Study Flash Cards

Front 1

Six Sigma is:

Back 1

A customer focused well defined problem solving methodology supported by a handful of powerful analytical tools

Front 2

Continuous improvement is:

Back 2

Driven by execution of carefully selected projects.

Front 3

The goal of the Six Sigma approach is to:

Back 3

take small steps forward and no steps backward.

Front 4

The purpose of all six sigma work and all improvement efforts is to

Back 4

better serve customer needs and expectations thereby providing increasing value to the customer and ensuring repeat business.

Front 5

Six Sigma Approach
Uses statistics solely as

Back 5

tools for interpreting and clarifying data

Front 6

Six Sigma Approach
Requires not just statistics but changes in

Back 6

the culture of the organization.

Front 7

Six Sigma Approach
Requires a deep commitment from?

Back 7

the highest levels of management

Front 8

Six Sigma Approach
Requires a tolerance for endlessly questioning the

Back 8

validity of sacred company beliefs and the traditional ways "things are done around here"

Front 9

Six Sigma says the law of diminishing returns

Back 9

does not apply

Front 10

For Six Sigma to be effective what 3 things must happen?

Back 10

there must be a process in place

the processes must be brought into control statistically

the processes must be improved (by reducing variation)

"Six Sigma Mantra"

Front 11

Six Sigma is:

Back 11

A customer focused well defined problem solving methodology supported by a handful of powerful analytical tools

Front 12

Continuous improvement is:

Back 12

Driven by execution of carefully selected projects.

Front 13

The goal of the Six Sigma approach is to:

Back 13

take small steps forward and no steps backward.

Front 14

The purpose of all six sigma work and all improvement efforts is to

Back 14

better serve customer needs and expectations thereby providing increasing value to the customer and ensuring repeat business.

Front 15

Six Sigma Approach
Uses statistics solely as

Back 15

tools for interpreting and clarifying data

Front 16

Six Sigma involves a series of steps designed to lead the organization through the gauntlet of process improvement. Major steps include:

Back 16

@DMAIIC
@D= Define
@M= Measure
@A=Analyze
@I= Improve (cost justification)
@I= Implement
@C= Control (standardize and Validate)

Front 17

D= Define the opportunity

Back 17

Improve on what matters most to the client and significantly impact the bottom line

Front 18

M= Measure the current performance

Back 18

Map the process gather initial performance data and determine current Sigma level.

Obtain client input factors critical to quality (CTQ)

Front 19

A= Analyze the current process

Back 19

Perform cause and effect analysis to determine reasons for gaps in performance.

Front 20

I= Improve process efficieency

Back 20

Determine breakthroughs design future state: new process, new "sigma" level

Create dashboards, scorecards and plans

Front 21

I= Implement improvements

Back 21

Execute plans, overcome barriers

Transition to the new process

Front 22

C= Control and adjust new processes

Back 22

Measure improvements and breakthroughs

Report dashboard, scorecard data and client feedback

Front 23

The study of variation begins with the teachings of

Back 23

Dr. Edward Deming... The theory of profound knowledge... essential for any organization that desired to be competitive in today's marketplace

Front 24

Profound knowledge is

Back 24

@Knowledge of systems
@Knowledge of Statistics (variations)
@Knowledge of psychology (Motivation)

Front 25

A system is

Back 25

a network of interdependent components that work together to try to accomplish an aim.

Front 26

Systems are

Back 26

managements responsibility.

Front 27

Managements job is to

Back 27

optimize the entire system over time.

Front 28

Variation happens it is

Back 28

the voice of the system or process

Front 29

Use statistics to make

Back 29

the invisible visible ...show patterns and types of variations

Front 30

Distinguish between

Back 30

special and common causes of variation

Front 31

Variation

Must act according to

Back 31

type of cause and avoid tampering.

Front 32

Shrink variation equals?

Back 32

reducing variation reduces costs

Front 33

Psychology

People want to

Back 33

do a good job and contribute and have pride and joy in their work

Front 34

Psychology

need to tap into

Back 34

intrinsic motivation

Front 35

Psychology

Must

Back 35

drive out fear and build trust.

