Glossary

Question 1

Mutually Exclusive

Answer 1

Two events are mutually exclusive if they cannot occur at the same time. An example is tossing a coin once, which can result in either heads or tails, but not both.

Question 2

sigma shift

Answer 2

Because processes will randomly fluctuate over time and will often deteriorate in the long run, Motorola introduced the practice of reducing their calculated sigma values by 1.5 standard deviations to provide a more conservative estimate of the long term performance of the process.
Use: If we failed to account for the random fluctuations over time, the probability of realizing a defect (for every one million opportunities for a defect), we would be living under a false sense of security. Performing at exactly six sigma equates to a probability of two (2) defects per BILLION. Normally distributed processes will fluctuate up to 1.5 sigma (either side of the mean) over time; therefore, after we account for that 1.5 sigma shift, the probability for a defect per 1 million opportunities turns out to be 3.4 DPMO (Defects per Million Opportunities).

Question 3

5-S

Answer 3

5-S derives its name from five Japanese terms beginning with the ‘S’. A conscientiously-applied program of 5-S creates a workplace suited for visual control and lean production. Collectively, the five S’s (detailed below) outline how to create a workplace that is visibly organized, free of clutter, neatly arranged, and sparkling clean. The 5-S system is often a starting place for implementing lean operations.
Use: If you’ve ever gone out into the garage for spring cleaning and have looked at the mess and didn’t even know where to start, you can appreciate the value of a 5-S methodology. You start with sorting everything. Put everything into homogeneous piles. Then, you find a place to put everything, but more importantly, you want to make it so that everything has a place, and everything is in its place. Included in the 5-S methodology is cleaning. That is the shine/sweep step. You then standardize best practices. And you would want to have a conscientiously-applied program to sustain the arrangement. Here are the 5-Ss (including alternate names that you may see).
1. SORT
2. SET IN PLACE / SET IN ORDER / STRAIGHTEN / STORE
3. SHINE / SWEEP
4. STANDARDIZE
5. SUSTAIN / SELF DISCIPLINE
OPTIONAL (SAFETY)

Question 4

7 Quality Tools

Answer 4

TBD

Question 5

7 New Management Tools

Answer 5

TBD

Question 6

Accuracy

Answer 6

Accuracy is the variation between the mean of a set of numbers and the true value.
Use: Many times you will hear someone say that some activity is accurate, when in reality, what they mean to say is that something is precise. Here’s the difference. Let’s say that you’re throwing darts at a dartboard. And let’s say that you are shooting for the bull’s-eye. If the darts are evenly distributed about the center even though they may be widely spread evenly around the dart board—consuming the whole dartboard, the dart throwing is accurate, but not precise. If however, all of the darts are showing up in the upper right-hand corner of the dart board, you have missed the target, but your distribution of darts is all clustered around the same spot in the upper right-hand corner, you are precise, but not accurate. What you want is to be precise and accurate. You want the darts to be in a tight cluster about the center, or bull’s-eye. If you can do that, you are throwing darts with accuracy and precision.

Question 7

Activity Network Diagram

Answer 7

An activity network diagram (also known as arrow diagram, pert chart, and critical path method is used to show activities that are in parallel and/or in series with each other. It will show the most optimistic times, the most pessimistic times, and the most likely times for the completion of projects.

Question 8

Affinity Diagram

Answer 8

An affinity diagram is used to show activities in homogeneous groupings known as affinity groupings.

Question 9

Alias

Answer 9

When using a design of experiments methodology, an alias is when the pattern of pluses (+) and minuses (-) into columns are identical. For example, a main effect is aliased with a two-factor interaction. An example of where this might be seen is in a resolution IV (fractional factorial) experiment. During the analysis, it is impossible to know whether a significant change is due to a main effect or due to an interaction because the columns are identical.

Question 10

Alpha Risk

Answer 10

An Alpha risk is the risk of concluding that something is significant when in fact it is really not. If an error occurs of this type, it is known as a Type I error.

Question 11

Alternative hypothesis

Answer 11

The alternate hypothesis is the compliment of the null hypothesis. The null hypothesis is what you anticipate through randomness. The alternative hypothesis, sometimes known as the alternate hypothesis is the opposite of the null hypothesis. The alternative hypothesis is what you would not anticipate through randomness. More often than not, you are trying to reject the null because you are trying to see a change in something - considering that most Six Sigma projects are trying to fix broken processes.

