LSSGB Lesson5 Improve

Copyright 2014, Simplilearn, All rights reserved.


Lesson 5—Improve

Lean Six Sigma Green Belt


● Describe the concepts of Design of Experiments (DOE)

● Explain the uses of Residuals Analysis and Box and Cox Transformation

technique

● Discuss the cause and effect matrix and the 5 Why tool in the root cause

analysis

● Explain the Lean and Kaizen techniques

After completing this lesson, you will be able to:

Objectives

2


Improve

Topic 1—Design of Experiments


Design of Experiments (DOE) is a

structured method that tests input

variables and their impact on the output

variable. DOE:

● is used as a one-stop method for

analyzing all influencing factors;

● is preferred over One Factor at a Time

(OFAT) experiments; and

● uses techniques such as blocking and

replication.

Design of Experiments—An Introduction

Factor A Factor B Factor C ResponseTrials

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4

5

6

7

8

3

2 1

1

1

2

2

2

2

1

1

2

2

1

1

2

2

1

2

1

2

1

2

1

2

Blocking

4


DOE—Plastic Molding Example

The objective of the experiment is to achieve uniform dimensions for a part at a particular target

value to reduce variations.

Molding

Process Output(Y)Inputs(X)

Cycle Time

Mold Temp

Holding Pressure

Holding Time

PartHardness

Material Type

Factorsin the

experiment

Responseof the

experiment

5


Components of DOE in the Molding Example

The components of DOE are:

Cycle time, mold temperature, holding pressure, holding time, and material type

Part hardness that is measured as a result of the experiment and is used to judge the effects of factors

Output Response

Factors

Mold temperature (600° and 700°) and Plastic type (Fillers and No Fillers)Levels

Time∗Temp (the best level for time depends on the set temperature)Interactions

6


Full Factorial Experiment—An Example

Full factorial experimental design:

● contains all combinations of all levels of all

factors;

● ensures no possible treatment combinations

get omitted; and

● is preferred over other designs.

Factor B: Oven Time (X2)

Factor A: Draw Temperature (X1)

700° 900°

30 minsY1

Y2

Y5

Y6

60 minsY3

Y4

Y7

Y8

The table shown here is for a two-way heat treatment experiment, where Y = Part Hardness.

7


Analysis of the Mean Effect

Based on the example, an analysis of the means helps

in understanding how:

● a change in temperature at which the material is

drawn creates a difference in the average part

hardness;

● a change in oven time creates a difference in the

average part hardness; and

● interaction between temperature and time affects

the average part hardness.

Factor B: Oven Time (X2)

Factor A: Draw Temperature (X1)

700° 900°

30 minsY1

Y2

Y5

Y6

60 minsY3

Y4

Y7

Y8

The table shown here is for a two-way heat treatment experiment, where Y = Part Hardness.

8


The calculation of main effect is shown below.

Main Effect

The table shown here is for a two-way heat treatment experiment.

Results: We can see that changing the draw temperature seems to change the average hardness.

Results: We can see that changing the oven time seems to have no major change in the average hardness.

Factor B: Oven Time

Factor A: Draw Temperature

A1 = 700° A2 = 900°

B1 = 30 mins90

87

84

87

B2 = 60 mins95

92

79

78

9


The calculation of interaction effect is shown below.

Interaction Effect

Factor B: Oven Time

Factor A: Draw Temperature

A1 = 700° A2 = 900°

B1 = 30 mins90

87

84

87

B2 = 60 mins95

92

79

78

The table on the right shows the mean of the factors.

Results: The interaction plot shows that low temperature and high oven time should be selected to achieve the highest desired output of hardness. The parallel lines indicate the output if no interactions occur between the main effects.

95

90

85

80

30 min. 60 min.

900

700

Bri

nn

ellH

ard

nes

s

A1 A2

B1 88.5 85.5

B2 93.5 78.5

A,B, =90+87

2= 88.5

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The number of experiments in a DOE setting is known as Runs. The number of runs in a:

Design of Experiments—Runs

Full factorial experiment without replication on 5 factors and 2 levels are: 25 = 32

Full factorial experiment with 1 replication on 5 factors and 2 levels are: 32 + 32 = 64

Half fractional factorial experiment without replication on 5 factors and 2 levels are: 25-1 = 16

Half factorial experiment with 1 replication on 5 factors and 2 levels are: 16 + 16 = 32

The difference between a full factorial and half fractional factorial experiment can be seen through the number of runs.!

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Improve

Topic 2—Root Cause Analysis


During regression analysis of linear or non-linear model, the value for Y at level X is predicted.

However, the actual value for Y observed at that level of X is different from the predicted value. This

difference is called Residual.

