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Unit 14 - The Lean Enterprise

The purpose of this unit:

• This unit looks at what you need to think about when

setting up a team for a Six Sigma project, focusing on

how you determine what you need as well as discussing

the roles within the team.

• It also gives an overview of the different belts and

explains the importance and relevance of

communications within a Six Sigma Project.

• Timing: 50 – 60 minutes

Copyright: Management and Strategy Institute

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What is Lean?

• Throughout this course, we have made several

references to Lean tools, Lean processes, and how Six

Sigma has borrowed from Lean. But, what is lean?


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What is Lean?

• Lean is a manufacturing process that was developed by Toyota in 1988.

• It is based upon 2 key principles – the removal of irregularity and the removal of irrelevance. That is, attempting to get uniformity within production and removing wasteful processes. It uses a whole set of tools:

– Kanban – enabling just-in-time delivery within production, so that stock is not held waiting to be used

– TIMWOOD – an acronym for Transport, Inventory, Motion, Waiting, Overproduction, Over processing, and Defects; all areas for examination of savings that are probably the most commonly referenced.


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What is Lean?

• This chart shows some of the most common tools you

will use.

Kaizen Poka yoke Takt time 5 Whys 5S Spaghetti

diagram Touch

time Work

times Kanban Theory of

Constraints Value

Stream Maps

Cycle times


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What is Lean?

• The main thrust of the Lean process is to get the most

streamlined process within a production environment.

• It wants to create the most consistently “good product” in

the most efficient way.


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What is Lean?

• As you can see with this very short summary of Lean,

there are many overlaps with Six Sigma.

• This has led many businesses to combine them together

and develop Lean Six Sigma teams which use both sets

of tools; which, given that there are some tools within Six

Sigma borrowed from Lean, is not such a huge change.

• However, it can create conflicts of delivery, and Project

Charters need to clearly state what is being sought by

the team in terms of Lean and Six Sigma improvements

and need to understand that benefits must be measured.


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The Lean Enterprise

• The measurement systems used in the Lean Enterprise

require modification to establish benchmarking for

performance comparison and uncover best practices for

gaining a competitive advantage.

• Standardization of metrics helps to collaboratively

measure, control, and manage processes. Sample

metrics for an enterprise such as supply chain include:

– Return on working capital

– Perfect order fulfillment

– Order fulfillment cycle time

– Supply chain management costs


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The Lean Enterprise

• When implementing Six Sigma and the Organization Enterprise wide deployment, it is important to understand these factors:

– Understand who your customers are and what is important to them

– Understand customer feedback through the Voice of the Customer and determine the requirements for your product

– Prioritize issues related to your product

– Determine internal processes and what causes variation

– Determine the causes of defects

– Develop ways to address defects

– Develop metrics to standardize and measure the changes made in the process


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The Lean Enterprise

• The Lean enterprise includes all types of enterprises

including manufacturing, service, transactional, product

and process design, as well as innovation.

• Six Sigma and Lean have over a 20-year history in

developing the Lean enterprise and creating culture,

process improvement tools, methodology, and

methodology for quality improvement. The results from

Six Sigma and Lean typically include:

Increased profits Less overhead Improved delivery

Lower operating costs Increased customer satisfaction Better supplier relations

Decreased costs Higher sales Less inventory

Benefits of the Lean Enterprise


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The Lean Enterprise

• Lean enterprise eliminates waste and non value-added

activities. Lean focuses on delivering more value to the

customer and addressing the voice of the customer. It

creates efficiency based on optimizing flow in a process

and empowers employees to improve their work. Lean

always asks, “how can we get better?”

• Lean does not eliminate people or employees, is not a

shortcut, and does not micromanage. Lean focuses on

removing the non value-added delay, waste, and rework

from your processes. Lean can be used in any industry or

business to improve speed, quality, and cost. When

implemented properly, Lean Six Sigma focuses on results,

not training.


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The Lean Enterprise

• Executive leaders are responsible to the organization to provide support for the Lean enterprise and process improvement initiatives.

• The success of Six Sigma and Lean is based on building a culture of support to support the program.

• Management supports these initiatives through development of a constant mindset of improvement by encouraging and supporting designing the best possible processes.

• Leadership also prioritizes projects, approves them, and celebrates the success of them. Leadership is then responsible for mentoring to ensure the tools, support, and knowledge are available to succeed.


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Understanding Lean

• Lean Six Sigma combines the process improvement

benefits of the Six Sigma method with the waste

reduction benefits of Lean. Lean seeks to reduce waste

in these forms:

– Elimination of defects

– Continuous improvement

– Elimination of non-value added activities

– Use of Kanban pull systems

– Flexibility to respond to variation


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The History of Lean

• The value of Lean begins with identifying activities to add value to processes or services and making sure that value-added activities are performed effectively and efficiently.

• By beginning with waste removal, Lean processes are able to build in synchronized activities which minimize unnecessary or excessive activities.

• Once processes have had waste removed, Black Belts typically review performance and design to reduce error rates and variability, thereby reducing waste and non value-added activities. These are the two main goals of Lean.


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Lean & Six Sigma

• Six Sigma is used to reduce error rate and process

variability, and Lean is used to reduce waste and non

value-added activities.

• The two work in an interrelated manner, because a high

error rate can lead to excessive waste, and excessive

waste can lead to a high error rate. By applying both,

organizations can address both waste and errors.


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Lean & Six Sigma

• There are several inherent differences in Lean and Six

Sigma:

Six sigma Lean

Is driven by leadership Is driven by middle management

Synchronizes employee skills Synchronizes resource utilization

Can be achieved without lean Provides tools to sustain lean

Builds on lean operations

Can be considered a prerequisite for six

sigma implemented for the purposes of

efficiency and waste reduction.


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Lean & Six Sigma

• Lean principles can work on any organizational improvement. Employees play a critical role in Lean implementation. Significant improvements can be the result of Lean initiatives for cycle times, lead times, productivity, throughput, and processes.

• Typical improvements include:

– Decreased setup time

– Reduced lead time

– Reduced people effort

– Reduced inventory

– Improved productivity

– Reduced facility floor space requirements


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The Seven Elements of Waste

Muda

• Transport

• Inventory

• Motion

• Waiting

• Over Producing

• Over Processing

• Defects

• Skills


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• The 7 classic wastes include overproduction, inventory,

defects, over-processing, waiting, motion and

transportation.

• An 8th element of waste is emerging called “knowledge

and latent skill.” This is where organizations fail to take

advantage of skills or talent or are not effective at

transferring learning between employees.

• These wastes can be found by determining the Rolled

Throughput Yield. A further explanation of each individual

term follows.


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• Defects are wastes of correction that are wastes, defects, or imperfections.

• Overproduction wastes are those of over producing and making too much.

• Transportation waste is that of material movement back and forth from storage.

• Waste of waiting is when resource labor and equipment could be making other products.

• Waste of inventory is any cost associated with items not on just-in-time inventory.

• Waste of over-processing is that with unneeded steps in production.

• Waste of motion is unnecessary travel around a factory.


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5S

• The 5S method is a tool to control outcomes and make

improvements to keep track of the changes

implemented.

• It stands for sort, straighten, shine, standardize, and

sustain. It can be used for any process or service.

• Sorting is just as it states, organizing and separating

what you need and don’t need.

• Straighten means to straighten up and arrange items you

need for your process or service so they are easily

identified.


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5S

• Shine means to clean up your area and set it up where

you can keep it clean.

• Standardize means to organize the first three S’s so

everything has a place.

• Sustain means to keep it going in all of your areas.

• The 5 S method improves safety and communication,

improves process flow, increases compliance, reduces

space requirements, boosts morale, removes non value-

added steps, and reduces time wasted looking for items.


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5S

• 5S helps by eliminating the unnecessary, establishing a

place for what remains, and cleaning up remaining

equipment, tools, and storage devices.

• This helps reduce clutter and needed items are readily

found.

• Visual cues and visual management are used to improve

consistency. These are signs, labels, stickers, and cards

marking where things go.


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5S

This graphic shows the Japanese origins and the literal

translations into English for the 5S processes.


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Unit Summary

In this unit, you have learned about:

• The history of Lean

• Waste reduction benefits of Lean

• Elimination of defects

• Continuous improvement

• Elimination of non value-added activities

• Use of Kanban pull systems and 5S


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Unit 15 - Selecting Lean Six Sigma Projects


• This unit looks at selecting Six Sigma Lean projects, how

to build the project charter, and how to build the

necessary business case.



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Selecting Lean Six Sigma Projects

• Lean Six Sigma projects are most often chosen by an

organization through a link to the overall strategy of the

organization and the return on investment for a project.

• There are several methods for selection of a Six Sigma

project.

• Matrix diagrams are a planning tool for displaying the

relationships among various data sets. During the

Champion phase or initial charter phase of the Define

stage, organizations often use a prioritization matrix to

rank competing priorities. This is a scoring or ranking

system which can also be a tool for the voice of the

customer. If the customer has multiple issues requiring

quality control, they can rank them in order of priority to

the organization.


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• Process decision program charts (PDPC) identify what may go wrong in a plan under development. Once possible issues are identified, prevention controls and countermeasures can be developed to prevent the problems.

• PDPC charts are used in these situations:

– When the price of failure is high

– Before implementing a plan

– When the plan must be completed on schedule

• The steps used to develop the PDPC chart include developing a tree diagram of the proposed plan, reviewing each task, brainstorming what could go wrong, reviewing all the potential problems and brainstorming possible countermeasures for each potential problem, and then determining which to implement.


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The sample shows the weighted section after weighted

scoring on quality, efficiency, and performance

aspects. The following chart shows a weighted Six

Sigma project prioritization matrix.

23.1 : Sample Project Selection Matrix


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Understanding Lean

• There are many tools which can be used for “leaning” a

process once the project is selected.

• The most common lean tools include:


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Lean Tools

Kaizen Poka Yoke Takt time 5 Whys

5S Spaghetti

diagram

Touch time Work times

Kanban Theory of

constraints

Value stream

maps

Cycle times


• In addition to the prioritization matrix, project viability

matrices are used to determine the viability of a project.

• Notice the weighting goes 1 to 5, with scoring of

definitely no, no, possibly, definitely, etc.


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Criteria

Number

1

Are customers

(internal/external)

dissatisfied or

defecting?

3 X

2Is the process relatively

stable?3 X

3

Is the specific defect

(defined by customer)

known?

4 X

4

Is data related to the

defect available or

collectable?

5 X

5Is the solution not

obvious?3 X

6

Are the expected

benefits significant

enough?

3 X

7

Will service and/or

quality be noticably

improved?

