7 Basic Tools for Quality Control

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PRODUCTIVITY & QUALITY

7 Basic Tools for Quality Control

Within any manufacturing environment, product information or "data" is collected for a variety of

reasons. Some of the common reasons for having this data are:

1. Data to assist in understanding the actual process or situation.

2. Data for product analysis.3. Data for process control (SPC).

4. Data for regulation a basis for raising or lowering a data standard, for example,temperature or thickness.

5. Acceptance or rejection data used to approve or reject products or parts.

The purpose of collecting data is usually to gather information about the product or to follow up

with some form of action. That is, after evaluating the actual conditions revealed by the data, someform of proper action should be taken. The first major critical step, however, is to ensure that the

data represents typical conditions, or is data taken from normal circumstances. The second major

critical step is to have a purpose for collecting the data. Therefore, before we actually collect data,

we should ask the following questions:

1. Define What we are measuring and Why are we measuring this information?

2. Define Where and When should we measure this information?

3. Define How should we be measuring this information and at what time intervals? Whatmeasurement tool?

4. Define Who should be measuring this information?

Data can be collected in many ways, depending upon the reason for the data, and the type of

information we are seeking. Thus, data can be basically divided into two main groups:

1. Measurement data: continuous data of length, weight, time, torque, etc.2. Countable data: enumerate data such as the number of defectives, percentage defective,

number count of each defect, etc.

Once we collect this data, it should be analyzed, and the information extracted through the use of

statistical methods. For that reason, data should be collected and organized in such a way as tomake data analysis more simple and meaningful. Therefore, you need to clearly record the nature

of the data collected. You should also record the purpose of the measurements and their

characteristics; the date; the instrument or method of measuring; the person performing themeasurement; and any other pertinent information to the collection process.

To properly record this data, you need to have a consistent time period, for example, measured

every hour or every two hours, and make sure you are measuring production parts. In the case ofcollecting data to count defects, ensure you count each and every item produced, as well as the

defects, during the collection period so you can compare how many defects were produced in

relation to the total production of parts.Let us now summarize our data collection steps:

1. Clarify the purpose of collecting the data it is useless if there is no real reason to collect

the data.

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2. Collect data efficiently make the method reliable, consistent and specify a standard period

of time.3. Take action according to the data once you have collected the data, make it effective by

analyzing the data and using it for improvement. Have an improvement action as a result of

the collection process.4. When establishing a basis for collecting data, be sure to ask and answer the What, When,

Where, How & Who questions mentioned above.

There are a large variety of Quality Tools and Statistical Process Control Methods (SPC) within

the realm of Total Quality Management. We are, however, going to only concern ourselves with 7Basic Quality Tools within this web site. They are:

1. Check Sheets

2. Pareto Diagrams

3. Histogram Diagram4. Cause-and-Effect or "Fishbone" Diagram

5. Scatter Diagrams

6. Control Charts7. NP Charts

Tool #1 - The Check Sheet

As previously mentioned, the intent and purpose of collecting data is to either control the

production process, to see the relationship between cause-and-effect, or for the continuousimprovement of those processes that produce any type of defect or nonconforming product. A

Check Sheet is used for the purpose of collecting data to compile in such a way as to be easily

used, understood and analyzed automatically. The Check Sheet, as it is being completed, actuallybecomes a graphical representation of the data you are collecting, thus you do NOT need any

computer software, or spreadsheet to record the data. It can be simply done with pencil and paper!

Check sheets have the following main functions:

1. Production process distribution checks - where the distribution lies.2. Defective item checks - to determine what kind of defects exist in the process.

3. Defect location checks - to determine where the common defects on a part are located.

4. Defective cause checks - type of defect and thus validate the cause thereof.5. Check-up confirmation checks - final phase of assembly to check the finished product or

work.

The methods that we will concentrate on and utilize here will be for Production Process

distribution and defective item checks. We will discuss the use and relevance of each individually.

Production Process Distribution

The size, weight, or diameter of parts, for example, are known as "continuous data". In a processwhere these types of data are gathered, the distribution they provide will often resemble a

Histogram (Histogram is Tool #3). A histogram can be used to investigate the distribution of the

process characteristics, and the average value can be calculated.