The assumptions on random errors can be validated, as they:

● are independent,

● exhibit normal distribution,

● have a constant variance (σ2) for all the settings of the independent variables, and

● have zero mean.

Residuals Analysis

Residual = Observed value − Predicted value

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As the linear regression model is not always appropriate for the data, the appropriateness of the

model should be assessed by defining residuals and examining the residual plots.

● If all the assumptions are satisfied, residuals is zero and no systematic patterns are observed.

● In residuals analysis, the values of both sum and the mean of the residuals are zero.

● Residuals and diagnostic statistics help in identifying the patterns that:

o either poorly fits in the model with strong influence upon the estimated parameters, or

o have a high leverage.

Residuals Analysis (contd.)

Interpret these diagnostics together to understand any potential problems with the model.!14


Box and Cox is a procedure used for estimating the best transformation to normality, within the

family of power transformation. It works by taking the current Y data and raising it to a power (λ).

The family of power transformations can be used for the following:

● Converting a dataset so that parametric statistics can be used.

● Any continuous data > 0 (This will not work when the values ≤ zero, transforming specs).

Data Transformation using Box Cox

Y∗=

(Yλ−1)

λwhere λ ≠ 0

Y∗= log (Y) where λ = 0

The use of the transformation does NOT guarantee normality.!15


The table shows how the data can be transformed using Lambda.

Data Transformation using Box Cox (contd.)

Lambda Value Transformed Value

-2 Y-2 = 1/Y2

-1 Y-1 = 1/Y1

-0.5 Y0.5 = 1/Y0.5 = 1/√Y

0 Log(Y)

0.5 Y0.5 = √Y

1 Y1 = Y (i.e., no transformation)

2 Y2

16


The difference between original data and the data transformed using Box Cox is shown.

Data Transformation using Box Cox—Example

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Figure 1: Original data plotted on histogram Figure 2: Transformed data plotted on histogram

Abnormal Normal

Max λ = −0.3


To improve a process, the steps to assess the level

of improvement are as follows:

● Check for the relationship between the input

and output variables.

● Measure key process variables through metrics.

● Identify critical variables by the process owners.

● Multiply process output priority with the input

variables.

● Add the values to determine the results for each

input variable.

Process Input and Output Variables

Process Output Variables

A B C D E

Prioritization Number 4 1 7 11 5 Results %

Process Input Variables

1 3 4 7 117 33

2 8 5 3 4 96 27

3 6 2 46 13

4 7 5 32 9

5 3 4 65 18

Totals 356 100

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The Cause and Effect Matrix gives the correlation between input and output variables.

Cause and Effect Matrix Template

Rating of Importance to

Customer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Process Inputs

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

10 0

11 0

12 0

13 0

14 0

15 0

16 0

17 0

18 0

19 0

20 0

0

0

Total 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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The steps for updating the Cause and Effect Matrix are:

Cause and Effect Matrix—Steps to Update

List the input variables vertically.

List the output variables

horizontally.

Rank the output variables based on customer priority.

1 2 3

The input variables with the highest score become the point of focus.!20


The Cause and Effect diagram is used to find the root cause and the potential solutions to a problem.

It breaks down a problem into bite-sized pieces and also displays the possible causes in a graphic

manner.

The four steps to construct a Cause and Effect diagram are:

Cause and Effect Diagram

Brainstorm possible causes of the problem.

Classify the major causes under various

headings.

Draw a Cause and Effect

diagram with the problem at the

point of the central axis line.

Write the causes on the diagram

under the classifications

chosen.

1 2 3 4

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The figure shows the Cause and Effect diagram for solder defects on a reflow soldering line. This

diagram helps in collecting data and discovering the root cause.

Cause and Effect Diagram—Example

Methods Machinery

Materials Manpower

Type of Solder Paste

Components

Size

Shape

ThicknessComponents Packaging

Technology

Full reflow

Batch size

Component density

Fine pitchPreventive Maintenance

Training

Skill

Knowledge

Self-sufficiency

Weekends

Tools

Schedule

Operator

Oven

Shifts

Feeder

Types of Screen

Temperature

Speed

Reflow Defect

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The 5 Why Technique

5 Why is one of the tools used to analyze the root cause of a problem. In this technique:

● the responsibility of the root cause analysis lies with the team;

● the ‘why’ question is asked for every problem till the root cause is obtained;

● the interrogation is not restricted to five questions.

The 5 Why technique can be used along with the Cause and Effect diagram.!23


The process for the 5 Why technique is given here:

The 5 Why Process

Identify the problem and the problem statement.

Arrange for a team brainstorming session. Explain the purpose.