2 X

8

Can the project be

completed within 6

months?

4 X

1 5.3 6 2.7 1

Total Score 2.8

Weighted Scores

Project Viability Matrix

Description Weighting Definite No (1) Mostly No (2) Possibly (3) Mostly Yes (4) Definite Yes (5)

23.2 : Sample Project Selection Matrix


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Building the Business Case and Project Charter

The actual project charter is a formal document used to summarize key deliverables and information for a Six Sigma project, and it provides the official authorization to move through with the project. The project charter includes these components:

• The key problem/s to be resolved

• The need for the resolution

• The mission statement or goal of the project

• The details of the project team composition

• Key stakeholders

• The scope of the project

• The resources required and authorization

• Project phase critical path and timelines


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23.3 : Sample Project Charter


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This is an actual project

charter for a restaurant

wanting to increase revenue

by reducing customer wait

times.


Your charter should include and ask the following questions in

the business case:

• The name of the project

• The aim of the project, called Aim statements

• Why the project is needed?

• What are the consequences of not doing the project?

Opportunity costs?

• What other projects have high priority? Priority Matrix

• What strategic goals are met by this project?

• Problem Statement: Summarizes and describes the problem,

opportunity, or objective in concise, measurable terms.

• Goal Statement: Describes the team’s improvement objective

this should tell what you intend to improve, reduce, eliminate

or control.


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• The Project Charter is composed

of details and tasks which may

vary with each organization and

project, but the basic structure for

projects and charters is to begin

with an executive summary. The

charter is written as a roadmap

for the project. It is geared

towards senior management as a

plan and for the project team as a

summary of what is to be done.

The next section of the charter

aligns the project with

organizational strategy.


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• Project Summary

• Strategic Alignment

• Goals

• Costs

• Risks

• Deliverables

• Timelines

• Constraints

Unit Summary


• How to select Six Sigma Lean projects

• How to build the project charter

• How to build the necessary business case


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Unit 16 - Six Sigma Statistics


• This unit goes through the statistical knowledge required

for Six Sigma Projects as well as how to use them most

effectively.

• There is also some discussion around software usage in

Six Sigma.



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Six Sigma Statistics

• Throughout a Six Sigma project, it is evident that there is

a large amount of reliance upon statistical analysis.

• In the very early stages, we stated that its use of

statistics is one of the key elements that separate Six

Sigma from many other quality methodologies.


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• They enable informed analysis of data from sampling

through to modeling potentials.

• They help the practitioner make an informed decision

with validity as the project progresses.


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• The main elements of statistics used within Six Sigma

are covered in the following section.

• Basic Statistics:

This includes understanding the core principles and

mathematics at the heart of statistics - how to work out

averages and variances amongst other commonly used

terms. These can be seen below:


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• Average – there are 3 types of averages most commonly

used in statistics: the mode, median, and mean average.

• The mode or modal point is a range that occurs most

frequently. So, if you have a set of results, the mode can

be attributed to that result found the most often.

• The median average is the center point of a range of

values, so if you have values ranging from 10 to 50, the

median point will occur at 30.


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• The mean average is the one most frequently used and

referred to as average, but it requires math to work it out.

• It is worked out by adding together all of the values and

dividing by the number of total values. For example, if

you receive 50 responses to an item and the results are

all added together, then this result is divided by 50 to

give the mean average. The mean average will often be

represented by a (“x bar”).


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• Variance is a calculation across a range of data that

shows how spaced out the information is.

• Similarly the standard deviation will be a spacing value

of information.

• The variance is the result of squaring the standard

deviation.

• So, where the Standard Deviation (SD) is often

represented by σ (the Greek letter sigma), then variance

(VAR) will be represented by σ 2 (sigma squared).


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• To calculate the SD from the variance, the first square

root is taken of it (√). The formula to calculate the VAR

can be seen below. Before working through the equation

the mean average must be calculated ( ).

• Variance σ 2= 2


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• In other words, you calculate the differences between

the results and the mean average, then square them,

add them up, and divide by the number of results. The

SD is just the square root of this result.


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• The standard deviation is used frequently in determining

confidence levels and probability of a result falling within

a certain range.

• This unit is not designed to give you a full understanding

of all potential mathematical formulas that you may

need.


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• But, you can see where the Six Sigma comes from,

where SD relates to a sigma, and that statistics states

that you can be 99.99966% confident of a containment

within 3 sigmas on either side of the mean average.

• There are many other statistical formulas, but these are

the core groundwork for any and all statistics work. If you

can understand these, then you can understand most.


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Charts and Graphs

• The use of graphical representation is fundamental

within statistics. There are many common types of

graphs, none more so than the normal distribution

graph.

• This graph is shaped like a bell and often referred to as a

bell chart. An example can be seen below. In a normal

distribution curve, the mean, median, and mode are all

the same value.


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• Charts and graphs allow us to plot occurrences,

frequencies, occasions, and all manner of data together

to be viewed without the need for the numbers.

• They are often used to see patterns or shapes to try and

make formulas fit into some shape of equation. There

are some statistics around the line of best fit, and that

relates to a frequency line showing the line of most likely

results, almost like a rolling average of results based on

the moving data displayed. An example of this can also

be seen below.


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10.1: A simple Normal distribution graph, where 0 is the mean average

point


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10.2: A line of best fit shown by the red central line


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Time Based Charts

• These are used to show change over a period of time

and can often be similar to the graphs as shown above,

but the horizontal or x-axis is reflective of the progress of

time.


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• These are useful for determining if a process has

optimum time periods or if things degrade after a certain

amount of time – particularly useful with monitoring

automated or semi-automated processes using

equipment in use for long periods of time.

• This can also demonstrate when employees should be

taking breaks, indicating periods of sustained

performance before degradation.


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Analysis of Variances and Tolerances

• We discussed what a variance is above. When you have

a number of events, you can compare the variances of

each event to see if there are lower variances with each

observation period.

• A good use of this occurs through the Analysis and

Improve stages of DMAIC when you are trying to

determine optimized processes.

• Looking at the variances with each simulation or walk-

through can allow you to determine which processes are

most effective and should be considered.


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• When considering variances, tolerance levels come into

play. A tolerance is the room around which a set value

can fluctuate, and an allowable tolerance is given in all

functionality.

• The aim of Six Sigma is to reduce that tolerance need as

much as possible.


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• If you consider the outcome of variance or SD

calculations – it can be linked that your tolerance level

should be no more than a set number of the SD on either

side of the perfect midpoint choice.

• This will allow you to factor in likely volumes of failure

while giving a verifiable mathematical explanation of your

choice of tolerance level.


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Regression

• Regression analysis is used to determine the strength of

a relationship between data output and input.

• In part 2, we mentioned the line of best fit; well, the

purpose of regression is to find that line and thus

describe that functional relationship between x and y.


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• This can be done either mathematically, using software,

or by just plotting the results and looking for a line of fit.

• Whichever way you choose to go will normally depend

on how obvious the answer is.

• The main benefit in doing this will be to enable

predictability of future events around the variables

without actually needing to undertake elongated

experiments.


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• You will also need to know the population parameter and

a sample statistic.

• A population parameter is a number describing

something about a whole population such as the

population mean or mode. It is fixed and used as the

value of a variable in a general distribution.

• The population parameter is used in statistics and is a

measurement of the population that is being studied.

Large populations are often measured by taking samples

to represent the entire population.


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• A sample statistic is something that describes a sample

such as the sample mean.

• In statistics, a sample statistic is one that is a subset of

individuals from within a statistical population to estimate

characteristics of the whole population.


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• The central limit theorem explains why many distributions

tend to be close to the normal distribution. The central limit

theorem describes the characteristics of the population of the

means created from the means of an infinite number of random

population sample sizes.

• The central limit theorem predicts that:

– The distribution of means will increasingly approximate a

normal distribution as the size N of samples increases.

– The standard deviation of the population of means is always

equal to the standard deviation of the parent population

divided by the square root of the sample size (N).

• The mean of the population of means is always equal to the

mean of the parent population from which the population

samples were drawn.


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• Independent probability occurs if the occurrence of one

event provides no information about whether or not the other

event will occur, meaning the events have no influence on

each other.

• Mutually Exclusive probability is a statistical term used to

describe a situation where the occurrence of one event is not

influenced or caused by another event.

• Conditional probability is the probability of an event given

the information that an event B has occurred, indicated by

P(A/B).

• Order of operators is important when simplifying expressions

and equations. The order of operations is a standard that

defines the order in which operations such as addition,

subtraction, multiplication, and division occur. Copyright: Management and Strategy Institute

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• The Order of Operations is shown below. The acronym PEMDAS is often used.

• Parentheses and brackets includes simplifying the inside of parentheses and brackets before the set of parentheses or removing the parentheses.

• Exponents includes simplifying the exponent of a number or of a set of parentheses before you multiply, divide, add, or subtract it.

• Multiplication and Division includes simplifying multiplication and division in the order that they appear from left to right.

• Addition and Subtraction includes simplifying addition and subtraction in the order that they appear from left to right.


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Software Use with Six Sigma

• There are 3 main types of software that may be used

within a Six Sigma project:

– Software that is used in day to day activity

– Task specific software

– Statistical analysis software


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• Software for day-to-1day use will normally include things

like Microsoft Office, Lotus Suite, or other business-

based word processing and spreadsheet packages.

• These will be used for daily communications and

creating documents and analyzing low level numbers or

collating data from observations.

• They are for general clerical use. It’s also likely that

email software and functionality will be used within the

team and for communicating out to the business.`


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• Task specific software relates to particular events or one

of the items that need creation. Project management

software is a good reference point for this and whether it

be a local computer with Microsoft Project or online

functionality like BaseCamp©, the importance of

planning the project cannot be underestimated.


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• The use of other software such as Adobe Acrobat for

creating graphics or other publishing software for

explaining the new process to the business are all

beneficial.

• You may also want to use software to create your

flowcharts or process maps, such as Microsoft Visio.


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• Don’t think that software will answer all your prayers; it

will just alleviate issues around the calculations.

• They are useful when you use them for simulations and

statistical modeling, and there are savings in time and

resources that can be made.


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• As of yet, there are no software tools that do the Six

Sigma process from start to finish, and they shouldn’t be

expected. The methodology needs human intervention

and succeeds on this basis.


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Unit Summary


• Statistics used in Six Sigma

• An overview of software in Six Sigma


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Unit 17 - Measurement Systems Analysis


• This unit explains when to use a Measurement Systems

Analysis when the apparent variation of a process is

caused by variations in the measuring system.



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Measurement Systems Analysis (MSA)

• Measurement Systems Analysis is designed to improve

the process when the apparent variation is caused by

variations in the measuring system.