Below is a sample Production Check Sheet



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In this sample sheet, we measured torque readings. The spec limit is 2.2Nm .5. From the examplesheet, you should see two dark vertical lines, on the left side it is labeled LSL (Lower Spec Limit)

which indicates the 2.2 - .5, or 1.7. On the right side, another dark line marked USL (Upper Spec

Limit) which indicates the 2.2 + .5, or 2.7. All product readings, or torque readings in this example,that conform (actually good product), need to fall within these limit boundaries. Anything that is

measured outside these limits is termed "Non-Conforming" since they are not within properspecification limits.Every time a measurement was taken, an "X", or check mark, was made on the check sheet. From

this sample sheet, you can see where most of the torque readings lie, the consistency of the

distribution, and how many are actually outside the spec limit. What has also been created here on

this Check Sheet is a "Histogram". We will discuss Histograms in a later lesson. Right now, whatis important to note from this example chart is that we do not have a good stable process. The

distribution is widespread and not well centered between the specification limits. The distribution

has dual peaks, or is what is known as "bi-modal".Bi-modal means that there are two points where most of the readings taken are charted, or that is

has two peaks. This means the frequency of readings rises and falls twice, rather than a more

proper and even distribution with one peak.



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A Check Sheet is used for:

1. distinguishing between fact and opinion (example: how does the community perceive the

effectiveness of the school in preparing students for the world of work?)2. gathering data about how often a problem is occurring (example: how often are students

missing classes?)

3. gathering data about the type of problem occurring (example: What is the most common

type of word processing error created by the students-grammar, punctuation, transposingletters, etc.?)


(continuous data use) No._ 741

PRODUCTION CHECK SHEET

Product Name__ Alternator Pulley Date_____ 12- 02- 02

Usage_________ Pulley Bolt Torque Factory________ Church Street

Specification____2.2 +/- .5 Section Name__ SI Line

No. of Inspections____ 185 Data Collector__ Sam The Man

Total Number_______ 185 Group Name___________________________

Lot Number_________ 1631 Remarks:_____________________________

Dimensions 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2

40

35 SpecLSL

SpecUSL

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TOTAL

FREQUENCY 1 2 7 13 10 16 19 17 12 16 20 17 13 8 5 6 2 1


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Steps to create a Check Sheet

1. Clarify the measurement objectives. Ask questions such as "What is the problem?", "Whyshould data be collected?", "Who will use the information being collected?", "Who will

collect the data?"

2. Create a form for collecting data. Determine the specific things that will be measured andwrite this down the left side of the check sheet. Determine the time or place being measured

and white this across the top of the columns.3. Collect the data for the items being measured. Record each occurrence directly on theCheck Sheet as it happens.

Tool #2 - The Pareto Diagram

The Pareto diagram is a graphical overview of the process problems, in ranking order of the most

frequent, down to the least frequent, in descending order from left to right. Thus, the Paretodiagram illustrates the frequency of fault types. Using a Pareto, you can decide which fault is the

most serious or most frequent offender.

The basic underlying rule behind Pareto's law is that in almost every case, 80% of the totalproblems incurred are caused by 20% of the problem cause types; such as people, machines, parts,

processes, and other factors related to the production of the product. Therefore, by concentratingon the major problems first, you can eliminate the majority of your problems. The few items thathave the largest amount of occurrence is your more frequent problem, than are the many items that

only happen once in a while. This is called the "vital few over the trivial many" rule. Quite often,

once you cure several of the "big hitters" you also eliminate some of the smaller problems at the

same time.So then, what exactly is a Pareto diagram? The Pareto prioritizes problem areas. Sometimes a

quality problem is so cluttered with so many smaller problems, it is difficult to know just where to

begin the solving process. Let's take an example. Below is a table from a manufacturing processthat charted all of their quality problems. While the original defect chart listed many problems at

various stages of the process, the overall problems were grouped into five main process areas. In

the left column is the name of the process where the defects occur. In the next column is theamount of defects recorded from their daily check sheets, recorded during a one week period. In

the third column is the percent of defectives from the overall production (N = 2165). In the fourth

and final column, is the percent of the total defects. That is, for example, of all the defects recorded

(416), poor Caulking is 47.6% of the entire problem. It should be obvious then, where the primaryproblem is and what should be focused upon first.



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Figure 1

From the chart above (figure 1), you can now create a Pareto chart in which you can graphically

display the quality problems. There is special software on the market that makes Pareto diagrams,however, an Excel barchart will basically create the same display. The below bar chart reflects the

above information charted in Excel.