Question 12

Analysis of Variance (ANOVA)

Answer 12

An analysis of variance is a statistical method for comparing the effect of the levels of a single factor or multiple factors on a response variable.

Question 13

Analysis of Variance (ANOVA) - One-way

Answer 13

A one-way ANOVA simply means that you are testing one factor. Now, you might test that particular factor at two, three, or four levels, etc. To contrast, a two-way ANOVA means that you are testing two factors.

Question 14

Analysis of Variance (ANOVA) - Two-way

Answer 14

A one-way ANOVA simply means that you are testing one factor. Now, you might test that particular factor at two, three, or four levels, etc. To contrast, a two-way ANOVA means that you are testing two factors. You might be testing those two factors each at two, three, four levels.

Question 15

Andon Board

Answer 15

A status board usually erected high enough to ensure viability by all of the workers.

Question 16

ARIZ

Answer 16

Algorithm in inventive problem solving.

Question 17

Arrow Diagram

Answer 17

See Activity Network Diagram

Question 18

Attribute Chart

Answer 18

An attribute chart is a type of control chart for measuring attribute data (vs. continuous data). There are four types of attribute charts: c chart, n chart, np chart, u chart. The choice of charts depends on whether you have a problem with defects or defectives, and whether you have a fixed or varying sample size.

Question 19

Attribute Data

Answer 19

Discrete data are only a finite number of values possible or if there is a space on the number line between two possible values. For example, it’s impossible to roll the 2.3 on a roll of the dice. You can either roll a two or three but nothing in between. One type of discrete data, sometimes referred to as attribute data are two-state (binary). This “attribute data” might be pass/fail, good/bad, the color matches/the color doesn’t match, etc.

Question 20

Balanced scorecard

Answer 20

A management technique used to align enterprise activities to the Key Business Objectives (KBO) of the organization.

Question 21

Baldrige

Answer 21

Malcolm Baldrige National Quality Award

Question 22

Bell-shaped

Answer 22

To be bell-shaped with a distribution means that most of the values are in the center, and fewer and fewer values tail out to either side symmetrically.

Question 23

Benchmarking

Answer 23

Webster stays that a benchmark is something that serves as as standard by which others may be measured or judged.

Question 24

Beta risk

Answer 24

Terminology that describes the risk that a project team is making when they conclude that something is not significant, when in fact, it really is significant. Think of a jury finding a defendant guilty, when in fact, the defendant is not guilty. There is a risk that when a defendant is sent to jail, the jury may have been incorrect in their verdict and sent an innocent person to prison. If an error of this type occurs, it is known as a type II error.

Question 25

Binomial Distribution

Answer 25

Binomial data are based on the existence of only two mutually-exclusive outcomes (or categories). These two outcomes are commonly referred to in statistics as successes and failures. In industry applications, the most common application of the binomial distribution is counting the number of defective (or good) items in a sample in order to determine a process yield. The binomial distribution is the probability upon which p and np attributes control charts are based.

Question 26

Binomial Experiment

Answer 26

For the binomial experiment, you need to have four things: 1) A fixed number of trials, 2) The number of trials need to be independent, 3) The probability of success on every trial needs to be the same, and 4) In each trial there can only be outcomes - typically successes and failures.

Question 27

Bivariate

Answer 27

Quite often we need to measure two characteristics on each sampled item. When this is the case - when we collect two pieces of information (measuring two variables) on each sampled unit - the resulting data are referred to as bivariate.

Question 28

Black Belt

Answer 28

The black belt is generally a full-time process improvement position within an organization. It might be full time, but it many times is a temporary position - perhaps a two-year assignment to help groom a high-potential employee for a senior level position.

Question 29

Box and whiskers plot

Answer 29

A graphical tool of exploratory data analysis that allows the comparison of groups by constructing a graph around five measures for each group: (1) the median, (2) the maximum, (3) the minimum, (4) the first quartile or 25th percentile, and (5) the third quartile or 75th percentile. This tool provides visibility of variation among items being evaluated and makes comparisons quickly and easily.

Question 30

BPM Business Process Management

Answer 30

Business process management is more of a holistic management approach, as compared to six Sigma, focusing on aligning all aspects of an organization with the wants and needs of clients. Six Sigma is generally looking at broken processes and fixing them.

