gIT FILE COPY

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Navy Personnel Research and Development Center V/

San Diego, CA 92152-6800

NPRDC TN 87-17

March 1987

Measurement of Work Processes Using Process Control: Instructor's Manual

(0Statistical

Approved for public release; distribution is unlimited.

SAPR 1 7 1987

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NPRDC TN 87-17

March 1987

Measurement of Work Processes Using Statistical Process

Control: Instructor's Manual

Archester Houston Vel Hulton Samuel B. Landau Marilyn Monda Joyce Shettel-Neuber

Approved and released by Richard C. Sorenson Director, Human Performance Department

Approved for public release; distribution is unlimited.

Best Available Copy Navy Personnel Research and Development Center San Diego, California 92152-6800 Preceding Page Blank In,

Document F"

FOREWORD This technical note contains the instructional materials for trainers of basic statistical methods used in process quality control. The training materials were developed as a classroom aid for implementing the statistical methods and techniques described by Ishikawa in his Guide to Quality Control; they were designed to teach managers and workers in Navy industrial activities these methods. Research for this work was sponsored by the Chief of Naval Operations (OP-04) with program element PE63739N. Claimant for this work was established within the Naval Supply Systems Command (PML5505). Support for courseware and workshops developed for test and evaluation was provided by the Naval Civilian Personnel Command (CEEP Code 02) with work request N0002385WR55N44.



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LOST AT SEA: INDIVIDUAL WORKSHEET

*

Name Group

_._

Instructions: You are adrift on a private yacht in the South Pacific. As a consequence of a fire of unknown origin, much of the yacht and its contents have been destroyed. The yacht is now slowly sinking. Your location is unclear because of the destruction of critical navigational equipment and because you and the crew were distracted trying to bring the fire under control. Your best esimate is that you are "approximately 1,000 miles south-southwest of the nearest land. Below is a list of 15 items that are intact and undamaged after the fire. In addition to these articles, you have a serviceable rubber life raft with oars large enough to carry yourself, the crew, and all the items listed below. The total contents of all survivors' pockets are a package of cigarettes, several books of matches, and five one-dollar bills. Your task is to rank the 15 items below in terms of their importance to your survival. Place the number I by the most important item, by the second most important place the number 2, and so on through number 15, the least important. •

_______

Sextant

____

Shaving mirror

•_-_

Five-gallon can of water

_______

Mosquito netting

4

One case of U.S. Army C rations Maps of the Pacific Ocean

.___.__

"2'?

Sea* cushion (floatation device approved by the Coast Guard),a' Two-gallon can of oil-gas mixture

___

Small transistor radio Shark repellent

"

______

Twenty square feet of opaque plastic One quart of 160-proof Puerto Rican rum

___-__

Fifteen feet of nylon rope

____,__

Two boxes of chocolate bars Fishing kit

Lab 1-3

*k'

I

LOST AT SEA: GROUP WORKSHEET Group

the This is an exercise in group decision making. Your group is to employ each Instructions. of the 1.5 survival items must be its agreed the group consensus method in reaching upon This decision. means that member the prediction by each group before for it

K

becomes a part of the group decision. Consensus is difficult to reach. Therefore, not every ranking will meet with everyone's complete approval. As a group, try to make each ranking one with which all group members can at least partially agree. Here are some guides to use in reaching consensus. I. Avoid arguing for your own individual judgments. basis of logic.

Approach the task on the

2. Avoid changing your mind if it is only to reach agreement and avoid conflict. Support only solutions with which you are able to agree at least somewhat. 3. Avoid "conflict-reducing" techniques such as majority vote, averaging, or trading in reaching your decision. 4.

View differences of opinion as a help rather than a hindrance in decision making. Sextant

_______

Shaving mirror

P

Five-gallon can of water

--

-

'

Mosquito netting

_•

One case of U.S. Arm), C rations

____

Maps of the Pacific Ocean Seat cushion (floatation device approved by the Coast Guard)

_J,

Two-gallon -an of oil-gas mixture

_____

Small transistor radio

"___

Shark repellent Twenty square feet of opaque plastic

_____

quart of 160-proof Puerto Rican rum

_One

Fifteen feet of nylon rope

i______

Two boxes of chocolate bars

-qi

_

kit

_Fishing

Lab 1-4 i v

q..I r

.

-

-

-

-

-.

% LOST AT SEA: ANSWER AND RATIONALE SHEET According to the "experts," the basic supplies needed when a person is stranded in mid-ocean are articles to attract attention and articles to aid survival until rescuers arrive. Articles for navigation are of little importance: Even if a small life raft were capable of reaching land, it would be impossible to store enough food and water to subsist during that period of time. Therefore, of primary importance are the shaving mirror and the two-gallon can of oil-gas mixture. These items could De used for signaling for air-sea rescue. Of secondary importance are items such as water and food, e.g., the case of Army C rations. A brief rationale is provided for the ranking of each item. These brief explanations obviously do not represent all of the potential uses for the specified items, but rather the primary importance of each.

