Six Sigma Terms & Concepts

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What is Six Sigma?

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Six Sigma is a scientific methodology that strives to achieve perfect.

It's a structured performance improvement process that hasnothing to do with martial arts other than the names were taken to dictate the levels of expertise. The terms Yellow Belt, Green Belt, Black Belt, and Master Black Belt are terms given to individuals that practice the Six Sigma methodology.

The methodology is a combination of traditional tools with recent breakthroughs in conjunction with statistics organized in a disciplined structure. Six Sigma strives to: •understand and reduce •variationimprove •performanceimprove customer satisfaction

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What is Lean?

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Defects Reduction

Home > What is Lean?> Principles
PRINCIPLES OF LEAN
The five-step thought process for guiding the implementation of lean techniques is easy to remember, but not always easy to achieve:
Specify value from the standpoint of the end customer by product family.
Identify all the steps in the value stream for each product family, eliminating whenever possible those steps that do not create value.
Make the value-creating steps occur in tight sequence so the product will flow smoothly toward the customer.
As flow is introduced, let customers pull value from the next upstream activity.
As value is specified, value streams are identified, wasted steps are removed, and flow and pull are introduced, begin the process again and continue it until a state of perfection is reached in which perfect value is created with no waste.

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What is 5S?

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The original 5S principles were stated in Japanese. Because of their proven value, they have been translated and restated in English. The 5S is a mantra of sorts designed to help build a quality work environment, both physically and mentally.

The 5S condition of a work area is critical to the morale of employees and the basis of customers’ first impressions. Management’s attitude regarding employees is reflected in the 5S condition of the work area.

The 5S philosophy applies in any work area. The elements of 5S are simple to learn and important to implement:

1) Sort—Eliminate whatever is not needed

2) Straighten—Organize whatever remains

3) Shine--- Clean the work area

4) Standardize--- Scheduleb regular cleaning and maintenance

5) Sustaon--- Make 5S a way of life


Benefits to be derived from implementing 5S include:.

Improved safety
Higher equipment availability
Lower defect rates
Reduced costs
Increased production agility and flexibility
Improved employee morale
Better asset utilization
Enhanced enterprise image to customers, suppliers, employees, and management

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What is Kano?

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Kano Model
A theory of customer satisfaction and product development that identifies five categories of product qualities based on how they affect the customer's perception of the product. This theory, developed in the 1980's by Noriaki Kano, describes customer preferences as attractive, one-dimensional, indifferent, must-have, and reverse (meaning dissatisfaction from too much of a good quality).

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What is Kaizen?

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What is Kaizen?
Kaizen is the practice of continuous improvement. Kaizen was originally introduced to the West by Masaaki Imai in his book Kaizen: The Key to Japan’s Competitive Success in 1986. Today Kaizen is recognized worldwide as an important pillar of an organization’s long-term competitive strategy. Kaizen is continuous improvement that is based on certain guiding principles:
Good processes bring good results
Go see for yourself to grasp the current situation
Speak with data, manage by facts
Take action to contain and correct root causes of problems
Work as a team
Kaizen is everybody’s business
And much more!
One of the most notable features of kaizen is that big results come from many small changes accumulated over time. However this has been misunderstood to mean that kaizen equals small changes. In fact, kaizen means everyone involved in making improvements. While the majority of changes may be small, the greatest impact may be kaizens that are led by senior management as transformational projects, or by cross-functional teams as kaizen events.
Kaizen Word Picture

KAI = CHANGE

ZEN = GOOD

"CHANGE FOR THE BETTER"
Kaizen = Continuous Improvement
...by Everybody! Everyday! Everywhere!

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What is DMAIC?

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Define, Measure, Analyze, Improve, Control

DMAIC

The DMAIC project methodology has five phases:

Define the system, the voice of the customer and their requirements, and the project goals, specifically.

Measure key aspects of the current process and collect relevant data.

Analyze the data to investigate and verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation.

Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future state process. Set up pilot runs to establish process capability.

Control the future state process to ensure that any deviations from the target are corrected before they result in defects. Implement control systems such as statistical process control, production boards, visual workplaces, and continuously monitor the process.

Some organizations add a Recognize step at the beginning, which is to recognize the right problem to work on, thus yielding an RDMAIC methodology.[13]

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What is DMADV or DFSS?

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Define, Measure, Analyze, Design, Verify

DMADV

The DMADV project methodology, known as DFSS ("Design ForSix Sigma"), [9] features five phases:

Define design goals that are consistent with customer demands and the enterprise strategy.

Measure and identify CTQs (characteristics that are Critical To Quality), Measure product capabilities, production process capability, and measure risks.

Analyze to develop and design alternatives

Design an improved alternative, best suited per analysis in the previous step

Verify the design, set up pilot runs, implement the production process and hand it over to the process owner(s).

Quality management tools and methods

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What is a Control Chart?

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Research

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What is a Statistical Process Control?

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Research

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Histogram

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Find

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Run Chart

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Find

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ADJUSTED R-SQUARED

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ADJUSTED R-SQUARED: modification of r-squared used in regression and multiple regression to compare models with different number of explanatory terms. Adjusted R-squared is more useful only if the R-squared is calculated based on a sample not the entire population.

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ANALYSIS OF VARIANCE (ANOVA):

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ANALYSIS OF VARIANCE (ANOVA): statistical technique for analyzing experimental data.

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ATTRIBUTE DATA:

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ATTRIBUTE DATA: pass/fail or go/no-go information. The control charts based on attribute data include percent chart, number of affected units chart, count chart, count-per-unit chart. Commonly used attricbute control charts are also called C,U,P,NP charts.

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AVAILABILITY

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AVAILABILITY: one of three components used to calculate the Overall Equipment Effectiveness (OEE). Availability can be expressed by the ratio of the uptime divided by the schedule time which is the same as the uptime plus downtime.

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BENCHMARKING

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BENCHMARKING: comparing a process or product to the others to determine improvement plan. Trying to obtain all similar concepts from worse to best to make decisions on improvements

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BLOCKING

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BLOCKING: Used in design of experiments (DOE) to neutralize background variables that can not be eliminated by randomizing through spreading them across the experiment.

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BOX-PLOT

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BOX-PLOT: also known as a box and whisker diagram is a graphing tool that displays centering, spread, and distribution of a continuous data set. See Box Plot for more information.

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BRAINSTORMING

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BRAINSTORMING: a technique that teams use to generate ideas on a particular subject. Each person in the team is asked to think creatively and write down as many ideas as possible. The ideas are not discussed or reviewed until after the brainstorming session.

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C CHART

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C CHART: count chart for attribute data. See C-Chart for more information.

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CAPABILITY INDEX

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CAPABILITY INDEX (Cp,Cpk,Pp,Ppk,Cpm,Cpkm) all assume normal data. Pp and Ppk are rarely used compared to Cp and Cpk. They should only be used as relative comparisons to their counterparts

• Cp: a short-term process capability index. The best a process can perform. A function of the standard deviation that numerically describes variation relative to the tolerance or specifications. Used to establish baseline measurement in the Measure phase of aDMAIC project and final score in the Control phase. The Cp is the best a process can perform if that process is centered on the midpoint and the Cp = Cpk.

• Cpk: a short-term process capability index that is a function of the mean and standard deviation, very commonly used.

• Pp: numerically describes the long-term capability (Cp is short term indicator) of a process assuming it was analyzed and stays in control. Similar to Cp, this capability index is only a function of the standard deviation, not a nominal (target) value that may be historical or provided by the customer.

• Ppk: numerically describes the long-term capability. account for centering of the process among the midpoint of the specifications. However, this performance indice may not be optimal if the customer wants another point as the target other than the midpoint. The calculation of Cpm accounts for the addition of a target value.

• Cpm: Cpm is a capability index, also known as the Taguchi capability index, that is a function of the specification limits, mean of the process, sample standard deviation, and a provided target, T.

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Cp

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Cp: a short-term process capability index. The best a process can perform. A function of the standard deviation that numerically describes variation relative to the tolerance or specifications. Used to establish baseline measurement in the Measure phase of aDMAIC project and final score in the Control phase. The Cp is the best a process can perform if that process is centered on the midpoint and the Cp = Cpk.

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Cpk

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Cpk: a short-term process capability index that is a function of the mean and standard deviation, very commonly used.

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Pp

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Pp: numerically describes the long-term capability (Cp is short term indicator) of a process assuming it was analyzed and stays in control. Similar to Cp, this capability index is only a function of the standard deviation, not a nominal (target) value that may be historical or provided by the customer.

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Ppk

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Ppk: numerically describes the long-term capability. account for centering of the process among the midpoint of the specifications. However, this performance indice may not be optimal if the customer wants another point as the target other than the midpoint. The calculation of Cpm accounts for the addition of a target value.

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Cpm

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Cpm: Cpm is a capability index, also known as the Taguchi capability index, that is a function of the specification limits, mean of the process, sample standard deviation, and a provided target, T.

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CAUSE-AND-EFFECT DIAGRAM

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CAUSE-AND-EFFECT DIAGRAM: a tool for seeking out all the causes, regardless of magnitude, that are contributing to the effect, Y. It is also referred to as the Ishikawa diagram after developer Kaoru Ishikawa and the Fishbone Diagram due to its configuration as the skeleton of a fish. This diagram is one of the seven tools of quality and is one of frequently used subjective screening tools in a DMAIC Six Sigma project.

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CENTRAL LIMIT THEOREM

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CENTRAL LIMIT THEOREM: States that given a distribution with a mean and variance, the sampling distribution of the mean approaches a normal distribution with a mean and variance/N as N, the sample size, increases. Also referred to as the Law of Large Numbers in the insurance and risk management field of study.

