Six Sigma Process

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The purpose of the Six Sigma Process is to identify and contain the vital few variables that are impacting an output variable in a negative way.

When this is done:

  • Variability in the output (Y) decreases,
  • The probability for defects decreases,
  • Yield increases,
  • Rolled Throughput Yield increases,
  • The Mean Time Between Failure (MTBF) increases,
  • Product reliability increases,
  • Work-in-Process (WIP) inventory decreases,
  • Quality improves,
  • Cycle-time (CT) decreases,
  • Costs decrease,
  • Customer satisfaction improves and,
  • Ultimately, the business is in a much better position to grow and prosper.

Six Sigma, Lean Six Sigma, and Design For Six Sigma PowerPoint Training Slides

The objective of most businesses is to maximize profit and grow. This is largely achieved by satisfying customers, and to the extent that we satisfy customers we ensure future survival and prosperity.

The Six Sigma process uses the expression Y=f(X). The goal of all Six Sigma projects is to solve this equation. In practical terms it's stated "Y is a function of X". It means that the "output (Y)" is a function of the "inputs (X)". It reflects the fact that a causal relationship exists in all process actions that we perform.

In reality, a result is seldom defined by a single input (X), so we extend the concept to say "Y is a function of one or many X's" where Y is the dependent variable and the X's are the independent variables.

Dependent & Independent Variables

Dependent and Independent Variables

Dependent And Independent Variable Examples

Customer satisfaction (Y) is a function of quality (X), delivery time (X) and cost (X) of the product and/or service that we provide. The term “Critical-to-Satisfaction” refers to any variable(s) that has significant influence on any of the three determinants of customer satisfaction.

Six Sigma Process Basics

six sigma process

Six Sigma utilizes specific tools, in a specific order, to solve business problems.

Six Sigma process tools are used to:

  • Scope and select projects, 
  • Modify and design new processes, 
  • Improve current processes, 
  • Decrease downtime and, 
  • Improve the internal and external customer experience.

Kicking-off a Six Sigma project you're faced with all possible causes - the “vital few” mixed in with the “trivial many". 

Using various Six Sigma tools and techniques, data is transformed into the useful knowledge needed to find the solution to the problem.

Six Sigma itself has not created new tools. It has packaged existing quality improvement tools and it's the appropriate application of the tools that makes all the difference.

Additionally, the use of the tools is additive; that is, information garnered from one leads to the use of another until such time as the sought after answers are obtained.

As you go through the application of the Six Sigma DMAIC process, your goal is to find the root causes to the problem you are trying to solve. 

To aid you in doing this, Six Sigma tools are used that take in a large number of the “trivial many contributors” and narrows them to the “vital few contributors”. 

six sigma factor funnel

Six Sigma tools are used to sift through the many inputs to define the key inputs driving the process.

Six Sigma Process Roadmap

Step 1: Select Output Characteristic

What’s The Big Picture (Y)?

You need to have a reason to enter the Six Sigma process and that reason must be that something is bothering a customer, either internal or external.

What’s critical-to-satisfaction, $$$$, cycle-time, defects, etc.?

  • If you can’t identify what’s important to work on; you shouldn't work on anything.
  • If you can’t put a name on it, you can’t do it!
  • If you can’t define the units of measure you will end up having to: define the units of measure and create the measuring system to measure it.



Critical-To-Tree Example (Quality, Delivery, Cost)

Step 2: Define Performance Characteristic

What‘s The Specification?

If you don’t know what makes something good or bad how can you measure it? The definition of what makes a "defect" must be crystal clear in the Six Sigma process and in the same units-of-measure as the CTS from step 1. If it's not you have the wrong specification.

Step 3: Validate The Measurement System

Can I See?

If you can’t measure something, how do you know where you are, where you have been, or where you are going? If your measuring system is incapable STOP and FIX IT before the project goes further.


measurement System Variation

Actual Value + Measurement System Error = Observed Measurement 

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Types of MSA

Attribute and Variable MSA

Variable and Attribute MSA Approaches

Step 4: Establish Capability

What's The Current Performance?

I know what needs to be fixed (step1), what defines it as good or bad (step 2), and that I can accurately measure it (step 3). Now we can say how the process is performing. Real improvement can happen from here!

Process Capability Study Output

Process Capability Study

Process Capability Study Key Statistics

Step 5: Define Performance Objectives

How Much Improvement Can We Make?

Where do you want to be at the end? Is it aligned with the business strategy? The Six Sigma process must be aligned with company strategy.

Process Cycle-Time Performance

Process Performance Objective

Cycle-Time Quality Improvement Objective

Step 6: Identify Variation Sources

What Makes It Tick?

List all potential inputs (X’s) that could be impacting the output (Y’s). This is filling the top of the funnel.

Think outside the box and identify all possible causes. If you fix it the same way you did last time, you will have the same problem you have now. The Six Sigma process is about permanent fixes.