ON VV

1.

Shaving mirror Critical for signaling for air-sea rescue.

,I

2.

Two-gallon can of oil-gas mixture Critical for signaling--the oil-gas mixture floats on the water and could be ignited with a dollar bill and a match (obviously, outside the raft).

3.

Five-gallon can of water "Necessary to replenish loss by perspiring, etc.

4.

One case of U.S. Army C rations Provides basic food intake.

5.

Twenty square feet of opaque plastic Utilized to collect rain water, provide shelter from the elements.

6.

Two boxes of chocolate bars A reserve food supply.

7.

Fishing kit Ranked lower than the candy bars because "one bird in the hand is worth two in the bush." There is no assurance that you will catch any fish.

8.

Fifteen feet of nylon rope May be used to lash equipment together to prevent it from falling overboard.

9.

Floating seat cushion If someone fell overboard, it could function as a life preserver.

".

-A

U

10.

Shark repellent Obvious.

1I.

One quart of 100-proof Puerto Rican rum Contains 80 percent alcohol--enough to use as a potential antiseptic for any injuries incurred; of little value otherwise; will cause dehydration if ingested. Lab 1-5

i

i

I

|p

LAB SESSION 2: FLOW CHARTS

Lab 2-0

Lab Session 2: Flow Charts

The purpose of the Sssion Two lab is to give the students practice in constructing a flow chart of a NARF process. familiar.

The process should be one with which the group is

You may select the process ahead of time or allow the students to help in its

selection.

Th'•

lab works best with two leaders--one person to moderate the discussion and

another to draw the flow chart on a blackboard as it is defined by the students.

These

roles may be filled by a trainer and a class member, respectively.

When the leaders and the process have been chosen, begin by asking the students to identify the main steps of the process.

-.. ,

Once this has been done, proceed to a more

detailed description of each step, as time permits.

After the lab, copy the flow chart

from the blackboard and make a copy for each class member.

Distribute and review

before you begin the next session.

Since much of the benefit of flow charting comes from the exchange of information that occurs during its construction, it is important that all students are given a chance to participate in both the definition and discussion of the flow chart. This exercise will also give the students a chance to practice working as a team.

Lab 2-1

¾.-•

I

IT

LAB SESSION 3: CAUSE-AND-EFFECT DIAGRAMS

.

M.0

WaW

Lab 3-0

Lab Session 3: Cause-and-Effect Diagrams

The purpose of the Session Three lab is to give the stu-,:.)ts practice in constructing aS,

cause-and-effect diagram. group is familiar.

The positive or negative effect should be one with which the

You may select the effect ahead of time, or allow the students to help

in its selection.

This lab works best with two leaders--one person to moderate the discussion and another to draw the cause-and-effect diagram on a t.lackboard as it is defined by the students. These roles may be filled by a trainer and a class member, respectively.

When the leaders and the effect have been chosen, begin by drawing the outline of a fishbone diagram on a blackboard.

Allow the students to define the labels for the effect

and main branch causes. Once this has been done, give each student a chance to suggest a possible cause under these main branches, filling them in on the diagram as the)' are suggested. After the lab, copy the diagram from the blackboard and make a copy for each class member. Distribute and review before you begin the next session.

Since much of the benefit of a cause-and-effect diagram comes from the exchange of information that occurs during its construction, it is important that all students are given a chance to participate in both the definition and discussion of the causes.

This exercise

will also give the students a chance to practice working as a team.

I.

3-1

.

LAB SESSION 4:. DATA COLLECTION

Lab 4-0

Lab Session 4: Data Collection Lab 1,

What do we need to know' a.

Determine purpose of data collection.

b.

Determine how data will be used.

..

(1) Feedback (2)

lJnderstanding

c. Types and sources of information that need to be gathered; for example, counts, frequencies, groups, individuals, other agencies, "experts" in the field. d. Ways in which information will be gathered existing data, computerized responses)? e,

(i.e., interviews, checksheets,

Format, that is, structure of the forms that will used to record information.

f. Sampling plan--a schedule of when and how often data will be recorded and reported.

2.

g.

Data collectors (e.g., supervisors, QA, artisans, trainers).

h.

Summarization and representation of data (choice of graphic tool).

Collect the DATA'

''4.

.1,

°=4.

Lab

I,

: : Emil

Lab Session 4: Practice Problem

%" \e have a plating process involving 5 employees and 10 tanks of different chemical'plating solutions. The percent of defectives has increased recently. why this is occurring.

We want to find out

What would be an appropriate data collection plan?

What are the

steps you would go through to find an answer to your problem? By what means would you collect and record your data? How would you graphically depict a summary of your data?