• Measurement systems are subject to variation and error.

The following are common MSA terms:


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• To properly build the required Measurement Systems

Analysis, the combination of analytical methods which

lead the team in the direction to developing a solution for

the problem may require a combination of multiple tools.


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• A Measurement Systems Analysis (MSA) is a designed

experiment that seeks to identify the components of

variation in the measurement.

• A Measurement Systems Analysis evaluates the test

method, measuring instruments, and the entire process

of obtaining measurements to ensure the integrity of

data.


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Precision and Accuracy

• Precision/tolerance (P/T) tells how well a given

measurement can be reproduced. This is a standard

deviation around a mean value. Tolerance may have two

errors: systematic and random.


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Bias, Linearity, & Stability

• Bias is a measure of the distance between the actual

value and the average value of a part.

• Bias occurs when the survey sample does not

accurately represent the population. The bias that results

from an unrepresentative sample is called selection

bias.


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• Under-coverage bias occurs when some members of

the population are inadequately represented in the

sample.

• Nonresponse bias occurs when individuals chosen for

the sample are unwilling or unable to participate in the

survey.

Voluntary response bias occurs when sample

members are self-selected volunteers; the resulting

sample tends to over-represent individuals who have

strong opinions.


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• Random sampling is a sampling from a population in

which the selection of a sample unit is based on chance

and every element of the population has a known, non-

zero probability of being selected.

• Random sampling helps produce representative samples

by eliminating voluntary response bias and guarding

against under coverage bias.


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• Response bias is bias that results from problems in the

measurement process. Bias due to measurement error

can occur with a poor measurement process.

• This includes leading questions where the wording of

the question may be loaded in some way to favor one

response over another.

• Social desirability occurs when survey respondents are

reluctant to admit to questions in the survey if the results

are not confidential. Their responses may be biased

toward what they believe is socially desirable.


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• Linearity measures the consistency of bias over the

range of the measuring device. Accuracy is the degree of

closeness to an expected mark.

• Linear Regression is a linear relationship with one input

and one output.

• Multiple Linear Regression is a linear relationship with

several inputs.

• Logistics Regression is regression where the output is a

probability.


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• Stability of a measurement system is analyzed using

control charts. Your goal is to ensure the measurements

taken by the appraisers for the process are stable and

consistent over time.

• Use the control charts you develop to monitor measures

over time so that you can make corrections to processes

and operating procedures.

• Don’t forget when thinking about measurement that

special causes can also occur with the process control

limits, and these must be given corrective action before

proceeding to validate the measurement system.


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Gage Repeatability & Reproducibility

• The Gage Repeatability and Reproducibility is the amount

of measurement variation introduced by a measurement

system, which consists of the measuring instrument itself

and the individuals using the instrument. Depending on

the source, you may see Gauge R&R or Gage R&R

spellings. A Gage R&R study quantifies 3 things:

– Repeatability – variation from the measurement

instrument

– Reproducibility – variation from the individuals using

the instrument

– Overall Gage R&R, which is the combined effect of (1)

and (2)


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Gage Repeatability & Reproducibility

The Repeatability and Reproducibility (GR&R) sample below

shows this process is at 17%, which is in the marginal range.

11.1: Sample Gage Repeatability and Reproducibility (GR&R) Chart


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Variable & Attribute MSA

• A Measurement Systems Analysis (MSA) is a designed

experiment that seeks to identify the components of

variation in the measurement.

• A Measurement Systems Analysis evaluates the test

method, measuring instruments, and the entire process

of obtaining measurements to ensure the integrity of

data.


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Unit Summary


• Measurement Systems Analysis

• Bias and linearity in sampling

• Repeatability & Reproducibility


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Unit 18 - Process Capability


• This unit explains process capability. Process Capability

Studies are used to determine whether a process is

capable of consistently achieving specifications using

system design, parameter design, and tolerances.



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Process Capability

• Process Capability Studies are used to determine

whether a process is capable of consistently achieving

the specifications. This design process has three stages:

– System design, which uses scientific and

engineering principles to create a prototype

– Parameter design for products and processes which

minimize variation

– Tolerances are used to set parameters to minimize

loss


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Process Capability

• Process Capability Indices show the value of the tolerance specified for the characteristic divided by the process capability. Cpk, Cp, Pp, and Ppk are most commonly used and defined as follows:

– Cp= Process Capability. A simple and straightforward indicator of process capability.

– Cpk= Process Capability Index. Adjustment of Cp for the effect of non-centered distribution.

– Pp= Process Performance. A simple and straightforward indicator of process performance.

– Ppk= Process Performance Index. Adjustment of Pp for the effect of non-centered distribution.


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Process Capability

• Process Capability Studies are short-term studies

conducted to collect information on the performance of

new or revised processes related to customer

requirements.

• This occurs when processes, employees, or equipment

change and as many possible measurements should be

used to get an accurate reflection.


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Process Capability

• Process capability examines the variability in process

characteristics and whether the process is capable of

producing products which conform to the required

specifications. These are the formulas to calculate

process capability:

– Cp = (USL-LSL)/6s

– Cpu = (USL-Xbar)/3s

– Cpl = (Xbar-LSL)/3s

– Cpk = Minimum of (Cpu,Cpl)


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Capability Analysis


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• A capability analysis is a graphical or statistical analysis

tool that visually or mathematically compares actual

process performance to a set of performance standards.

• It is used to assess whether a system is statistically able

to specifications or requirements of a process. Capability

analysis is most easily calculated using prebuilt

calculations and formulas.

• The sample process below has a capability of 4.8 and is

within specs at 4.5 Sigma.

Capability Analysis

12.1: Sample Capability Analysis Chart


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Concept of Stability

• Control chart monitoring can display historical record of

the behavior of a process, allow for monitoring a process

for stability, detecting changes from a previously stable

pattern of variation, signaling the need for the adjustment

of a process, and helping detect special causes of

variation.


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Attribute & Discrete Capability

• Discrete variables are data that cannot be broken down

into smaller units. Only a finite number of values are

possible.

• Discrete data has one set of discrete values such as

pass or fail or yes or no.


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Monitoring Techniques

• Monitoring and measurement of processes is a

continuous process for organizational quality

improvement.

• Methods for monitoring should demonstrate the ability of

processes to achieve planned results.

• When the planned results are not achieved, corrective

action should be taken.


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Measurement tools will aid in monitoring techniques. These are tools you can use to measure processes and change:

• Process Maps • Takt Time • Data Sampling - Population vs. Sample • Data Classification • Data Collection • SPC-Charts to assess Measurement System Stability • MSA - Measurement System Analysis • Statistical Process Control (SPC) Charts • Root Cause Analysis • 5-WHY • Fishbone Diagram / Cause and Effect Diagram • Correlation Matrix

- continued on next slide


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- continued

• FMEA - Failure Mode Effects and Analysis

• Overall Equipment Effectiveness (OEE)

• Spaghetti Diagram

• Establishing a Baseline Measurement

• DPU - Defects per Unit

• DPO - Defects per Opportunity

• DPMO - Defects per Million Opportunities

• Process Yield Metrics

• FY - Final Yield

• TPY - Throughput Yield

• RTY - Rolled Throughput Yield

Unit Summary


• How a capability analysis is performed

• Process capability indices including tolerance for Cpk,

Cp, Pp, and Ppk of process capability.

• Measurement tools and monitoring techniques.


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Unit 19 - Inferential Statistics


• This unit provides information on statistical theories

which involve inference.



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Inferential Statistics

• The central limit theorem explains why many

distributions tend to be close to the normal distribution.

The central limit theorem describes the characteristics of

the population of the means created from the means of

an infinite number of random population samples of size.


– The distribution of means will increasingly

approximate a normal distribution as the size N of

samples increases.

- continued


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- continued

– The standard deviation of the population of means is

always equal to the standard deviation of the parent

population divided by the square root of the sample

size (N).

– The mean of the population of means is always equal

to the mean of the parent population from which the

population samples were drawn.


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• Independent probability occurs if the occurrence of one

of the events provides no information about whether or

not the other event will occur, meaning the events have

no influence on each other.

• Mutually Exclusive probability is a statistical term used

to describe a situation where the occurrence of one

event is not influenced or caused by another event.

• Multiplication

• Conditional probability is the probability of an event

given the information that an event B has occurred

indicated by P(A/B).


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Understanding Inference

• Inferential statistics are used to draw conclusions (an

inference) about a population based on sample data.

Inferential statistics may also be called Analytical

statistics.

• Enumerative statistics tell about the specific data that is

being analyzed. Enumerative may also be called

Descriptive statistics.


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Sampling Techniques & Uses

• When sampling data, a good sample is representative of

the population. And each sample point represents the

attributes of a known number of population elements.

• All probability sampling methods rely on random

sampling.


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• Random sampling is a sampling from a population in

which the selection of a sample unit is based on chance,

and every element of the population has a known, non-

zero probability of being selected.

• Random sampling helps produce representative samples

by eliminating voluntary response bias and guarding

against under coverage bias.


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• Random sampling helps achieve unbiased sample

results in a study by choosing subjects from a population

through unpredictable means.

• All of the subjects have an equal chance of being

selected out of the population being researched.

• Three methods are commonly used for random

sampling: random number tables, mathematical

algorithms, and physical randomization devices.


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• Stratified sampling is used to ensure smaller sub-

groups are not overlooked.

• Stratified sampling is used when there are smaller sub-

groups that need to be investigated, when you want to

reduce standard error, and when you want to achieve

greater statistical significance in a smaller sample.


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• Sample homogeneity means the characteristics of the

sample are uniform and representative of the population.

• Data integrity is maintained by using a Measurement

Systems Analysis which evaluates the test method,

measuring instruments, and the entire process of

obtaining measurements to ensure the integrity of data.


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Central Limit Theorem

• The central limit theorem explains why many

distributions tend to be close to the normal distribution.

• The central limit theorem describes the characteristics of

the population of the means created from the means of

an infinite number of random population samples of size.


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Central Limit Theorem


– The distribution of means will increasingly

approximate a normal distribution as the size N of

samples increases.

– The standard deviation of the population of means is

always equal to the standard deviation of the parent

population divided by the square root of the sample

size (N).

– The mean of the population of means is always equal

to the mean of the parent population from which the

population samples were drawn.


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Unit Summary


• Inferential statistics

• Sampling techniques for population samples

• The central limit theorem for distribution


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Unit 20 - Hypothesis Testing


• This unit provides information on hypothesis testing to

see if a potential solution will work.



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General Concepts & Goals of Hypothesis Testing

• Hypothesis testing is used in Six Sigma to screen for

potential causes of a problem.