Pareto Example

The left vertical axis (border) shows the number of defects for each defective category, and the

right vertical axis shows the percentage of each defect of the total defects. The horizontal axis

(bottom) lists the defective items starting with the most frequent one on the left (Caulking),progressing over to the least frequent occurrence on the right side (Torque). Therefore, the Pareto

diagram visually indicates which problem should be solved first, or in this case, the Caulking

problem. With this bar graph, it is easier to see which defects are most important of all the defectsthat exist. If we solve all or most of the problems in Caulking, it could affect some of the problems

observed in connecting, gapping, fitting, and torque.

During the "brain-storming" session (we'll cover this later), it is wise to ask, "Does the Caulkingproblem have any impact on the other problems listed?" In some cases it might. If there was proper

caulking, would part of the "Gapping" problem be eliminated?" If there were proper caulking,

would the "Torque" have a better value and thus not be part of the defects? Sometimes your major

problems have impact on the smaller problems. Several problem areas may all be attributed to

ONE ROOT CAUSE, even though several failure modes are observed. For this reason, it is alwayswise to choose the most frequent problem first.

STEPS IN CONSTRUCTING A PARETO CHART WITH STEP-BY-STEP EXAMPLE:

1. Determine the categories of problems or causes to be compared. Begin by organizing the

problems or causes into a narrowed down list of categories (usually 8 or less).2. Select a Standard Unit of Measurement and the Time Period to be studied. It could be a

measure of how often something occurs (defects, errors, tardies, cost overruns, etc.);



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frequencies of reasons cited in surveys as the cause of a certain problem; or a specific

measurement of volume or size. The time period to be studied should be a reasonablelength of time to collect the data.

3. Collect and Summarize the Data. Create a three-column table with the headings of "error or

problem category", "frequency", and "percent of total". In the "error or problem category"column list the categories of problems or causes previously identified. In the "frequency"

column write in the totals for each of the categories over the designated period of time. Inthe "percent of total" column, divide each number in the "frequency" column by the totalnumber of measurements. This will provide the percentage of the total.

Error Category Frequency Percent of TotalPunctuation 22 44%

Grammar 15 30%

Spelling 10 20%

Typing 3 6%TOTAL 50 100%

1. Create the framework for the horizontal andvertical axes of the Pareto Chart. The

horizontal axis will be the categories ofproblems or causes in descending order with

the most frequently occurring category on the

far left (or at the beginning of the horizontal

line). There will be two vertical axes-one onthe far left and one on the far right. The

vertical axis on the far left point will indicate

the frequency for each of the categories. Scaleit so the value at the top of the axis is slightly

higher than the highest frequency number.The vertical axis on the far right willrepresent the percentage scale and should be

scaled so that the point for the number of

occurrences on the left matches with the corresponding percentage on the right.

2. Plot the bars on the Pareto Chart. Using a bargraph format, draw the corresponding bars in

decreasing height from left to right using the

frequency scale on the left vertical axis. To plot

the cumulative percentage line, place a dot aboveeach bar at a height corresponding to the scale on

the right vertical axis. Then connect these dots

from left to right, ending with the 100% point atthe top of the right vertical axis.

3. Interpret the Pareto Chart. Use common sense-just because a certain problem occurs most often doesn't necessarily mean it demands your

greatest attention. Investigate all angles to help solve the problems-What makes the biggestBMS/Quality & Productivity


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difference? What will it cost to correct the problems? What will it cost if we don't correct

this problem?

A PARETO CHART IS USED FOR:

1. Focusing on critical issues by ranking them in terms of importance and frequency

(example: Which course causes the most difficulty for students?; which problem with

Product X is most significant to our customers?)2. Prioritizing problems or causes to efficiently initiate problem solving (example: Which

discipline problems should be tackled first? or, What is the most frequent complaint by

parents regarding the school?; solution of what production problem will improve quality

most?)3. Analyzing problems or causes by different groupings of data (e.g., by program, by teacher,

by school building; by machine, by team)

4. Analyzing the before and after impact of changes made in a process (example: What is themost common complaint of parents before and after the new principal was hired?; has the

initiation of a quality improvement program reduced the number of defectives?)

Tool #3 - The Histogram

The common person believes that if a part is made in mass production from a machine, all of theparts will be exactly alike. The truth is that even with the best of machines and processes, no two

parts are exactly the same. The product will have a main or "mean" specification limit, with

plus/minus tolerance that states that as long as the part is produced within this range, to that range,it is an acceptable part. The object is to hit the target specification, however, that is not always

totally possible.