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CHECK SHEET

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CHECK SHEET: "sheet" used to collect data in real-time and at the location where the data is generated. Data can be of any type and is the first steps to any improvement project which can be either quantitative or qualitative (attribute, discrete, variable, continuous).

• Classification: A trait such as a defect or failure mode must be classified into a category.

• Location: The physical location of a trait is indicated on a picture of a part or item being evaluated.

• Frequency: The presence or absence of a trait or combination of traits is indicated. Also number of occurrences of a trait on a part can be indicated.

• Measurement: A measurement scale is divided into intervals, and measurements are indicated by checking an appropriate interval.

• Check List: The items to be performed for a task are listed so that, as each is accomplished, it can be indicated as having been completed.

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Check List

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Check List: The items to be performed for a task are listed so that, as each is accomplished, it can be indicated as having been completed.

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COEFFICIENT OF CORRELATION

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COEFFICIENT OF CORRELATION: that measures only a linear relationship between two variables and is denoted by an "r" value. The "r" value is used to measure the correlation and it will always range from -1.0 (anticorrelation) to +1.0. As the value approaches 0 their is less linear correlation, or dependence) of the variables.

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COEFFICIENT OF DETERMINATION:

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COEFFICIENT OF DETERMINATION: The COD ranges from 0-1 (0%-100%) represented by r-squared. The proportion of variability of the dependent variable (Y) accounted for or explained by the independent variable (x) equal to the coefficient of correlation value squared.

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COMMON CAUSES

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COMMON CAUSES: causes of variation that are inherent in a process over time. They affect every outcome of the process and everyone working in the process. (See also "special causes.")

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CONFORMANCE

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CONFORMANCE: an affirmative indication or judgment that a product or service has met the requirements of a relevant specification, contract, or regulation

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CONTINUOUS IMPROVEMENT

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CONTINUOUS IMPROVEMENT: the ongoing improvement of products, services, or processes through incremental and breakthrough improvements.

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CONTROL CHART

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CONTROL CHART: a chart with upper and lower control limits on which values of some statistical measure for a series of samples or subgroups are plotted. The chart frequently shows a central line to help detect a trend of plotted values toward either control limit, the LCL or UCL.

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CONTROL LIMIT

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CONTROL LIMIT: control limit for points above the central line in a control chart. There is a lower control limit (LCL) and an upper control limit (UCL) and this is provided by the process where as the specification limits (used in process capability assessments) are provided by the customer.

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COROLLARY

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COROLLARY: An inference derived from axioms or propositions that follow from axioms or other proven propositions.

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CORRECTIVE ACTION

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CORRECTIVE ACTION: the implementation of solutions resulting in the reduction or elimination of an identified problem.

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CRITICAL TO QUALITY (CTQ)

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CRITICAL TO QUALITY (CTQ): key measurable characteristics of a product or process whose performance standards, or specification limits, must be met in order to satisfy the customer. Sought in the DEFINE phase of Six Sigma projects as a part of gathering the Voice of the Customer (VOC). They align improvement or design efforts with critical issues that affect customer satisfaction.

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CUMULATIVE SUM CONTROL CHART

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CUMULATIVE SUM CONTROL CHART: a control chart on which the plotted value is the cumulative sum of deviations of successive samples from a target value. The ordinate of each plotted point represents the algebraic sum of the previous ordinate and the most recent deviations from the target.

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CUSTOMER DELIGHTER

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CUSTOMER DELIGHTER: delighter features are used in Kano Model that are characteristics that pleasantly surprise and exceed customer expectations. 

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CUSTOMER SATISFACTION

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CUSTOMER SATISFACTION: the result of delivering a product or service that meets customer requirements.

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DEFECT

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DEFECT: a non-conforming characteristic for a product, process, or service. A defective unit may have one or more defects.

Class 1 = Very Serious

Class 2 = Serious

Class 3 = Major

Class 4 = Minor

Causes that have higher lower class of defect are often given higher subjective scores relative to their impact on the effects. Such as "severity" scoring withing the FMEA creation process.

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FACTOR

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FACTOR: also known as a predictor variable (x, PIV). This input variable may be controlled or uncontrolled variable whose influence is being studied.

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F-DISTRIBUTION

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F-DISTRIBUTION: The F-value is a measurement of distance between individual distributions. As F goes up, P goes down saying that there is more confidence in there being a difference between two means and to accept the alternative hypothesis. To calculate take the Mean Square of X divided by the Mean Square of Error Compare this value to the F-critical value found in a table is another way to test hypothesis used inANOVA.

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FIT

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FIT: Predicted value of the response variable provided a specific combination of factor settings.

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GANTT CHART

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GANTT CHART: Visual project planning device used for production scheduling. It is a horizontal bar chart that serves as a visual tool for project management. It illustrates dependent steps and where the project is at any given time. The chart was developed by Henry Laurence Gantt, born in 1861.

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INFERENCE SPACE

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INFERENCE SPACE: Operating range of factors that are being analyzed.

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KURTOSIS

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KURTOSIS: measure of peakness or flatness of a distribution.

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LINEARITY

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LINEARITY: Variation between a known standard across the low and high end of the gage. It is the difference between an individual's measurements and that of a known standard or truth over the full range of expected values.

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MEAN TIME BETWEEN FAILURES

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MEAN TIME BETWEEN FAILURES: common metric used in Predictive Maintenance programs. It measures the average amount of time between failures for a machine or product.

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MULTICOLLINEARITY

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MULTICOLLINEARITY: when two or more predictor variables (x, input variables) are found to be correlated with each other.

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NOMINAL GROUP TECHNIQUE

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NOMINAL GROUP TECHNIQUE: a brainstorming technique used by teams to generate ideas. Team members are asked to confidentially write down as many ideas as possible. Each member is then asked to share one idea with the rest of the team which is recorded. Once everyone on the team has shared an idea, the ideas are prioritized by the entire group.

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POISSON

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POISSON: Poisson formula describes rare events and is also referred to as the law of improbable events. The formula is shown below to calculate the probability of occurrences over an interval. It can provide an approximation to the Binomial Distribution if the number of samples (n) is large and probability of a success (p) is small. Click here to visit the module on the Poisson Distribution.

The Poisson Distribution is a discrete distribution named after French mathematician Simeon-Denis Poisson.

Unlike the Binomial Distribution that has only two possible outcomes as a success or fail, this distribution focuses on the number of discrete occurrences over a defined interval.

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R-SQUARED

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R-SQUARED: The amount of variation explained by the regression equation. Used in inference space to predict future outcomes on the basis of other related information. It is the sum of the squares of the regression model divided by total sum of squares. The square root of it is the correlation coefficient, r.

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RESIDUAL

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RESIDUAL: Difference between an actual and fitted (predicted) experimental value. Term commonly found when using regression or multiple regression.

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RESPONSE VARIABLE (Y, KPOV)

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RESPONSE VARIABLE (Y, KPOV): process output. This is linked to the customer Critical To Quality (CTQ) characteristics.

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ROLLED THROUGHPUT YIELD (RTY)

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ROLLED THROUGHPUT YIELD (RTY): The probability of the entire process producing zero defects. RTY is more important as a metric to use where the process has excessive rework. RTY is the product of each process’s throughput yield, TPY.

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SEVEN TOOLS OF QUALITY

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SEVEN TOOLS OF QUALITY: commonly used tools in just about every DMAIC Six Sigma project to help understand an existing process and drive most effective improvements.

1) Fishbone diagram (finding the all the root causes)

2) Check sheet (collecting data)

3) Flowchart (Process Map-what is really happening)

4) Control chart (determining the initial and final state of process)

5) Histogram (determining the data distribution)

6) Pareto chart (finding the vital few)

7) Scatter diagram (for correlation/regression)

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SHEWHART CYCLE

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SHEWHART CYCLE: named after father of statistical control and creator of the control chart, Walter Shewhart. Also called "plan-do-check-act cycle", or PDCA cycle, or PDSA cycle representing "plan-do-study-act".

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SIGNAL-TO-NOISE (S/N) RATIO

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SIGNAL-TO-NOISE (S/N) RATIO: a mathematical equation that indicates the magnitude of an experimental effect above the effect of experimental error due to chance fluctuations

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SIX-SIGMA QUALITY

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SIX-SIGMA QUALITY: a term used to generally indicate that a process is well within specifications or has received "perfection". Process can be better than six-sigma. Technically the specification range is ±6 standard deviations.

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SPEARMAN'S RHO CORRELATION COEFFICIENT

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SPEARMAN'S RHO CORRELATION COEFFICIENT: Similar to Pearson's Correlation Coefficient (r) it is a measure of statistical dependence of two variables in matched pairs. It is a non-parametric test that will also have a value range from -1 to +1 and zero indicating no association. Spearman's Rho test can determine association of non-linear relationships but it has its limitations too. Recall the Pearson's Correlation Coefficient only measures linear correlation.

A value of +1 indicates perfectly positive monotonic correlation. All data points with greater x values than that of a given data point will have greater y values.

A value of 0 indicates no correlation

A value of -1 indicates perfectly negative monotonic correlation. All data points with lower x values than that of a given data point will have lower y values.

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SPC: Statistical Process Control

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SPC: Statistical Process Control

An Overview of Six Sigma Statistical Process Control
Categorized in: Six Sigma (General), Six Sigma Implementation, Six Sigma Tools & Metrics


Six Sigma is a quality control methodology that relies heavily on statistical theory to improve the quality and efficiency of business processes. This is done in order to decrease the defects in the end result of the processes; a business’ product or service.

The Six Sigma quality system relies heavily on the statistical analysis and statistical process control (SCP). Six Sigma statistical process control tools allow you find out whether the business process in questions is manageable and stable, or if it is trending towards variability which could lead directly to errors in the end product.

