Test the probability of a sample median being equal to hypothesized value.

H0: m1=m2=m3=m4 (null hypothesis)
Ha: At least one is different (alternate hypothesis)

2 - Sample t Test : The two sample t-Test is used for testing hypothesis about the location two sample means being  equal.

1 - Sample t Test : The one sample t-Test is used for testing hypothesis about the location of the sample mean and a target mean being equal.

A 3D model of TQM, having People, Product and Process as the 3 axis.
For Implementing TQM, all the 3 parameters should be improved.

1. People: Satisfaction of both Internal and External customer.

2. Product: Conforming to the requirements specified.

3. Process: Continuous Improvement of all the operations and activities is at the heart of TQM.

5 Laws of Lean Six Sigma have been formulated to provide direction to improvement efforts. The laws are a conglomeration of Key Ideas of Six Sigma and Lean.

Law 0: The Law of the Market - Customer Critical to Quality defines quality and is the highest priority for improvement, followed by ROIC (Return On Invested Capital) and Net Present value. It is called the Zeroth law as it is the base on which others are built.

Law 1: The Law of Flexibility - The velocity of any process is proportional to the flexibility of the process.

Law 2: The Law of Focus - 20% of the activities in a process cause 80% of the delay. (Related to Pareto Principle)

Law 3:The Law of Velocity - The velocity of any process is inversely proportional to the amount of WIP. This is also called "Little's Law".

Law 4: The complexity of the service or product offering adds more non-value, costs and WIP than either poor quality (low Sigma) or slow speed (un-Lean) process problems.

The 5 why's typically refers to the practice of asking, five times, why the failure has occurred in order to get to the root cause/causes of the problem. There can be more than one cause to a problem as well. In an organizational context, generally root cause analysis is carried out by a team of persons related to the problem. No special technique is required.

An example is in order:
You are on your way home from work and your car stops:

• Why did your car stop? Because it ran out of gas.
  
  

• Why did it run out of gas? Because I didn't buy any gas on my way to work.
  
  

• Why didn't you buy any gas this morning? Because I didn't have any money.
  
  

• Why didn't you have any money? Because I lost it all last night in a poker game.
  
  I hope you don't mind the silly example but it should illustrate the importance of digging down beneath the most proximate cause of the problem. Failure to determine the root cause assures that you will be treating the symptoms of the problem instead of its cause, in which case, the disease will return, that is, you will continue to have the same problems over and over again.
  
  Also note that the actual numbers of why's is not important as long as you get to the root cause. One might well ask why did you lose all your money in the poker game last night?
  
  _____
  Here's another example. I learned the example using the Washington Monument used when demonstrating the use of the 5 Whys.
  
  The Washington Monument was disintegrating
  Why? Use of harsh chemicals
  Why? To clean pigeon poop
  Why so many pigeons? They eat spiders and there are a lot of spiders at monument
  Why so many spiders? They eat gnats and lots of gnats at monument
  Why so many gnats? They are attracted to the light at dusk.
  Solution: Turn on the lights at a later time.

5C ia a 5 step technique very similar to 5S to stabilise, maintain and improve the safest, best working enviroment to support sustainable Quality, Cost and Delivery.

What are the 5Cs?

Clear Out: Separate the essential from the non essential

Configure: A place for everything and everything in its place.

Clean and Check: Manualy clean to spot abnormal conditions.

Conformity: Ensures that the standard is maintained and improved.

Custom and Practice: Everyone follows the rules, understands the benefits and contributes to the improvement.

5S is the Japanese concept for House Keeping.

1.) Sort (Seiri)
2.) Straighten (Seiton)
3.) Shine (Seiso)
4.) Standardize (Seiketsu)
5.) Sustain (Shitsuke)
____________________________________________
I think the concept of 5S has been twisted and its real meaning and intention has been lost due to attempts to keep each element in English word to start with letter 'S', like the real Nippongo words (seiri, seiton, seiso, seiketsu, and shitsuke). Well, whoever deviced those equivalent English words did a good job,they're close, but the real interpretation is not exactly the correct one. For the benefit of the readers who would like to develop and establish their own understanding and applications, the following are the real meaning of each element in English:

Japanese - English Translations
-------- --------------------
Seiri - Put things in order
(remove what is not needed and keep what is needed)
Seiton - Proper Arrangement
(Place things in such a way that they can be easily reached whenever they are needed)
Seiso - Clean
(Keep things clean and polished; no trash or dirt in the workplace)
Seiketsu - Purity
(Maintain cleanliness after cleaning - perpetual cleaning)
Shitsuke - Commitment (Actually this is not a part of '4S', but a typical teaching and attitude towards any undertaking to inspire pride and adherence to standards established for the four components)

This standard defines the procedure of “5Z Accreditation” which is the scheme to promote, evaluate, maintain and improve process control using the Genba Kanri principles.
“5Z” is a general term for the following five actions ending with “ZU”…meaning “Don’t” in Japanese.

-UKETORAZU (Don’t accept defects)
-TSUKURAZU (Don’t make defects)
-BARATSUKASAZU (Don’t create variation)
-KURIKAESAZU (Don’t repeat mistakes)
-NAGASAZU (Don’t supply defects)

The traditional 6Ms are:

• Machines
• Methods
• Materials
• Measurements
• Mother Nature (Environment)
• Manpower (People)

Other definitions:

• Machines
• Methods
• Materials
• Measurements
• Milieu (Mother Nature, surroundings, environment)
• Manpower (People/mainly physical work)
• Mindpower (Also people/mainly brain work)
• Management (separate from Manpower/People because it considers Tampering)
• Money
• Miscellaneous
• (the) Moon (so far unknown cause)

Remember Rudyard Kipling's famous poem that reads as under?

"I have Six Stalwart Serving Men,
They taught me all I know,
Their Names are What and Where and When,
And Why and How and Who."

After ascertaining the methods etc. of a process, by using the 5 questions of What, Where, When, How and Who, then question each and every detail Why?... Why?... Why?...
This is the secret of creativity.

Your project planning should answer following question:

WHAT : What will you make/do this?
WHY : Why will you make/do this?
WHERE : Where will you make/do this?
WHO : Who will make/do this?
WHEN : When will you start/stop this (time scheduling)?
WHICH : Which will you make/do this (process, tooling, material sources etc…)?

Histograms
Cause and Effect Diagram
Check Sheets
Pareto Diagrams
Graphs
Control Charts
Scatter Diagrams

These are 7 QC tools also known as ISHIKAWAS 7QC tools which revolutionised the Japane & the World in Sixties & Seventies

The 7 wastes are at the root of all unprofitable activity within your organization.

The 7 wastes consist of:

1. Defects
2. Overproduction
3. Transportation
4. Waiting
5. Inventory
6. Motion
7. Processing

Use the acronym 'DOTWIMP' to remember the 7 Wastes of Lean.

The worst of all the 7 wastes is overproduction because it includes in essence all others and was the main driving force for the Toyota JIT system, they were smart enough to tackle this one to eliminate the rest.

The 8D Process is a problem solving method for product and process improvement. It is structured into 8 steps (the D's) and emphasizes team. This is often required in automotive industries. The 8 basic steps are: Define the problem and prepare for process improvement, establish a team, describe the problem, develop interim containment, define & verify root cause, choose permanent corrective action, implement corrective action, prevent recurrence, recognize and reward the contributors.