• The hypothesis test calculates the probability that an

observed difference between two or more sets of data

can be explained by random chance alone and not a

fundamental difference between the underlying sample

populations that the samples came from.

• The p-value is most often used to represent this.


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• Hypothesis testing allows you to evaluate if a project or

proposed process improvement calculation is statistically

significant, or if the same thing could have occurred by

random chance.

• It also helps you to understand the probability distribution of

the data sample. The normal distribution is used to determine

the probability of occurrence of an event based on historical

data.

• Normal distribution is bell shaped and the majority of the data

falls in the center.

• Exponential distribution is used for reliability engineering

where the failure rate is constant.

• Hypothesis testing can also help determine which factors in

process inputs and outputs are significant.


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Hypothesis Testing

• Chi square is used to test whether a sample is drawn

from a population that conforms to a specified

distribution. Chi square is also called goodness of fit. Chi

square is calculated by summing the Chi square

contributions from each category in the hypothesis.

• The hypothesis is:

– H0 the sample conforms to the specified distribution

– H1 the sample does not conform to the distribution


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Hypothesis Testing

• Hypothesis testing

• Terminology

• The significance level is the probability of making a Type

I Error. In a Hypothesis Test, a Type I error occurs when

statistically unlikely test results lead to the incorrect

conclusion that the null hypothesis should be rejected.


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Hypothesis Testing

• Analysis of Variance (ANOVA) is a statistical technique for

analyzing experimental data.

• It subdivides the total variation of a data set into meaningful

component parts associated with specific sources of variation

in order to test a hypothesis on the parameters of the model

or to estimate variance components.

• ANOVA is used to test whether the means of many samples

differ, but it does so using variation instead of mean.

• It compares the amount of variation within the samples to the

amount of variation between the means of samples.

• ANOVA is effective to separate inherent variance and special

cause variance, and it also provides a methodology to

evaluate the robustness of a process to various levels of a

factor.


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Hypothesis Testing

• The Chi-Square Goodness of Fit is used to test whether

a sample is drawn from a population conforms to a

specified distribution.

• The hypothesis for Chi square is:




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Hypothesis Testing

• Power The power of a hypothesis test is a measure of the probability of rejecting the null hypothesis when it is false.

• Sample size is typically annotated by (n) and is the number of units in a sample.

• Balance is evenly distributing both the quantity and variety of work across available work time, avoiding overburden and underuse of resources.

• Repetition is synonymous with replication.

• Replication is used to remove systematic errors. The number of tests should equal the number of replications.

• Order is the numeric progression in which a product or service is produced.

• Efficiency is the lowest possible variance from any estimator divided by the expected variance of the selected estimator.


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Hypothesis Testing

• Randomization is used in experimental designs to

prevent systematic patterns and to convert patterns into

variation that can be detected in analysis.

• Blocking is a technique used to manage nuisance factors

that may affect the results of an experiment. The

experiment is organized into blocks, and the nuisance

factor is maintained at a constant level in each block.

• Interaction is the combined effect of inputs instead of the

sum of the individual effects.

• Confounding is used in experimental design. Factors are

confounded when the design array is configured so that

the effect of one factor is combined with the other.

• Resolution is the criteria used to select a fractional

factorial design.


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• Hypothesis testing uses two propositions:

– Ho where the mean = 0 (the null hypothesis)

– H1 where the mean ≠ 0 (the alternative hypothesis)


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Significance: Practical vs. Statistical


I Error.

• In a Hypothesis Test, a Type I error occurs when




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Risk: Alpha & Beta

• If the p Value is greater than the alpha risk, reject the null

hypothesis, and accept the alternative hypothesis.


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Types of Hypothesis Tests

These are key hypothesis testing terms.

• The power of a hypothesis test is a measure of the

probability of rejecting the null hypothesis when it is

false.

• The sample size is typically annotated by (n) and is the

number of units in a sample.

• Balance is the even distribution of the quantity and

variety of work across available work time when avoiding

overburden and underuse of resources.

- continued


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Types of Hypothesis Test

- continued

• Replication is used to remove systematic errors. The

number of tests should equal the number of replications.


• Efficiency is the lowest possible variance from any

estimator divided by the expected variance of the

selected estimator.

• Order is the numeric progression in which a product or

service is produced.


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Hypothesis Testing with Normal Data


I Error.

• In a Hypothesis Test, a Type I error occurs when




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• These are the three main types of hypothesis tests:

– Two Population Means

– Matched or Paired Samples

– Two Population Proportions

• In the Two Population Means test, populations are

independent and population standard deviations are

unknown, or populations are independent and population

standard deviations are known (which is not likely).


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• In the Matched or Paired Samples hypothesis test,

two samples are drawn from the same set of objects,

and the samples are dependent.

• In the Two Population Proportions hypothesis test,

populations are independent.

1 & 2 Sample T-Tests

• T-tests compare the mean against a specified value

using a sample of 30 items or less.

• Two sample t-tests compare the means of two samples

of 30 items or less.

• Paired t-tests compare the means of two samples of 30

items or less, when the items in the two samples can be

paired.

• Z-tests compare the mean against a specified value

when the sample has more than 30 items or the

standard deviation is known.


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1 Sample Variance

• The Chi-Square Goodness of Fit is used to test whether

a sample drawn from a population conforms to a

specified distribution. The hypothesis for Chi square is:



• Contingency tables

– Select, develop, and use contingency tables to

determine statistical significance.

– Contingency tables are an application of the chi-

square test used to test the relationship between two

variables. The tables are calculated by:


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One Way ANOVA

• Analysis of Variance (ANOVA) is a statistical technique for analyzing experimental data.

• It subdivides the total variation of a data set into meaningful component parts associated with specific sources of variation in order to test a hypothesis on the parameters of the model or to estimate variance components.

• ANOVA is used to test whether the means of many samples differ, but it does so using variation instead of mean. It compares the amount of variation within the samples to the amount of variation between the means of samples.

• ANOVA is effective to separate inherent variance and special cause variance, and it also provides a methodology to evaluate the robustness of a process to various levels of a factor.


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Tests of Equal Variance, Normality Testing and

Sample Size Calculation, Performing Tests, and

Interpreting Results

• p Control Charts are used to plot units nonconforming

when the samples are not of equal size.

• np Control Charts are used to plot units nonconforming

when samples of equal size are taken from the

process.

• c Control Charts are used to plot the number of

nonconformities per unit when the sample size is

constant. It is used in situations where each unit can

have several nonconformities.

• u Control Charts are used to plot the number of

nonconformities per unit when the sample size varies.


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For best data accuracy:

• Accept all data as it is collected and scrutinize it later.

• Record the data at the time it occurs.

• Avoid rounding the data.

• On the data collection plan, record as many details around the data, such as the exact source, machine, operator, conditions, collector’s name, material, gage, and time.

• Record legibly and carefully.

• Screen data for misplaced decimal points, duplicate data entries, improper recording procedures, and missing date points.

• Verify the gages are accurate through calibration.


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Hypothesis Testing with Non-Normal Data

Wilcoxon Rank Sum Test Is the same as the Mann Whitney Test

Mood's Median Test Is a nonparametric alternative to ANOVA

Friedman's Test Is a nonparametric alternative to two way ANOVA

Mann Whitney Test Is a nonparametric alternative to two sample t-test

Levene's Test Tests for equality of variances of several samples

Kruskal Wallis Test Is a nonparametric alternative to ANOVA


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• Non parametric tests can be used when the population

does not conform to a particular distribution or normal

distribution. The most common nonparametric tests and

their uses are:

Mann-Whitney

• Mann-Whitney is a nonparametric test which compares

the means of two samples.

• It tests the hypothesis that:

– H0 the samples are drawn from populations with

equal means

– H1 the mean of the population of sample 'm' is less

than/greater than/not equal to that of sample 'n’


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Kruskal-Wallis

• Kruskal-Wallis is a nonparametric test used to compare

several samples and test the hypothesis that:

– H0 the samples are all drawn from populations that

have equal means

– H1 the mean of at least one of the populations is

different


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Mood’s Median

• Mood’s Median is a nonparametric equivalent of ANOVA

where the hypothesis is

– H0 the samples are all drawn from populations with

equal medians

– H1 the median of at least one of the samples

populations is different


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Friedman

• Friedman's Test is a nonparametric alternative to two-

way analysis of variance.

• The hypothesis is:

– H0 the means of all the samples equal

– H1 the mean of at least one of the samples is different


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1 Sample Sign

• The One Sample Sign Test is a simple nonparametric

test equivalent to the parametric One Sample t-test and

used to test the probability of a sample median being

equal to hypothesized value.

• This is where:

– H0: m1=m2=m3=m4 (null hypothesis)

– Ha: At least one is different (alternate hypothesis)


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1 Sample Wilcoxon

• The1-sample Wilcoxon test is used to estimate the

population median and compare it to a target or

reference value.

• The 1-sample Wilcoxon test is used to determine

whether the median of a group differs from a specified

value.

• It is a nonparametric alternative of the 1-sample t-test.


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One and Two Sample Proportion

• The One Sample t-test is a hypothesis test used to test

the mean of a small sample taken from a population with

a normal distribution against a specified value.

• The Two Sample t-test is a hypothesis test used to

compare the means of two small samples to see if they

may come from the same population using:

– H0 the population means are equal

– H1 the popular means are different


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Chi-Squared (Contingency Tables)

• The Chi-Square test is used to test whether a sample is

drawn from a population that conforms to a specified

distribution.

• Chi squared contingency tables are called Yates

correction. The 2 x 2 tables allow teams to evaluate the

accuracy of the approximation and the value of this

correction.


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• Sample size is typically annotated by (n) and is the

number of units in a sample.

• One-factor experiments are a method of designing

experiments involving the testing of factors, or causes,

one at a time instead of all separately.


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• Randomized block is a method used in the design of

experiments similar to stratified random sampling where

block designs are constructed to reduce noise or

variance in the data.


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• Two-level fractional factorial experiments include three

key ideas:

– The sparsity of effects principle, where there may

be lots of factors, but few are important.

– The projection property, where every fractional

factorial contains full factorials in fewer factors.

– The sequential experimentation, where runs can be

added to a fractional factorial to resolve difficulties (or

ambiguities) in interpretation.


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• Full factorial experiments test several factors at two

levels, high and low.

• In a full factorial experiment, every possible combination

of factors and permutations is tested.

• The following chart shows possible solutions for a full

factorial experiment.