Front 36

Everything does what

Back 36

Varies

nothing is exactly 100% repeatable.

Front 37

About Variation

Special Cause

Back 37

find special causes and remove.

Those closest to the process are most likely to find the special causes of variations

Front 38

About Variation

Common Cause

Back 38

Take action on the system

Management is responsible for the system

Front 39

When only normal variation (common cause) is present in a process the process is said to

Back 39

be stable

Front 40

Stable processes are

Back 40

predictable

Front 41

Stable processes are in

Back 41

(statistical) control

Front 42

Stable processes have a

Back 42

known process capability

Front 43

A process is said to be in statistical control when

Back 43

through the use of past experience we can predict how the process will vary in the future. CONTROL = PREDICTABILITY

Front 44

CONTROL EQUALS

Back 44

PREDICTABILITY

Front 45

Theorem
Basic theorem of variation

Back 45

If you always do what you always done you will always get what you always got.

Front 46

Corollary to the Basic Theorem of Variation

Back 46

Insanity... always doing the same thing over and over again expecting a different result

Front 47

In order to monitor any process effectively there are several pieces of information that must be known:

Back 47

@ Central location
@ Spread
@ Shape (bell shaped curve)
@ Relationship of the variation to time

Front 48

A Histogram is

Back 48

A graphical representation of data in a bar chart format.

Front 49

What is used to observe the "SHAPE" of the data?

Back 49

Histograms

Front 50

Central location

Back 50

@Mean
@Median
@Mode

Front 51

Variability

Back 51

@Range
@Standard Deviation
@Shape

Front 52

Measures of Central Location

Back 52

@Average
@Mode
@Median
@Arithmetic Mean

Front 53

The Average is

Back 53

the expected value or the balance point

Front 54

Mode is

Back 54

the most frequently occurring value

Front 55

Median is the

Back 55

middle value

Front 56

Arithmetic Mean is

Back 56

the total of the individual values divided by the number of individual values.

Front 57

-
x

Back 57

Represents the symbol for average.

Front 58

If the shape of the distribution is not symmetrical use what

Back 58

median of the mode

Front 59

if the shape of the distribution is symmetrical use

Back 59

arithmetic mean

Front 60

6,7,7,8,8,8,9,9,9,9,10,10,11,11,12,13

The mode is?

Back 60

9 because it is the most frequently occurring value.

Front 61

6,7,7,8,8,8,9,9,9,9,10,10,11,11,12,13

The median is?

Back 61

9 because it is the middle value

Front 62

6,7,7,8,8,8,9,9,9,9,10,10,11,11,12,13

The mean is?

Back 62

9.1875 because it is the actual average.

Front 63

Measures of Variability are

Back 63

Range

Standard deviation

Front 64

The range is

Back 64

the difference between the largest and the smallest values in the sample.

Front 65

The symbol for range is

Back 65

R

Front 66

Standard Deviation is

Back 66

a mathematical measure of the variability of the data about the mean.

Front 67

The symbol for Standard Deviation is

Back 67

S

6

Front 68

Between plus and minus one standard deviation of the mean we normally expect to find about what % of the values

Back 68

68%

Front 69

With in 2 standard deviations of the mean we would expect to find approximately what % of the values

Back 69

95.5%

Front 70

Within 3 standard deviations of the mean we would expect to find about what % of the values?

Back 70

99.73%

Front 71

3 standard deviations 99.73% of the values, represent what?

Back 71

virtually all the values and the expected limits of common cause variation necessary for a stable and predictable process

Front 72

Six Sigma Quality

Customer requirements are ?

Back 72

6 standard deviations from the mean in either direction.

Front 73

3 sigma equals ? errors per million

Back 73

27 errors per million

Front 74

6 sigma equals ? errors per million?

Back 74

3.4 errors per million

Front 75

six sigma total range is ?

Back 75

12 which means 6 standard deviations each side of the mean.