Limits are categorized into lower and upper. The lower control limit (LCL), would be set three sigma levels under the mean, while the upper control limit (UCL) is usually set at three sigma levels, over the mean. Since around 99 percent of average process unpredictability will take place within plus or minus three sigma, if the procedure is managed, it should approximate a standard distribution around the mean, and every data point needs to be inside the pre-defined limits.

In order to compute restrictions, you should first identify the mean. Begin with a trial of 30 or more procedure observations, for instance the altitude of a solder bump on top of a circuit board, calculated in thousandths of an inch. Compute the mean through adding up all the values and dividing by the number of evaluation or observation. If the trial size is 30 and the total of the experimental values is 173, the method would be 173/30 = 5.8.

The standard deviation (SD) is easier to compute with the use of the programmed standard deviation calculator in a numerical analysis program or the STDEV function that can be found in a spreadsheet program. For instance, let's presume the SD is 1.8.

The formula to compute the UCL is (3 x SD) + (Process Mean) = UCL. In this example, this would appear to be (3 x 1.8) + 5.8 =11.3. The LCL would be computed as (Process Mean)-(3 x SD) = LCL. Going back to the example, this would appear to be 5.8 - (3 x 1.8) = 0.3. To sum up, the process mean for this trial should be 5.8, and should be exactly focused between the LCL of 0.3 and the UCL of 11.3. These values will be applied in the next section to produce charts.

A control chart is simply a line chart presenting chronological measurements of a procedure characteristic, such as the size of a machined part, with additional lines to illustrate the lower and upper limits. Statistical software packages will have automated chart functions that will create these charts for you. When you assess a chart, you're looking for signals that the procedure could be unmanageable or trending toward being unmanageable.

If any of the following caution signs are present, the procedure could be unmanageable or is trending toward becoming unmanageable:
• A particular point that is outside either of the control limits
• Two out of three points in a line that are on the related side of the middle line and two sigma or more away from it
• Four of five consecutive points on one side of the middle line and higher than one sigma from it
• Eight or more points in a line that are moving the same way.

While the measurements might still be inside the acceptable ranges, if the procedure isn't manageable, it is time to make a move because you'll soon see faulty units generated by the procedure. Six Sigma statistical process control is not easy to understand, even though it is crucial to the entire Six Sigma Methodology and Goals. It is best to perform these tests within the capable hands of a trained and certified Six Sigma Black Belt.

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SPECIAL CAUSES

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SPECIAL CAUSES: causes of variation that arise because of non-inherent and special circumstances. Special causes are also referred to as assignable causes that will result initially a process being out of control. Not all special causes are found outside of the process control limits. Two types of variation: common cause and special cause

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SPECIFICATION

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SPECIFICATION: a document or customer provided evidence that states the requirements to which a given product or service must conform. Commonly found as the lower specification limit (LSL) and/or the upper specification limits (USL). Customer may also provide a target that may or may not be in the middle of the LSL and USL.

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STABILITY

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STABILITY: represents variation due to elapsed time. It is the difference between an individual's measurements taken of the same parts after an extended period of time using the same techniques.

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STEM AND LEAF PLOT

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STEM AND LEAF PLOT: Graphical plot used to display in a bar chart format categories or variable data. The stems are groups of data by class intervals. The leaves are smaller increments of each data point that are built onto the stems.

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TAKT TIME

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TAKT TIME: The rate at which products or services should be produced to meet the customer demand. The value, in conjunction with current loading (production) rates, is used to analyze process loads, bottlenecks, and excess capacity. Click here to see the module on Takt Time.

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THROUGHPUT YIELD (TPY)

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THROUGHPUT YIELD (TPY): The number of acceptable pieces at the end of the end of a process divided by the number of starting pieces excluding scrap and rework (meaning they are a part of the calculation).

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U CHART

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U CHART: count per unit chart used with attribute data

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Deming's 14 Points on Quality Management

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Deming’s 14 Points on Quality Management, a core concept on implementing total quality management, is a set of management practices to help companies increase their quality and productivity.

1) Create constancy of purpose for improving products and services.
2) Adopt the new philosophy.
3)Cease dependence on inspection to achieve quality.
4) End the practice of awarding business on price alone; instead, minimize total cost by working with a single supplier.
5) Improve constantly and forever every process for planning, production and service.
6) Institute training on the job.
7) Adopt and institute leadership.
8) Drive out fear.
9) Break down barriers between staff areas.
10) Eliminate slogans, exhortations and targets for the workforce.
11) Eliminate numerical quotas for the workforce and numerical goals for management.
12) Remove barriers that rob people of pride of workmanship, and eliminate the annual rating or merit system.
13) Institute a vigorous program of education and self-improvement for everyone.
14) Put everybody in the company to work accomplishing the transformation.

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Affinity Diagram

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Affinity Diagram
A technique for organising individual pieces of information into groups or broader categories.

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ANOVA

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ANOVA
Analysis of Variance: A statistical test for identifying significant differences between process or system treatments or conditions. It is done by comparing the variances around the means of the conditions being compared.

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Average

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Average
Also called the mean, it is the arithmetic average of all of the sample values. It is calculated by adding all of the sample values together and dividing by the number of elements (n) in the sample.

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Bar Chart

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Bar Chart
A graphical method that depicts how data falls into different categories.
A graphical method which depicts how data fall into different categories.

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Black Belt

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Black Belt
An individual who receives approximately four weeks training in DMAIC, analytical problem solving, and change management methods. A Black Belt is a full time Six Sigma team leader solving problems under the direction of a Champion.
Breakthrough Improvement
A rate of improvement at or near 70% over baseline performance of the as-is process characteristic.

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Capability

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Capability
A comparison of the required operation width of a process or system to its actual performance width. Expressed as a percentage (yield), a defect rate (dpm, dpmo,), an index (Cp, Cpk, Pp, Ppk), or as a sigma score (Z).

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Cause and Effect Diagram
Fishbone Diagram

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Cause and Effect Diagram
Fishbone Diagram: A pictorial diagram in the shape of a fishbone showing all possible variables that could affect a given process output measure.

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Central Tendency

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Central Tendency
A measure of the point about which a group of values is clustered; two measures of central tendency are the mean, and the median.

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Champion

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Champion
A Champion recognises, defines, assigns and supports the successful completion of Six Sigma projects; they are accountable for the results of the project and the business roadmap to achieve Six Sigma within their span of control.

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Characteristic

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Characteristic
A process input or output which can be measured and monitored.
Common Causes of Variation
Those sources of variability in a process which are truly random, i.e. inherent in the process itself.

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Complexity

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Complexity
The level of difficulty to build, solve or understand something based on the number of inputs, interactions and uncertainty involved.

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Control Chart

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Control Chart
The most powerful tool of statistical process control. It consists of a run chart, together with statistically determined upper and lower control limits and a centerline.

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Control Limits

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Control Limits
Upper and lower bounds in a control chart that are determined by the process itself. They can be used to detect special or common causes of variation. They are usually set at 3 standard deviations from the central tendency.

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Correlation Coefficient

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Correlation Coefficient
A measure of the linear relationship between two variables.

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Cost of Poor Quality (COPQ)

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Cost of Poor Quality (COPQ)
The costs associated with any activity that is not doing the right thing right the first time. It is the financial qualification of any waste that is not integral to the product or service.

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CP

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CP
A capability measure defined as the ratio of the specification width to short-term process performance width.

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Cpk

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CPk.
An adjusted short-term capability index that reduces the capability score in proportion to the offset of the process centre from the specification target.

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Critical to Quality (CTQ)

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Critical to Quality (CTQ)
Any characteristic that is critical to the perceived quality of the product, process or system. See Significant Y.

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Critical X

Back 92

Critical X
An input to a process or system that exerts a significant influence on any one or all of the key outputs of a process.

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Customer

Back 93

Customer
Anyone who uses or consumes a product or service, whether internal or external to the providing organisation or provider.

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Cycle Time

Back 94

Cycle Time
The total amount of elapsed time expended from the time a task, product or service is started until it is completed.

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Defect

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Defect
An output of a process that does not meet a defined specification, requirement or desired outcome such as time, length, color, finish, quantity, temperature etc.

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Defective

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Defective
A unit of product or service that contains at least one defect.
Deployment (Six Sigma)
The planning, launch, training and implementation management of a Six Sigma initiative within a company.

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Design of Experiments (DOE)

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Design of Experiments (DOE)
Generally, it is the discipline of using an efficient, structured, and proven approach to interrogating a process or system for the purpose of maximising the gain in process or system knowledge.

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Design for Six Sigma (DFSS)

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Design for Six Sigma (DFSS)
The use of Six Sigma thinking, tools and methods applied to the design of products and services to improve the initial release performance, ongoing reliability, and life-cycle cost.

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DMAIC

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DMAIC
The acronym for core phases of the Six Sigma methodology used to solve process and business problems through data and analytical methods. It stands for define, measure, analyse, improve and control.

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DPMO

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DPMO
Defects per million opportunities. The total number of defects observed divided by the total number of opportunities, expressed in parts per million. Sometimes called Defects per Million (DPM).

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DPU

Back 101

DPU
Defects per unit. The total number of defects detected in some number of units divided by the total number of those units.

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SS Terms E thru Z

Back 102

Rizalito Garcia is the author of Six Sigma for Newbies, released in 2009.

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Entitlement

Back 103

Entitlement
The best demonstrated performance for an existing configuration of a process or system. It is an empirical demonstration of what level of improvement can potentially be reached.

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Epsilon S

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Epsilon S
Greek symbol used to represent residual error.

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Experimental Design

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Experimental Design
See Design of Experiments.

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Failure Mode and Effects Analysis (FMEA)

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Failure Mode and Effects Analysis (FMEA)
A procedure used to identify, assess, and mitigate risks associated with potential product, system, or process failure modes.