Of course, different companies have their different twists on what they call the steps, etc...but that is the basics.

8 D is short for Eight Disciplines which oOriginated from the Ford TOPS (Team Oriented Problem Solving) program. (First published approximately 1987)
D#1 - Establish the Team
D#2 - Describe the problem.
D#3 - Develop an Interim Containment Action
D#4 - Define / Verify Root Cause
D#5 - Choose / Verify Permanent Corrective Action
D#6 - Implement / Validate Permanent Corrective Action
D#7 - Prevent Recurrence
D#8 - Recognize the Team

An easy way I learned at a seminar to remember the wastes, they spell TIM WOODS

T - Transport - Moving people, products & information
I - Inventory - Storing parts, pieces, documentation ahead of requirements
M - Motion - Bending, turning, reaching, lifting

W - Waiting - For parts, information, instructions, equipment
O - Over production - Making more than is IMMEDIATELY required
O - Over processing - Tighter tolerances or higher grade materials than are necessary
D - Defects - Rework, scrap, incorrect documentation
S - Skills - Under utilizing capabilities, delegating tasks with inadequate training

A-squared is the test statistic for the Anderson-Darling Normality test. It is a measure of how closely a dataset follows the normal distribution. The null hypothesis for this test is that the data is normal. So if you get an A-squared that is fairly large, then you will get a small p-value and thus reject the null hypothesis. Small A-squared values imply large p-values, thus you cannot reject the null hypothesis.

Acceptable Quality Level. Also referred to as Assured Quality Level. The largest quantity of defectives in a certain sample size that can make the lot definitely acceptable; Customer will definitely prefer the zero defect products or services and will ultimately establish the acceptable level of quality. Competition however, will 'educate' the customer and establish the customer's values. There is only one ideal acceptable quality level - zero defects - all others are compromises based upon acceptable business, financial and safety levels.

The highest number of nonconforming units or defects found in the sample that permits the acceptance of the lot.

The planned utilization of remnant material for value-added purposes.

Conditional personal or professional liability “after” the fact, determined by action or responsibility. Accountability to action assumes the willingness to be held accountable for adequate expertise and capability. (see responsibility)

A person holds themselves accountable for an item when they are willing to explain

1) how the item should be and

2) what they did to cause it to be the way it actually is.

1) Accuracy refers to clustering of data about a known target. It is the difference between a physical quantity's average measurements and that of a known standard, accepted 'truth,' vs. 'benchmark.' Envision a target with many arrows circling the bullseye, however, none of them are near each other.

2) Precision refers to the tightness of the cluster of data. Envision a target with a cluster of arrows all touching one another but located slightly up and to the right of the bullseye.

In practice it is easier to correct a process which has good precision than it is to correct a process which is accurate. This is due to the increased amount of variation associated with accurate but not precise process.

Actively and purposefully make changes in our data to monitor the corresponding impact and results on the Xs and Ys.

A form of cost accounting that focuses on the costs of performing specific functions (processes, activities, tasks, etc.) rather than on the costs of organizational units. ABC generates more accurate cost and performance information related to specific products and services than is available to managers through traditional cost accounting approaches.

A tool used to organize and present large amounts of data (ideas, issues, solutions, problems) into logical categories based on user perceived relationships and conceptual frameworking.

Often used in form of "sticky notes" send up to front of room in brainstorming exercises, then grouped by facilitator and workers. Final diagram shows relationship between the issue and the category. Then categories are ranked, and duplicate issues are combined to make a simpler overview.

Lost interactions in a Design of Experiment. An alias indicates that you've changed two or more things at the same time in the same way. Aliasing is a critical feature of Plackett-Burman, Taguchi designs or standard fractional factorials.Lower the resolution higher is the aliasing issue. Aliasing is a synonym for confounding.

Alpha risk is defined as the risk of rejecting the Null hypothesis when in fact it is true.

Synonymous with: Type I error, Producers Risk

In other words, stating a difference exists where actually there is none. Alpha risk is stated in terms of probability (such as 0.05 or 5%).

The value (1-alpha) corresponds to the confidence level of a statistical test, so a level of significance alpha = 0.05 corresponds to a 95% confidence level.

The alternate hypothesis (Ha) is a statement that the means, variance, etc. of the samples being tested are not equal. In software program which present a p value in lieu of F Test or T Test When the P value is less than or equal to your agreed upon decision point (typically 0.05) you accept the Ha as being true and reject the Null Ho. (Ho always assumes that they are equal)

Analysis of variance is a statistical technique for analyzing data that tests for a difference between two or more means by comparing the variances *within* groups and variances *between* groups. See the tool 1-Way ANOVA.

A software or other service component modelling technique using tools based on mathematical models.

After you have plotted data for Normality Test, Check for P-value.

P-value < 0.05 = not normal.

normal = P-value >= 0.05


Note: Similar comparison of P-Value is there in Hypothesis Testing.
If P-Value > 0.05, Fail to Reject the H0

The Anderson-Darling test is used to test if a sample of data came from a population with a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more weight to the tails than does the K-S test. The K-S test is distribution free in the sense that the critical values do not depend on the specific distribution being tested. The Anderson-Darling test makes use of the specific distribution in calculating critical values. This has the advantage of allowing a more sensitive test and the disadvantage that critical values must be calculated for each distribution.

In 'ancient' Japan, Andon was a paper lantern (a handy vertically collapsible paper lampshade with an open top and a candle placed at the central section of the closed bottom). To the ancient Japanese, Andon functioned as a flashlight, a signaling device in distance, or even a commercial sign.

Nowadays, Andon at many manufacturing facilities is an electronic device: audio and/or color-coded visual display. For example, suppose an Andon unit has three color zones (red, green, and orange) and when the orange zone flashes with a distinctive sound, it calls for an attention of and is signaling operator to replenish certain material.

A tool of visual management, originating from the Japanese for "Lamp". Lights placed on machines or on production lines to indicate operation status. Commonly color-coded are:

- Green: normal operations
- Yellow: changeover or planned maintenance
- Red: abnormal, machine down

Often combined an audible signal such as music or an alarm.

Analysis Of VAriance (ANOVA), a calculation procedure to allocate the amount of variation in a process and determine if it is significant or is caused by random noise. A balanced ANOVA has equal numbers of measurements in each group/column. A stacked ANOVA: each factor has data in one column only and so does the response.

Appraisal Cost is a component of 'Cost of Quality'
This is the cost incurred on Preventing the defects. e.g
Cost to establish Methods & Procedures
Cost to Plan for Quality
Cost incurred on Training.

Advanced Product Quality Planning

Phase 1 -
Plan & Define Programme - determining customer needs, requirements & expectations using tools such as QFD
review the entire quality planning process to enable the implementation of a quality programme how to define & set the inputs & the outputs.

Phase 2 -
Product Design & Development - review the inputs & execute the outputs, which include FMEA, DFMA, design verification, design reviews, material & engineering specifications.

Phase 3 -
Process Design & Development - addressing features for developing manufacturing systems & related control plans, these tasks are dependent on the successful completion of phases 1 & 2 execute the outputs.

Phase 4 -
Product & Process Validation - validation of the selected manufacturing process & its control mechanisms through production run evaluation outlining mandatory production conditions & requirements identifying the required outputs.