4.. '

if-

Lab 4-2

V

Lab Session 4: Steps to Take for Problem Solution 1. The PURPOSE would be to find the reason for the increase in the percent of defects. So we might be interested in which defects are most numerous, and which factors are "causing" the defects. 2. What will this information be used for? the extent that we can? 3.

What will we need to know? a.

Swithin

Will we change the situation or conditions to

Types of defects by frequency (i.e., rank order the types by how often they occur a specified time period). You will need to determine the length of this period.

b. Possible causes. else can we do? (1)

We have information on the employees and the tanks.

What

Construct cause-and-effect diagram.

(2) Possible factors: training levels.

materials, maintenance, equipment age, work methods,

4.

Decide what information we will gather and how it will be recorded.

5.

Develop a sampling plan for data collection.

6. Determine who will collect the data. Make sure they understand what it is they will be recording and that they have the requisite training to make these judgments. *

7. Ensure that identifying features of data collection will be obtained (i.e., dates, times, who collected the data, etc.).

An

Lab 4-3

Example of Data Collection Sheet

Frequency of Defects by Chemical Solutions Date: Section: Inspector: Total Number of Items Inspected:

*Defect

Nickel Part #

Cadmium Part #

Peeling Cracking Over-/Under-plating Fitting Fisheyes Burning Double-plating Shadowing Hardening Nodules Total *Other

Areas of Interest

1. Day of Week 2. Worker

L

S~Lab

4

4-4

Chrome Part #

Silver Part #t

Other Part I/

TOTAL Part #

X Number of Defects by Workers and Machines

Workers

Tanks

A

B

C

D

2 3 4 (a-

5 6 7

'10 49

How might the above two data collection sheets be combined?

Lab 4-5

E

N LAB SESSION 5: PARETO DIAGRAMS

L.. 5

Lab 5-0

,•

Lab Session 5: Pareto Diagrams

The purpose of the Session Five lab is to give the students practice in constructing

"Pareto diagrams. Three examples are provided. You may use these examples or develop your own. All of the numbers are taken from actual slant-threes and handwrite data.

WW

Begin the lab by working the calculations for the first problems with the help of the students. Encourage the students to compute the calculations on their own handouts while you demonstrate them on the blackboard.

Complete the example by requesting a

volunteer to graph the Pareto on the blackboard.

The other students may use the graph

_

paper provided to graph the example at their desks.

Allow the students the remainder of the lab period to compute the final two examples. While the students work, circulate throughout the room and give help with the calculations and graphing. Use the beginning of the next session to review the results of A

the final two examples.

'¼.o

"Lab 5-1

Pareto \Worksheet Variable:

Date: 12/20/84

Malfunction Code

Total Number Inspected = 191

Category

Number

Plating Improper Paperwork Improper or Lost

69

Tech Dir not Comp

37

Burred

11

Out of Tolerance

10

Percentage

Cumulative Percentage

64

Totals Notes. Some formulas helpful in computing a Pareto diagram: = number/total number inspected. 1. Percentage for a category a category for category. 2. Cumulative percentage that percentage for previous to it +

Lab 5-2

cumulative percentage for the category

1

I

Ii

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-_

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Lab

-3

--.--.

~.---.

._.-

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Pareto Worksheet Date: 12/20/S4

Variable: Resp. Shop on Handwrite Total Number Inspected = 185

Category

Number

Plating

99

Grinders & Hones

IS

Sandblasting

35

Exam & Routing

19

Cmpnts & Metal, Div

14

Totals

Percentage

Cumulative Percentage

T

Notes. Some formulas helpful in computing a Pareto diagram: 1.

Percentage for a category = number/total number inspected.

2. CumuatIve percentage for a category previous to it + percentage for that category.

LI

Lab 5-4

cumulative percentage for the category

I!5-5

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Pareto

',

orksheet

V'riable: Shop Initiating Handwrite

Date: 12/2/384 Total Number lnspected

Category

Number

Grinders & Hones

135

Percentage

Cumulative Percentage

77 7

Processing Sandblasting

2(

Rotor Heads

14

Exam & Routing

II

Totals

Notes, Some formulas helpful in computing a Pareto diagram: 1.

Percentage for a category = number/total number inspected.

2. prev~,,s Cumulative for a category t •÷ * •'"'•'*" percentaget1'"÷ category. Prev. .s to it percentage .f.......

cumulative percentage for the category

177

Lab.5,

S~Lab

5-6

7-

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.

Pareto Worksheet Variable:

Date: Total Number Inspected

Category

Number

Percentage

Cumulative Percentage

Stotals Notes, Some formulas helpful in computing a Pareto diagram: 1.

Percentage for a category = number/total number inspected.

2. Cumulative percentage for a category previous to it + percentage for that category.

cumulative percentage for the category

A.-

'17

..I,,-

Lab 5-3

LAn SESSION 6: iHISTOGRAMS

'I 'N

Lab 6-0

• -

IV Lab Session 6: Histograms

*rhe purpose of this lab is to give the students practice in constructing histograms. Two examples are provided.