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Finance Representative

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Finance Representative
An individual who provides an independent evaluation of a Six Sigma project in terms of hard and/or soft savings. They are a project support resource to both Champions and Project Leaders.

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Fishbone Diagram
See cause and effect diagram.

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Fishbone Diagram
See cause and effect diagram.

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Flowchart

Back 109

Flowchart
A graphic model of the flow of activities, material, and/or information that occurs during a process.

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Gage R&R

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Gage R&R
Quantitative assessment of how much variation (repeatability and reproducibility) is in a measurement system compared to the total variation of the process or system.

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Green Belt

Back 111

Green Belt
An individual who receives approximately two weeks of training in DMAIC, analytical problem solving, and change management methods. A Green Belt is a part time Six Sigma position that applies Six Sigma to their local area, doing smaller-scoped projects and providing support to Black Belt projects.
Hidden Factory or Operation
Corrective and non-value-added work required to produce a unit of output that is generally not recognised as an unnecessary generator of waste in form of resources, materials and cost.

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Histogram

Back 112

Histogram
A bar chart that depicts the frequencies (by the height of the plotted bars) of numerical or measurement categories.

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Implementation Team

Back 113

Implementation Team
A cross-functional executive team representing various areas of the company. Its charter is to drive the implementation of Six Sigma by defining and documenting practices, methods and operating policies.

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Input

Back 114

Input
A resource consumed, utilised, or added to a process or system. Synonymous with X, characteristic, and input variable.

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Input-Process-Output (IPO) Diagram

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Input-Process-Output (IPO) Diagram
A visual representation of a process or system where inputs are represented by input arrows to a box (representing the process or system) and outputs are shown using arrows emanating out of the box.

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lshikawa Diagram

Back 116

lshikawa Diagram
See cause and effect diagram and fishbone diagram.

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Least Squares

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Least Squares
A method of curve-fitting that defines the best fit as the one that minimises the sum of the squared deviations of the data points from the fitted curve.

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Long-Term Variation

Back 118

Long-Term Variation
The observed variation of an input or output characteristic which has had the opportunity to experience the majority of the variation effects that influence it.

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Lower Control Limit (LCL)

Back 119

Lower Control Limit (LCL)
For control charts: the limit above which the subgroup statistics must remain for the process to be in control. Typically, 3 standard deviations below the central tendency.

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Lower Specification Limit (LSL)

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Lower Specification Limit (LSL)
The lowest value of a characteristic which is acceptable.

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Master Black Belt

Back 121

Master Black Belt
An individual who has received training beyond a Black Belt. The technical, go-to expert regarding technical and project issues in Six Sigma. Master Black Belts teach and mentor other Six Sigma Belts, their projects and support Champions.

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Mean

Back 122

Mean
See average.

Front 123

Measurement

Back 123

Measurement
The act of obtaining knowledge about an event or characteristic through measured quantification or assignment to categories.

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Measurement Accuracy

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Measurement Accuracy
For a repeated measurement, it is a comparison of the average of the measurements compare to some known standard.

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Measurement Precision

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Measurement Precision
For a repeated measurement, it is the amount of variation that exists in the measured values.

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Measurement Systems Analysis

Back 126

Measurement Systems Analysis (MSA)
An assessment of the accuracy and precision of a method of obtaining measurements. See also Gage R&R.

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Median

Back 127

Median
The middle value of a data set when the values are arranged in either ascending or descending order.

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Metric

Back 128

Metric
A measure that is considered to be a key indicator of performance. It should be linked to goals or objectives and carefully monitored.

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Natural Tolerances of a Process

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Natural Tolerances of a Process
See Control Limits.

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Nominal Group Technique
A structured method that a team can use to generate and rank a list of ideas or items.

Back 130

Nominal Group Technique
A structured method that a team can use to generate and rank a list of ideas or items.

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Non-Value Added (NVA)

Back 131

Non-Value Added (NVA)
Any activity performed in producing a product or delivering a service that does not add value, where value is defined as changing the form, fit or function of the product or service and is something for which the customer is willing to pay.

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Normal Distribution

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Normal Distribution
The distribution characterised by the smooth, bell-shaped curve. Synonymous with Gaussian Distribution.

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Objective Statement

Back 133

Objective Statement
A succinct statement of the goals, timing and expectations of a Six Sigma improvement project.

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Opportunities

Back 134

Opportunities
The number of characteristics, parameters or features of a product or service that can be classified as acceptable or unacceptable.

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Out of Control

Back 135

Out of Control
A process is said to be out of control if it exhibits variations larger than its control limits or shows a pattern of variation.

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Output

Back 136

Output
A resource, item or characteristic that is the product of a process or system. See also Y, CTQ.

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Pareto Chart

Back 137

Pareto Chart
A bar chart for attribute (or categorical) data categories are presented in descending order of frequency.

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Pareto Principle

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Pareto Principle
The general principle originally proposed by Vilfredo Pareto (1848-1923) that the majority of influence on an outcome is exerted by a minority of input factors.

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Poka-Yoke

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Poka-Yoke
A translation of a Japanese term meaning to mistake-proof.

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Probability

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Probability
The likelihood of an event or circumstance occurring.

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Problem Statement

Back 141

Problem Statement
A succinct statement of a business situation which is used to bound and describe the problem the Six Sigma project is attempting to solve.

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Process

Back 142

Process
A set of activities and material and/or information flow which transforms a set of inputs into outputs for the purpose of producing a product, providing a service or performing a task.

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Process Characterisation

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Process Characterisation
The act of thoroughly understanding a process, including the specific relationship(s) between its outputs and the inputs, and its performance and capability.

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Process Certification

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Process Certification
Establishing documented evidence that a process will consistently produce the required outcome or meet required specifications.

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Process Flow Diagram

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Process Flow Diagram
See flowchart.

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Process Member

Back 146

Process Member
A individual who performs activities within a process to deliver a process output, a product or a service to a customer.

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Process Owner

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Process Owner
Process Owners have responsibility for process performance and resources. They provide support, resources and functional expertise to Six Sigma projects. They are accountable for implementing developed Six Sigma solutions into their process.

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Quality Function Deployment (QFD)

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Quality Function Deployment (QFD)
A systematic process used to integrate customer requirements into every aspect of the design and delivery of products and services.

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Range

Back 149

Range
A measure of the variability in a data set. It is the difference between the largest and smallest values in a data set.

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Regression Analysis

Back 150

Regression Analysis
A statistical technique for determining the mathematical relation between a measured quantity and the variables it depends on. Includes Simple and Multiple Linear Regression.

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Repeatability (of a Measurement)

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Repeatability (of a Measurement)
The extent to which repeated measurements of a particular object with a particular instrument produce the same value. See also Gage R&R.

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Reproducibility (of a Measurement)

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Reproducibility (of a Measurement)
The extent to which repeated measurements of a particular object with a particular individual produce the same value. See also Gage R&R.

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Rework

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Rework
Activity required to correct defects produced by a process.

Front 154

Risk Priority Number (RPN)
In Failure Mode Effects Analysis:

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Risk Priority Number (RPN)
In Failure Mode Effects Analysis, the aggregate score of a failure mode including its severity, frequency of occurrence, and ability to be detected.

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Rolled Throughput Yield (RTY)

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Rolled Throughput Yield (RTY)
The probability of a unit going through all process steps or system characteristics with zero defects.

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RUMBA

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RUMBA
An acronym used to describe a method to determine the validity of customer requirements. It stands for reasonable, understandable, measurable, believable, and achievable.

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Run Chart

Back 157

Run Chart
A basic graphical tool that charts a characteristic's performance over time.

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Scatter Plot

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Scatter Plot
A chart in which one variable is plotted against another to determine the relationship, if any, between the two.

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Screening Experiment

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Screening Experiment
A type of experiment to identify the subset of significant factors from among a large group of potential factors.

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Short Term Variation

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Short Term Variation
The amount of variation observed in a characteristic, which has not had the opportunity to experience all the sources of variation from the inputs acting on it.

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Sigma Score (Z)

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Sigma Score (Z)
A commonly used measure of process capability that represents the number of short-term standard deviations between the centre of a process and the closest specification limit. Sometimes referred to as Sigma level, or simply Sigma.

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Significant Y

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Significant Y
An output of a process that exerts a significant influence on the success of the process or the customer.

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Six Sigma Leader

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Six Sigma Leader
An individual that leads the implementation of Six Sigma, co-ordinating all of the necessary activities, assures optimal results are obtained and keeps everyone informed of progress made.

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Six Sigma Project

Back 164

Six Sigma Project
A well defined effort that states a business problem in quantifiable terms and with known improvement expectations.

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Six Sigma (System)

Back 165

Six Sigma (System)
A proven set of analytical tools, project management techniques, reporting methods and management techniques combined to form a powerful problem solving and business improvement methodology.

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Special Cause Variation

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Special Cause Variation
Those non-random causes of variation that can be detected by the use of control charts and good process documentation.

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Specification Limits

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Specification Limits
The bounds of acceptable performance for a characteristic.

Front 168

Stability (of a Process)

Back 168

Stability (of a Process)
A process is said to be stable if it shows no recognisable pattern of change and no special causes of variation are present.

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Standard Deviation

Back 169

Standard Deviation
One of the most common measures of variability in a data set or in a population. It is the square root of the variance.

Front 170

Statistical Problem

Back 170

Statistical Problem
A problem that is addressed with facts and data analysis methods.

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Statistical Process Control (SPC)

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Statistical Process Control (SPC)
The use of basic graphical and statistical methods for measuring, analysing, and controlling the variation of a process for the purpose of continuously improving the process. A process is said to be in a state of statistical control when it exhibits only random variation.

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Statistical Solution

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Statistical Solution
A data driven solution with known confidence/risk levels, as opposed to a qualitative, "I think" solutions.