Phase 5 -
Launch, Feedback, Assessment & Corrective Action - focuses on reduced variation & continuous improvement identifying outputs & links to customer expectations & future product programmes.

Control Plan Methodology -
discusses use of control plan & relevant data required to construct & determine control plan parameters
stresses the importance of the control plan in the continuous improvement cycle.

A tool used for working out optimal schedules and controlling them effectively. It shows relationships among tasks needed to implement a plan using nodes for events and arrows for activities. Arrow diagrams are used in PERT (Program Evaluation and Review Technique) and CPM (Critical path method).

Known for pioneering efforts to invent or create that which has never existed, it is one of a family of four work process types and is characterized as a temporary endeavor undertaken to create a unique product or result which is performed by people. (Artisan Process, Project Process, Operations Process, Automated Process)

See "Special Cause".

Providing an optimal degree of confidence to Internal and External Customers regarding establishing and maintaining in the organization, practices, processes, functions and systems for accomplishing organizational effectiveness.

Establishing and maintaining an optimal degree of confidence in the organizational practices, processes, functions and systems for accomplishing organizational effectiveness.

Alternate definition:
Establishing and maintaining the commitments made to Internal and External Customers.

Attribute data is the lowest level of data. It is purely binary in nature. Good or Bad, Yes or No. No analysis can be performed on attribute data.

Attribute data must be converted to a form of Variable data called discrete data in order to be counted or useful.

It is commonly misnamed discrete data.

Attributes data are qualitative data that can be counted for recording and analysis.

Examples include the presence or absence of a required label, the installation of all required fasteners.

Attributes data are not acceptable for production part submissions unless variables data cannot be obtained.

The control charts based on attribute data are percent chart, number of affected units chart, count chart, count-per-unit chart, quality score chart, and demerit chart.

Attribution theory (B. Weiner) explains how individuals interpret events and how this relates to their thinking and behavior.

This theory has been used to explain the difference in motivation between high and low achievers. According to attribution theory, high achievers will invite rather than avoid tasks that could lead them to success because they believe success results from high ability and effort, and they are confident of their ability and effort. However, they believe failure is caused by bad luck or a poor exam, i.e. things that are beyong their range of control. Thus, failure doesn't affect their self-esteem but success builds pride and confidence.

On the other hand, low achievers avoid success-related actions because they tend to doubt their ability and/or assume success is related to luck or influence or to other factors beyond their control. Thus, even when successful, it isn't as rewarding to the low achiever because he/she doesn't feel responsible. Suceess does not increase his/her pride and confidence.

A timely process or system, inspection to ensure that specifications conform to documented quality standards. An Audit also brings out discrepencies between the documented standards and the standards followed and also might show how well or how badly the documented standards support the processes currently followed.

Corrective, Preventive & Improvement Actions should be undertaken to mitigate the gap(s) between what is said (documented), what is done and what is required to comply with the appropriate quality standard. Audit is not only be used in accounting or something that relates to mathematics but also used in Information Technology.

The granting or taking of power and liability to make decisions and influence action on the behalf of others.

Autocorrelation means that the observations are not independent. Each observation will tend to be close in value to the next. This can result in under estimating sigma. A little bit of autocorrelation will not ruin a control chart.

Known for eliminating labor costs, it is one of a family of four work processes characterized as an on-going endeavor undertaken to create a repetitive product or result which planned, executed and controlled. (Artisan Process, Project Process, Operations Process, Automated Process)

Availability is the state of able readiness, of a product, process, practicing person or organization to perform satisfactorily its specified purpose, under pre-specified environmental conditions, when called upon.

AIQ - Average Incoming Quality: This is the average quality level going into the inspection point.

AOQ - Average Outgoing Quality: The average quality level leaving the inspection point after rejection and acceptance of a number of lots. If rejected lots are not checked 100% and defective units removed or replaced with good units, the AOQ will be the same as the AIQ.

B10 Life is the time by which 10% of the product population will get failed

The start and due dates for each operation in the manufacturing process are calculated back from the ship date. (See also Ship Date).

An experiment is balanced when all factor levels (or treatment groups) have the same number of experimental units (or items receiving a treatment). Unbalanced experiments add complexity to the analysis of the data but hopefully for good reason. For example, some levels are of less interest to the researcher than others. Some levels are expected to produce greater variation than others and so more units are assigned to those levels.

Balance is nonessential but desirable if equal accuracy, power, or confidence interval width for treatment comparisons is important. Severe imbalance can induce factor confounding (correlated factors or non-independent treatment levels).

The balanced scorecard is a strategic management system used to drive performance and accountability throughout the organization.

The scorecard balances traditional performance measures with more forward-looking indicators in four key dimensions:

• Financial
• Integration/Operational Excellence
• Employees
• Customers

Benefits include:

• Alignment of individual and corporate objectives
• Accountability throughout the organization
• Culture driven by performance
• Support of shareholder value creation

See Malcolm Baldrige National Quality Award.

A bar chart is a graphical comparison of several quantities in which the lengths of the horizontal or vertical bars represent the relative magnitude of the values.

This test is used to determine if there is a difference in variance between 3 or more samples/groups. It is usefull for testing the assumption of equal variances, which is required for one-way ANOVA.

A snapshot of the state of inputs/outputs frozen at a point in time for a particular process.  A baseline should be recordered to establish a starting point to measure the changes achieved with any process improvement.

Process by which the quality and cost effectiveness of a service is assessed, usually in advance of a change to the service.  Baselining usually includes comparison of the service before and after the Change or analysis of trend information.  The term Benchmarking is normally used if the comparison is made against other enterprises.

"Business As Usual" The old way of doing business, considering repetitive tasks with no critical sense of improvement.

The concept of discovering what is the best performance being achieved, whether in your company, by a competitor, or by an entirely different industry.

Benchmarking is an improvement tool whereby a company measures its performance or process against other companies' best practices, determines how those companies achieved their performance levels, and uses the information to improve its own performance.

Benchmarking is a continuous process whereby an enterprise measures and compares all its functions, systems and practices against strong competitors, identifying quality gaps in the organization, and striving to achieve competitive advantage locally and globally.

A way or method of accomplishing a business function or process that is considered to be superior to all other known methods.

A lesson learned from one area of a business that can be passed on to another area of the business or between businesses.

Beta risk is defined as the risk of accepting the null hypothesis when, in fact, it is false.

Consumer Risk or Type II Risk.

Beta risk is defined as the risk of accepting the null hypothesis when, in fact, the alternate hypothesis is true. In other words, stating no difference exists when there is an actual difference. A statistical test should be capable of detecting differences that are important to you, and beta risk is the probability (such as 0.10 or 10%) that it will not. Beta risk is determined by an organization or individual and is based on the nature of the decision being made. Beta risk depends on the magnitude of the difference between sample means and is managed by increasing test sample size. In general, a beta risk of 10% is considered acceptable in decision making.

The value (1-beta) is known as the "power" of a statistical test. The power is defined as the probability of rejecting the null hypothesis, given that the null hypothesis is indeed false.