You may use these or develop your own. All of the data are

fictitious,.

Example I.

The data involve tank solution concentrations (ounces of deruster per gallon of

_

water). These data represent possible concentrations measured at various intervals.

_

Example 2.

The data involve the amount of time it took to input a Handwrite, after ii was initiated, into a computer.

The value of "I" (one) day represents a Handwrite that was

entered into the computer on the same day it was initiated.

Each of these examples contains worksheets designed to organize the data and provide the necessary formulas.

Use the information on how to construct a histogram

from Session Six to guide the class through the worksheets in the first example.

A

"description of the three worksheets and some critical steps follow. Histogram Worksheet One.

This worksheet contains the raw data for the example. form to fill in the blanks at the bottom of Worksheet One.

Use the information on this XL is the largest value

identified. XS is the smallest value in the data set. Compute the range. Lab 6-1

EXAMPLE I HISTOGRAM WORKSHEET ONE Variable: Ounces of deruster per gallon of water.

Data Values 41.85

44.45

51.30

50.85

48.60

50.85

41.85

46.35

46.94

41.85

51.30

43.65

40.95

35.51

45.00

33.30

44.61

49.50

40.33

46.80

42.75

44.12

48.60

47.70

31.50

Specification

44.10

34.45

44. i 0

47.25

47.70

43.36

53.55

47.70

57.60 38.25

Lim its: 40-48 oz./gal.

53.10

54.45

48.60

40.51

39.95

39.15

47.25

39.15

40.50

57.60

34.20

50.40

47.70

37.35

53.23

Total number of data values observed (N) XL= XS xs Range

XL

-X

CA.

-0A

Lab 6-3

EXAMPLE I ,H

HISTOGRAM WORKSHEET TWO Formulas and Tables Helpful in Computing a Histogram

Number of Data Values Observed

Number of Intervals (K)

Under 50

5-7

50-100

6-10

101-250

7-12

Over 2.50

13-20

SRange

= X large - Xsmall

To computer the class interval: H

range/K-I

N

Range

Number of intervals (K)

Interval size (H) =4

=

IL

Lab 6-4

EXAMPLE I HISTOGRAM WORKSHEET THREE Class Boundaries and Frequencies

Class Number

Class Boundaries Upper Lower

f

Frequency Tally

Frequency

It

S_

____I

Total

S__

___

Range

=

Class interval size (K) =

--- .11111i

-

-

6 -.S~Lab 5

- -

II :

11

I

,

i

-

II ab_6-6

4

EXAMPLE 2 HISTOGRAM WORKSHEET ONE Variable: Lag Time from Initiation on Hardwrite to Entry on the Computer (Days)

Data Values 1

3

2

2

2

1

2

1

1

2

4

2

5

1

7

2

1

3

1

20

1

7

2

1

9

Specification

2

4

2

1

6

Limits:

2

4

1

3

4

2

3

3

4

3

5

1

6

5

2

5

2

8

10

2

Total number of data values observed (N) XL L SXS

Range = XL -XS

I

Same day lag time is considered to be "1." Next day lag time is considered to be "2."

La4-

Lab 6-7

EXAMPLE 2 HISTOGRAM WORKSHEET TWO Formulas and Tables Helpful in Computing a Histogram

Number of Data Values Observed

Number of Intervals (K)

Under 50 50-100 101-250

5-7 6-10 7-12

Over 250

13-20

Range = Xlarge - Xsmall To computer the class interval: H = range/K-: N =

Range =

Number of intervals (K)

Interval size (H)

41

Lab 6-8 !MUM

EXAMPLE 2 HISTOGRAM WORKSHEET THREE Class Boundaries and Frequencies Class Boundaries Class Number

Lower

Upper

Frequency Tally

Total

N= Range= Class interval size (H)

Lab 6-9

Frequency

TI I

-

_

H

7

.-

_

7

I

_

F

T

--

t

IL

4-i I-----l .Lab 6"1

Lab 6-10

•

LAB SESSION 7: SCATTER DIAGRAMS

Lab 7-0

I

Lab Session 7: Scatter Diagrams

The purpose of this lab is to give the students practice in constructing scatter diagrams. Two examples are provided.

You may use these or develop your own. All of

the data are fictitious.

Example 1.

The data for this example give the speed of a conveyor (X variable) and the length of threads (Y variable) as they move along the conveyor.

The data pairs in the table were

collected over a 1-week period.

Example 2.

The data for this example give the number of months of training a worker received (X variable) and the number of errors the worker made on a training-related task (Y

I

variab!e). The data pairs in the table were collected over a 1-month period.

Begin the lab by helping the class to graph the first example.

After the pairs of

points have been plotted (be sure axes are properly labeled), draw in an approximate "line of best fit."