Ask ‘why’ for the answers obtained.

1 2 3

5

Analyze the problem and brainstorm backwards.

4

If a problem occurs, it is usually due to the process and not because of a person or team.!24


Nutri Worldwide Inc. investigated erroneous deliveries by the Delivery Management team using the 5

Why technique.

5 Why—Example

Why? Incomplete addresses given on the parcel.

Why? Complete addresses were not collected from the customers.

Why? The addresses were given to the operators over phone calls.

Why? The operators were unable to gage if the addresses were incomplete.

Why? An official format for capturing delivery addresses from customers was unavailable.

PROBLEM STATEMENT: Delivery of parcels to incorrect addresses

COUNTER MEASURE: Prepare a template capturing details such as flat number, street name and number, region, city,

and zip code along with the customer’s contact number.

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Improve

Topic 3—Lean Tools


Techniques Description

Kaizen

(Continuous Improvement)

All incremental changes routinely applied and sustained over a long period of time result in significant improvement.

Poka Yoke

(Mistake Proofing)

It is good to do it right the first time and even better to make it impossible to do it wrong the first time.

5S

(Sort, Set in order, Shine, Standardize, and Sustain)

Sorting items according to a rule, setting them in order, cleaning the area, forming and circulating a set of written standards, and sustaining the process.

Just in Time (JIT)Producing the necessary units, in the necessary quantity, at the necessary time, with the required quality.

Lean Techniques

The first four Lean techniques are:

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Techniques Description

Kanban

(Signboard)

Utilizing visual display cards to signal movement of material between steps of a product process.

Jidoka

(Autonomation)

Implementing supervisory function in production line and stopping the process as soon as a defect is encountered. The process does not start again till the root cause of the defect is eliminated.

Takt time

(TAKT Time = Time Available/Demand)

The maximum time in which the customer demands his needs to be met.

Heijunka Reducing waste occurring due to fluctuation in customer demand.

Lean Techniques (contd.)

The last four Lean techniques are:

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Cycle time reduction refers to the reduction in the time taken for a complete process. Low cycle time:

● increases productivity and throughput;

● releases resources early;

● reduces internal and external waste;

● simplifies the operational process; and

● decreases product damage.

Cycle Time Reduction

Low cycle time indirectly helps in satisfying the customer and staying ahead of competition.!29


The changes brought by implementing Lean techniques on an existing process are illustrated in the

diagram below.

Cycle Time Reduction—Example

Old process Improved process

Process 5

Process 1

Process 4

Process 2

Process 1

Process 4Process 5

Process 2

Process 3

Process 3

In

Operator 4

Operator 3

Operator 5

Operator 3

In

outOperator 2

out

Operator 1

Operator 1 Operator 2

Lean Techniques

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Kaizen and Kaizen Blitz—Introduction

Kaizen is a continuous improvement method to improve the functions of an organization. The

improvements could be in process, productivity, quality, technology, and safety.

Kaizen Blitz is known as Kaizen event or Kaizen workshop. Teams improve processes through

structured workshops.

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The differences between Kaizen and Kaizen Blitz are given here:

Kaizen and Kaizen Blitz—Differences

Kaizen

● Is a method

● Brings in incremental change

● Standardizes, measures, and compares the process

● Is lead by the management and the team

● Relatively delays the process of decision making

● Is an improvement process

Kaizen Blitz

● Is a workshop or an event

● Brings rapid solutions when required

● Plans, executes the event, decides a solution, and follows it through

● Is lead by the management

● Accelerates the process of decision making

● Is a part of improving the process

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Kaizen and Kaizen Blitz are practiced in many organizations. The examples are as follows:

Kaizen and Kaizen Blitz—Examples

Kaizen

In Toyota, the production cycle is resumed after the problem is identified and solved.

Kiazen Blitz

In a wood window company, scraps are eliminated, work areas are reorganized, and inventory is reduced.

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Quiz


a.

b.

c.

d.

QUIZHow many runs are there in a full factorial experiment with 3 factors, each at 2 levels?

6

12

8

9

1

35


Copyright 2012-2014,Simplilearn,All rights reserved

a.

b.

c.

d.

QUIZHow many runs are there in a full factorial experiment with 3 factors, each at 2 levels?

6

12

8

9

1

Answer: d.

Explanation: The answer is given by the total no. of levels for each factor to the power of no. of factors, which is equal to 23 = 8.

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a.

b.

c.

d.

QUIZThe input categories for a classical cause and effect diagram would not include:

manpower.

machine.

material.

maintenance.

2

37



a.

b.

c.

d.

QUIZThe input categories for a classical cause and effect diagram would not include:

manpower.

machine.

material.

maintenance.