Front 76

The Grand Average

Back 76

is the process average. it is usually the average of the sample averages. (as long as all samples are the same size)

It is also the averge of all the individuals.

Front 77

The symbol for grand average is?

Back 77

_
_
x

Front 78

Calculate the grand average for
@2,3,6,9,1
@3,6,4,1,11
@2,7,22,0,9
@-4,8,0,14,0

Back 78

@2,3,6,9,1....4.2
@3,6,4,1,11...5
@2,7,22,0,9...8
@-4,8,0,14,0...3.6
@20.8/4=5.2

Front 79

Average Range is the ?

Back 79

estimate for total process variability.

The average range is the average of the sample ranges

Front 80

the symbol for average range is?

Back 80

_
R

Front 81

The Grand Average

Back 81

is the process average. it is usually the average of the sample averages. (as long as all samples are the same size)

It is also the averge of all the individuals.

Front 82

The symbol for grand average is?

Back 82

_
_
x

Front 83

Calculate the grand average for
2,3,6,9,1
3,6,4,1,11
2,7,22,0,9
-4,8,0,14,0

Back 83

2,3,6,9,1....4.2
3,6,4,1,11...5
2,7,22,0,9...8
-4,8,0,14,0...3.6
20.8/4=5.2

Front 84

Average Range is the ?

Back 84

estimate for total process variability.

The average range is the average of the sample ranges

Front 85

the symbol for average range is?

Back 85

_
R

Front 86

The Grand Average

Back 86

is the process average. it is usually the average of the sample averages. (as long as all samples are the same size)

It is also the averge of all the individuals.

Front 87

The symbol for grand average is?

Back 87

_
_
x

Front 88

Calculate the grand average for
2,3,6,9,1
3,6,4,1,11
2,7,22,0,9
-4,8,0,14,0

Back 88

2,3,6,9,1....4.2
3,6,4,1,11...5
2,7,22,0,9...8
-4,8,0,14,0...3.6
20.8/4=5.2

Front 89

Average Range is the ?

Back 89

estimate for total process variability.

The average range is the average of the sample ranges

Front 90

the symbol for average range is?

Back 90

_
R

Front 91

Calculate R bar for the following data:
2,3,6,9,1
3,6,4,1,11
2,7,22,0,9
-4,8,0,14,0

Back 91

2,3,6,9,1-------8
3,6,4,1,11------10
2,7,22,0,9------22
-4,8,0,14,0-----18
--------------------58/4=14.5

Front 92

Data is described to assist with the analysis in six sigma. in order to completely describe data we need to know the following?

Back 92

Location (Histogram/ranges)
Spread (Histogram/ranges)
Shape (Histogram/ranges)
Variation over time (off the control chart)

Front 93

Most measures have ?

Back 93

Targets. for example an organization may promise delivery in 24 hours.

Front 94

A histogram shows us the ? of distribution

Back 94

shape. bell shape or normal curve

Front 95

Sometimes the shape is not normal what must we do?

Back 95

we must compare our shape with the expected shape to see if the process is behaving like it always has.

Front 96

Use the Histogram to compare the

Back 96

observed shape compared to the expected shape

Front 97

If the Histogram pattern is different from what we expect then?

Back 97

we may not be doing what we always have. or we may not be predictable or we may not be stable.

Front 98

Process monitoring is performed to

Back 98

determine the type and amount of variation that is present in a process as time goes by

Front 99

The two types of variation are

Back 99

common cause
&
Special Cause

Front 100

Control Charts are

Back 100

Statistical tools which shows the amount and type of variation present in any process that is being monitored.

and describe the representative nature of a stable, predictable, in a control process

Front 101

Control chart shows how much?

Back 101

variation we have and what type of causes

Front 102

5 Components of control charts are:

Back 102

UCL upper control limit

LCL lower control limit

CL Center line (shows where the character istic average falls

USL Upper specification limit or upper customr requirement (come from customer)

LSL Lower specification or lower customer reuirement.