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Unit Summary


• Hypothesis tests and specific types of tests including

parametric and nonparametric

• Tests of Equal Variance, Normality Testing and Sample

Size calculation, performing tests and interpreting results


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Unit 21 - Simple Linear and Multiple

Regression Analysis


• This unit provides information on performing regression

analysis.



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Simple Linear Regression

• Regression analysis finds the line of best fit through a

series of points. The least squares method is used to

calculate regression.

• The types of regression are:

– Linear Regression: a linear relationship with one input

and one output.

– Multiple Linear Regression: a linear relationship with

several inputs.

– Logistics Regression: regression where the output is

a probability.


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Simple Linear Regression

• Multivariate analysis is used to analyze processes that have several inputs and outputs.

• Factor analysis reduces a large number of continuous factors to a small number for analysis. Discriminant analysis has one output and it is categorical in nature.

• Manova has several outputs which must be correlated.

• Cyclical variations are time based with a repeated pattern.

• Temporal variation is variation with time.

• Logit is used to find the relationship between a probability and quantity.


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Correlation

• Two variables are said to be closely correlated if there is

a strong relationship between them.

– Measurement correlation measures the strength of

a linear relationship between two variables.

– Scatter diagrams are also used and they show the

correlations between variables.


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Correlation

• This is an example of Strong correlation.

• This is an example of Weak correlation.

Narrow and tight

placement of data

values

Scattered and

sporadic

placement of data

values

18.1: Strong and Weak Correlation


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Regression Equations

• The regression equation shows the relation between the

inputs ('X') and the output ('Y') that is created by either a

regression analysis or the Design of Experiments.

• The equation for linear regression is Y = A + Bx


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Residual Analysis

• Regression Analysis is a data model that predicts a

regression so that variation in a process can be

reviewed, comparing the actual values from a process to

the predicted regression values which are the residuals.

• The residuals should conform to a normal distribution. If

they do not, this may be an indicator of a pattern or

cause for the regression.

• Residual analysis is an important part of the analysis in

regression analysis and experimental design.


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Multiple Regression Analysis

• Multiple regression is used to examine a relationship to

any other factors which may be nonlinear independent

variables (either quantitative or qualitative) to determine

the effects of a single variable or multiple variables.


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Non- Linear Regression

• Nonlinear regression is a technique to fit a curve through

data.

• It fits data to any equation that defines Y as a function of

X.

18.2: Y as function of X Non Linear regression


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Multiple Linear Regression

• Multiple Linear Regression follows the equation Y=A =

Bx and shows in a linear display responses to several

inputs as seen here:

• The purpose of linear regression is to find the line that

comes close to the data.

18.3: Multiple linear regression inputs and outputs


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Confidence & Prediction Intervals

• Confidence intervals define the area around a sample

mean which the true population will fall into with some

degree of confidence.

• There is a 95% probability the true population mean will

lie within the 95% confidence interval of the sample

mean.


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Confidence & Prediction Intervals

• Using the statistical definition of the 95% confidence

interval, if a poll of 100 people were taken, 95% of

respondents would be within the calculated confidence

intervals, and five percent would be either higher or

lower than the range of the confidence intervals.


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Residual Analysis

• Residual Analysis show how well the regression model

represents a process being studied and the special

causes. Residual analysis is part of experimental design

and the residuals should conform to a normal

distribution.


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Data Transformation, Box Cox

• Box Plots are used to represent relatively small data

sets. The outliers are points that are more than 1.5 times

the interquartile range above the third quartile or below

the first quartile.

• The whiskers extend to the largest and smallest data

values that are not outliers.

18.4: Box Cox Diagram


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Unit Summary


• Regression analysis

• Confidence and prediction intervals for data

• Regression analysis determining relations for data

variables


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Unit 22 - Designed Experiments


• This unit will show the design of experiments and

factorial experiments to determine the x and y variables

that affect a response.



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Designed Experiments

• The design of experiments is a systematic series of tests

used to determine relationships of x and y variables that

affect a process and response.

• The results of the experiment are analyzed to find the

regression equation that relates the factors to the

response.


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• The Taguchi Method (Design of Experiments) and other

methodologies have made major contributions in the

reduction of variation and greatly improved engineering

quality and productivity by reducing environmental

variation.

• Design of experiments deals with planning, conducting,

analyzing, and interpreting controlled tests to evaluate

the factors that control the value of a parameter.


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Design principles:

• Power: the power of a hypothesis test is a measure of the probability of rejecting the null hypothesis when it is false.






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DOE terms include:


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Interaction Main Effects Confounding Replication

Balanced Design Blocking Dependent

Variables Randomized

Block

Response variable

Random Effects Model Treatment Factor


Design principles:



• Randomization is used in experimental designs to prevent systematic patterns and to convert patterns into variation that can be detected in analysis.

• Blocking is a technique used to manage nuisance factors that may affect the results of an experiment. The experiment is organized into blocks and the nuisance factor is maintained at a constant level in each block.

- continued


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- continued

• Interaction is the combined effect of inputs instead of

the sum of the individual effects.

• Confounding is used in experimental design. Factors

are confounded when the design array is configured so

that the effect of one factor is combined with the other.


factorial design.

Experiment Objectives

• The goal of the Design of Experiments is to discover the

relationship between the factors that affect a process

and the response.

• In the experiment, the factors are varied systematically

and then the resulting response observed.

• The results of the experiment are analyzed to find the

regression equation for the response.


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Experimental Methods

• Experimental methods include one and two-level

fractional factorial experiments.

• One level include Randomized and Latin square design.

• Two level include:

– The sparsity of effects principle, where there may be

lots of factors, but few are important.







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Experiment Design Considerations

• Design for Six Sigma (DFSS) is different from DMAIC as

it is used for designing a completely new product or

process that meets customer specifications.

• DFSS vary from organization to organization depending

on the characteristics of the product or business process

that needs to be developed.

• DFSS is an approach-based methodology rather than a

stand-alone optimization methodology such as DMAIC.

• DMADV and IDOV are variations of the DSS. Design For

Six Sigma uses the DMADV or DMADOV sequence

rather than the DMAIC sequence.


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Full Factorial Experiments


levels, high and low. In a full factorial experiment, every

possible combination of factors and permutations is

tested.




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2k Full Factorial Designs


key ideas:

– The sparsity of effects principle, where there may be

lots of factors, but few are important.



– The sequential, where runs can be added to a

fractional factorial to resolve difficulties (or



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Linear & Quadratic Mathematical Models

• In a linear mathematical model, the plotted data follows a

straight line.

• Every data point may not fall on the line but the majority

of them will. The overall pattern of the data follows the

shape of a line and is the overall shape of the data.

• Linear mathematical models use the formula y = mx + b.


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Linear & Quadratic Mathematical Models

• In a quadratic model, the plotted data follows the shape

of what is called a quadratic.

• A quadratic shape is one that is curved and shaped

similar to the letter “u.”

• The formula for the quadratic mathematical model is

Y = ax^2 + bx + c.


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Balanced & Orthogonal Designs

• A Balanced Design (Balanced Experiment) is a factorial

design in which each factor is run the same number of

times at the high and low levels.

• An experimental design is orthogonal if each factor can

be evaluated independently of all the other factors. In a

two level factorial design, this is achieved by matching

each level of each factor with an equal number of each

level of the other factors.


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Fit, Diagnose Model, and Center Points

• Fit finds the line of best fit through a series of points. The

least squares method is used to calculate regression. As

mentioned earlier, the types of regression are:

– Linear Regression is a linear relationship with one

input and one output.

– Multiple Linear Regression is a linear relationship with

several inputs.

– Logistics Regression is regression where the output is

a probability.


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• The pattern of variation in a set of data is called distribution. Distribution is most often viewed for the shape, spread, and center.


– The distribution of means will increasingly approximate a normal distribution as the size N of samples increases.

– The standard deviation of the population of means is always equal to the standard deviation of the parent population divided by the square root of the sample size (N).

– The mean of the population of means is always equal to the mean of the parent population from which the population samples were drawn.


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• Normal distribution is a continuous distribution where any two data values may have an interval in between. The bell shaped normal curve has probabilities that are found as the area between any two z values. Normal distribution has 5 characteristics:

– The mean, mode, and median are equal.

– Most values concentrate near the mean and decrease in frequency further from the mean.

– Symmetrical about the central value

– The curve has only one mode.

– All data points fall within the curve, either 50% to the left or 50% to the right.


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• This picture shows a normal distribution of data.


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19.4: Center point of normal distribution


• Binomial distribution is different from a normal

distribution, although the shape of the curve will be

similar.

• Binomial distribution is a discrete probability distribution.

It shows the probability of getting “X” successes in a

sample of “N” from an 'infinite' population, where the

probability of a success is “Y.”


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• Poisson distribution is a discrete probability distribution.

It is used when the sample size is not restricted and it is

not possible to specify the number of occurrences, but

you do know the average number of occurrences.

• The formula is shown here where d = the number of

occurrences and λ = the average number of

occurrences.


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• Chi square is used to test whether a sample is drawn

from a population that conforms to a specified

distribution.

• Chi square is also called goodness of fit. Chi square is

calculated by summing the chi square contributions from

each category in the hypothesis. The hypothesis is:




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• Student’s t is a Probability Distribution Function which

gives the height of the distribution.

• The shape of the t distribution is similar to the normal

distribution and converges on the normal distribution as

the number of degrees of freedom increases.


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• F distributions are a continuous probability

distribution formed from the ratios of two Chi-

squared variables. If X1 and X2 are independent

Chi-square variables.


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• T-tests compare the mean against a specified value

using a sample of 30 items or less.

• Two sample t-tests compare the means of two samples

of 30 items or less.

• Paired t-tests compare the means of two samples of 30

items or less, when the items in the two samples can be

paired.

• Z-tests compare the mean against a specified value

when the sample has more than 30 items or the

standard deviation is known.


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Fractional Factorial Experiments


factorial design.

• Factorial designs involve testing several (n) factors at

high and low levels using fractional factorials such as

half (2n-1) or a quarter (2n-2).


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Designs


key ideas:

– The sparsity of effects principle, where there may

be lots of factors, but few are important.







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Designs


levels, high and low.

• In a full factorial experiment, every possible combination

of factors and permutations is tested.




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Treatment A B AB

1 -1 -1 +1

2 +1 -1 -1

3 -1 +1 -1

4 +1 +1 +1

Confounding Effects

• Confounding effects are when the design is configured

so that the effect of one factor is combined with the

other.

• Ultimately, the effect of the individual factors cannot be

isolated by the analysis.


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Experimental Resolution

• In the design of experiments, the Design Resolution is a

key criteria for selecting a fractional factorial design:

– Resolution III RIII: two factor interactions are aliased

with main effects.