2

Answer: a.

Explanation: The 4M of the cause and effect diagram includes categories like machine, material, method, and manpower, but not maintenance.

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a.

b.

c.

d.

QUIZCause and Effect diagram is also known as:

Gantt chart.

Ishikawa diagram.

Tree diagram.

Pareto chart.

3

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a.

b.

c.

d.

QUIZCause and Effect diagram is also known as:

Gantt chart.

Ishikawa diagram.

Tree diagram.

Pareto chart.

3

Answer: c.

Explanation: The Cause and Effect diagram was developed by Dr. Ishikawa and therefore also called the Ishikawa diagram.

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a.

b.

c.

d.

QUIZThe 5 Why analysis can be used as a part of:

Fishbone diagram.

DOE.

OFAT.

Cause and Effect matrix.

4

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a.

b.

c.

d.

QUIZThe 5 Why analysis can be used as a part of:

Fishbone diagram.

DOE.

OFAT.

Cause and Effect matrix.

4

Answer: b.

Explanation: The 5 Why analysis can be used along with the Cause and Effect diagram, which is also known as a fishbone diagram.

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a.

b.

c.

d.

QUIZWhat is the benefit of using cycle time reduction?

Reduction in managerial control

Streamlining of process

Effective risk management

Increase in resources

5

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a.

b.

c.

d.

QUIZWhat is the benefit of using cycle time reduction?

Reduction in managerial control

Streamlining of process

Effective risk management

Increase in resources

5

Answer: c.

Explanation: Cycle time reduction decreases the time taken for the entire process by streamlining it. It reduces waste using Lean methodology and thereby reduces cost and increases productivity.

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a.

b.

c.

d.

QUIZ A production line uses signs at specific points on the line to indicate when components or raw materials need to be replenished. This practice is an example of:

Kaizen.

Poka Yoke.

FMEA.

Kanban.

6

45



a.

b.

c.

d.

QUIZ A production line uses signs at specific points on the line to indicate when components or raw materials need to be replenished. This practice is an example of:

Kaizen.

Poka Yoke.

FMEA.

Kanban.

6

Answer: a.

Explanation: Kanban literally means signboards. Kanban uses display cards to signal movement of material.

46


a.

b.

c.

d.

QUIZKaizen Blitz is:

a methodology.

an event.

a part of the control plan.

an eliminating variation.

7

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a.

b.

c.

d.

QUIZKaizen Blitz is:

a methodology.

an event.

a part of the control plan.

an eliminating variation.

7

Answer: c.

Explanation: Kaizen Blitz is known as Kaizen event or Kaizen workshop. It is part of Kaizen.

48


a.

b.

c.

d.

QUIZWhich of the following statement is true for residuals analysis?

8

A random pattern of residuals exhibits a linear model.

A random pattern of residuals exhibits a non-linear model.

When the sum of all the residuals is less than zero, the data set is nonlinear.

When the sum of all the residuals is greater than zero, the data set is nonlinear.

49



a.

b.

c.

d.

QUIZWhich of the following statement is true for residuals analysis?

A random pattern of residuals exhibits a linear model.

A random pattern of residuals exhibits a non-linear model.

When the sum of all the residuals is less than zero, the data set is nonlinear.

When the sum of all the residuals is greater than zero, the data set is nonlinear.

8

Answer: b.

Explanation: A random pattern of residuals will always exhibit a linear model. If you have a non-random pattern it will always exhibit a non-linear model. In Residuals Analysis, the sum of residuals is always zero.

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a.

b.

c.

d.

QUIZThe Box Cox Transformation is used for:

9

fixing the data issues.

estimating the best transformation to normality.

analyzing if the data is normal.

plotting Box Cox.

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a.

b.

c.

d.

QUIZThe Box Cox Transformation is used for:

fixing the data issues.

estimating the best transformation to normality.

analyzing if the data is normal.

plotting Box Cox.

9

Answer: c.

Explanation: Box and Cox developed a procedure for estimating the best transformation to normality, within the family of power transformation.

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● DOE are a series of planned and scientific experiments that test the impact

of independent variables on dependent variables.

● During regression analysis of linear or non-linear model, the value for Y at

level X is predicted.

● Residuals Analysis helps you understand the accuracy of the analysis of

linear or non-linear model.

● Box and Cox procedure is used for estimating the best transformation to

normality within the family of power transformation.

● The 5 Why tool is used to analyze the root cause of a problem.

● Cycle time reduction, Kaizen, and Kaizen Blitz help in improving processes.

Summary

Here is a quick recap of what we have learned in this lesson:

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THANK YOU

LSSGB Lesson5 Improve

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