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Actually, the term "risk" really means probability or chance of making an incorrect decision. The actual risks of making a wrong decision are unique to the decision being made and may be realized only if a wrong decision is made. For example, the probability of a false negative (type II error) on an a test for aids in a patient might be calculated as 0.001. The risk of concluding that a person does not have aids when in fact they do is quite a different concept - the "risk" is propagation of a deadly disease.

A formal analysis of the effect on the business if a specific process fails or loses efficiency.  It will also identify the minimum performance level of a given process that an organization requires to continue operating.

Bias in a sample is the presence or influence of any factor that causes the population or process being sampled to appear different from what it actually is. Bias is introduced into a sample when data is collected without regard to key factors that may influence it. A one line description of bias might be: "It is the difference between the observed mean reading and reference value."

Distinguishing the professional and concepts of Quality from that of the word quality.

Bimodal Distribution is one in which 2 values occur more frequently in data set than rest of the values.

In a situation where there are exactly two mutually exclusive outcomes (Ex: Success or Failure) of a trial, to find the x success in N trials with p as the probability of success on a single trial.

Ex:
Team A has won 15 Cricket Matches out of 50 played. What is the probability of winning atmost 5 matches in the next 10 matches?

x = 5, N = 10 and p = 15/50 = 0.3
Mean = N * p = 10 * 0.3 = 3

A discrete random variable which represents the number of successes out of n (the sample size) identical and independent trials.

Six Sigma team leaders responsible for implementing process improvement projects (DMAIC or DFSS) within the business -- to increase customer satisfaction levels and business productivity. Black Belts are knowledgeable and skilled in the use of the Six Sigma methodology and tools.

Black Belts have typically completed four weeks of Six Sigma training, and have demonstrated mastery of the subject matter through the completion of project(s) and an exam.

Black Belts coach Green Belts and receive coaching and support from Master Black Belts.

Sources of variation which are non-random (Special Cause)

Blocking neutralizes background variables that can not be eliminated by randomizing. It does so by spreading them across the experiment.

You can think of a block as an kind of uncontrolable factor that is added to the experiment. A block is ususally used when this uncontrolable factor cannot be avoided during the experiment, so it is incorporated into the experiment in a controlled way. The idea is to pull the variation due to the blocks out of the expermental error in order to reduce the experimental error and give the test more power.

Common examples of when blocking factors are used:

• When you don't have enough units from one lot and are forced to use another lot, AND you suspect (maybe know) that there are important differences between lots. You then use (in a controlled manner) half of the units from one lot and half of the units from the other lot.
  
  
• When you don't have enough test chambers for all the parts, AND you suspect (maybe know) that there are important differences in the effects between test chambers. You then assign (in a controlled manner) half of your units to one chamber and half to the other chamber.
  
  
• The original use of blocking involved agricultural experiments. Researchers needed to use multiple fields or fields that has important intra-field variation. They would the potential fertility of a field as a block factor. Similar blocking might have been needed due to differences between edge-effects and center effects on crops grown.

The Box-Cox transformation can be used for converting the data to a normal distribution, which then allows the process capability to be easily determined.

A box plot, also known as a box and whisker diagram, is a basic graphing tool that displays centering, spread, and distribution of a continuous data set.

A box and whisker plot provides a 5 point summary of the data.
1) The box represents the middle 50% of the data.

2) The median is the point where 50% of the data is above it and 50% below it. (Or left and right depending on orientation).

3) The 25th quartile is where, at most, 25% of the data fall below it.

4) The 75th quartile is where, at most, 25% of the data is above it.

5) The whiskers cannot extend any further than 1.5 times the length of the inner quartiles. If you have data points outside this they will show up as outliers.

Business Process Management System (BPMS)- a nine step model enables companies to model, deploy and manage mission-critical business processes, that span multiple enterprise applications, corporate departments. BPMS is usually used for lesser mature processes to make them Repeatable & Reliable.

The nine step approach includes:

1. Create Process Mission
2. Document Process
3. Document Customer & Process requirements
4. Identify Output & Process Measures
5. Build process management system
6. Establish data collection plan
7. Process performance monitoring
8. Develop dashboards with spec limits & targets
9. Identify improvement opportunities

A method to generate ideas. Groundrules such as -no idea is a bad idea- are typical. Benefit of brainstorming is the power of the group in building ideas of each others ideas.

A problem solving approach/technique whereby working members in a group are conducting a deductive methodology for identifying possible causes of any problem, in order to surmount poor performance in any process or activity pursued by the group members and facilitator.

BRM---Business Risk Management.It is to evaluate the business risk involved for any change in the process

The location between each operation in a production line that contains in-process parts. Typically a conveyor, roller-rack, or CML (continuously-moving-line).

The size of the buffer is governed by the average cycle times for each operation. A machine with a low cycle time feeding to a machine with a higher cycle time will typically have a large buffer in order to prevent blocking the first machine.

See also Level of Buffering and Lean Buffering.

Small insect. Also a problem in software.

The part of code that makes the program behave in an unwanted manner. The sooner a bug is detected in the Software Lifecycle, lesser would be the cost involved in fixing it

The term bug came from the fact that a moth flew into an early computer that ran on vacuum tubes.

A high level existing management performance indicator that champions care a lot about. Example: Profitability percentage, Customer satisfaction, Inventory levels, Time to market, Yield etc.

Business metrics are infulenced by Multiple processes or many many outputs.

Also called Process Management or Reengineering. The concept of defining macro and micro processes, assigning ownership, and creating responsibilities of the owners.

The critical activities of an enterprise that must be performed to meet the organizational objective and are solution independent.

A step or change made to the product which is necessary for future or subsequent steps but is not noticed by the final customer.

Typical term used to describe CEO, CFO, COO, CIO, and other senior executives within an organization.

Calibration is simply the comparison of instrument performance to a standard of known accuracy. It may simply involve this determination of deviation from nominal or include correction (adjustment) to minimize the errors. Properly calibrated equipment provides confidence that your products/services meet their specifications. Calibration:

increases production yields,
optimizes resources,
assures consistency and
ensures measurements (and perhaps products) are compatible with those made elsewhere.

Change Acceleration Process are a set of critical tools that helps orgnizations/groups towards a common goal for achieving path breaking improvements in the change initiatves.

The need for CAP can well be understood using simple law of mechanics,
Vg=Ug(Initial Group velocity)+Ag(Group Acceleration)*Tg(Group Time).

The final velocity with which the organization or the group achieve their change initiatve objectives depends on their initial velocity or enthusiasm for change and the positive acceleration with which they move forward together.

---------------------- other version ----------------------

CAP ( Change Acceleration Process ) is a change management framework with a set of tools...to gauge the political/strategic/cultural environment in the organization and plan for action which will eventually determine how much success a change initiative can bring in within the existing operating boundaries.
Some of the CAP tools are ARMI, GPRI , Includes/Excludes , Threat Vs Opportunity , In Frame / Out Frame , More of..Less of excerice , Elevator Speech.

Acronym for Corrective and Preventive Action.

Corrective action:
Action taken to eliminate the cause of the existing non-conformity to prevent its recurrence.

Preventive action:
Action taken to eliminate the cause of potential non-conformity.

Both of these are prevention oriented.

The quick fix type actions are called as corrections

The capability of a product, process, practicing person or organization is the ability to perform its specified purpose based on tested, qualified or historical performance, to achieve measurable results that satisfy established requirements or specifications.

