The document discusses risk management and quality control through statistical process control charts. It defines key risk management terms and outlines different types of risks. It then covers control charts for variables, including how to calculate control limits and determine whether a process is in or out of control. The document provides examples of X-bar and R charts and discusses how to revise control limits over time.
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
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 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
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 + 3σ
LCL = X − 3σ
σ = standard deviation
26. Determining an alternative value for
the standard deviation
m
∑R i
R = i=
1
m
UCL = + 2 R
X A
LCL = − 2 R
X A
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.
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
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
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.
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 of
Level 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 Methodology
Project 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 Outcome
3.Risk can fixed only when it is scalable
4.More than one form of risk can be present in a
project
5.100% assurance on risk control can be guranteed
6.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
Notes de l'éditeur
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.