– Resolution IV RIV: two factor interactions are aliased

with other two factor interactions, but not with main

effects. Main effects are aliased with three factor

interactions.

– Resolution 5 RV: two factor interactions are not

aliased with each other, but are aliased with three

factor interaction.


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Unit Summary


• Design for Six Sigma (DFSS)

• The value of proper fit and diagnose

• Distribution and factorial experiments


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Unit 23 - Statistical Process Control


• This unit describes control tools and statistical process

control tool usage.



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Statistical Process Control (SPC)

• This course covers statistical process control measures

that a Black Belt would use in the Control phase of Six

Sigma.

• Not all tools used in Six Sigma are intuitive such as a bar

graph or Pareto chart.

• Some of the tools used in six sigma have significant

mathematical formulas (particularly in the manufacturing

arena), which take the user time and training to develop.

• A Black Belt can typically recommend the best tool for

the process, but the project team may need significant

training on how to use it.


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• Statistical Process Control is used to monitor processes

and may be applied to production or business

processes.

• Statistical Process Control uses statistical methods to

ensure that the process is stable and monitor the

processes for timely identification of special causes.

• All processes are subject to variation.


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• Statistical Process Control (SPC) is used to help

understand variation and process capability and

performance by using control charts of Cp, Cpk, Pp,

Ppk, x-bar, and R chart, and attribute charts of p, np, c

and u.

• The Black Belt will be able to develop control charts for

x-bar and s charts, median charts, XmR/ImR charts,

short run SPC, and moving average (MA) charts.


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u Control Charts U Charts

c Control Charts Moving Average Control Charts

np Control Charts X-Bar & R Control Charts

p Control Charts ImR (Individual Moving Range) Control Charts


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• Process Control is most often monitored by control charts which have an upper and lower control limit indicating a special cause. There are two types of SPC charts: control charts for attributes and control charts for variables. These are the most common SPC charts:


Attribute charts Use for:

U charts Defects (errors) per unit in a subgroup

Np charts Number of defective units in a sample for go or no-go

P charts Percent or proportion of defects in a subgroup

C charts Number of defects in a subgroup

Control charts Use for:

Xbar-R charts Plot of the mean and range of a group less than 5

X bar-s charts Plot the mean and standard deviation of a group more

than 5

X-R charts Plot of mean and range of individual measurements.

This chart will help explain the application of attribute

and control charts.


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• Correctly interpreting control charts is important to

effectively adjusting and improving processes.

• After you have charted your data, there are 8 standard

rules for interpreting what the control charts mean. The 8

rules follow.


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These are the indications of a process out of control:

1. Any point beyond control limits

2. Two out of three points in a row beyond two sigma

3. Four out of five points in a row beyond one sigma

4. Fifteen points in a row within one sigma


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5. Eight points in a row on both sides of the centerline

within two sigma

6. Nine points in a row on one side of the center line

7. Six points in a row increasing or decreasing

8. Fourteen points in a row alternating up or down


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Data Collection for SPC

• Data collection and analysis allows for quality assurance

as a project progresses to assure the project satisfies

the required quality standards.

• Use a data collection chart to track subsequent data to

monitor your controls once they are implemented.

• The next two slides will show you some of the common

data collection parameters.


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• Data collection for SPC explains how and by whom data is collected for improvement. This data includes the following common descriptors. – The Process Name: Enter the process description

and identify the process owner or owners.

– Description of Measure: List a detailed description of your specific measures.

– Justification for the Measure: What influenced you to pick this as the critical measure? How do you substantiate your choice of control?

– Link to Strategic Initiative / Key Business Driver: Identify the key business and strategic influences for this measure.

- continued


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- continued

• Numerator: Denominator:

• Data Source: Identify all your data sources

• Baseline, Target, Current Results Best Practice, and Measurement Period identify your starting point for all of these.

• Measurement or Process Pattern: What are the normalized steps in your process?

• Time Documentation: Document time intervals and timestamps of the process.

• Include any comments of relevant information, exceptions, unexpected outcomes, and findings.


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I-MR Chart

• The Individual Chart plots each measurement as a

separate data point, where each data point stands on its

own and where the subgroup size is or = 1.

• The Moving Range Chart uses a default value of 2 so

that each data point plots the difference or range

between two consecutive data points as they come from

the process in sequential order.

• There will be one less data point in the Moving Range

chart than the Individual chart.


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Xbar-R Chart

• The Xbar-R Chart is a common statistical process control used to sample averages. The X-bar chart is most often used for large sample sizes and large volume processes.

• The chart is used with the X as the range chart (R). The range is the highest minus lowest numbers placed in a chart plot of the range for each sample with calculated control limits.

• The range shown in the chart is sensitive to shifts in process width, and it shows the uniformity of a process. Unfortunately, the X-bar chart does not show problems quickly because of the effect of averaging averages.


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Xbar-R Chart

• This is an example of an Xbar Chart.

This shows the range of the

highest and lowest numbers

for a large sample process

22.1 : Sample Xbar-R Chart


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U Chart

• The U Chart is used to track the number of

characteristics of interest per item or unit examined. The

measurement scale for u charts is continuous. The u

chart is similar to the c chart except that the sample size

can change over time.

• U chart sample sizes can vary in size but should be kept

to within 25% of the average sample size.

• The U chart center line (mean) (u-bar) is calculated by:

U= Total number of non-conformances in all the items sampled

Total number of items sampled


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U Chart

• This is an example of a U Chart.

This chart shows the specific number of characteristics of

interest per item or unit examined, on a continuous scale. In

this chart, there are four characteristics being tracked.

22.2 : Sample U Chart


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P Chart

• The P Chart is used to track a proportion of a population

or whole, and each unit is considered conforming or

nonconforming.

The formula for its calculation is:

p = Total number of nonconforming in all samples being considered

Total number of items reviewed in all samples being considered


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P Chart

• This is an example of a P Chart.


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This chart shows how each characteristic

measure is or is not meeting a measure

(conforming).

22.3 : Sample P Chart

NP Chart

• The NP Chart is used to measure the number of defective items in a sample when the sample size (n) is constant.

• The NP chart is used to track the number of items that are nonconforming, and each item may be counted only once.

• It would be used when the number of items with the characteristic is a small number within the total population under consideration.

• The formula to calculate the NP chart is:

Total number of nonconforming items in all samples being considered Total number of samples


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NP Chart

• This is an example of an NP chart.

This shows the defective

items in this sample

22.4 : Sample NP Chart


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Cum Sum Chart

• The Cum Sum Chart is used to plot the cumulative sum

of the deviations from a target value.

• It identifies process changes. Cum Sum charts are

constructed by calculating and plotting a cumulative sum

based on the data.

• The chart is built using the graph of the equation:

X= X1 + X2 + X3….X15

15


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EWMA Chart

• The (Exponentially Weighted Moving-Average) EWMA

Chart is used as a control chart for variable data.

• EWMA applies weighting factors that decrease

exponentially where the weighting for each older data

point decreases exponentially, which gives more

importance to recent observations.


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EWMA Chart

• This is an example of an EWMA chart.


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This shows the most recent

observations of the process.

22.5 : Sample EWMA Chart

Control Methods

• Control methods will include combinations of these:

– Chart Champion: Name of the process owner

– Critical to Quality Characteristic: End-product

characteristic proven to be important to the customer,

along with hierarchical reference number

– Significant Characteristic Description: Process

characteristics that have a significant impact on the Critical

to Quality Characteristic

– Significant Characteristic Number: Reference number to

organize Significant Characteristics within a hierarchy that

relates to the corresponding CTQCs

– Chart Type: X-bar & R chart, P-chart, C-Chart, Trend chart


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Control Methods

• Chart Location: Location where the cart is kept

• Measurement Method: Method used to collect the measurement data (e.g., scale, caliper)

• Measurement Study: Denote whether a measurement system analysis has been completed. If Yes, show the % total error.

• Gage Number: Reference number for the gage that corresponds to the calibration tracking system.

• Sampling Plan: How many samples are drawn at what frequency.

• Process Stability: Is the process in a state of statistical control - Yes or No.

• Cp/Cpk: If process is stable, calculate Cp and Cpk.


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Control Chart Anatomy

• Statistical Process Control is most often monitored by

control charts, which have an upper and lower control

limit indicating a special cause.

• There are two types of SPC charts: control charts for

attributes and control charts for variables.


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Subgroups, Impact of Variation,

Frequency of Sampling

• Subgroups are used to compare the control limits and

patterns of variation between each subgroup through

analysis.


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• The Impact of Variation can be random or systematic.

• Random variation and systematic variation should be

evaluated for the critical causes of your process and

their impact on variation, either for (spread) or for central

tendency (centering).

• Hypothesis testing can help determine the cause based

on your target for specified limits.


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There are four primary sampling strategies:

• Random sampling

• Stratified random sampling

• Systematic sampling

• Rational sub-grouping


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• Random sampling is used in population sampling when

reviewing historical or batch data.

• Stratified random sampling is used in population

sampling when reviewing historical or batch data.

• Systematic sampling is used in process sampling when

data is collected in real time during process operation.

• Rational sub-grouping is used in process sampling when

data is collected in real time during process operations.


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Center Line & Control Limit Calculations

Control limit calculations are found by:

• Calculating the mean by summing the measurements

and dividing by the sample size.

• Then, calculating the standard deviation by subtracting

each measurement from the mean and squaring the

results individually.

• Then, summing the set of individual numbers. Take that

number and divide the sum by the sample size minus

one.

• Then, the last step is to square the result to compute the

standard deviation.


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Center Line & Control Limit Calculations

Once the control limits are calculated, they are

placed on a control chart seen here, which

shows you the upper control limit, control, and

lower control limit of the process.

22.6 : Sample of centerline and control limit


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Six Sigma Control Plans

• The control plan could possibly be the most important

part of the entire DMAIC methodology.

• Control plans help verify that the Voice of the Customer

is being met by verifying improvement processes,

documenting procedures, and updating standard

operating procedures and policies.

• This can be accomplished using any combination of

these tools: the balance scorecard, control chart, control

plan document, or control plan form.


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Elements of the Control Plan

• The control plan is a document that lists what is

monitored in a product, service, or process as far as

characteristics of quality.

• The control plan plays an important part in sustaining

product quality long after a manufacturing process is

developed and launched.


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Elements of the Control Plan

• There is no set type or style of project control plan for a

specified particular improvement, but use metrics

important to your sustainment.

• Use a combination of spreadsheets and text documents

to keep track of the measures being improved.

• In general, you determine all of the headers and labels

for your control plan which are critical to quality, proven

to be important to the customer, and include a

hierarchical reference number.