Allow the students the remainder of the lab period to complete the final

example.

While the students work, circulate throughout the room and give help with the graphing. Use the beginning of the next session to review the results of the examples.

At

this time, have the students identify the type of correlation indicated by the "line of best fitI." Lab 7-1

.

SCATTER DIAGRAM WORKSHEET EXAMPLE I X Variable (Horizontal Axis) = Conveyor Speed (crn/sec) Y Variable (Vertical Axis) = Severed Length of Threads (mm)

X Variable

X Variable

Y Variable

Y Variable

8.1

1046

8.0

1040

7.7

1030

5.5

1013

7.4

1039

6.9

1025

5.8

1027

7.0

1020

7.6

1028

7.5

1022

6.8

1025

6.7

1020

7.9

1035

8.1

1035

6.3

1015

9.0

1052

7.0

1038

7.1

1021

8.0

1036

7.6

1024

8.0 8.0

1026 1041

8.5 8.0

1029 1031

7.2

1029

7.5

1005

6.0

1010

5.5

1023

6.3

1020

8.0

1030

6.7

1024

5.2

1010

8.2

1034

6.5

1025

8.1

1036

8.0

1030

6.6

1023

5.2

1010

6.5

1011

6.5

1025

IN

Lab 7-2

£4

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SCATTER DIAGRAM WORKSHEET EXAMPLE 2 X Variable (Horizontal Axis) = Number of Months of Training Y Variable (Vertical Axis) = Number of Errors Made in Previous Month Y

Variab I•

SX

Variable

Y

Variable

Variable

S3

5

3

3•

7

2

4

7

3

11

3

4

2

12

2

4

6

4

3

3

6

1

7

8

3

3

6

2 9

S 1

10 74

1

3

7

5

3

12

2

4

2

5

5

4 3

"3 9

8

S

2

1

5

3

6

2

2

9

5

3

3

6

46

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X

Fictional Data

Lab 7-14

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ab 7-5'-I

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SCATTER DIAGRAM WORKISHEET X Variable (Horizontal Axis) Y Variable (Vertical Axis) = X

Y

X

Y

Variable

Variable

Variable

Variable

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LAB SESSION 8: RUN CHARTS

4.,

Lab 8-0

Lab Session 8: Run Charts

The purpose of this lab is to give the students practice in constructing run Five examples, each based on a different measure, are provided.

charts.

You may use these

examples or develop your own. All of the data are fictitious.

"

In order to construct the run charts, you must calculate:

1.

The frequency (freq.) or the total of each day's production. Example: For day 4, the frequency is found by adding up the products made by each

of the five workers, or 28

2.

+

23

+

28

+

23

4

26 = 128.

The mean (x) or average daily production of the workers. Example: For day 4, the mean equals the frequency of production (128) divided by

the number of people working (5), or 128/5 = 25.6.

This indicates that the workers

averaged 25.6 products on day 4.

3.

The range (R) or difference between the highest versus the lowest performance for

each day. Example:

LL

For day 4, the range is equal to the highest production (28) minus the

lowest production (23), or 28 - 23 = 5.

4.

The number of defective products (NP) for each day. Example:

For day 4, the number of defective products is found by adding up the

number of defective products made by each worker, or 5 + 3 + 0 + 3 + 5 =16.

Lab 8-1

Defects found in gizmos produced in shop XX

for the period of March 4-29, 1986

OPERATORS

DATE

C

D

E

4 5 6 7 8

5 2 2 5 1

3 3 3 3 2

0 1 0 3 2

3 5 1 2 2

5 5 2 2 3

9

X

X

X

X

X

X

X 2

X 04

X

X .4

12 13 14

3 1 4

2 4 4

2 1 4

4 4 2

4 4 2

15

4

2

3

3

4

16

X

X

X

X

X

17 18

X 1

X 0

X 1

X 0

X 1

19 20 21 22

0 4 3 3

1 3 2 4

3 3 4 0

2 1 1 5

1 1 0 2

23

X

X

X

X

X

24

X

X

X

4

X

25

1 2

4 1

1 1

4 4

3 3

27 28 29

2 4 0

1 3 4

4 4 1

2

10 Ii1

•26

B

L

8

Lab 8-3

0 1

1

3

U Production in gizmo shop XX for the period of March 4-29, 1986

OPERATORS

DATE

A

B

C

D

E

4 5 6

28 29 35

23 26 25

28 29 26

23 21 23

26 38 21

7 8

32 38

26 25

35 32

19 17

38 35

9

Xx

XX

XX

XX

XX

10

xx

xx

xx

xx

xx

-•11

33

20

22 118

i •12

27

23

29

13

22

28

26

28

25

15I

37

20

16

xx xx

18 19

S14

%,17

,

30

15

32

21

20

35

xx xx

xx )LX

xx xx

xx KXx

22

20

24

16

39

24

26

24

24

42

20 21 22

32 38 35

22 29 23

20 25 27

20 18 20

40 40 41

2.3

XX

XX

XX

lu'

xx

XX

x

-2

29

32 "26

23

S.,2s

x

xx

31

xx

XX

26

21

16

47

27

29 33

24 29

30 29

18 18

46 49

28 29

37 39

24 27

27 30

24 14

46 51

Lab 8-4

•f

21

24

Freq.

