_{}most popular approach to process improvement is Six Sigma. Since this short course is very limited in scope, we cannot address all of

_{}Six Sigma tools and techniques, but will highlight

_{}basic concepts behind Six Sigma. As we discussed earlier, we want to reduce variation (improve quality) to continuously improve our processes. Six Sigma provides a methodology for getting to

_{}root cause of variation and reducing it. Despite what some might say, Six Sigma is not about forcing you to obtain perfection at any costs. It’s more about giving you a wide range of tools, applied in a discipline way for improving a process on a project by project basis. When applied to

_{}right kinds of projects, Six Sigma can yield significant results.

### Project Selection

Six Sigma is executed through projects and since Six Sigma is very precise, it’s often better to start with smaller projects that have limited scope as opposed to large, organizational wide projects that are too difficult to manage. Additionally, projects need to have some justification behind being selected. So in_{}world of Six Sigma, it is very common to see a series of toll-gates or a formal business case to justify

_{}project. For example, projects will consume resources and time. There needs to be a clear payoff or return for doing

_{}project. Additionally, it is useful to clearly define

_{}expected impact of projects and match these impacts against critical issues confronting

_{}organization. For example, a high level of customer complaints or product returns is a critical issue that might be ripe for a Six Sigma type project.

### Five Phases - DMAIC

_{ }life cycle of six sigma work consists of five phases:

1.

**D**efine Opportunities: What must we do to meet VOC - Voice of

_{}Customer. In this phase, you must clearly identify your customers and analyze customer related information, translating this into Critical to Quality (CTQ). CTQ’s are requirements that your processes must perform up to if you expect to meet customer expectations. Once you understand this, then you can initiate six sigma projects to address

_{}specific performance issues.

2.

**M**easure Performance: How much variation is taking place in our processes? In this phase, you will measure your variation in relation to an acceptable level of performance or specification limit. This is driven by

_{}characteristics of your CTQ. Certain statistical tools are used, such as sampling, frequency distribution, and control charts.

3.

**A**nalyze Opportunities: What are

_{}root causes behind this variation? In this phase, you identify

_{}sources of variation. A good place to start is with a nonstatistical tool: Root Cause Analysis, including

_{}Five Whys. Then you can begin to use certain statistical tools, such as Analysis of Variance, to better understand

_{}sources of process variation.

4.

**I**mprove Performance: What can we do to reduce this variation?

_{}vital few or root sources of variation are now identified. One of

_{}more popular tools used for improvement is called Design of Experiments (DOE).

5.

**C**ontrol Performance: How can we design

_{}process so that we never cross

_{}Upper or Lower Control Limits? This is where you sustain your desired performance levels and where practical, seek to improve it by removing more variation from

_{}process.

The Basics

Sigma is a statistical measure of process capability in relation to how much deviation takes place in_{}population of data. It measures

_{}variability of

_{}failure to meet customer requirements.

_{}higher

_{}more process outputs are able to meet customer requirements given fewer defects.

_{}following DPMO scale is used to express

_{}different sigma levels:

For various processes, we set targets which we will call "critical to" such as Critical to Quality (CTQ). This might be making pizzas in our pizza restaurant that are produced in 8 minutes. Each time we bake a pizza, there is some variation from this target of 8 minutes. If we plot each of these bake times, we can show

_{}distribution on a graph. Additionally, our customers are willing to accept pizzas baked in 10 minutes, but likewise it takes us at least 6 minutes to put all

_{}ingredients together for baking

_{}pizza. These limits represent

_{}goal is to "control" what happens within this range and when we bake

_{}pizza at exactly 8 minutes, we have Six Sigma quality - zero deviation from standard. As we get better and better at our baking process, we start to narrow

_{}range, USL and LSL, so that

_{}normal distribution curve becomes tighter. This is how we express continuous improvement in

### CTQ and VOC

Critical to Quality is customer driven and so we have to tap into_{}customer to understand our requirements (CTQ). Six Sigma (as well as lean) requires that you are listening to

_{}Customer or VOC. In

_{}world of Six Sigma, you are "insync" with VOC when you:

1. Provide a 100% solution to

_{}customer’s problem.

2. Minimal effort involved - not wasting

_{}customer’s time and efforts.

3. Giving

4. Provide

_{}customer wants it.

5. Provide

_{}customer wants it.

6. Compress

_{}decision making process for

_{}decision.

This is perhaps one of

_{}biggest reasons why Six Sigma and Lean have become so popular -

_{}bar has been raised in terms of customer satisfaction. Additionally, any variation from

"There is a parable of

_{}elephant. Each is asked to identify what they are touching.

_{}

_{}elephant and identifies he is touching a spear.

_{}

_{}torso and claims what he is touching is a wall.

_{}

_{}tail and think it’s a snake. This parable parallels Six Sigma. As its popularity has grown, different experts have marketed Six Sigma to fit their needs, not necessarily that of their customers. Of course, Six Sigma includes significant amounts of statistical tools. But many see Six Sigma as only statistics. They are wrong. Touch part of

_{}work that constitutes Six Sigma and it will look eerily similar to other quality approaches. Touch another part of Six Sigma and it only vaguely resembles a quality approach at all." - Six Sigma Execution by George Eckes

The Six Sigma Equation

Six Sigma begins with a simple equation that says - All outcomes are_{}result of inputs and

_{}process that acts on these inputs may introduce errors. Errors create variation and in

Y = f (X) + E

Y: Desired outcome

f: Activities and Functions that convert inputs to outcomes

X: Inputs that are needed to produce

_{}desired outcome

E: Errors

If we go back to our pizza example, we bake pizzas with different outcomes or Y’s. Several different inputs are required before we can bake