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Six Sigma Control Plans

• This is a sample control plan.

Notice the characteristics that

are tracked.

22.7 : Sample Six Sigma Control Plan


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Cost Benefit Analysis

• The Cost Benefit Analysis (CBA) is similar to the return

on investment calculation or a go-no-go determination.

• The CBA will help determine costs of not doing a Six

Sigma project, the costs if the project fails, and

opportunity costs.


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Elements of the Response Plan

• A Six Sigma response plan is a flowchart that tells the

data-plotter what to do in the event of an out-of-control or

out-of-specification condition.

• An example of a reaction plan would be a printing press

operator who receives an alert for an offset of print and

is able to stop the line and make an adjustment based

on the data.


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Unit Summary


• Statistical process control (SPC) and process capability

and performance by using control charts of Cp, Cpk, Pp,

Ppk, x-bar, and R chart, and attribute charts of p, np, c

and u

• Six Sigma control plan requirements

• Process data sampling requirements


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Unit 24 - Leading Six Sigma Teams


• This unit goes through the very basic principles of project

management, project teams, and the role of the Black

Belt leading the team.

• It also looks at the communications of the project leader

and Black Belt.



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Project Management

• There are libraries full of books discussing project

management and various certifiable qualifications that

can be obtained; a variety of methodologies for

managing projects, software, and “good advice” in most

organizations.

• The nature of a project is that it is a structured and

accountable method for delivering an end product or

goal.

• Thus its transfer of use to Six Sigma is pretty obvious.


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Project Management

There are a few key things that define a project.

• It has an end goal to achieve – a desired outcome.

• It has a schedule or timeline for delivery.

• It has a budget.

• It has a plan of activity.

• It has a sponsor and manager.

• It has project team members.

• It has structure and documentation of activity.

• It is monitored on progress and reports achievement.


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Project Management

• A quick read through the above and you will immediately

see the overlap between Six Sigma projects and general

project management.

• Thus, a Six Sigma project can be ‘project managed’ -

following whatever project management philosophy

suits, but having this structured, accountable, and

sequential approach to activity.


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Project Management

• As a project manager, the Black Belt leading the project

would be responsible for monitoring expenditure within

the project, determining tasks to be undertaken and

assigning them, monitoring achievement against

expectations of a project plan, and ultimately arriving at

an end state on time, in budget, and using the expected

resources agreed with the sponsor at the outset of the

project.


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Project Management

You may choose to use some good project management

practices to help move along development of your Six

Sigma project.

• Consider releasing regular project reports detailing

progress.

• Consider using risk and issue logs that identify likely or

potential challenges to the successful delivery.

• Consider the use of checkpoint meetings as a structure

for regular engagement with your business customer,

stakeholders and team.


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Communications

• As a Six Sigma Black Belt and project leader, you will be

expected to undertake a significant amount of

communications within and outside of your team.


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Communications

• You should be prepared to communicate regularly within

your team and ensure that task and role allocations are

clearly explained and understood.

• You should show confidence in your team and

particularly Green Belt members, demonstrating that you

can trust them to deliver by shaping a task for them

without detailing the specific elements.

• That is good leadership as much as project

management.


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Communications

• You will have regular engagement with your Champion

to update on progress and discuss challenges within the

project.

• There should be a good understanding between you and

a clear direction that you both agree on for the future of

the project.

• You should be prepared to offer challenge and exert your

Six Sigma knowledge within these discussions to ensure

that the project stays focused on the problem statement

to be solved.


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Communications

• You will also need to engage with stakeholders, process

owners, and customers affected by the project.

• These people will probably engage with you at structured

meetings, so it is important that these meetings take

place regularly and clearly convey information about the

progress and development aspects of whichever part of

the Six Sigma project you have reached at that point.


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Communications

• Ensure meetings have agendas and are recorded for

reference should any decisions be made.

• Try to make sure they have a set time so that they do not

drift into discussion forums but stay on track and on

purpose.

• You may also get requests to have one-to-one

discussions with those involved who are concerned

about the process.

• You need to be sure to explain what is happening and

why it is happening and then take on their views and

discuss them freely.


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Communications

• Often, it is just a misunderstanding that causes

concerns, but if there is something you cannot answer

then work with your Champion to find common ground,

and work together with the business strategy in mind.

• Sometimes this one-to-one discussion may take place

on the phone, but the same guidance applies.


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Communications

• As a final point, both Black and Green Belt Six Sigma

practitioners should be able to facilitate a discussion to

get to the cause of issues and help within the workings

of the DMAIC approach.

• It may not happen, as this work will often be undertaken

by Green Belts, but be prepared to step up and facilitate

a solution-workshop whenever needed.

• That can instill faith in your abilities as well as

demonstrate to others how to progress a challenging

period within the DMAIC approach.


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Unit Summary


• Change management and the requirements to build a

sustainable culture for quality improvements

• Operational excellence and business process

reengineering

• The criticality of project management


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Unit 25 - Ending Six Sigma Projects


• This unit provides information on how and when to end a

Six Sigma project, how to monitor benefits, and the

responsibilities being passed on to the business.



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When Does a Six Sigma Project Finish?

• We have discussed some of the end elements of a Six

Sigma project in other units, particularly unit 10 – the

Control stage.

• Effectively, a Six Sigma project will end when all benefits

have been signed off by the sponsor and the process

has become a normal part of the business.

• That sounds simple enough, but there wouldn’t be a

section devoted to ending a project if it was that

straightforward.


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• At the end of the Control phase of the DMAIC journey,

the organization’s financial department or company

accountant should confirm the benefits in dollars.

• The Champion or sponsor will then take these agreed

figures into the business and discuss their value and

what it means.

• This will begin the process of confirming benefits and

developing a plan for benefits monitoring that will

become the responsibility of the business.

• Only once everything is agreed upon and the business is

ready, can the project begin to be closed down.


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• Unfortunately, some perfectionists may think that Six

Sigma teams should keep working on improvements

indefinitely.

• There comes a point of minimal return and there are no

more process improvements that can be done. This is

when the process is optimized.

• When optimized, benefits can be calculated and

presented to the business for agreement.


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When does a Six Sigma project finish?

• When the benefits are agreed upon and the plans put in

place to monitor not only the benefits, but control the

process in a way that ensures its longevity.

• When the training for existing staff has been completed

and created for new recruits.

• When the project charter has been fulfilled, then the

project can close.


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• Closing the Six Sigma project is much like the closure of

a general project.

• It will involve a meeting of all relevant stakeholders,

customers, and process owners, led either by the project

leader or the Champion.

• The meeting will examine the contents of the original

charter and gain agreement from all present that the

aims and goals of the charter have been met.


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• Then the project manager will prepare a written

document that outlines the results for each stage of the

project and confirms achievement of the solution to the

original problem.

• This document is distributed for agreement and in some

organizations, they physically gain sign off.


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• At the same time, a benefits report is created based

around agreed benefits.

• These may or may not have been discussed at the

closure meeting, depending upon the confidentiality in

relation to those present.

• However, this will detail not just the high-level saving in

terms of cost, but will detail how this is made up in terms

of time, quality, and cost savings.


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• The benefits report includes expected realization numbers or ranges for each to fulfill the global savings figure.

• The benefits report also states how these elements will be measured, using what tools and by whom.

• It should also clearly state how often or when the measure will take place. Within a month of closure is a good starting point, with monthly or quarterly thereafter for a period of 1 -2 years recommended.

• This will enable benefits to be not only achieved, but any overshoots or undershoots clearly identified as stability of the process is achieved following integration within the business.


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What Are the Benefits?

• Cost savings will likely include people savings, where the required numbers of staff to perform an action may reduce.

• Other cost savings may result from reduced storage times, reduction in packaging waste, new supplier costs, or just reduced running times of machinery.

• All of these are likely to be easy to quantify in terms of dollars by just comparing before and after figures.

• It may be a slightly staggered approach in realizing staff savings as some initial retraining costs and redeployment costs would have to be borne against the reductions in need costs at the outset.


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• When looking at quality savings, you should consider

accuracy of production numbers at each stage of the

process.

• Internal assurance staff should be able to identify this

and potentially reduced workload for quality control

personnel.

• You may also choose to assess the customer’s view of

achievement, a recommended option, by undertaking

surveys, focus groups, or stakeholder liaison meetings.

• The metrics here are likely to be in terms of accuracy or

satisfaction levels being achieved.


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• In terms of timing benefits, this can be a timing

improvement across the whole process or within certain

stages of the process.

• If it is a particularly complex or multi-faceted process,

timings for each part of the process should be given and

taken.

• Sampling exercises to monitor times can be undertaken.

If you are looking at shipping of products, perhaps this

would be another factor for customer engagement

discussions.


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• Overall, when measuring benefits, it is worth having

these individual factors feeding into the overall total

savings figure.

• It can be encouraging for all involved to see the greater

figure being achieved and even surpassed as full

realization is achieved.


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Passing on the Hard Work

• When the agreement to close the project has been

achieved, then it is time to close.

• One of the final acts of the project lead will be to ensure

the business has all the necessary tools to maintain and

monitor the improvement into the future.


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• The benefits plan will be passed on with its detail as

described above.

• The control plan will be handed over to the process

owner together with all associated documents agreeing

how continued compliance will be achieved.

• The Champion will also get a copy of these documents

as the executive level sponsor for the project, since they

typically report to the finance or executive boards of the

business on the success of the Six Sigma Project.


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• All created training information and content, new

documents, technology, contracts, and environmental

controls will also be passed on to the business for use

and application.

• The project can then be closed down and remaining

papers filed away.


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• As a courtesy, it is worth making the effort to thank

stakeholder representatives and their home teams for

their efforts as well as recognizing the efforts of Yellow

Belts that will be returning to their day job at the end of

the project.

• A simple letter of thanks or personal thank you is often

sufficient, but if your organization permits other rewards

then consider these also.


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• The Black Belt will now get ready for the organization’s

next Six Sigma project to commence the hard work all

over again.


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Team Dynamics

• When any team comes together for the first time, it is

recommended that they take some time to get to know

each other and develop as a cohesive unit.

• Six Sigma teams are no different, but the expectation

placed upon it by the business may be to get up and

running very quickly.

• It is key that the right people are pulled into the team to

ensure its success.


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Team Dynamics

• Once stakeholders have been identified, they should be

grouped into key and non-key stakeholders, depending

upon the decision making and range of influence – a

simple stakeholder analysis may help this split.

• Once these 2 groups are set up, the team needs to

include one member from each group. Each of these will

be a supporter of the change, enthusiastic in their

approach but also credible within the group.