The purpose of a Histogram is to take the data that is collected from a process and then display itgraphically to view how the distribution of the data, centers itself around the mean, or main

specification. From the data, the histogram will graphically show:

1. The center of the data.2. The spread of the data.3. Any data skewness (slant, bias or run at an angle).

4. The presence of outliers (product outside the specification range).

5. The presence of multiple modes (or peaks) within the data.

A HISTOGRAM IS USED FOR:

1. Making decisions about a process, product, or procedure that could be improved after

examining the variation (example: Should the school invest in a computer-based tutoring

program for low achieving students in Algebra I after examining the grade distribution?;are more shafts being produced out of specification that are too big rather than too small?)

2. Displaying easily the variation in the process (example: Which units are causing the mostdifficulty for students?; is the variation in a process due to parts that are too long or parts

that are too short?)

STEPS IN CONSTRUCTING A HISTOGRAM:

1. Gather and tabulate data on a process, product, or procedure. This could be time, weight,

size, frequency of occurrences, test scores, GPA's, pass/fail rates, number of days to

complete a cycle, diameter of shafts built, etc.



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2. Calculate the range of the data by subtracting the smallest number in the data set from the

largest. Call this value R.3. Decide about how many bars (or classes) you want to display in your eventual histogram.

Call this number K. This number should never be less than four and seldom exceeds 12.

With 100 numbers, K=7 generally works well. With 1000 pieces of data, K=11 works well.4. Determine the fixed width of each class by dividing the range, R by the number of classes

K. This value should be rounded to a "nice" number, generally a number ending in a zero.For example 11.3 would not be a "nice" number. 10 would be considered a "nice" number.Call this number i, for interval width. It is important to use "nice" numbers else the

histogram created will have wierd scales on the X axis.

5. Create a table of upper and lower class limits. Add the interval width i to the first "nice"

number less than the lowest value in the data set to determine the upper limit of the firstclass. This first "nice" number becomes the lowest lower limit of the first class. The upper

limit of the first class becomes the lower limit of the second class. Adding the internal

width (i) to the lower limit of the second class determines the upper limit for the secondclass. Repeat this process until the largest upper limit exceeds the biggest piece of data.

You should have appriximately K classes or categories in total.

6. Sort, organize, or categorize the data in such a way that you can count or tabulate howmany pieces of data fall into each of the classes or categories in your table above. These are

the frequency counts and will be plotted on the Y axis of the histogram.

7. Create the framework for the horizontal and vertical axes of the histogram. On the

horizontal axis plot the lower and upper limits of each class determined above. The scale onthe vertical axis should run from zero to the first "nice" number greater than the largest

frequency count determined above.

8. Plot the frequency data on the histogram framework by drawing vertical bars for each class.The height of each bar represents the number or frequency of values occuring between the

lower and upper limits of that class.

9. Interpret the histogram for skew and clustering problems:

Interpreting skew problems:

Data may be skewed to the left or right. If the histogram shows a long tail of data on the left side of

the histogram, the data is termed left or negatively skew. If a tail appears on the right side, the data

is termed right or positively skew. Most process data should not typically appear skew. Data that is

seriously skew either to the left or right may be an indication that there are inconsistencies in theprocess or procedures, etc. Decisions may need to be made to determine the appropriateness of the

direction of the skew.

It should be noted, however, that some process data is, by its very nature, skew. This situationoccurs in arrival processes (for example, people arriving at a McDonalds within a fixed unit of

time) and in service processes (for example, the time it takes to wait on a customer in a bank).

Interpreting clustering problems:

Data may be clustered on opposite ends of the scale or display two or more peaks indicating

serious inconsistencies in the process or procedure or the measurement of a mixture of two or moredistinct groups or processes that behave very differently.



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EXAMPLE

The data below are the spelling test scores for 20 students on a 50 word spelling test. The scores(number correct) are: 48, 49, 50, 46, 47, 47, 35, 38, 40, 42, 45, 47, 48, 44, 43, 46, 45, 42, 43, 47.