Capability analysis is a graphical or statistical tool that visually or mathematically compares actual process performance to the performance standards established by the customer.

To analyze (plot or calculate) capability you need the mean and standard deviation associated with the required attribute in a sample of product (usually n=30), and customer requirements associated with that product.

See the tool Capability Analysis.

The maximum amount of parts that may be processed in a given time period.

Is constrained by the bottleneck of the line--that is, the capacity of a production system depends on what is usually the slowest operation.

Capacity = 1 / Cycle Time

Typically the above formula is used when cycle time is expressed in shifts/part, thus measuring capacity as parts/shift.

Procedure used in response to a defect. This implies that you are reporting on a detected Non Conformance (NCR or NCMR) and have determined root cause to correct this from reoccuring.

A cause is anything that affects a result. But in root cause analysis we generally think of causes as bad. Therefore we need a different term to include both adverse influences and beneficial influences. Therefore, see "Factor."

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A factor (X) that has an impact on a response variable (Y); a source of variation in a process or a product or a system.

Anything that adversely affects the nature, timing, or magnitude of an adverse effect.

A cause and effect diagram is a visual tool used to logically organize possible causes for a specific problem or effect by graphically displaying them in increasing detail. It helps to identify root causes and ensures common understanding of the causes. It is also called an Ishikawa diagram.

Cause and Effect relationships govern everything that happens and as such are the path to effective problem solving. By knowing the causes, we can find some that are within our control and then change or modify them to meet our goals and objectives. By understanding the nature of the cause and effect principle, we can build a diagram to help us solve everyday problems every time.

Acronym for Critical Business Requirements.

Capacity Constraint Resource - Higher cycle time machine in a assembly line.

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CCR----Critical Customer Requirement

The center of a process is the average value of its data. It is equivalent to the mean and is one measure of the central tendency.

A center point is a run performed with all factors set halfway between their low and high levels. Each factor must be continuous to have a logical halfway point. For example, there are no logical center points for the factors vendor, machine, or location.

The central limit theorem states that given a distribution with a mean m and variance s2, the sampling distribution of the mean appraches a normal distribution with a mean and variance/N as N, the sample size, increases.

The central limit theorem explains why many distributions tend to be close to the normal distribution.

Here's a great learning example website: www.math.csusb.edu/faculty/stanton/m262/central_limit_theorem/clt.html

Addend:
If you are are averaging your measurements of a particular observable, your average's distribution may seem to tend toward a normal distribution. If the random variable that you are measuring is decomposable into a combination of several random variables your measurements may also seem to be normally distributed

YOU CAN STOP HERE IF YOU DO NOT WANT THE CALCULATIONS. However, I suggest just reading the words to keep yourself safe - the stuff between the dollar signs should suffice. I hope that my notation is clear for those venturing into the formulas.

Just to be on the safe side and preclude easy misinterpretations, here are some perspectives with three Central Limit Theorems. NO PROOFS! Immediately below you have one strong theorem and one weak one. At the very bottom is a theorem that is only referenced for completion and is for those who have fun proving limits of weighted sums of L2 integrals. Except for the third theorem, I trust that this will provide everyone with more light than heat!

$$$$$$One Strong Central Limit Theorem states the following: The average of the sum of a large number of independent, identically distributed random variables with finite means and variances converges "in distribution" to a normal random variable. {Example: "independent" production runs for the manufacturing of a computer (or appliance) circuit component, or board; milling shafts, polishing 1000s of microscope or phased array telescope lenses (Hawaii, where are you?), software modules, etc.} One must be careful about the type of convergence, such as "convergence in measure (or almost everywhere)" vs. "mean-square convergence" vs. "convergence in distribution". {Please note: "convergence in distribution" is a much weaker than "convergence in measure", but it is also weaker than "mean-square convergence"}$$$$$$

$$$$$$So, here we go: the average of the sum of a large number of independent, identically distributed random variables X1, X2, ....., Xn with finite means M(j) and finite variances Var(j) converges IN DISTRIBUTION to a normally distributed random variable X' with a finite mean M and a finite variance Var.$$$$$$ The formula follows (my apologies for my notation):

X1 + X2 + X3 + ....---> X' , where X' ~ N(M, Var), i.e., Normally Distributed with finite mean = M, and finite variance Var.

" -------> " denotes "converges toward"

If for each of the Xj, M(j) = 0 and Var(j) = 1, then X' ~ N(0,1)

$$$$$$A Weaker Central Limit Theorem: A sequence of jointly distributed random variables X1, X2, X3, ...., Xn with finite means and variances obeys the classical central limit theorem, IF the sequence Z1, Z2, Z3, ....., Zn converges IN DISTRIBUTION to a random variable Z ~ N(0,1) (WO! BACK UP! BE VERY CAREFUL HERE! THAT WAS AN IF!!!! THE TABLES HAVE BEEN TURNED!!!!)$$$$$$,

where

Zn = [Sn - E(Sn)]/[Std Dev(Sn)], and Sn = X1 + X2 + X3 + .... + Xn, Std Dev (Sn) = Square Root {Var(Sn)} is the standard deviation, and E(Sn) is the Expectation of Sn, the sum of the random variables Xj, 1<= j <= n.

" <= " denotes " less than or equal to"

The random variables Z1, Z2, ...., Zn are called the sequence of normalized consecutive sums of the sequence X1, X2, ...., Xn.

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In terms of the characteristic functions (see Section **** below), the sequence {Xj} obeys the central limit theorem, IF for every real number a:

In the limit as n goes positively to infinity, the Characteristic Function (CF) of Zn(a) converges to exp(-a^2/2)

The limit CF(Zn(a)) --------> exp(-a^2/2), as n ------> infinity, where a^2 = "a squared", and exp( ) is the exponential function.

" ^ " denotes exponentiation

The gold nugget here is that the function exp(-a^2/2) is the Characteristic Function (CF) for a random variable that is distributed normally N(0,1)!

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****[Characteristic Functions, i.e., the Fourier Transforms of the probability density functions of random variables (when they exist!). However, the spectral densities (the transforms of the Distribution Functions) always exist!)]

Two important concerns: the types of convergence, and what they mean. Two random variables with exactly the same distributions will often differ from one another to the vexation of the observer. However, they will tend to hop, skip, and jump around there central moments (i.e., means, variances, etc.) similarly.
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Two important cases (Recommendation: Leave Case 2 for those who are most comfortable with probabilistic L2 calculus):

Case 1. Independent, identically distributed random variables X, and {Xj} with finite means M, and M(j) and variances Var, and Var(j).

Then for Zj = [(X1+....+Xj) - jE(X)]/[Sqrt(j)*Var(X)], j=1,.....n,..... The limit of the characteristic function for Zj will converge to a normal characteristic function.

" * " denotes multiplication

Case 2. Independent random variables with finite means and (2 + delta)th central moment {i.e. a little bit more exponentiation than the variance's square}. Delta is some very small number, and

the (2 + delta)th central moment for Xj = mu(2+delta; j) = E[|Xj - E(Xj)|^(2 + delta)]. Please recall E[g] is the expectation of g.