Question 31

Brainstorming

Answer 31

Brainstorming is a method for generating a large number of ideas in a short period of time. Brainstorming should be full of energy, moves along rapidly, and is synergistic in nature. Brainstorming should create a large list of ideas which may eventually be boiled down, or final down, to a smaller list of priority items later in the project.

Question 32

Business case

Answer 32

A business case is one of the major components of the project charter. The other two major components include the problem statement and the problem scope. The business case to scribes, in nonquantifiable terms, what the project does it describes the impact of the strategic business objectives. It’s used as a motivational tool that describes why the project is worth doing and explains the consequences of not doing the project.

Question 33

Capability ratio

Answer 33

The capability ratio is the inverse of the CP index. Remember that the CP index is the specifications bread divided by the process bread of six standard deviation’s. Flip that formula around and you would be dividing the process spread by the specification spread. In the case of a capability ratio, you would be looking for a smaller is better characteristic. With the CP index, you were looking for a larger is better characteristic.

Question 34

Capability study

Answer 34

As mentioned in the course, there are two ways to make a bad part, or to make for an unhappy customer experience when dealing with continuous data. either the process variation centering is off, or the dispersion – fatness or slop in the process – is too wide. A capability study will tell you with two numbers represented by either CP CPK or PP PPK whether your process is capable of meeting customer requirements in either the short-term or the longer-term.

Question 35

Cause-and-effect diagram

Answer 35

A cause-and-effect diagram was first known by the name Fishbone diagram because it looks like the skeleton of a fish. It was first made popular by Dr. Ishikawa back in the late 70s and early 80s. Usually the cause of fact diagram is drawn on a large whiteboard or a flip chart. The effect is usually written at the 3 o’clock position. A horizontal line divides the whiteboard into two equal parts. Many times the 6 AM – man machine material method measurement and mother nature – are used as branches off the horizontal line lead to the effect on the right-hand side of the whiteboard. The idea is to list out as many possible causes, and said causes, and said causes of sub causes, until the team runs out of ideas. As with brainstorming you want this to be a fast-moving exercise so that the team can solicit as many ideas in a short period of time as possible.

Question 36

Charter

Answer 36

A project charter is a document that contains the basic elements – business case, problem statement, and scope dash of the improvement project.

Question 37

Check sheet

Answer 37

Check sheet is a tool used for data collection. And designing the checksheet to collect the data for particular purpose, we usually adapt the basic data collection function of the checksheet to the issue at hand. You would use a tally sheet to simply keep track of defects are defectives. You would use a defect location checksheet to collect and analyze data when you need a visual image of the item being evaluated.

Question 38

Chi-square distribution

Answer 38

A statistical tool used to test for independence or dependence between random variables taken from different populations. With this you are comparing a target variance with an observed variance. It is just just independence of two nominal variables (names or categories only).

Question 39

Common-Cause vs. Special-Cause

Answer 39

Common-cause variation is where no one, or combination of factors is unduly affected the process variation (random variation). Special-cause variation is when one or more factors are affecting the process variation in a non-random way. With special-cause variation, one should be able to identify, or put their finger on the reason behind the unexpected variation.

Question 40

Confounding

Answer 40

An alias when employing the use of a designed experiments methodology is the pattern of pluses (+) and minuses (-) into columns are identical. For example, a main effect is aliased with a two-factor interaction. During the analysis, it is impossible to know whether a change is due to a main effect or due to an interaction since the columns are identical. Confounding is similar, but it doesn’t mean 100% overlap with the pattern of pluses and minuses in the columns. Perhaps the column might be 80 percent confounded, or 90 percent confounded. It would be better if there was no confounding as far as resolution is concerned.

Question 41

Continuous Data

Answer 41

Continuous data can be measured on a continuum. Think of it as being able to divide a measure by one half, and in half again, and in half again, - to infinity. Contrast continuous data with discrete/attribute data that is binary, or two-state – pass/fail, go/no go, good/bad, and so on.
Use: A Six Sigma practitioner should always strive to work upstream (if necessary) to gather continuous data because with continuous data you can get a handle on the magnitude of the problem. If you only have discrete/attribute data, you can only tell whether it’s good or bad, or whether it passes or fails.