The largest number is 50 and the smallest is 35. Thus, the range, R = 15. We will use 5 classes, so

K=5. The interval width i= R/K = 15/5=3.The we will make our lowest lower limit, the lower limit

for the first class 35. Thus the first upper limit is 35+3 or38. The second class will have a lower limit of 38 and anupper limit of 41. The completed table (with frequencies

tabulated) will look like the following:

Class Lower Limit Upper Limit Frequency

1 35 38 1

2 38 41 2

3 41 44 4

4 44 47 5

5 47 50 8

Tool #4 - Cause-and-Effect Diagram

After collecting data from a process, and then preparing a pareto or histogram diagram, it's time to

consider the reasons for the variation and those defects created. This data collected will reveal thatitems produced do not always turn out the same on a consistent basis. That is, parts produced can

vary from production line to production line, from day shift to night shift, and from day to day, and

so forth. In other words, you seldom get consistent parts produced every time. What causes thesedifferences, or variation within the process? Basically, the variation created can originate from one

or more of the following sources:

1. Raw Materials

2. Machinery, equipment or tooling3. Work method or process

4. Work force - new people, trained different, etc.

5. Measurement method, or inconsistency in ways of measurement6. Environment - high humidity, cold temperatures, dust, etc.

The real problem becomes which one of the above factors is either totally, mostly, or somewhatresponsible for the cause of our problem? Or is it a combination of several causes?

A Cause-and-Effect diagram is useful in sorting out the causes of dispersion and organizing mutual

relationships. This is an excellent team problem solving tool, where a team can gather together to

"brain storm" the potential causes and resolutions to solve the variation problem.

The Cause-and-Effect Diagram was created by Dr. Kaoru Ishikawa, an engineer and professor in

Japan. The Cause-and-Effect Diagram is also referred to as a "Fishbone" diagram, getting the namefrom its resemblance to a fish skeleton when created. The main purpose of this diagram is to define

a problem, identify a possible cause, isolate the cause, and then develop a solution. Below is an

example of a generic Cause-and-Effect Diagram.

A CAUSE AND EFFECT DIAGRAM IS USED FOR:



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1. Identifying potential causes of a problem or issue in an orderly way (example: Why has

membership in the band decreased?; why isn't the phone being answered on time?; why isthe production process suddenly producing so many defects?)

2. Summarizing major causes under four categories (e.g., People, Machines, Methods, and

Materials or Policies, Procedures, People, and Plant)

STEPS IN CONSTRUCTING A CAUSE AND EFFECT DIAGRAM:1. Write the issue (problem or process condition) onthe right side of the Cause and Effect Diagram.

2. Identify the major cause categories and write them

in the four boxes on the Cause and Effect Diagram.You may summarize causes under categories such

as:

3. Methods, Machines, Materials, People4. Places, Procedures, People, Policies,

5. -Surroundings, Suppliers, System, Skills

6. Brainstorm potential causes of the problem. As possible causes are provided, decide as a group

where to place them on the Cause and Effect Diagram. It is acceptable to list a possible causeunder more than one major cause category.

7. Review each major cause category. Circle the most likely causes on the diagram.

8. Review the causes that are circled and ask "Why is this a cause?" Asking "why" will help getto the root cause of the problem.

9. Reach an agreement on the most probable cause(s).

EXAMPLE OF COMPLETED CAUSE/EFFECT DIAGRAM:



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Tool #5 - The Scatter Diagram

The Scatter Diagram is another Quality Tool that can be used to show the relationship between"paired data", and can provide more useful information about a production process. What is meant

by "paired data"? The term "cause-and-effect" relationship between two kinds of data may also

refer to a relationship between one cause and another, or between one cause and several others. Forexample, you could consider the relationship between an ingredient and the product hardness;

between the cutting speed of a blade and the variations observed in length of parts; or therelationship between the illumination levels on the production floor and the mistakes made inquality inspection of product produced.

A SCATTER DIAGRAM IS USED FOR:

1. Validating "hunches" about a cause-and-effect relationship between types of variables(examples: I wonder if students who spend more time watching TV have higher or lower

average GPA's?; is there a relationship between the production speed of an operator and the

number of defective parts made?; is there a relationship between typing speed in WPM anderrors made?)

2. Displaying the direction of the relationship (positive, negative, etc.) (examples: Will testscores increase or decrease if the students spend more time in study hall?; will increasing

assembly line speed increase or decrease the number of defective parts made?; do fastertypists make more or fewer typing errors?)

3. Displaying the strength of the relationship (examples: How strong is the relationship

between measured IQ and grades earned in Chemistry?; how strong is the relationshipbetween assembly line speed and the number of defective parts produced?; how strong is

the relationship between typing faster and the number of typing errors made?)