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Supplier

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Supplier
An individual or entity responsible for providing an input to a process in the form of resources or information.

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Trend

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Trend
A gradual, systematic change over time or some other variable.

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TSSW

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TSSW
Thinking the Six Sigma way. A mental model for improvement which perceives outcomes through a cause and effect relationship combined with Six Sigma concepts to solve everyday and business problems.

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Two-Level Design

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Two-Level Design
An experiment where all factors are set at one of two levels, denoted as low and high (-1 and +1).

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Upper Control Limit (UCL) for Control Charts

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Upper Control Limit (UCL) for Control Charts
The upper limit below which a process statistic must remain to be in control. Typically this value is 3 standard deviations above the central tendency.

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Upper Specification Limit (USL)

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Upper Specification Limit (USL)
The highest value of a characteristic which is acceptable.

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Variability

Back 179

Variability
A generic term that refers to the property of a characteristic, process or system to take on different values when it is repeated.

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Variables

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Variables
Quantities which are subject to change or variability.

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Variable Data

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Variable Data
Data which is continuous, which can be meaningfully subdivided, i.e. can have decimal subdivisions.

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Variance

Back 182

Variance
A specifically defined mathematical measure of variability in a data set or population. It is the square of the standard deviation.

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Variation

Back 183

Variation
See variability.

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Voice of the Business (VOB)

Back 184

Voice of the Business (VOB)
Represents the needs of the business and the key stakeholders of the business. It is usually items such as profitability, revenue, growth, market share, etc.

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Voice of the Customer (VOC)

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Voice of the Customer (VOC)
Represents the expressed and non-expressed needs, wants and desires of the recipient of a process output, a product or a service. Its is usually expressed as specifications, requirements or expectations.

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Voice of the Process (VOP)

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Voice of the Process (VOP)
Represents the performance and capability of a process to achieve both business and customer needs. It is usually expressed in some form of an efficiency and/or effectiveness metric.

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Waste

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Waste
Waste represents material, effort and time that does not add value in the eyes of key stakeholders (customers, employees, investors).

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X

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X
An input characteristic to a process or system. In Six Sigma it is usually used in the expression of Y=f(X), where the output (Y) is a function of the inputs (X).

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Y

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Y
An output characteristic of a process. In Six Sigma it is usually used in the expression of Y=f(X), where the output (Y) is a function of the inputs (X).

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Yellow Belt

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Yellow Belt
An individual who receives approximately one week of training in problem solving and process optimisation methods. Yellow Belts participate in process management activates, participate on Green and Black Belt projects and apply concepts to their work area and their job.

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Z Score

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Z Score
See Sigma Score.

Front 192

Rizalito Garcia

Back 192

Rizalito Garcia is the author of Six Sigma for Newbies, released in 2009.

Front 193

Six Sigma Terminology

Back 193

Six Sigma Terminology
By Rizalito Garcia
A comprehensive glossary of Six Sigma terms and acronyms used in managing Six Sigma projects.
Affinity Diagram
A technique for organising individual pieces of information into groups or broader categories.

ANOVA
Analysis of Variance: A statistical test for identifying significant differences between process or system treatments or conditions. It is done by comparing the variances around the means of the conditions being compared.

Attribute Data
Data which on one of a set of discrete values such as pass or fail, yes or no.

Average
Also called the mean, it is the arithmetic average of all of the sample values. It is calculated by adding all of the sample values together and dividing by the number of elements (n) in the sample.

Bar Chart
A graphical method that depicts how data falls into different categories.
A graphical method which depicts how data fall into different categories.

Black Belt
An individual who receives approximately four weeks training in DMAIC, analytical problem solving, and change management methods. A Black Belt is a full time Six Sigma team leader solving problems under the direction of a Champion.

Breakthrough Improvement
A rate of improvement at or near 70% over baseline performance of the as-is process characteristic.

Capability
A comparison of the required operation width of a process or system to its actual performance width. Expressed as a percentage (yield), a defect rate (dpm, dpmo,), an index (Cp, Cpk, Pp, Ppk), or as a sigma score (Z).

Cause and Effect Diagram
Fishbone Diagram: A pictorial diagram in the shape of a fishbone showing all possible variables that could affect a given process output measure.

Central Tendency
A measure of the point about which a group of values is clustered; two measures of central tendency are the mean, and the median.

Champion
A Champion recognises, defines, assigns and supports the successful completion of Six Sigma projects; they are accountable for the results of the project and the business roadmap to achieve Six Sigma within their span of control.

