• Environment
• Temperature, pressure, humadity• Operator
• Operator performs- physical & emotional
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Control Chart Viewpoint
Variation due to Common or chance causes Assignable causes
Control chart may be used to discover “assignable causes”
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Run chart - without any upper/lower limits
Specification/tolerance limits Control limits - statistical
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Control chart functions• Control charts are powerful aids to understanding the
performance of a process over time.
PROCESSPROCESS
Input Output
What’s causing variability?
Noise
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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”
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Types of Data
• Continuous data• Product characteristic that can be measured
• Length, size, weight, height, time, velocity
• Discrete dataProduct characteristic evaluated with a discrete choice
• Good/bad, yes/no
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Control chart for variables
• Variables are the measurablemeasurable characteristics of a product or service.
• Measurement data is taken and arrayed on charts.
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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.
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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
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The Control Chart Method
R Control Chart: UCL = D4 x Rmean LCL = D3 x Rmean CL = Rmean Capability Study:PCR = (USL - LSL)/(6s); where s = Rmean /d2
X bar Control Chart: UCL = XDmean + A2 x Rmean LCL = XDmean - A2 x Rmean CL = XDmean
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Control Chart ExamplesControl Chart Examples
Nominal
UCL
LCL
Sample number
Var
iati
on
s
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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
m
i
i 1
XX
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UCL & LCL calculation
deviation standard
3XLCL
3XUCL
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Determining an alternative value for the standard deviation
m
m
ii
1
RR
RAXUCL 2
RAXLCL 2
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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 0.082 5.01 5.03 5.07 4.95 4.96 5.00 0.123 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.145 4.95 4.92 5.03 5.05 5.01 4.99 0.136 4.97 5.06 5.06 4.96 5.03 5.01 0.107 5.05 5.01 5.10 4.96 4.99 5.02 0.148 5.09 5.10 5.00 4.99 5.08 5.05 0.119 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
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Calculation
From Table above:• Sigma X-bar = 50.09• Sigma R = 1.15• m = 10Thus;• 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.
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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
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Trial control limitTrial 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
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X-bar Chart
4.94
4.96
4.98
5.00
5.02
5.04
5.06
5.08
5.10
0 1 2 3 4 5 6 7 8 9 10 11
Subgroup
X b
ar
LCL
CL
UCL
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R Chart
0.00
0.05
0.10
0.15
0.20
0.25
0 1 2 3 4 5 6 7 8 9 10 11
Subgroup
Ran
ge
LCL
CL
UCL
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Run Chart
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 5 10 15 20 25
Subgroup number
Ra
ng
e, R
6.30
6.35
6.40
6.45
6.50
6.55
6.60
6.65
6.70
0 5 10 15 20 25
Subgroup numberM
ean
, X
-bar
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X-bar ChartX-bar Chart
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R ChartR Chart
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Trial Control Limits & Revised Control Limit
6.30
6.35
6.40
6.45
6.50
6.55
6.60
6.65
0 2 4 6 8
Subgroup
Mea
n, X
-bar
0.00
0.05
0.10
0.15
0.20
0 2 4 6 8
Subgroup
Ran
ge,
R
UCL = 6.46
CL = 6.40
LCL = 6.34
LCL = 0
CL = 0.08
UCL = 0.18
Revised control limits
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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.
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The NormalThe NormalDistributionDistribution
-3 -2 -1 +1 +2 +3Mean
68.26%95.44%99.74%
= Standard deviation
LSL USL
-3 +3CL
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• 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.
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Define the 3-sigma limits for sample means as follows:
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
Normal Distribution Review
94345
0503015
3
07755
0503015
3
.).(
. Limit Lower
.).(
. Limit Upper
n
n
3
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Common CausesCommon Causes
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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.
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Assignable CausesAssignable Causes
(a) Mean
Grams
Average
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Assignable CausesAssignable Causes
(b) Spread
Grams
Average
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Assignable CausesAssignable Causes
(c) Shape
Grams
Average
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Improvement
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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.
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What Is Six Sigma?
• Degree of variation;• Level of performance in terms of defects;• 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
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Sigma is a letter
in the Greek Alphabet
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Six Sigma Definitions
• Business DefinitionA break through strategy to significantly improve
customer satisfaction and shareholder value by reducing variability in every aspect of business.
• Technical DefinitionA statistical term signifying 3.4 defects per million
opportunities.
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Sigma Level
Defects Per Million Opportunities
Rate of Improvement
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
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Six Sigma Project Methodology
ControlControlDefineDefine MeasureMeasure AnalyzeAnalyze ImproveImprove
Establish standards to maintain process;
Design the controls, implement and monitor.
Evaluate financial impact of the project
Identify, evaluate and select projects for improvement
Set goalsForm
teams.
Collect data on size of the selected problem,
identify key customer requirements,
Determine key product and process characteristic.
Analyze data, establish and confirm the “ vital few “ determinants of the performance.
Validate hypothesis
Improvement strategy
Develop ideas to remove root causes
Design and carry out experiments,
Optimize the process.
Final solutions
Project Phases
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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
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E-procurement system of Honeywell & VedantaBEN
CHM
ARK – 2 0 1 1
PSGIM, Coimbatore
Fri25 Feb
Questions Please ????