X

R

DATE 4 5 6 7 8 11 12 13 14 S15 18 19 20 21

_

_

22 25 26 27 28

.8

Lab 8-5

NP

% Defective

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Lab 9-0

S~

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,% Lab Session 9: Control Charts

This lab presents an overview of control charts and does not emphasize computation. It does not include a lab to give students practice in constructing control charts. You may wish to use this time to discc,.ss the flow chart that follows.

The flow chart describes the

process within the Act stage of the PDCA cycle. Until now, we have not mentioned the activities that occur in the Act phase.

Be sure to emphasize the importance of the

implementation and testing of possib!e solutions developed by the project team. The flow

"chartincludes a decision point at which time the team must decide if a solution needs to be passed up to a higher level quality board for action. be used

as an

The discussion of this chart may

introduction to the final session that deals with

the structure

and

relationships of quality teams.

Following the flow chart are two sets of instructions for the calculation of control charts.

Distribute these to your students for future reference. "7,

X

-,q

,

Lab 9- 1

Perform P~an

Do

Check

Work

Develop set of possible sclutIonS

k

Select a solution

for trial

Higner level group informs project team of reasons for 7ejection No

Can solution be al te ast •e

no Iby proaect

No

Is o

h

f featb

level group Higher ev

Submt solution to

m

v

Yes

,

amodifies measure its effei t

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support

olution

level group___

an] S toResults

_ _

solution as neded and provides ne

i -0

Ys

ig e •

of trial and reasons tor failure

cghc- group level

are collected

coit-nues

Figure 9-1. Flow chart d'splaying the Act stage of the PDCA cycle. Lab 9-2

.4

Definitions and Symbols for x n

=

and R Charts

An individual reading for observation. The number of observations in a group, often known as the sample size. The sample size may be 2, 3, 4, 5, or more, but no greater than 10.

x (x bar)

The average of a group of x's. The x is calculated by the formula: xl + x2 + x3

. . .

xn

or

sum of sample subgroup

n

sample size

x (x double bar) = The average of a series of x values. The x is calculated by the formula: k = Number of groups in a sample:

x

= xl + x2 + x3 .. .xn k

or

sum of sample means number of sample groups

R (range) = The difference between the largest and the smallest reading in the sample. R = Largest x - smallest x.

S(R bar)

=

The average of a series of R values.

Z =

RI

4

R2 + R3 ...

rn

sum or ranges number of sample groups

k

A= A factor used in calculating the control limits for the x chatt. D4 = A factor used in caiculatinq, the upper control limit for the R chart. D = A factor used in calculating the lower control limit for the R chart. The factors A2 , D4 , ar-d D3 vary with) the size of the sample. provided in the material on computing x and R charts.

Lab 9-3

A table of factors is

Calculation of x and R Charts As stated earlier, the x and R charts are used together to monitor process performance. The following material gives a general outline of how the x and R charts are calculated. For detailed information, refer to the Ishikawa textbook (1985), pages 6670. Here are the steps for calculating x and R charts. 1. Collect measurements.

The

the data.

sample

total

size should

consist

of

100 or more

Arrange the data into subgroups of two to five measures each. Sample su_)groups 2. are usually arranged according to such factors as time, date, lot, operator, equipment, etc. The number of measures in a subgroup determines the size of the subgroups. Size is represented by the letter n. The number of subgroups in a sample is represented by the letter k. 3. Record the data on a data sheet. The data sheet should be designed so that it is easy to cOmpute values of x and R for each subgroup. 4. Compute the means or x for each subgroup. Add the measures within a subgroup and divide the sum by the subgroup size (n). 5. Compute the range or R for each subgroup. The range for a subgroup is found by subtracting the lowest measurement from the highest measurement. 6. Compute the overall means or x. Add the means of all the subgroups and divide the sum by the number of subgroups (k). 7.

Compute the average value of the range (R) by adding the ranges of all the .

subgroups and dividing the sum by the number of subgroups (.).

"8. Compute the control limit lines. The factors A 2 , D , and D, refer to values that change depending on subgroup size. Those values are presented in the following table: Table of Control Chart Coefficients g.

n __ _

__ _

__

_

_

_

_

_

_

A2

2_

D _

_

_

1D 3

4

2

1.880

3.267

3

1.023

2.575

I

4

0.729

2.282

1 > Do not apply

5

6

0.577 0.483

2.115 2.004

7

0.419

1.924

I -

--

0.076

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N LAB SESSION !0: QIJAL!TY TEAMSNI

A.