If the {Xj} are independent and the Zj are defined as in Case 1, the characteristic functions (CFs) will converge to the normal CF exp(-a^2/2), IF the Lyapunov Condition holds:

The Lyapunov Condition:

In the limit as j goes to infinity {1/Var(2+delta)[Sj]}*{Sum(mu(2+delta; j)|1<=j<=n)} = 0, where
Var(2+delta)[Sj] = E[|Sj - E(Sj)|^(2 + delta)]

The numerical average (e.g. mean, median or mode) of a process distribution. Can also be displayed as the centerline of a process control chart.

An indication of the location or centrality of the data. The most common measures of central tendency are: mean (numerical average), median (the midpoint of an order data set such that half of the data points are above and half are below it) and the mode (the value that occurs most frequently)

Certified Six Sigma Black Belts (CSSBB) are those individuals who have displayed proven knowledge and expertise in implementing Six Sigma. This involves both "textbook" knowledge of the subject matter (methodologies, tools, principles, and related topics such as leadership and change management), as well as real-world, successful application of the methdology and tools in more than one Six Sigma projects.

An individual can be become a Certified Six Sigma Black Belt (CSSBB) in a variety of ways: from a not-for-profit society, from a consulting company, or from their private company (e.g. GE, Motorola, etc.). No one way is necessarily better than another, however, it is widely accepted that private companies with mature Six Sigma programs serve as the best vehicles for certification. In other words, this becomes the most valuable certification in marketability of individuals who become certified.

What matters most, arguable, is the results the Certified Six Sigma Black Belt (CSSBB) has delivered and can prove to a potential new employer.

Japanese for "load load", Chaku Chaku is an efficient style of production in which all the machines needed to make a part are situated in the correct sequence very close together.

The operator simply loads a part and moves on to the next operation. Each machine performs a different stage of production, such as turning, drilling, cleaning, testing or sandblasting.

Business leaders and senior managers who ensure that resources are available for training and projects, and who are involved in project tollgate reviews.

A person who leads a change project or business-wide initiative by defining, researching, planning, building business support and carefully selecting volunteers to be part of a change team. Change Agents must have the conviction to state the facts based on data, even if the consequences are associated with unpleasantness.

The Service Management process responsible for controlling and managing requests to effect changes (RFCs) to the business infrastructure or any aspect of business services to promote business benefit while minimizing the risk of disruption to services. 

Change Management also controls and manages the implementation of the changes subsequently approved.

A characteristic is a definable or measurable feature of a process, product, or variable.

A document or sheet that clearly scopes and identifies the purpose of a Quality improvement project. Items specified include background case, purpose, team members, scope, timeline.

The Chi Square Test is a statistical test which consists of three different types of analysis 1) Goodness of fit, 2) Test for Homogeneity, 3) Test of Independence.

The Test for Goodness of fit determines if the sample under analysis was drawn from a population that follows some specified distribution.

The Test for Homogeneity answers the proposition that several populations are homogeneous with respect to some characteristic.

The Test for independence (one of the most frequent uses of Chi Square) is for testing the null hypothesis that two criteria of classification, when applied to a population of subjects are independent. If they are not independent then there is an association between them.

Chi Square is the most popular discrete data hypothesis testing method.

The sum of essential facts or events accompanying, conditioning, or determining the probability or improbability of an event.

Machine Capability index, should be 2.00 or higher. See also Cpk.

Calculated using continuous / Uninterupted samples. Also known as short term capability. Cmk > 1.67 is the preferable situation. Usually, The long term capability studies shall be done in a machine / Process after achieving the required Cmk value.

The Capability Maturity Model for Software (also known as the CMM and SW-CMM) has been a model used by many organizations to identify best practices useful in helping them increase the maturity of their processes.

Also: Co-ordinate Measuring Machine is a CNC measuring machine capable of performing Reverse engineering and Dimentional inspection of Critical components.

See "Cost Of Conformance"

Certification of Conformity

Coefficient of variation is defined as the relative measure of dispersion it relates the mean and standard deviation by expressing the Std deviation as a % of mean. The benefit of standard deviation is a absolute measure which explains the dispersion in the same unit as original data.

A source of *Quality* failure that is always present as part of the random *Variation* inherent in the *Process* itself.

Its origin can usually be traced to an element of the process which only management can correct.

The less well-defined a process is, the more it is subject to random variation, resulting in a higher level of quality failures (bugs).

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In general, and very approximately, Common Causes outweigh *Special Causes* as origins of quality failures by four to 1 (*Pareto* distribution).

Common cause variation is fluctuation caused by unknown factors resulting in a steady but random distribution of output around the average of the data. It is a measure of the process potential, or how well the process can perform when special cause variation removed.

Common cause variability is a source of variation caused by unknown factors that result in a steady but random distribution of output around the average of the data. Common cause variation is a measure of the process's potential, or how well the process can perform when special cause variation is removed. Therefore, it is a measure of the process technology. Common cause variation is also called random variation, noise, noncontrollable variation, within-group variation, or inherent variation. Example: Many X's with a small impact.

Common cause variation is the remaining variation after removing the special causes (non-normal causes) due to one or more of the 5Ms and an "E" causes (Manpower, Material, Method, Measurement, Machine, and Environment), also known as 6Ms (Man power, Mother nature, Materials, Method, Measurements or Machine).

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See also *Common Cause*, *Special Cause*, *Special Cause Variation*.

Communication is the process of delivering and sending messages through various channels.

The ability of any organization or enterprise to dominate in the national and international markets, through offering quality products or services,which are exceeding the requirements of the customers.

See "Cost Of Non-Conformance".

Develop an understanding of customer's needs and environment, involve actual customer's to develop voice of customer (VOC), and operationally define requirements for downstream development.

In statistics: an incidental or subordinate variable

A restricting premise or provision upon which the fulfillment an occurrence or outcome of a cause and effect relationship depends.

Measurement of the certainty of the shape of the fitted regression line. A 95% confidence band implies a 95% chance that the true regression line fits within the confidence bands. Measurement of certainty.

How "wide" you have to cast your "net" to be sure of capturing the true population parameter. If my estimate of defects is 10%, I might also say that my 95% Confidence Interval is plus or minus 2%, meaning that odds are 95 out of 100 hundred that the true population parameter is somewhere between 8 and 12%.

This is the last and also one of the most critical steps of a DOE. This is the phase of setting your process at the settings you have calculated to see if you really get what your equation says you should.

This should always be done before advertising results & implementing new factor settings, confirm the results.

If you are still unable to have a confirmation, there is likely a problem with the DOE. There may be an interaction involved and/or the DOE may have been botched.

Factor or interaction effects are said to be confounded when the effect of one factor is combined with that of another. In other words, the effects of multiple factors on a response can not be separated. This occurs to some degree in all situations, and least frequently when the data is obtained from a carefully planned and executed experiment having a predefined objective.

Consequential metrics can be both Business and process metric, which measures anything that goes wrong as a result of improving the primary metric.

Measures any negative consequences hence called as consequential metrics. Also called as a secondary metric.

There can be multiple consequential metrics in a project of improving one process or one primary metric.

Concluding something is good when it is actually bad (TYPE II Error)

See also Alpha Risk, Beta Risk, Error (Type I), Error (Type II), Null Hypothesis, Alternative Hypothesis and Hypothesis Testing

The systematic search and quarantine of potentially nonconforming product/material throughout the delivery chain and subsequent delivery of known conforming prouct/material.