STEPS IN CONSTRUCTING A SCATTER DIAGRAM:

1. Collect two pieces of data (a pair of numbers) on a student, process, or product. Create asummary table of the data.

2. Draw a diagram labeling the horizontal and vertical axes. It is common that the "cause"

variable be labeled the horizontal (X) axis and the "effect" variable be labeled the vertical

(Y) axis. The values should increase up the vertical scale and toward the right on the

horizontal scale. The scale on both the X and Y axes should be sufficient to include boththe largest and the smallest X and Y values in the table.

3. Plot the data pairs on the diagram by placing a dot at the intersections of the X and Y

coordinates for each data pair.4. Interpret the scatter diagram for direction and strength

a. Interpreting thedirection:

Data patterns may be positive, negative, or display no relationship. A positiverelationship is indicated by an ellipse of points that slopes upward demonstrating that an

increase in the cause variable also increases the effect variable. A negative relationship

is indicated by an ellipse of points that slopes downward demonstrating that an increasein the cause variable results in a decrease in the effect variable. A diagram with a

cluster of points such that it is difficult or impossible to determine whether the trend is

upward sloping or downward sloping indicates that there is no relationship between the

two variables.



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b. Interpreting the strength:

Data patterns, whether in a positive or negative direction, should also be interpreted forstrength by examining the "tightness" of the clustered points. The more the points are

clustered to look like a straight line the stronger the relationship.

EXAMPLES



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Tool #6 - The Control Chart

Processes and Process Variability

The concept of process variability forms the heart of statistical process control. For example, if a

basketball player shot free throws in practice, and the player shot 100 free throws every day, the

player would not get exactly the same number of baskets each day. Some days the player would get

84 of 100, some days 67 of 100, some days 77 of 100, and so on. All processes have this kind ofvariation or variability.

This process variation can be partitioned into two components. Natural process variation,frequently called common cause or system variation, is the naturally occurring fluctuation or

variation inherent in all processes. In the case of the basketball player, this variation would

fluctuate around the player's long-run percentage of free throws made. Special cause variation istypically caused by some problem or extraordinary occurrence in the system. In the case of the

basketball player, a hand injury might cause the player to miss a larger than usual number of free

throws on a particular day.

Statistical Process Control

Shewhart's discovery statistical process control or SPC, is a methodology for charting the processand quickly determining when a process is "out of control" (e.g., a special cause variation is

present because something unusual is occurring in the process). The process is then investigated to

determine the root cause of the "out of control" condition. When the root cause of the problem isdetermined, a strategy is identified to correct it. The investigation and subsequent correction

strategy is frequently a team process and one or more of the TQM process improvement tools are

used to identify the root cause. Hence, the emphasis on teamwork and training in process

improvement methodology.It is management's responsibility to reduce common cause or system variation as well as special

cause variation. This is done through process improvement techniques, investing in new

technology, or reengineering the

process to have fewer steps andtherefore less variation. Management

wants as little total variation in a process as possible--both common

cause and special cause variation.

Reduced variation makes the processmore predictable with process output

closer to the desired or nominal value.

The desire for absolutely minimal

variation mandates working toward the goal of reduced process variation.The process above is in apparent statistical control. Notice that all points lie within the upper

control limits (UCL) and the lower control limits (LCL). This process exhibits only common causevariation.

The process above is out of

statistical control. Notice that asingle point can be found outside the

control limits (above them). This

means that a source of special cause



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variation is present. The likelihood of this happening by chance is only about 1 in 1,000. This small

probability means that when a point is found outside the control limits that it is very likely that asource of special cause variation is present and should be isolated and dealt with. Having a point

outside the control limits is the most easily detectable out-of-control condition.

The graphic above illustrates the typical cycle in SPC. First, the process is highly variable and out

of statistical control. Second, as special causes of variation are found, the process comes into

statistical control. Finally, through process improvement, variation is reduced. This is seen fromthe narrowing of the control limits. Eliminating special cause variation keeps the process in

control; process improvement reduces the process variation and moves the control limits in towardthe centerline of the process.

Tool #7 - The Np Attribute Control Chart

The use of attribute control charts arises when items are compared with some standard and then areclassified as to whether they meet that standard or not. The Np control chart is used to determine if

the rate of nonconforming product is stable, and will detect when a deviation from stability has

occurred. There are those who argue that there should only be an Upper Control Limit (UCL), andNOT a Lower Control Limit (LCL) since rates of nonconforming product outside the LCL is

actually a good thing. However, if we treat the LCL violations as another search for an assignablecause, we could learn where lower nonconformity rates lie and perhaps eliminate them further.


7 Basic Tools for Quality Control

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