Characteristic
A process input or output which can be measured and monitored.
Common Causes of Variation
Those sources of variability in a process which are truly random, i.e. inherent in the process itself.
Complexity
The level of difficulty to build, solve or understand something based on the number of inputs, interactions and uncertainty involved.
Control Chart
The most powerful tool of statistical process control. It consists of a run chart, together with statistically determined upper and lower control limits and a centerline.
Control Limits
Upper and lower bounds in a control chart that are determined by the process itself. They can be used to detect special or common causes of variation. They are usually set at 3 standard deviations from the central tendency.
Correlation Coefficient
A measure of the linear relationship between two variables.
Cost of Poor Quality (COPQ)
The costs associated with any activity that is not doing the right thing right the first time. It is the financial qualification of any waste that is not integral to the product or service.
CP
A capability measure defined as the ratio of the specification width to short-term process performance width.
CPk.
An adjusted short-term capability index that reduces the capability score in proportion to the offset of the process centre from the specification target.
Critical to Quality (CTQ)
Any characteristic that is critical to the perceived quality of the product, process or system. See Significant Y.
Critical X
An input to a process or system that exerts a significant influence on any one or all of the key outputs of a process.
Customer
Anyone who uses or consumes a product or service, whether internal or external to the providing organisation or provider.
Cycle Time
The total amount of elapsed time expended from the time a task, product or service is started until it is completed.
Defect
An output of a process that does not meet a defined specification, requirement or desired outcome such as time, length, color, finish, quantity, temperature etc.
Defective
A unit of product or service that contains at least one defect.
Deployment (Six Sigma)
The planning, launch, training and implementation management of a Six Sigma initiative within a company.
Design of Experiments (DOE)
Generally, it is the discipline of using an efficient, structured, and proven approach to interrogating a process or system for the purpose of maximising the gain in process or system knowledge.
Design for Six Sigma (DFSS)
The use of Six Sigma thinking, tools and methods applied to the design of products and services to improve the initial release performance, ongoing reliability, and life-cycle cost.
DMAIC
The acronym for core phases of the Six Sigma methodology used to solve process and business problems through data and analytical methods. It stands for define, measure, analyse, improve and control.
DPMO
Defects per million opportunities. The total number of defects observed divided by the total number of opportunities, expressed in parts per million. Sometimes called Defects per Million (DPM).
DPU
Defects per unit. The total number of defects detected in some number of units divided by the total number of those units.
Entitlement
The best demonstrated performance for an existing configuration of a process or system. It is an empirical demonstration of what level of improvement can potentially be reached.
Epsilon S
Greek symbol used to represent residual error.
Experimental Design
See Design of Experiments.
Failure Mode and Effects Analysis (FMEA)
A procedure used to identify, assess, and mitigate risks associated with potential product, system, or process failure modes.
Finance Representative
An individual who provides an independent evaluation of a Six Sigma project in terms of hard and/or soft savings. They are a project support resource to both Champions and Project Leaders.
Fishbone Diagram
See cause and effect diagram.
Flowchart
A graphic model of the flow of activities, material, and/or information that occurs during a process.
Gage R&R
Quantitative assessment of how much variation (repeatability and reproducibility) is in a measurement system compared to the total variation of the process or system.
Green Belt
An individual who receives approximately two weeks of training in DMAIC, analytical problem solving, and change management methods. A Green Belt is a part time Six Sigma position that applies Six Sigma to their local area, doing smaller-scoped projects and providing support to Black Belt projects.
Hidden Factory or Operation
Corrective and non-value-added work required to produce a unit of output that is generally not recognised as an unnecessary generator of waste in form of resources, materials and cost.
Histogram
A bar chart that depicts the frequencies (by the height of the plotted bars) of numerical or measurement categories.
Implementation Team
A cross-functional executive team representing various areas of the company. Its charter is to drive the implementation of Six Sigma by defining and documenting practices, methods and operating policies.
Input
A resource consumed, utilised, or added to a process or system. Synonymous with X, characteristic, and input variable.
Input-Process-Output (IPO) Diagram
A visual representation of a process or system where inputs are represented by input arrows to a box (representing the process or system) and outputs are shown using arrows emanating out of the box.
lshikawa Diagram
See cause and effect diagram and fishbone diagram.
Least Squares
A method of curve-fitting that defines the best fit as the one that minimises the sum of the squared deviations of the data points from the fitted curve.
Long-Term Variation
The observed variation of an input or output characteristic which has had the opportunity to experience the majority of the variation effects that influence it.
Lower Control Limit (LCL)
For control charts: the limit above which the subgroup statistics must remain for the process to be in control. Typically, 3 standard deviations below the central tendency.
Lower Specification Limit (LSL)
The lowest value of a characteristic which is acceptable.
Master Black Belt
An individual who has received training beyond a Black Belt. The technical, go-to expert regarding technical and project issues in Six Sigma. Master Black Belts teach and mentor other Six Sigma Belts, their projects and support Champions.
Mean
See average.
Measurement
The act of obtaining knowledge about an event or characteristic through measured quantification or assignment to categories.
Measurement Accuracy
For a repeated measurement, it is a comparison of the average of the measurements compare to some known standard.
Measurement Precision
For a repeated measurement, it is the amount of variation that exists in the measured values.
Measurement Systems Analysis (MSA)
An assessment of the accuracy and precision of a method of obtaining measurements. See also Gage R&R.
Median
The middle value of a data set when the values are arranged in either ascending or descending order.
Metric
A measure that is considered to be a key indicator of performance. It should be linked to goals or objectives and carefully monitored.
Natural Tolerances of a Process
See Control Limits.
Nominal Group Technique
A structured method that a team can use to generate and rank a list of ideas or items.
Non-Value Added (NVA)
Any activity performed in producing a product or delivering a service that does not add value, where value is defined as changing the form, fit or function of the product or service and is something for which the customer is willing to pay.
Normal Distribution
The distribution characterised by the smooth, bell-shaped curve. Synonymous with Gaussian Distribution.
Objective Statement
A succinct statement of the goals, timing and expectations of a Six Sigma improvement project.
Opportunities
The number of characteristics, parameters or features of a product or service that can be classified as acceptable or unacceptable.
Out of Control
A process is said to be out of control if it exhibits variations larger than its control limits or shows a pattern of variation.
Output
A resource, item or characteristic that is the product of a process or system. See also Y, CTQ.
Pareto Chart
A bar chart for attribute (or categorical) data categories are presented in descending order of frequency.
Pareto Principle
The general principle originally proposed by Vilfredo Pareto (1848-1923) that the majority of influence on an outcome is exerted by a minority of input factors.
Poka-Yoke
A translation of a Japanese term meaning to mistake-proof.
Probability
The likelihood of an event or circumstance occurring.
Problem Statement
A succinct statement of a business situation which is used to bound and describe the problem the Six Sigma project is attempting to solve.
Process
A set of activities and material and/or information flow which transforms a set of inputs into outputs for the purpose of producing a product, providing a service or performing a task.
Process Characterisation
The act of thoroughly understanding a process, including the specific relationship(s) between its outputs and the inputs, and its performance and capability.
Process Certification
Establishing documented evidence that a process will consistently produce the required outcome or meet required specifications.
Process Flow Diagram
See flowchart.
Process Member
A individual who performs activities within a process to deliver a process output, a product or a service to a customer.
Process Owner
Process Owners have responsibility for process performance and resources. They provide support, resources and functional expertise to Six Sigma projects. They are accountable for implementing developed Six Sigma solutions into their process.
Quality Function Deployment (QFD)
A systematic process used to integrate customer requirements into every aspect of the design and delivery of products and services.
Range
A measure of the variability in a data set. It is the difference between the largest and smallest values in a data set.
Regression Analysis
A statistical technique for determining the mathematical relation between a measured quantity and the variables it depends on. Includes Simple and Multiple Linear Regression.
Repeatability (of a Measurement)
The extent to which repeated measurements of a particular object with a particular instrument produce the same value. See also Gage R&R.
Reproducibility (of a Measurement)
The extent to which repeated measurements of a particular object with a particular individual produce the same value. See also Gage R&R.
Rework
Activity required to correct defects produced by a process.
Risk Priority Number (RPN)
In Failure Mode Effects Analysis, the aggregate score of a failure mode including its severity, frequency of occurrence, and ability to be detected.
Rolled Throughput Yield (RTY)
The probability of a unit going through all process steps or system characteristics with zero defects.
RUMBA
An acronym used to describe a method to determine the validity of customer requirements. It stands for reasonable, understandable, measurable, believable, and achievable.
Run Chart
A basic graphical tool that charts a characteristic's performance over time.
Scatter Plot
A chart in which one variable is plotted against another to determine the relationship, if any, between the two.
Screening Experiment
A type of experiment to identify the subset of significant factors from among a large group of potential factors.
Short Term Variation
The amount of variation observed in a characteristic, which has not had the opportunity to experience all the sources of variation from the inputs acting on it.
Sigma Score (Z)
A commonly used measure of process capability that represents the number of short-term standard deviations between the centre of a process and the closest specification limit. Sometimes referred to as Sigma level, or simply Sigma.
Significant Y
An output of a process that exerts a significant influence on the success of the process or the customer.
Six Sigma Leader
An individual that leads the implementation of Six Sigma, co-ordinating all of the necessary activities, assures optimal results are obtained and keeps everyone informed of progress made.
Six Sigma Project
A well defined effort that states a business problem in quantifiable terms and with known improvement expectations.
Six Sigma (System)
A proven set of analytical tools, project management techniques, reporting methods and management techniques combined to form a powerful problem solving and business improvement methodology.
Special Cause Variation
Those non-random causes of variation that can be detected by the use of control charts and good process documentation.
Specification Limits
The bounds of acceptable performance for a characteristic.
Stability (of a Process)
A process is said to be stable if it shows no recognisable pattern of change and no special causes of variation are present.
Standard Deviation
One of the most common measures of variability in a data set or in a population. It is the square root of the variance.
Statistical Problem
A problem that is addressed with facts and data analysis methods.
Statistical Process Control (SPC)
The use of basic graphical and statistical methods for measuring, analysing, and controlling the variation of a process for the purpose of continuously improving the process. A process is said to be in a state of statistical control when it exhibits only random variation.
Statistical Solution
A data driven solution with known confidence/risk levels, as opposed to a qualitative, "I think" solutions.
Supplier
An individual or entity responsible for providing an input to a process in the form of resources or information.
Trend
A gradual, systematic change over time or some other variable.
TSSW
Thinking the Six Sigma way. A mental model for improvement which perceives outcomes through a cause and effect relationship combined with Six Sigma concepts to solve everyday and business problems.
Two-Level Design
An experiment where all factors are set at one of two levels, denoted as low and high (-1 and +1).
Upper Control Limit (UCL) for Control Charts
The upper limit below which a process statistic must remain to be in control. Typically this value is 3 standard deviations above the central tendency.
Upper Specification Limit (USL)
The highest value of a characteristic which is acceptable.
Variability
A generic term that refers to the property of a characteristic, process or system to take on different values when it is repeated.
Variables
Quantities which are subject to change or variability.
Variable Data
Data which is continuous, which can be meaningfully subdivided, i.e. can have decimal subdivisions.
Variance
A specifically defined mathematical measure of variability in a data set or population. It is the square of the standard deviation.
Variation
See variability.
Voice of the Business (VOB)
Represents the needs of the business and the key stakeholders of the business. It is usually items such as profitability, revenue, growth, market share, etc.
Voice of the Customer (VOC)
Represents the expressed and non-expressed needs, wants and desires of the recipient of a process output, a product or a service. Its is usually expressed as specifications, requirements or expectations.
Voice of the Process (VOP)
Represents the performance and capability of a process to achieve both business and customer needs. It is usually expressed in some form of an efficiency and/or effectiveness metric.
Waste
Waste represents material, effort and time that does not add value in the eyes of key stakeholders (customers, employees, investors).
X
An input characteristic to a process or system. In Six Sigma it is usually used in the expression of Y=f(X), where the output (Y) is a function of the inputs (X).
Y
An output characteristic of a process. In Six Sigma it is usually used in the expression of Y=f(X), where the output (Y) is a function of the inputs (X).
Yellow Belt
An individual who receives approximately one week of training in problem solving and process optimisation methods. Yellow Belts participate in process management activates, participate on Green and Black Belt projects and apply concepts to their work area and their job.
Z Score
See Sigma Score.
Rizalito Garcia is the author of Six Sigma for Newbies, released in 2009.

Front 194

SWOT Analysis

Back 194

SWOT Analysis

A scan of the internal and external environment is an important part of the strategic planning process. Environmental factors internal to the firm usually can be classified as strength (S) or weaknesses (W), and that external to the firm can be classified as opportunity (O) or threats (T). Such an analysis of the strategic environment is referred to as a SWOT analysis.

The SWOT analysis provides information that is helpful in matching the firm’s resources and a capability to the competitive environment in which it operates. As such, it is instrumental in strategy formulation and selection.

Front 195

Process Capability (Cp, Cpk) and Process Performance (Pp, Ppk) – What is the Difference?

Back 195

Process Capability (Cp, Cpk) and Process Performance (Pp, Ppk) – What is the Difference?
In the Six Sigma quality methodology, process performance is reported to the organization as a sigma level. The higher the sigma level, the better the process is performing.

Another way to report process capability and process performance is through the statistical measurements of Cp, Cpk, Pp, and Ppk. This article will present definitions, interpretations and calculations for Cpk and Ppk though the use of forum quotations. Thanks to everyone below that helped contributed to this excellent reference.

Jump To The Following Sections:

Definitions
Interpreting Cp, Cpk
Interpreting Pp, Ppk
Differences Between Cpk and Ppk
Calculating Cpk and Ppk
Definitions
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.