(That's my first pass at defining this word used througout the automotive influenced manufacturing world. I was looking for a definition from you.)

con·tin·u·ous (kn-tny-s)
adj.
Uninterrupted in time, sequence, substance, or extent.

Continuous data is information that can be measured on a continuum or scale. Continuous data can have almost any numeric value and can be meaningfully subdivided into finer and finer increments, depending upon the precision of the measurement system.

As opposed to discrete data like good or bad, off or on, etc., continuous data can be recorded at many different points (length, size, width, time, temperature, cost, etc.).

Continuous data is data that can be measured and broken down into smaller parts and still have meaning. Money, temperature and time are continous.Volume (like volume of water or air) and size are continuous data.

Let's say you are measuring the size of a marble. To be within specification, the marble must be at least 25mm but no bigger than 27mm. If you measure and simply count the number of marbles that are out of spec (good vs bad) you are collecting attribute data. However, if you are actually measuring each marble and recording the size (i.e 25.2mm, 26.1mm, 27.5mm, etc) that's continuous data, and you actually get more information about what you're measuring from continuous data than from attribute data.

Data can be continuous in the geometry or continuous in the range of values. The range of values for a particular data item has a minimum and a maximum value. Continuous data can be any value in between.

It is the data that can be measured on a scale.

Continuous Improvement (CI): Adopting new activities and eliminating those which are found to add little or no value. The goal is to increase effectiveness by reducing inefficiencies, frustrations, and waste (rework, time, effort, material, etc). The Japanese term is Kaizen, which is taken from the words "Kai" means change and "zen" means good.

An "in statistical control" process is one that is free of assignable/special causes of variation. Such a condition is most often evidence on a control chart which displays an absence of nonrandom variation.

A technical function in nature and a continuous process by which the expected results are measured againist a predetermined criteria or standards. In the case of variances, a disciplinary action will be undertaken or improvement actions will be pursued.

A graphical tool for monitoring changes that occur within a process, by distinguishing variation that is inherent in the process(common cause) from variation that yield a change to the process(special cause). This change may be a single point or a series of points in time - each is a signal that something is different from what was previously observed and measured.

Control limits define the area three standard deviations on either side of the centerline, or mean, of data plotted on a control chart. Do not confuse control limits with specification limits. Control limits reflect the expected variation in the data. Bi latral specification/tolerances have two limits on both side of the tolerances which is not appreciated in the Unilatral tolerances.

The intent of a process control plan is to control the product characteristics and the associated process variables to ensure capability (around the identified target or nominal) and stability of the product over time.

The process Failure Modes and Effects Analysis (FMEA) is a document to identify the risks associated with something potentially going wrong (creating a defect - out of specification) in the production of the product. The FMEA identifies what controls are placed in the production process to catch any defects at various stages on the processing.

Every completed Six Sigma project should have not only a control chart (if applicable), but a control plan. This ensures that the process doesn't revert to the way it previously operated.

Cpk = Z(short-term) which is sigma level / 3

However, if you are starting with DPMO, convert it to a decimal value(divide by 1,000,000), look this decimal value up in a standard normal curve(z table) and find the corresponding z. Minitab can do this as well. Anyway this is long term z. To convert to short term z which is sigma level:

z(short term) which is sigma level = Z(long term) + 1.5

then you can plug into the equation above to get Cpk

Customer Operations Performance Center

Customer, Output, Process, Input, Supplier.
Similar to the more common SIPOC but COPIS is a term used for an outside-in approach. Used when completing a high level 'wing-to-wing' map of what a customer experiences. Gives you the steps in the process from a customers view point.

COPQ stands for Cost of Poor Quality. See Cost of Poor Quality for definition.

Cost of Quality. See Cost of Poor Quality.

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See "Cost Of Quality".

Action to eliminate the cause of a detected nonconformity.

Correction is taken to rectify a known nonconformance; Corrective Action is taken to prevent recurrence of said nonconformance.

Action to eliminate the cause of a detected nonconformity. There can be more than one nonconformity. Corrective action is taken to prevent recurrence. Correction relates to containment whereas corrective action relates to the root cause. See Preventive Action.

Preventive Action is action to prevent the occurrence of a potential nonconformance; Corrective Action is taken to prevent recurrence of a known nonconformance. Examples of Preventive Action include (but are not limited to): Reviews (contracts, purchasing, processes, designs), Statistical Process Control (SPC) Analysis, Software Validation and Verification, Supplier Surveillance, Preventive Maintenance & Calibration Controls, Management Review of Quality Management System, Capability Studies, FMEA, Capability Maturity Model (CMM)/Capability Maturity Model Integration (CMMI) Processes, Employee Training Programs that train employees prior to commencing work, Suggestion Boxes, Disaster Recovery Planning, Trend Analysis, Benchmarking

Correlation is a technique for investigating the relationship between two quantitative, continuous variables.

Correlation is the degree or extent of the relationship between two variables. If the value of one variable increases when the value of the other increases, they are said to be positively correlated. If the value of one variable decreases when the value other variable is increasing it is said to be negatively correlated. If one variable does not affect the other they are considered to not be correlated.

The correlation coefficient quantifies the degree of linear association between two variables. It is typically denoted by r and will have a value ranging between negative 1 and positive 1.

In order to calculate the costs of providing service it is necessary to design and build a framework in which all costs can be recorded and allocated or apportioned to specific Customers or other activities.  Such 'Cost Models' can be developed to show, for example, the cost of each service, the cost for each Customer or the cost for each location.  The usual start point is to develop a Cost-by-Customer Cost Model.

(COC) A component of the *Cost Of Quality* for a work product. Cost of conformance is the total cost of ensuring that a product is of good *Quality*. It includes costs of *Quality Assurance* activities such as standards, training, and processes; and costs of *Quality Control* activities such as reviews, audits, inspections, and testing.

COC represents an organisation's investment in the quality of its products.

Contrast *Cost Of Non-Conformance*.

(CONC.) The element of the *Cost Of Quality* representing the total cost to the organisation of failure to achieve a good *Quality* product.

CONC includes both in-process costs generated by quality failures, particularly the cost of *Rework*; and post-delivery costs including further *Rework*, re-performance of lost work (for products used internally), possible loss of business, possible legal redress, and other potential costs.

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See also "Cost of Poor Quality - COPQ"

COPQ consists of those costs which are generated as a result of producing defective material.

This cost includes the cost involved in fulfilling the gap between the desired and actual product/service quality. It also includes the cost of lost opportunity due to the loss of resources used in rectifying the defect. This cost includes all the labor cost, rework cost, disposition costs, and material costs that have been added to the unit up to the point of rejection. COPQ does not include detection and prevention cost.

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See also *Cost Of Non-Conformance*.

COPQ should contain the material and labor costs of producing and repairing defective goods, you can include a portion of the appraisal cost if you have an inspection point, but never should you include prevention costs.
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COPQ – Suppliers
Cost of Poor Quality from Suppliers

Suppliers can generally affect our cost due to:
a) Producing defective material.
b) Damaging material during delivery.

Our COPQ will generally cover the followings:
1) Cost of labor to fix the problem.
2) Cost of extra material used.
3) Cost of extra utilities .
4) Cost of lost opportunity
a) Loss of sales/revenue (profit margin)
b) Potential loss of market share
c) Lower service level to customers/consumers

The cost associated with the quality of a work product.