7p7 Lab10,

HWSO PROJECT TEAM Date:

, Member

Code

Present

Rick Jar, es* Tom '1tilliarns hris Thompson -Robin Stevens Jirn Palmer "Ed Daley Terry Mason Tony Gerra

56140 56340 57230 62110 62120 63310 97311 97211

X X X X X

bSUMMARY

Substitute

____ Code

Jack Larsen

63310

___

X X

-

AND ACTION ITEMS

Reviewed report of study regarding HWSOs conducted by Code 232 (3 August 1983).

"Listed causes for writing a HWSO in E & R:

-9 ,_-

Jobs without documentation Repair without (nonroutine) No link jobs on Wip (manydecks reasons)

.

Lost paperwork Additional work (3Rs) Paperwork out of sync Customner service jobs (routine and nonroutine) Subgroup from feeder shop

KI IZ

Peter Landers reported results of recent study conducted on HWSOs. Reasons for H\VSOs are not clearly listed on form. ACTION:

T. Williams, R. Stevens, and C. Thompson will organize a log to be completed in E & R when HIWSO is written. number, program, and reason for writing.

Log will include link

The possibility of producing a new MDR deck when one was unavailable was discussed. ACTION:

R. James will look into the feasibility of issuing new decks.

*These names are fictitious.

Lab 10-1

PROGRESS REPORT Team:

Plating Team

Date:

iuqly 31, 19S5

Ma

Anode Cleaning in Chrome Tanks

Subject: Objective:

Maintain clean utility anodes for the purpose of obtaining better current. Action Codes: *Allen Jones--34200 Tony Benson--61300 James Peterson--97324

*

New utility anodes are difficult to pull and clean (4' long, lead).

*

Anodes are cleaned infrequently (about every 6 months).

4

Heavy deposits of lead salts build on anodes after extended use without cleaning- -leads to bad current and contributes to bad plating. Oxides build up in tanks. Anodes shrink.

0

Anodes require sandblast cleaning. About 50 percent of anodes are no good and must be thrown away. Lead goes into sand and sand must be changed. When anodes are cleaned, tanks are down for days or weeks.

*

Cleaning standards are vague.

Plan of t~ction: * * 1

Contact other plating facilities. Test anode cleaner/steam clean method. Clean anodes put in all chrome tanks. SPull and clean anodes every 6 weeks. Person Contacted: Fred Jones, McClellan AFB* Mr. McMichael, NARF Norfolk

"•The names listed in this example are fictitious.

Lab 10-2

'"j

Information Obtained: Twenty-six anodes were cleaned using new method. *

All cleaned up.

Other facilities have some problems.

Action Taken0

0

*

For present time, pull anodes every 6 weeks and clean with two-stage process. Bulletin board will be made for shop to show anode change schedule. Keep spare anodes to replace others being cleaned. To make cleaning process easier for shop personnel, move tank PS-7 behind chrome tanks and use for cleaning solution. Move tank P8-8 next to it and equip it with air-water power spray. (P8-7 and P8-8 are currently scheduled for removal.)

r"

Recommendation: *

For the future, look into what is the optimal time period for cleaning utility anodes. Six weeks is an arbitrary time period.

Potential Problems: At this time, moving the tanks in the shop is Priority 3 and may take a couple of years to accomplish. •-

Lab 10-3

$

*.•.

•' 7,:-F

iI V4

!I

144 '9S

t"4

U.-0

5X "GLOSSARY Cause-and-effect diagram: A diagram that shows the different factors or causes of a certain effect and how they interrelate. Central tendency: The typical or representative score in a group of scores. central tendency include the mean, median, and mode. Continuous data: temperature, Control chart:

Measures of

Data that are measured; for example, length, width, time, and

.

.

ALM

A graph that compares samples of process performance to a statistically

predicted range of performance. Control limits: conditions.

Statistical estimates of how a process should perform under stable

Correlation: A measure of the degree of relationship between two variables. Data; Information collected in a systematic and objective manner to answer a specific question or set of questions. Discrete data:

N•i

Data that are based on counting (e~g., number of defects, types of defects,;•._,'-

sex of workers, number of injuries per shot). Dispersion: How data are positioned around their central value. dispersion are range and spread.

Some measures of

Distribution: The arrangement of a set of data according tc frequency, time, or location. A distribution can visually summarize qualities of the data such as dispersion, peakedness, "and skewness. Flow chart: A diagram that depicts the steps in a process and how they interrelate. Frequency: The number of times an event is observed.

""41

Histogram: A vertical bar graph that depicts the distribution of a set of continuous data.

I ~that

Normal curve: The shape of , distribution that resembles a bell. is normal is symmetrical in appearance. Normal variation: or operation. PDCA Cycle;

-• .'