Interpreting Cp, Cpk
“Cpk is an index (a simple number) which measures how close a process is running to its specification limits, relative to the natural variability of the process. The larger the index, the less likely it is that any item will be outside the specs.” Neil Polhemus

“If you hunt our shoot targets with bow, darts, or gun try this analogy. If your shots are falling in the same spot forming a good group this is a high Cp, and when the sighting is adjusted so this tight group of shots is landing on the bullseye, you now have a high Cpk.” Tommy

“Cpk measures how close you are to your target and how consistent you are to around your average performance. A person may be performing with minimum variation, but he can be away from his target towards one of the specification limit, which indicates lower Cpk, whereas Cp will be high. On the other hand, a person may be on average exactly at the target, but the variation in performance is high (but still lower than the tolerance band (i.e., specification interval). In such case also Cpk will be lower, but Cp will be high. Cpk will be higher only when you r meeting the target consistently with minimum variation.” Ajit

“You must have a Cpk of 1.33 [4 sigma] or higher to satisfy most customers.” Joe Perito

“Consider a car and a garage. The garage defines the specification limits; the car defines the output of the process. If the car is only a little bit smaller than the garage, you had better park it right in the middle of the garage (center of the specification) if you want to get all of the car in the garage. If the car is wider than the garage, it does not matter if you have it centered; it will not fit. If the car is a lot smaller than the garage (Six Sigma process), it doesn’t matter if you park it exactly in the middle; it will fit and you have plenty of room on either side. If you have a process that is in control and with little variation, you should be able to park the car easily within the garage and thus meet customer requirements. Cpk tells you the relationship between the size of the car, the size of the garage and how far away from the middle of the garage you parked the car.” Ben

“The value itself can be thought of as the amount the process (car) can widen before hitting the nearest spec limit (garage door edge).
Cpk =1/2 means you’ve crunched nearest the door edge (ouch!)
Cpk =1 means you’re just touching the nearest edge
Cpk =2 means your width can grow 2 times before touching
Cpk =3 means your width can grow 3 times before touching” Larry Seibel

Interpreting Pp, Ppk
“Process Performance Index basically tries to verify if the sample that you have generated from the process is capable to meet Customer CTQs (requirements). It differs from Process Capability in that Process Performance only applies to a specific batch of material. Samples from the batch may need to be quite large to be representative of the variation in the batch. Process Performance is only used when process control cannot be evaluated. An example of this is for a short pre-production run. Process Performance generally uses sample sigma in its calculation; Process capability uses the process sigma value determined from either the Moving Range, Range or Sigma control charts.” Praneet

Differences Between Cpk and Ppk
“Cpk is for short term, Ppk is for long term.” Sundeep Singh

“Ppk produces an index number (like 1.33) for the process variation. Cpk references the variation to your specification limits. If you just want to know how much variation the process exhibits, a Ppk measurement is fine. If you want to know how that variation will affect the ability of your process to meet customer requirements (CTQ’s), you should use Cpk.” Michael Whaley

“It could be argued that the use of Ppk and Cpk (with sufficient sample size) are far more valid estimates of long and short term capability of processes since the 1.5 sigma shift has a shaky statistical foundation.” Eoin

“Cpk tells you what the process is CAPABLE of doing in future, assuming it remains in a state of statistical control. Ppk tells you how the process has performed in the past. You cannot use it predict the future, like with Cpk, because the process is not in a state of control. The values for Cpk and Ppk will converge to almost the same value when the process is in statistical control. that is because sigma and the sample standard deviation will be identical (at least as can be distinguished by an F-test). When out of control, the values will be distinctly different, perhaps by a very wide margin.” Jim Parnella

“Cp and Cpk are for computing the index with respect to the subgrouping of your data (different shifts, machines, operators, etc.), while Pp and Ppk are for the whole process (no subgrouping). For both Ppk and Cpk the ‘k’ stands for ‘centralizing facteur’ – it assumes the index takes into consideration the fact that your data is maybe not centered (and hence, your index shall be smaller). It is more realistic to use Pp and Ppk than Cp or Cpk as the process variation cannot be tempered with by inappropriate subgrouping. However, Cp and Cpk can be very useful in order to know if, under the best conditions, the process is capable of fitting into the specs or not.It basically gives you the best case scenario for the existing process.” Chantal

“Cp should always be greater than 2.0 for a good process which is under statistical control. For a good process under statistical control, Cpk should be greater than 1.5.” Ranganadha Kumar

“As for Ppk/Cpk, they mean one or the other and you will find people confusing the definitions and you WILL find books defining them versa and vice versa. You will have to ask the definition the person is using that you are talking to.” Joe Perito

“I just finished up a meeting with a vendor and we had a nice discussion of Cpk vs. Ppk. We had the definitions exactly reversed between us. The outcome was to standardize on definitions and move forward from there. My suggestion to others is that each company have a procedure or document (we do not), which has the definitions of Cpk and Ppk in it. This provides everyone a standard to refer to for WHEN we forget or get confused.” John Adamo

“The Six Sigma community standardized on definitions of Cp, Cpk, Pp, and Ppk from AIAG SPC manual page 80. You can get the manual for about $7.” Gary

Calculating Cpk and Ppk
“Pp = (USL – LSL)/6*Std.dev
Cpl = (Mean – LSL)/3*Std.dev
Cpu = (USL – Mean)/3*Std.dev
Cpk= Min (Cpl, Cpu)” Ranganadha Kumar

“Cpk is calculated using an estimate of the standard deviation calculated using R-bar/d2. Ppk uses the usual form of the standard deviation ie the root of the variance or the square root of the sum of squares divided by n – 1. The R-bar/D2 estimation of the standard deviation has a smoothing effect and the Cpk statistic is less sensitive to points which are further away from the mean than is Ppk.” Eoin

“Cpk is calculated using RBar/d2 or SBar/c4 for Sigma in the denominator of you equation. This calculation for Sigma REQUIRES the process to be in a state of statistical control. If not in control, your calculation of Sigma (and hence Cpk) is useless – it is only valid when in-control.” Jim Parnella

“You can have a ‘good’ Cpk yet still have data outside the specification, and the process needs to be in control before evaluating Cpk.” Matt

Front 196

PROCESS CAPABILITY (CP & CPK)
Six Sigma Study Guide

Back 196

PROCESS CAPABILITY (CP & CPK)
Six Sigma Study Guide
Cp and Cpk are considered short-term potential capability measures. Evaluating process capability with Cp & Cpk mirror what is done (and why it is done) when following the Pp & Ppk approach. The main difference is that you use Cp & Cpk after a process has reached stability or statistical control.

How to Use Cp & Cpk
Calculating Cp & Cpk.
Just as you use Cp & Cpk when a process is stable and Pp & Ppk when a process is new, the way you calculate each are a bit different, too.

Let’s revisit Pp

Pp = (USL – LSL) / 6* s

In Pp, s is the standard deviation, or the ‘fatness’ or dispersion of the bell curve.

In Cp, we replace s with and estimate of σ. To do that we leverage the Moving Range concept [ R Bar / d2] from a Moving R Bar chart or an XMR Chart.

So,

Cp = (USL – LSL) / ( 6* R Bar / d2 )

Where d2 comes from the chart depending on how many subgroups were in the sample.

d2 subgroup values
d2 subgroup values

Calculating Cpk usig a Z Value
If you have a Z value, the equation is very easy;

Cpk can be determined by dividing the Z score by three.

A z score is the same as a standard score; the number of standard deviations above the mean.

z_popZ = x – mean of the population / standard deviation.

Cpk = (USL – LSL)/ 3 σ

Cpk for Process Mean close to USL
If your Process Mean (central tendency) is closer to the USL, use: [ USL – x(bar) ] / [3 * R Bar / d2], where x(bar) is the Process Mean.

Cpk for Process Mean close to LSL
If your Process Mean (central tendency) is closer to the LSL, use: [x(bar) – LSL ] / [3 * R Bar / d2], where x(bar) is the Process Mean.

Notes on Cp Values
If the ratio is greater than one, then the Engineering Tolerance is greater than the Process Spread so the process has the “potential” to be capable (depending on process centering).
If, however, the Process Spread is greater than the Engineering tolerance, then the process variation will not “fit” within the tolerance and the process will not be capable (even if the process is centered appropriately).
Process Capability Cp Cpk example
That was poorly centered!

Cpk Video
Great, clear, concise video on this subject.

“If you were producing a Cpk equal to 1, than you could expect to produce at least 99.73% good parts.”

Useful Links on Cpk
More about Cpk

Cpk quiz

Front 197

QUALITY FUNCTION DEPLOYMENT (QFD) AND HOUSE OF QUALITY (HOQ)

Back 197

QUALITY FUNCTION DEPLOYMENT (QFD) AND HOUSE OF QUALITY (HOQ)
Six Sigma Study Guide
Quality Function Deployment is a planning process for products and services that starts with the voice of the customer. Basicall, it enables people to think together.

What is the House of Quality?
The House of Quality is a voice of customer analysis tool and a key component of the Quality Functional Deployment technique. It starts with the voice of the customer. It is a tool to translate what the customer wants into products or services that meet the customer wants.

Typically the first chart used in Quality Function Deployment
Data intensive and is capable of capturing large amounts of information.
The Roof: used to show how the design requirements interact with each other.
Competitive Section: based primarily on the customer’s perspective.
Lower level / Foundation: used to rank the ‘hows’. These are the actions your organization will take to satisfy your customers.
House of Quality

OK, this isn’t the best example of CTQs. They should be written from the perspective of the end user. Better ones would be:

Website is easy to find.
Website has great, fresh content
I would buy from this website

Front 198

Fault Tree Analysis

Back 198

Fault Tree Analysis is a Process Improvement Tool. It is a method of identifying the root causes of potential faults using a tree type structure:

fault tree

It is usual to use the Boolean 'and' and 'or' symbols. A possible cause of motor failure is overheating (1). This can happen if the ventilation slots are blocked (4) OR the motor load is overloaded (5)

Front 199

Guidelines and Matrices for Picking Six Sigma Candidates
Profile photo of Sanjoy Kumar ParialSanjoy Kumar Parial 0

Back 199

Guidelines and Matrices for Picking Six Sigma Candidates
Profile photo of Sanjoy Kumar ParialSanjoy Kumar Parial 0
Green Belt and Black Belt candidate selection is a critical step in ensuring that a Six Sigma program provides the benefits intended. Overlooking the importance of this step can lead to slow progress and incomplete results.

Finding Green Belt candidates with the right traits is a proven method for kick-starting a Six Sigma program. This approach to candidate selection also is useful when filtering through a list of candidates who express an interest in moving from a Green Belt role to the role of Black Belt. Using a qualitative rating matrix provides a systematic process for selecting either level of Belt candidates. The process can make a significant difference in the effectiveness of the organization’s Six Sigma initiative.

Green Belt Candidate Traits

The Green Belt role requires candidates to demonstrate a skill set that includes starting and completing projects and using a data-based approach to solving practical problems. A list of these skills is outlined below:

1. Interest in Six Sigma – Interest in process improvement initiatives is critical. Voluntary participation in the program and demonstration of quality consciousness in previous work experience are indicators for this criterion.