As defined by Crosby ("Quality Is Free"), Cost Of Quality (COQ) has two main components: *Cost Of Conformance* and *Cost Of Non-Conformance* (see respective definitions).

Cost of quality is the amount of money a business loses because its product or service was not done right in the first place. From fixing a warped piece on the assembly line to having to deal with a lawsuit because of a malfunctioning machine or a badly performed service, businesses lose money every day due to poor quality. For most businesses, this can run from 15 to 30 percent of their total costs.

This value represents the maximum allowable expenditure for material, labor, outsourcing, overhead, and all other expenses associated with that project. (See also OCT: Operation Cost Target)

Covariates are random variables you treat as concomitants (see Concomitant Variable) or as other influential variables that also affect the response. Covariates in DOE are uncontrolled variables that influence the response but do not interact with any of the other factors being tested at the time. Therefore, if they are present during the experiment then they would show as measurements of error.

Process Capability index: a measure of the ability of a process to produce consistent results - the ratio between the permissible spread and the actual spread of a process. Permissible spread is the difference between the upper and lower specific limits of acceptibility (a.k.a. total tolerance); actual spread is defined, arbitrarily, as the difference between upper and lower 3 x sigma deviations from the mean value (representing 99.7% of the normal distribution). As a formula, Cp = (USL-LSL)/(6 x sigma). Note this takes no account of how well the output is centered on the target (nominal) value. For that see Cpk.

You can think of the process capability index Cp in 3 ways:

1. Cp measures the capability of a process to meet its specification limits. It is the ratio between the required and actual variability.

2. More mathematically, the Cp is the ratio of the Spec difference (upper - lower) divided by 6-sigma, which is the spread of a normal curve. Minitab gives the following explanation: 'Capability statistics are basically a ratio between the allowable process spread (the width of the specification limits) and the actual process spread (6s)'

3. Graphically, think of positioning a normal curve centered between the specs. Now look at the tail areas that exceeds the specs. The smaller the area, the larger the Cp. In this sense it is equivalent to looking at the popular PPM measure (parts-per-million) which gives the area of the normal curve that exceeds the specs.

Process Capability index ('equivalent') taking account of off-centredness: effectively the Cp for a centered process producing a similar level of defects - the ratio between permissible deviation, measured from the mean value to the nearest specific limit of acceptability, and the actual one-sided 3 x sigma spread of the process. As a formula, Cpk = either (USL-Mean)/(3 x sigma) or (Mean-LSL)/(3 x sigma) whichever is the smaller (i.e. depending on whether the shift is up or down). Note this ignores the vanishingly small probability of defects at the opposite end of the tolerance range. Cpk of at least 1.33 is desired.

Capability analysis indice.

A critical element is an X that does not necessarily have different levels of a specific scale but can be configured according to a variety of independent alternatives. For example, a critical element may be the routing path for an incoming call or an item.

CTQs (Critical to Quality) are the key measurable characteristics of a product or process whose performance standards or specification limits must be met in order to satisfy the customer. They align improvement or design efforts with customer requirements.

CTQs represent the product or service characteristics that are defined by the customer (internal or external). They may include the upper and lower specification limits or any other factors related to the product or service. A CTQ usually must be interpreted from a qualitative customer statement to an actionable, quantitative business specification.

To put it in layman's terms, CTQs are what the customer expects of a product... the spoken needs of the customer. The customer may often express this in plain English, but it is up to us to convert them to measurable terms using tools such as DFMEA, etc.

Customer Relationship Management (CRM) is a philosophy that puts the customer at the design point, it is being customer-centric. It should be viewed as a strategy rather than a process. It is designed to understand and anticipate the needs of current and potential customers. There is a plethora of technology out there that helps capture customer data and external sources, and consolidate it in a central warehouse to add intelligence to the overall CRM strategy. "We are in business because of our customers. So it only makes sense to build and intimate relationship with the customer." Now that's CRM!

Characteristic Selection Matrix

CTC or Critical To Customer.

This is the input to the Quality Function Deployment activity, for the customer requirements side of the analysis. Not same as CTQ.

CTQ's are the internal critical quality parameters that RELATE to these customer-critical parameters. QFD relates the two, and leads to the DFMEA efforts which quantify the severity and frequency of occurance of failure to meet the CTQ's and thus the CTC's by relationship. Car door sound when closing might be a CTC, while the dimensional tolerances and cushioning that produce those conditions are CTQ's for the auto maker.

A CRT starts with the identification of Undesirable Effects (UDEs) present in our reality.
Such UDEs are not only present, they hurt; they take away some, or much, of the joy that we take in our work.
They contribute to form the “prison” created by the way people interact. These UDEs cover a fairly large span; they originate from different sources and have different “weights.”

If we want to be effective we need to identify the minimum necessary things that need to change. To do this we should identify the few things causing the majority of the current problems. The fewer the elements we find that cause the problems the more powerful and focused our improvement process will be. We call problems 'UnDesirable Effects' to remind us that these are not things that exist in isolation but are the negative effects of some cause. They are symptoms and they result from a cause. Therefore to identify the few things that need to be changed we should rely on cause and effect relationships. We use a diagram called a 'Current Reality Tree' to show the relationships and links between the current UnDesirable Effects. The process used to identify how the UDE are linked together results in a Current Reality Tree (CRT).

Customer:
A person who receives the product or service of a process.

In a laymans language:
A customer is one who buys or rates our process/product (In terms of requirements), and gives the final verdict on the same. This in turn acts as a hidden feedback which can be implemented leading to improvement to all the parameters of the Process Management.

The concept that the customer is the only person qualified to specify what Quality means. This leads to detailed analyses of who are the customers, what are their needs, what features (or new) are required of our products/services, how do customers rate our products/services versus our competitors and why, how can we keep our customers satisfied?

The wants or voice-of-customer in Stated or ImpliedTerms.

Most of the times the customer is enabled to state the requirements precisely. (Like please bring me a glass of luke warm water to drink). However customer may not always be able to precisely state or equipped to realize the basic attributes of his requirements. It is therefore the responsibility of the supplier to reconsider the attributes of desired/ supplied product in terms of the 'implied or real' requirements. For example the hygiene of the environment in which food is cooked in a resturant.

A cusum chart is a type of control chart (cumulative sum control chart). It is used to detect small changes between 0-0.5 sigma. For larger shifts (0.5-2.5), Shewart-type charts are just as good and easier to use. Cusum charts plot the cumulative sum of the deviations between each data point (a sample average) and a reference value, T. Unlike other control charts, one studying a cusum chart will be concerned with the slope of the plotted line, not just the distance between plotted points and the centerline. Critical limits for a cusum chart are not fixed or parallel. And a mask in the shape of a V is usually laid over the chart with the origin over the last plotted point. Previous points covered by the mask indicate the process has shifted.

So, who uses these types of charts? Typically chemical industries.

Cycle time is the total time from the beginning to the end of your process, as defined by you and your customer. Cycle time includes process time, during which a unit is acted upon to bring it closer to an output, and delay time, during which a unit of work is spent waiting to take the next action.

In a nutshell - Cycle Time is the total elapsed time to move a unit of work from the beginning to the end of a physical process. (Note, Cycle Time is not the same as Lead Time).