A distribution curve

Random differences that are a natural part of any system, procedure,

The Plan-Do-Check-Act cycle of continuous improvement developed by

Shewhart and Deming. Q1.

Pareto diagrams: A vertical bar graph that displays categories in decreasing order of frequency from left to right,

G-1

• • I

Pattern interpretation: Using the placement of data points on a control chart to determine if a system is in or out of control. Peakedness: A measure of the height of a distribution that describes its flatness or curve. Population: All possible units with defined characteristics.

.'-

Process: The operations and resources contained within a particular stage of a system.

"

Process in control: The state arising when the production process is stable and there is no abnormality in the points on the control chart. Process out of control: The state arising when the production process is not stable and there are points outside the control limits on the control chart.

•

\.

Quality: A characteristic or the value of a product. It is a factor of the appropriateness of the design and specifications and the adherence to these specifications in production. Random sampling: Sampling from a population such that each unit has an equal chance of being selected for the sample. Run chart: A run chart is a line graph that shows data plotted over time. Sample: A subset of units taken from a population or a group of measures taken from the Samples are used to estimate overall overall output of a process or a system. performance and quality of an output.

"-

Sample subgroup: Samples divided into small units in order to show the effect of a particular factor or set of factors. Sampling technique: stratified.

The way a sample is selected from a population, i.e., ranuom or

Scatter diagram: A diagram that shows the relationships between two variables that have been measured in pairs.

€.

Skewness: A bunching of data on one side of distribution.

1•

Special variation: Nonrandom variation that indicates specific problems in a system, procedure, or operation. Spread: The width of a histogram. product or service.

The more narrow

"

I

1.

the spread, the more uniform the . 1

ii

G-2

.A.'

•IM

Stratified sampling:

Sampling from different parts of a population in the same proportion

that they occur in that population.

;'..

Structured problem solving: A group approach to solving problems. I1 involves identifying problems in a systematic way, suggesting solutions to them, and determining the effect of implementing them. System: A set of procedures and resources that work together to produce a given service or prod ict. Variable: Sometimes called a measure, it is a general name for anything being measured. Differences in units or the measure of units due to errors in materials, Variation: machines, manpower, or methods. The more variation, the less uniform the unit.

V.

'.". "I'.

or.

4

G-

BIBLIOGRAPHY

7,17

B-0

BIBLIOGRAPHY T. (1984). Weirton Steel bets on quality. SAdams, Qua!ity, 23(4), 23-25. American Supplier Institute, Inc. (Producers). minutes). Romulus, MI: Author. Andrews, M. (1985). Statistical Production, 95(4), 56-59.

Continuous improvement (videotape, 15

process control:

Mandatory

management

tool. n

The ASQC software directory. (1985). Quality Progress, 18(3), 28-63. AT&T Technologies. Author. Aubrey. C. A.,

(1956).

Statistical quality control handbook.

II, & Eldridge, L. A. (1981).

Banking on high quality.

Charlotte, NC: Quality Progress,

The Derning philosophy of continuing Baker, E. M., & Artinian, H. L. (1985). improvement in a service operation: The case of Windsor Export Supply. Quality Progress, 18(6), 61-69. Barbour, W. B. (1984).

Step-by-step SPC. Quality, 23.(8), 14-15.

Bean, E. (April 10, 1985). Cause of quality-control problems might be managers - not workers. Wall Street Journal, p. 31. Beels, G. J. (1985). Strategy for survival. Quality, 2(), Berry, B. H. (198!). 22. (29), 59, 61, 63.

16-18, 20, 22.

Preventing defects through statistical quality control.

Iron Age,

Box, G. E. P., & Draper, N. R. (1969). Evolutionary operation. New York: John Wiley. Box, G. E. P., Hunter, W. G., & Hunter, 3. S. York: John Wiley. tv

,17.

(1978).

Statistics for experimenters.

New

Brittanica Films (Producers). Lake Orlon, MI: Author.

Management's five deadly diseases (videotape, 16 minutes).

Brittanica Films (Producers). MI: Author.

Roadmapfor change (videotape, 30 minutes). Lake Orion,

Bryson, M. C. (1984). Sample size determination - revised and simplified. P g 1's,( 12), 40-41.

,uaL.i

Burr, 1. W. (1984). Management needs to know statistics. Quality Progress, 17(7), 26-30.

"

~B-I ,.°E

.°4

Callahan, J. M. 161(l), t45-47..''

(1981).

The Deming era arrives in Detroit.

Automotive Industries,

Callahan, J. M. (1981). The Deming era: Industries, 161(12), 73-74.

A new US industrial revolution?

Callahan, J. M. (1981). Insight analysis: Automotive Industries, 161(11), 13.

Detroit.

Callahan, J. M. 162(1), 32.

(1982).

Cambridge Corporation. Tokyo: Author.

New religion impacts auto industry.

Deming involves suppliers, engineers.

(1983).

Automotive

,

-

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