_{}pizza - preparing

_{}pizza. All of these inputs are

_{}

### Statistical Concepts

One of_{}attractions behind Six Sigma has to do with statistics. Statistics removes much of

_{}subjectivity that often plagues other forms of analysis. Opinions and speculation are replaced by applying statistical concepts to data. Some of these statistical concepts include:

1. Mean and Standard Deviation: Expressing process performance begins with

_{}average of your sample values; sum of all values divided by

_{}spread of data around

_{}mean. Standard Deviation is calculated by going through

a. Calculate

_{}difference from

b. Take

_{}square of each difference.

c. Sum all of your square values and divide by

_{}standard deviation for a sample (as opposed to

_{}calculation so that

_{}entire population.

d. Take

_{}square root of your value from step c (variance). This gives you

Let’s go back to our pizza example. Suppose we made 6 observations of how long it takes to bake pizza. Our upper control limit is 8 minutes; i.e. we don’t want to take more than 8 minutes to bake pizzas.

_{}results of our six observations are:

2. Sigma Value: After calculating

_{}Mean and Standard Deviation, you need to express this performance related to CTQ - customer requirements. This is done by calculating

_{}Z-Score) which represents

_{}mean. However, in order for this to work we need a normal distribution of data. So it’s useful to do a histogram and plot your data, observing

3. t test: Since we use samples to represent populations, we will most likely not know

_{}population. And when our sample size is small (less than 30 observations), we can use

_{}t test to help us with a hypothesis test about

_{}population.

4. F test: We may want to take samples from different segments of

_{}population, such as sampling only cheese pizzas, then sampling deluxe pizzas to see if this yields different results. You can use

5. ANOVA: Used to conduct hypothesis testing when you have two or more groups of data. Like

_{}purpose of ANOVA is to test

_{}data groups. When you test and analyze only one variable (such as oven temperature in baking our pizzas), this is a One-Way ANOVA. If we tested two factors (such as oven temperature and dough texture of pizzas), this would be Two-Way ANOVA.

_{}testing of a combination of factors simultaneously in one test is referred to as a factorial experiment.

### Design of Experiments (DOE)

_{}number of inputs can be numerous (people, materials, equipment, technology, practices, methods, applications, etc.), making our six sigma equation look like:

Y = f (X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X19, X20)

What we really need to do is find out which of these inputs (X) is having

_{}most influence on our outcome (Y). By focusing on

_{}vital few. Experiments are managed based on:

1. Factors:

_{}possible X’s in our equation

2. Levels:

_{}range of values for each factor

3. Main Effects:

_{}change in Y from our experiment as we change our factor (X) from

_{}highest level.

Factors are

_{}independent variable and we want to quantify

Each combination is an equation, contained within a matrix for all factors in our experiment. In order to get

_{}most information, a full matrix is needed which contains all possible combinations of factors and levels. If this creates too many experimental runs, fractions of

_{}matrix can be taken.

"Probably few people know exactly what is meant by quality. Quality actually has different dimensions, which are all considered by consumers purchasing products. Although we as consumers may not know precisely what we mean by quality, we all recognize quality when we see it." -

_{}

### Design for Six Sigma

_{}"DMAIC" approach to Six Sigma seeks to improve existing processes. However, this is only half of

_{}six sigma management process.

_{}other half is to design and develop new processes to improve how we meet customer expectations. This is called Design for Six Sigma (DFSS). DFSS is used under two circumstances: Existing processes cannot be improved or a process to meet CTQ does not exist. Some of

_{}tools used in DFSS type projects include:

1. Quality Function Deployment (QFD): A methodology for identifying and categorizing customer requirements into a matrix.

_{}matrix prioritizes customer expectations on a scale from 1 (least important) to 5 (most important). Causeeffect requirements are also ranked; i.e. what is

_{}"roof" matrix that sits on top of

_{}main house matrix. Depending upon your approach, QFD may include several matrixes for capturing important relationships:

2. Failure Mode Effects Analysis (FMEA): Analytical approach directed toward problem prevention through which every possible failure mode is identified and risk rated.

_{}basic steps for FMEA are:

a. Identify various failure modes (spoiled materials, labor input mistakes, flawed method, equipment failure, etc.)

b. Identify

_{}effects

c. Determine

d. Identify

_{}causes

e. Determine

f. Assess current control processes in place

g. Evaluate

_{}ability to detect

h. Assign a risk rating (A x B x C) relative to:

A: Severity of Impact - On a scale of 1 to 10, rate

_{}seriousness of

B: Probability of Occurrence -

_{}likelihood that a cause and failure mode will occur with 10 as failure is certain and 1 is highly unlikely.

C: Ability to Detect - Rating your ability to detect

_{}failure mode before putting

_{}customer. A rating of 10 indicates that you cannot detect

i. Take corrective actions on those failure modes with high risk ratings.

_{}results of your FMEA can be summarized on a worksheet.

3. Poka Yoke: Mistake proofing a product or service. Errors lead to defects and if you can catch

_{}defects. Certain work conditions tend to introduce errors: Adjustments, Infrequent Activities, Rapid Repetition Involved, and High Volume Loads with Compressed Time Frames. Once you’ve identified

_{}root causes and see if you can design an error proof way of doing

_{}work.

"A very few American companies are counted among

_{}world-class leaders in quality management. But thousands upon thousands of other companies have yet to take that all important first step to ensure their products and services deliver to each customer a dependable high level of quality.

_{}

_{}only market that really matters anymore:

_{}

- Quality in America: How to Implement a Competitive Quality Program by V. Daniel Hunt

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