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Team Dynamics

• It may not be possible to get all these elements right, so

a more skeptical member may be involved, but whose

credence within the group is respected and who can be

leveraged to embrace buy-in over time.

• The total team should range between 5 and 7 members

for ideal production with meeting, workshops, and other

activities, pulling in people from various areas of the

business at the appropriate times throughout the

schedule of the project.


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Team Dynamics

• The team has collective responsibility for the success or

failure of the project.

• There should be no blame or finger pointing, but

acceptance to learn from errors and move forward.

• The team leader will be responsible for planning,

managing, and delivering against the charter and

keeping the team focused on achieving the goals of the

charter throughout the whole DMAIC process.


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Team Dynamics

• The team must also be receptive to the customers’

comments and value the contributions of all with

objective viewpoints.

• They must be understanding and rationalize these at

each point to progress the project.


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Team Dynamics

• Most Green and all Black Belts will demonstrate

facilitation skills, and as such, they should be clearly able

to overcome conflicts and disagreements within the

team.

• Occasionally, it may be of benefit to utilize an

independent facilitator if project conflicts continue.


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Unit Summary


• How to bring a Six Sigma project to closure. All projects

have a specified time length and duration of course.

• How to pass on roles for sustainment

• The specific dynamics of teams, which is a crucial to

manage a process which is ongoing


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Unit 26 - Making the Change


• This unit gives an overview of change management

within the context of Six Sigma projects, how to assess

the organizational culture, and how to gain buy in from

those experiencing the change.



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What is Change Management?

• There are many definitions of change management, and there is much confusion around the term.

• It does not relate to project management or technology changes, but relates to the experience people have of something changing within their workplace

– How they are affected and how the business needs to consider the impact of change upon its workforce through the linking of technology, training, Human Resources, environment, and communications to not only deliver changes but to successfully bring about a change within the workplace that is accepted and embraced by the workforce.


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• Some of the key considerations around change

management link to organization structures, where the

reporting or managerial roles may change priority or

seniority.

• It also ties in with workforce development, which ensures

that the workforce is trained and has sufficient skills to

perform their required duties.

• It will also look at organizational culture and

communications, determining the nature of the business

culture and how best to work with it to gain acceptance

of change.


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• As you can imagine, introducing a Six Sigma process

change overlaps with a considerable amount of change

management.

• New processes may require the workforce to be

redistributed and the managerial structure changed.

Change managers can work with Six Sigma projects to

effectively deal with these restructures.

• The new process will definitely need staff training, and

once again, developing the workforce potential can

enable the Six Sigma project to take forward the learning

events required in a structured and appropriate way.


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• When considering the collaboration with any business’

change management professional, it should be noted

that many businesses do not have one.

• It may be that an external contractor is brought in to help

with the change management. Even if the change

manager is present, be prepared for them to be engaged

in several other changes at the same time.

• The Black Belt project lead will normally be the person

engaging with the change manager, but it may be

suitable for Green Belts to engage as the task in-hand

relates.


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Culture and Perceptions

• Most businesses have a culture which dominates the

way they do things. This can likely be something that

reflects the personalities of the people who have

developed the business or simply evolved over time.


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• It can be difficult to get a grasp of the nature of a culture

at first, but using an experienced change management

professional who has either experienced cultural

assessments or works within the culture already can give

insight into this.

• It is important to understand the culture’s tendencies,

and these are a sample of the typical questions that can

be answered in gaining an understanding of a

company’s culture.


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• Do they like change?

• Do they strongly resist change?

• Do they question decisions a lot or are they an organization that accepts without challenge?

• Do they enjoy training?

• Do they prefer class based training, CBT, paper?

• What is their perception of management?

• What is their perception of reward and recognition?

• Do they know what Six Sigma is?

• How do they communicate?

• How do they like to communicate?


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• You will see some reference to perceptions, and this is

the most used word when discussing business culture:

how the business is perceived by the people within it.

• When you have an understanding of the culture, you can

begin to think how best to approach the roll out of the

change to match their perceived needs and cause the

least amount of disruption as possible within the

business.


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Gaining Buy-in

• One of the key requirements for introducing any Six

Sigma change is gaining the buy-in of the business and

the people impacted.

• This can be no easy feat, but considering what we have

discussed above about change management and

cultural awareness, then it is possible.


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Gaining Buy-in

• When you are ready to introduce the change, have a few

change agents within the mix of staff.

• These are people who embrace change as a general

rule, will be ready to talk to their colleagues about the

change, and be encouraging of the need to make the

change.


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Gaining Buy-in

• Make sure you have good and suitable communications.

• Ensure that the way of the organization is followed when

communicating the change – don’t try and change the

way they communicate – one change is more than

enough at a time for most to deal with.


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Gaining Buy-in

• If there is to be any downsizing or reorganization, ensure

that the full details of this are transparent and clearly

understand.

• Allow people to go through the change curve from denial

to acceptance. Make sure that the process for

determining relocations or redundancy is clearly

explained, and management endorses the process

publicly.


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Gaining Buy-in

• Ensure training and education is readily available to train

those relocating or having to change their existing ways.

• They need to see the willingness to invest in them as a

sure way that you are serious about the change.


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Gaining Buy-in

• Finally, have an escape route available for those who

don’t want to make the change – that is somewhere for

those people who just don’t want to transition and cannot

find the enthusiasm for it.

• Whether it be deployment elsewhere or options to leave

the business, they need to see that option as well.


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Operational Excellence

• Combining multiple methodologies is nothing new, and

there have been several attempts to combine business

improvement teams. Probably the most successful has

been the move to Operational Excellence.


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• Operational Excellence is a philosophy of leadership,

teamwork, and problem solving resulting in continuous

improvement throughout the organization.

• This is accomplished by focusing on the needs of the

customer, empowering employees, and optimizing

existing activities in the process.

• It stresses the need to continually improve by promoting

a stronger team using greater ownership of activities and

making the environment better for both employees and

customers.


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• Operational Excellence is based on the fact that you

cannot improve if you do not measure.

• Metrics and KPI definition for any process is of pivotal

importance, and this means that it has great sway within

Six Sigma process improvement as it uses metrics to

validate all changes.

• It also encourages continuous improvement by

continuously improving on existing metrics and

performance measures. OE'S main objective is to reduce

operation cost and wastes without affecting quality, time,

delivery, and cost of products and services being offered.


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Business Process Reengineering (BPR)

• Business Process Reengineering (BPR) is the analysis

and design of workflows and processes within an

organization.

• It defines a business process as a set of logically-related

tasks performed to achieve a defined business outcome.


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• Re-engineering is the basis for many recent

developments in management, including ideas around

multi-skilled and cross-functional teams.

• Business Process Reengineering may also be referred

to as Business Process Redesign, Business

Transformation, or Business Process Change

Management.


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• As we’ve looked at Six Sigma, we have been highlighting

the need to examine processes and to change them to

meet the needs of the project goal.

• BPR was originally developed within the IT industry, and

as such, it uses a lot of terms and descriptors that are

akin to software development. However, its methods of

looking at shortest point routes and repetition reduction

are found within Six Sigma, and an understanding of

BPR philosophy and its existence can be a benefit to any

Black Belt.


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The Black Belt Role

• There are a couple of key differences in the role of the

Black Belt as opposed to that of the Green. The main

elements are greater statistical knowledge and

capability, as well as managerial ability, either in a

general sense or as a project manager.

• There will also be greater experience and general

knowledge of the Six Sigma methodology through longer

practice and continued use.


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The Black Belt Role

• It is often the case that the Six Sigma Black Belt will be

someone within a junior executive position, maybe a

Director level within the business, or, if not, sitting in a

broadly equivalent standing.

• A lot of companies will have only a few Black Belts and

may look to get one trained up to be a Master Black Belt.


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The Black Belt Role

• Within the realm of training, this set of units gives you an

overview or general idea of the requirements and

responsibilities to become a Black Belt.

• Strictly speaking, to truly obtain and understand your

Black Belt certification title, you will need to complete

one or two projects before your certification will be

completely recognized. This is true of all training, online

or physical.


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Black Belt Discussion

• The role of the Black Belt is to manage DMAIC projects

and processes at an organizational high level.

• The Black Belt should be skilled in project management,

leadership, analytical thinking, adult learning, and

organizational change management.


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Black Belt Discussion

• This is a general balance of how you should spend your

time.

• 25 percent should be running projects as the project

lead.

• 20 percent should be used helping Green Belts who are

project leads.

• 20 percent should be used for teaching Six Sigma.

• 25 percent should be used doing analytical work.

• 10 percent should be used defining and developing

additional projects.


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Review of Applicable Activities and Tools

Using the DMAIC methodology

DMAIC Phase Activities involved Tools you can use DEFINE

Define the problem, agree on the goals,

and listen to the voice of the customer.

Identify Process Owner, Champion, Team

Define Customers and Requirements CTQ

Align Goals with Business Initiatives

Determine Projected ROI

Develop Project name and Purpose

Complete Project Charter

Develop a High-Level Process Map

Project Charter Template

Brainstorming

Graphs

Stakeholder Analysis

Historical Data

Voice of the Customer

MEASURE

What is your baseline?

Develop Detailed Process Maps

Measure your Measurement System

Collect Data

Take Measurements

Data Collection Plan

Benchmarking

CTQs, Histogram, Pareto Chart, Scatter

Diagram, Control Charts, Sigma Level, ROI,

FMEA, Validate, Gage R&R

ANALYZE

Analyze the data for variation and root

causes.

Analyze Data

Define Performance Objectives

Identify Value and Non Value Processes

Determine the Root Cause

Value-Stream, Historical Data

5 Whys, Fishbone, Hypothesis testing, DOE,

Histogram, Pareto Chart, Scatter Diagram,

Control Charts, Statistical Analysis

IMPROVE

Choose the solution/s, pilot the solution,

mistake proof, roll out the improvement

and evaluate the results.

List Potential Solutions

Rank Solutions

Select Solution and Try

Check Results

Roll Out

Evaluate Improvement

Analysis, Brainstorming

Decision Matrix

Capability Study

Pilot

Implementation Plan

CONTROL

Verify the Voice of the Customer is being

met, check your ROI, implement your

control plan and close out the project.

Update Standard Operating Procedures and

Policies

Build a Transition Plan

Close Out the Project

Verify Improvement Processes

Document Procedures

Sigma, ROI, Balance Scorecard, Control Chart

Control Plan Document

Control Plan Form

Transition Plan

Project Management Methods for Closing


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Unit Summary

In this unit, you have learned:

• What change management is within the context of Six

Sigma projects

• How to assess the organizational culture

• How to gain buy in from those experiencing the change


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