Variation Identification Tools

Variation Identification Tools

Identifying The "Viatal Few" Input Variables - Process Map, Fishbone and Pareto Results Feeding Into C&E Matrix

Assessing Initial Risk

Failure Modes & Effects Analysis (FMEA)

Assessing Risk - Process Map and Pareto Results Feeding Into Failure Modes & Effects Analysis (FMEA) Tool

FMEA Risk Analysis

Assessing Risk - Process Map and Pareto Results Feeding Into Failure Modes & Effects Analysis (FMEA) Tool

Developing An Initial Control Plan

Control Plan Development

Process Map, Capability, MSA and FMEA Results Feeding Into The Development Of A Control Plan

Step 7: Screen Potential Sources

Discover Input (X's) Output (Y) Relationships

This is getting to the middle of the funnel. Screen using graphical tools, experiments, and hypothesis tests to identify and prove which are the vital inputs (X’s).

Discover input ( X’s) and output (Y) relationships. For simple projects you may get to the bottom of the funnel with 1 vital input (X).

Matrix Plot

Correlation Matrix

Matrix Plot Results Exploring Relationships Between Variables

Step 8: Discover Variable Relationships

How Are The Inputs (X’s) Affecting The Output (Y)?

Evaluate how the vital inputs ( X’s) affect the output (Y), either independently or in combination. This is primarily done with regression and DOE. This is the bottom of the funnel.

I know which inputs (X’s) are impacting my output (Y). And now I know how they affect the output (Y).

The function Y = f(X1, X2,…, Xn) is called the “transfer function” – it describes how a change in one or more of the inputs (X’s) transfers to a change in the output (Y).

In practical terms it means “the output (Y) is a function of the inputs (X’s). "Y=f(x) is the Six Sigma process".

Simple Regression Plot

Simple Linear Regression

Exploring The Strength Of Relationship. Developing The Sigma Sigma Equation.

Step 9: Establish Operating Tolerances

How Do I Set The Inputs (X's) To Maximize The Output (Y)?

We know which inputs (X’s) are important and how they impact the output. What settings of the inputs (X’s) do I use to make the improvement?

In the case of variable inputs (X’s), my settings must be a target value and tolerance (amount ± about the target). In the case of non-variable inputs (X’s), the best value of the input (X) variable that provides the best value of output variable (Y).

Step 10: Validate Measurement Systems(s)

Can I See When My Critical Inputs Change?

Validate the measuring systems of the vital inputs (X’s).

Also, you might have improved your output (Y) so much that you can no longer “see” the process improvement. You may need to improve the measurement system to truly measure the improvement.

Step 11: Determine Process Capability

How Much Improvement Did I Make?

Measure the capability of controlling the inputs ( X’s) at their optimal settings. Conduct a process capability study on the output (Y). Determine results by comparing the new capability with the baseline capability (step 4) and your goals (step 5).

Step 12: Implement Process Controls

Let’s Not Repeat This Again!

Before you can say that a project is complete; the inputs (X’s) you determined as vital, their settings, and other actions you have taken to make the improvement must be:

  • Nailed down and set in concrete,
  • Fully implemented - NOT just agreed to,
  • Put into a rigorous audit schedule,
  • Documented in a Control Plan.

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Six Sigma Tools Refreshers


Distribution - Binomial

Distribution - ChiSquare

Distribution - F

Distribution - Lognormal

Distribution - Normal

Distribution - Poisson

Distribution - Prob Plot

Distribution - t

Distribution - Weibull


Blocking and Confounding

Center Point

Designed Experiment (DOE)

Factorial Experiments

Inner Array

Interaction Plot

Main Effects Plot

Randomizing and Replication

Runs and Resolution


Statistical Process Control

Control Charts

Control Chart Selection

Control Limits

Control Chart - MR

Control Chart - np

Control Chart - p

Control Chart - R

Control Chart - S

Control Chart - u

Control Chart - XBar


Individuals Chart

Pre-Control Chart

Rational Subgrouping


Affinity Diagram

Alpha and Beta Risk

Alternate Hypothesis


ANOVA 2-Way Random


Basic Probability

Box Plot


ChiSq Goodness Of Fit

Confidence Interval

Correlation Coefficient

Contingency Table

Continuous Data Tools

CTx Tree

CTY Tree

Data Collection

Data Transformation

Decision Making Tools


Discrete Data Tools


Degrees of Freedom

Failure Mode Effects Analysis


Fishbone Diagram

Fitted Line Plot

F-Test - 2 Variances

Gage R and R

Hidden Factory


Homogeneity of Variance

Kano Model

Line Plot

Linear Regression

Mean, Median and Mode

Measurement Scale


Measures of Location

Measures of Variation

Mistake Proofing

Nonparametric Tests

Null Hypothesis

Operational Definitions

Probability of a Defect

Process Capability Indices

Process Flow Diagram

Process Mapping

Rational Subgrouping

Residuals Plots

Rolled Throughput Yield

Sampling and Sample Size

Scatter Plot

Shift and Drift

Standard Normal Z Score

Standard Deviation

Test Sensitivity


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