Front 103

UCL

Back 103

upper control limit

Front 104

LCL

Back 104

lower control limit

Front 105

CL

Back 105

Center line (shows where the characteristic average falls

Front 106

USL

Back 106

Upper specification limit or upper customer requirement (come from customer)

Front 107

LSL

Back 107

Lower specification Limit or lower customer requirement.

Front 108

Control limits describe the

Back 108

representative nature of a stable process. specifically control limits identify the expected limits of normal, random or chance variation that is present in the process being monitored.

Front 109

Control limits are set by

Back 109

the process. The process sets the limits itself. the only way to change control limits is by changing process.

Front 110

Specification Limits are those limits that describe what?

Back 110

the characteristics the product (or process) must have in order to conform to customer requirements or to perform properly in the next operation

Front 111

Specific limits come from

Back 111

directly from the customer

*what the product or process that may be to keep the customer happy

*easy to change

Front 112

Control limits different types of charts come in two categories

Back 112

Variables & Attributes

Front 113

Control limits different types of charts.

Variables

Back 113

* Averages and ranges, Green Belts are expected to be able to construct and interpret this control chart

* individuals and Moving ranges. This is useful in service applications

* Averages and standard Deviations

* Medians and Ranges

Front 114

Control limits different types of charts.

Attributes (Things we count)...

Back 114

*Percent Defective
*Number Defective
*Number of defects
*Defects per unit

Front 115

Constructing an Xbar and Rbar chart

Back 115

Identify characteristic and sampling scheme

Record Data

Calculate sample average and sample range

Calculate grand average and average range

If stable (histogram of individual measurements) calculate limits

Calculate control limits

Construct control charts

Plot initial data points

Interpret chart with respect to variation common cause special cause.

Front 116

What is needed for a sampling scheme (3)

Back 116

Determine sample size

sampling frequency (how often do we measure)

Make sure we have a big enough sampling to be representative

Front 117

Charts do what?

Back 117

Monitor processes and provide a record of behavior over time.

Front 118

When out of control it means

Back 118

not stable

special cause variation is present

Stop and identify the special cause

Front 119

Control limits describe what?

Back 119

the representative nature of a stable process. Specifically they identify the expected limits of normal, random or chance variation that is present in the process being monitored.

Front 120

Process Capability is ?

Back 120

the measured, inherent reproducibility of the product turned out by the process.

Front 121

Process capability can be quantified from what?

Back 121

data which in turn are the results for measurements of work performed by the process. It defines limits we would normally expect virtually all individuals to fall within.

Front 122

By definition a capable process is operating at?

Back 122

3 sigma level or 99.73%

Front 123

Process capability is the range

Back 123

over which the natural variation of a process occurs as determined by the system of common causes.

Front 124

Process capability it is the ability of

Back 124

the combination of people, machines, methods, materials and measurements to produce a product or service that will consistently meet design specifications.

Front 125

Process capability is measured by

Back 125

the proportion o output that can e produced within design specifications.

Front 126

Process capability is a measure of the uniformity of

Back 126

the process

Front 127

Process Capability can be measured only

Back 127

if all special causes have been eliminated and the process is in a state of statistical control.

Front 128

If process is not in control

Back 128

don't estimate its capability, its meaningless.

Front 129

Components of Process Capability are?

Back 129

Design specifications
Centering of natural
variation

Range or spread of variation

Front 130

Capability measures...Short term show the

Back 130

capability oat a specific instance in time e.g.5 out of 90 samples did not meet customer requirements

Front 131

Capability measures .... Long term show

Back 131

the expected capability of the process based on the statistical projections using inherent process variability

Front 132

What must be performed prior to calculating any process capability measures?

Back 132

Histogram
&
Control Chart

Front 133

Greenbelts are expected to be able to calculate

Back 133

Percent or Proportion Non Conforming

Cp Index
Cpk Index

Front 134

Percent or proportion Non Conforming tells us what?

Back 134

once we know this we know defects/million

known sigma level

Front 135

Cp Index and Cpk Index are what

Back 135

quality metrics ... a quick way to tell how we are doing relative to how we should be doing

Front 136

Percent Non Conforming reflects what

Back 136

the proportion of the population that we normally expect not to meet the process specifications.