2. Passion – Excitement about being part of the Six Sigma culture change is essential. Passion brings the required dedication level.

3. Process orientation – A focus on the complete process instead of viewing things in isolation is important. Green Belts must visualize how different parameters and resources interact with each other to give a desired output.

4. Process knowledge – Knowledge of the organization impacted by the project is especially important. Green Belt projects typically focus on localized improvements. Without sufficient knowledge about the organization, the Green Belt will find it difficult to complete the project as well as gain acceptance from those who are involved in the process day to day.

5. Ability to spend required time – The time Green Belts are required to spend on a Six Sigma project is typically anywhere from 30 percent to 50 percent of their total hours. If a Green Belt is responsible for service support, a key processing function or another critical project, the Six Sigma training and project quickly become lower priorities. Selected candidates are expected to do justice to the Six Sigma activities.

6. Zeal to learn – During Six Sigma training, the Green Belt is taught many new tools and techniques. To gain confidence in using the methodology and tools, the Green Belt is required to practice the tools not only during training but also beyond training hours with live examples.

7. Inclination toward data analysis – Six Sigma is a data-based methodology using statistical calculations and techniques. Candidates are not required to have formal education in mathematics or statistics but an interest in mathematical analysis is desirable.

8. Customer orientation – Six Sigma is all about consistently meeting customer expectations. A Green Belt with little or no customer experience is less likely to appreciate this aspect of Six Sigma.

Traits for Black Belt Candidates

The Black Belt role is leadership focused. Hence, the desired qualities in a Black Belt are different from those of a Green Belt. Middle managers are typically best suited for the role. In addition to the Green Belt criteria listed above, the Black Belt should possess the following characteristics:

1. Technical aptitude – A high level of technical skill in applying the Six Sigma methodology within the organization is a key factor. Six Sigma skills are taught during the Black Belt training course and an evaluation of the candidate’s skill during this time will provide an indicator of future Black Belt success.

2. Ability to influence – A Black Belt leads project teams, and in that role must direct team members, communicate effectively to multiple levels of management and assist the organization in implementing change.

3. Business acumen – In the leadership role, the Black Belt should understand the current market environment of the organization, map the business-level challenges to the day-to-day working of individual functional areas and drive the program accordingly.

4. Problem-solving approach – Candidates demonstrating cause-and-effect thinking and data-driven analysis in previous assignments are equipped in part for success as Black Belts.

5. Ability to train/mentor – One of the most important functions of a Black Belt is to coach Green Belts during their project execution and to provide expert help so that possible roadblocks are proactively removed. Many Six Sigma deployments also require Black Belts to conduct Green Belt and Six Sigma awareness training.

6. Functional competencies – A Black Belt must understand how different functions work together and influence the organization. Black Belt projects are usually large in scope and commonly involve multiple functions such as finance, sales, marketing, human resources and IT.

Using a Pugh Matrix to Identify Candidates

The Pugh matrix is a tool used to facilitate a disciplined, team-based process for concept selection. Several concepts are evaluated, comparing their strengths and weaknesses against each other, to arrive at an optimum solution. The Pugh matrix encourages comparison of several different concepts against different criterion and is a useful tool because it does not require a great amount of quantitative data on the concepts. The matrix process also is applicable in the identification of Six Sigma candidates, providing a systematic method to evaluate and select Green Belts and Black Belts.

The following steps describe the process to construct a Pugh matrix:

1. Establish the selection criteria – The candidate selection team members create individual lists of selection criteria including critical-to-quality elements. The team should not become bogged down in refining the criteria. If information is needed, note it and resolve the uncertainty before the team meets again. The criteria for selection are based on prerequisites and the expected roles of the Green Belt and Black Belt.

2. Set up the matrix – Create the matrix on a flip chart with selection criteria entered in the rows and candidate designators entered as the column headings. This is a good opportunity to reaffirm the selection team’s common understanding and commitment to the established criteria.

3. Compare the concepts – In each cell of the matrix, enter the appropriate rating “S,” “+” or “-” for each candidate-criterion intersection. S indicates an average rating whereas + and – indicate above and below averages.

4. Evaluate the ratings – Create a weighting method according to the individual organization’s need versus the criteria selected for consideration. Choose candidates based on the weighted positive and same ratings.

Below, Table 1 shows a Pugh matrix for selecting Green Belt candidates and Table 2 shows a Pugh matrix for selecting Black Belt candidates.

Table 1: Pugh Matrix for Green Belt Selection
Selection Criteria

Importance

GB1

GB2

GB3

GB4

GB5

GB6

GB7

GB8

GB9

Interest in Six Sigma
5

Passion
3

Process orientation
3

Process knowledge
4

Ability to spend required time
5

Zeal to learn
4

Inclination toward data analysis
4

Customer orientation
4

Weighted Sum of Positives (+)
Weighted Sum of Sames (S)
Weighted Sum of Negatives (-)
+ = Good S = OK, at Par - Not Satisfactory
Table 2: Pugh Matrix for Black Belt Selection
Selection Criteria

Importance

BB1

BB2

BB3

BB4

BB5

BB6

BB7

BB8

BB9

Interest in Six Sigma
5

Passion
5

Technical aptitude
4

Ability to influence
4

Business acumen
4

Zeal to learn
5

Problem-solving approach
5

Ability to train/mentor
4

Functional competencies
4

Deep process/organization knowledge
4

Customer orientation
5

Weighted Sum of Positives (+)
Weighted Sum of Sames (S)
Weighted Sum of Negatives (-)
+ = Good S = OK, at Par - = Not Satisfactory
Five Stars, Six Sigma

Front 200

Design for Six Sigma (DFSS)

Back 200

Design for Six Sigma
Design for Six Sigma (DFSS) is a business-process management "methodology" related to traditional Six Sigma.[citation needed]. It is used in many industries, like financial, marketing, basic engineering, process industries, waste management, electronics, etc. It is based on the use of statistical tools, like linear regression and is basically a empirical research tool and is similar to research as done in -for example- social science. While the tools and order used in Six Sigma require a process to be in place and functioning, DFSS has the objective of determining the needs of customers and the business, and driving those needs into the product solution so created. DFSS is relevant for relative simple items / systems. It is a manufacturing process generation in contrast with process improvement.[citation needed] Measurement is the most important part of most six sigma or DFSS tools. This is done mostly when prototypes are available (when measurements can be done). In most cases after a first Critical Design choise has been made (or trade off studies are done) and manufacturing was involved and made products. It is related to mostly time invariant problems.

There are different options for the implementation of DFSS. Unlike Six Sigma, which is commonly driven via DMAIC (Define - Measure - Analyze - Improve - Control) projects, DFSS has spawned a number of stepwise processes, all in the style of the DMAIC procedure. [1]

DMADV, define – measure – analyze – design – verify, is sometimes synonymously referred to as DFSS. The traditional DMAIC Six Sigma process, as it is usually practiced, which is focused on evolutionary and continuous improvement manufacturing or service process development, usually occurs after initial system or product design and development have been largely completed. DMAIC Six Sigma as practiced is usually consumed with solving existing manufacturing or service process problems and removal of the defects and variation associated with defects. It is clear that manufacturing variations may impact product reliability. So, a clear link should exist between reliability engineering and Six Sigma (quality). Furthermore, DFSS (or DMADV and IDOV) strives to generate a new process where none existed, or where an existing process is deemed to be inadequate and in need of replacement. DFSS aims to create a process with the end in mind of optimally building the efficiencies of Six Sigma methodology into the process before implementation; traditional Six Sigma seeks for continuous improvement after a process already exists.

DFSS as an approach to design
DFSS seeks to avoid manufacturing/service process problems by using advanced Voice of the Customer techniques to avoid process problems at the outset (e.g., fire prevention). When combined, these methods obtain the proper needs of the customer, and derive engineering system parameter requirements that increase product and service effectiveness in the eyes of the customer and all other people. This yields products and services that provide great customer satisfaction and increased market share. These techniques also include tools and processes to predict, model and simulate the product delivery system (the processes/tools, personnel and organization, training, facilities, and logistics to produce the product/service). In this way, DFSS is closely related to operations research (solving the knapsack problem), workflow balancing. DFSS is largely a design activity requiring tools including: quality function deployment (QFD), axiomatic design, TRIZ, Design for X, design of experiments (DOE), Taguchi methods, tolerance design, robustification and Response Surface Methodology for a single or multiple response optimization. While these tools are sometimes used in the classic DMAIC Six Sigma process, they are uniquely used by DFSS to analyze new and unprecedented products and processess. It is a concurrent analyzes directed to manufacturing optimization related to the design.

Front 201

Payback Period Calculation

Back 201

[2/15, 11:37 PM] [phone]: A six sigma project requires $23000 of initial investment and training cost of $6000 , spread over a 6 month period. The project is expected to save the company $3000 per month, starting in the fifth month. Ignoring interest and taxes, what is the payback period?

A. 9 months
B. 12 months
C. 13 months
D. 14 months

[2/15, 11:37 PM] [phone]: Darrah : Questions like this are covered in Payback period
[2/15, 11:39 PM] ‪[phone]‬: 14 months
[2/15, 11:43 PM] [phone]: For payback period, first total cost of the project is calculated which in this case is initial investment + training cost = $23000 + $ 6000 = $29000
The payback period means time in which you will get the profit equal to what you in invested that is $29000
If you are getting $3000 per month.. You will reach $27000 in 9 months and $30000 in 10 months... So you reaching the invested amount in 10th month
Since payback period is calculated from start of project, the total time in receiving back this profit = 4 initial months in which there is no profit + 10 months in which profit reaches investment = 14 months