Front 137

Percent Non Conforming corresponds to

Back 137

the tail areas on the normal curve sketch

Front 138

Capability Indices show the relationship

Back 138

between the process capability and the process specifications

Front 139

Cp measures

Back 139

potential capability assuming that the process average is equal to the midpoint of the specification limits and the processes operating in statistical control.

Front 140

Cpk reflects the

Back 140

current process mean's proximity to either specification limit. (when the process is centered Cp = Cpk) Although the indices are calculated differently the interpretation is the same. always use Cpk

Front 141

If Cpk is less than 1

Back 141

we are not capable

Front 142

if Cpk is equal to 1

Back 142

then equivalent to 3 sigma level and therefore capable.

Front 143

If Cpk is greater than 1

Back 143

then we are more than capable

Front 144

Understand Who? and Understand What?

Back 144

Who your customers are and what you provide for them.

Front 145

W@hat distinguishes Six Sigma from the other Quality Improvement Methods?

Back 145

Justifying improvements int eh language of management.

Front 146

D in DMAIIC IS

Back 146

Define. the hardest part of DMAIIC.

Front 147

D in DMAIIC means what

Back 147

Define
it includes identifying the problem, and setting the scope. It also includes:

Identifying the customers and what is important to them
Determining the outputs
Determining the inputs
Determining what is critical

Front 148

Tools used in Defining are

Back 148

Process Analysis
Flow Charting
Check Sheets
Pareto Analysis
Cause and ?Effect Diagrams
FMEA(Failure Mode Effects Analysis)

Front 149

The second step in DMAIIC is

Back 149

M for Measure.
Identify and verify critical quality characteristics

Estimate current capability

Determine where you are relative to desired objectives

Front 150

Common tools used to measure

Back 150

Process Capability (Percent Nonconforming, Capability Indices)

Measurement Systems Analysis

Cost of Quality (Appraisal, Detection, Failure)

Front 151

Third Step in DMAIIC is

Back 151

Analyze

Front 152

Analyze does what

Back 152

makes sense out of the data that is collected during measure.

This shows the amount of improvement that might be possible to make the critical quality characteristic "best in class"

Front 153

Possible Analysis tools are

Back 153

Descriptive Statistics
Inferential Statistics
Probability
FMEA prioritize potential failures according to their risk and drives actions to eliminate or reduce their likelihood of occurrence

Front 154

Improvement

Back 154

in order to improve, possible improvements are evaluated in a logical and planned fashion

Front 155

Improvement tools

Back 155

Design of experiments (ANOVA, Factorial)

Simulation
Cost Justification
FMEA
Project Management
Correlation
Regression (linear, Multivariate)

Front 156

Implement means

Back 156

Improvements are implemented in a logical and planned fashion

a project plan is developed and managed.

Front 157

C stands for

Back 157

Control

Front 158

In the control phase

Back 158

measures have been implemented and steps are taken to make sure improvements are maintained.

Front 159

Tools for control are

Back 159

Statistical process control cost of Quality
Cost Analysis
ISO 9000

Front 160

Implementation Strategy items

Back 160

Top Management Support and Participation

Project Identification

Resource Allocation

Data Based Decision Making

Measurement and feedback

Front 161

Implementation process of Six Sigma must be

Back 161

Top down approach.
Responsibility must lie with senior management.

Senior management must drive the process through the organization.

Elements of this include selection of projects allocation of resources and decisions based on measurements

Front 162

Steering committee

Back 162

Identifies projects

Identifies black belts

allocates resources

monitors progress

reviews effectiveness

establish implementation strategy and policies

Front 163

Cost is the

Back 163

sum of the labor raw materials and overhead used to produce it. The minimum cost required to accomplish the functions desired is necessary cost and cost above this is unnecessary

Front 164

Variable cost is directly

Back 164

associated with the production of a product: direct labor direct materials

Front 165

Fixed costs is

Back 165

not affected by a change in level of production.

Front 166

Break Even Point

Back 166

Point at which total costs equals total revenue.