part 3: pareto analysis and check sheets - inpeperondi/19.10.2009/exemplo_ferramentas... ·...

Scott Leavengood, Extension wood products,Washington County; and James E. Reeb,Extension forest products manufacturingspecialist; Oregon State University.

Part 3: Pareto Analysis and Check Sheets S. Leavengood and J. Reeb

EM 8771 • January 2002$2.50PERFORMANCE EXCELLENCE

IN THE WOOD PRODUCTS INDUSTRY

Part 1 in this series introduced the reader to Statistical Process Control,and Part 2 provided an overview of how and why SPC works.

Part 3 begins the step-by-step process of building the practical skillsnecessary for hands-on implementation of SPC. This report discusses Paretoanalysis, a tool we can use to help decide how and where to begin usingSPC. We also discuss check sheets, which are data collection tools that maybe used in Pareto analysis.

Part 4 discusses flowcharts. Future publications in the series will discusscase histories of wood products firms using SPC, providing real-worldevidence of the benefits of SPC and examining pitfalls and successfulapproaches.

Where to begin an SPC program?Most manufacturing processes are sufficiently complex that at first glance

it may seem impossible to decide where to begin using SPC techniques. SPCprograms that attempt to monitor too many process variables are quicklyoverwhelmed by the time and labor required to collect, analyze, plot, andinterpret the data. In such cases, SPC seems too time consuming and expen-sive to be of any benefit.

The life expectancy of SPC in a company depends heavily on the resultsof the first few projects undertaken. With this kind of pressure, how do youdecide where to begin?

Obviously, we cannot measure everything. We must focus initially on themost important quality problems to get the “biggest bang for the buck.” Thisis especially true in the early stages of an SPC program when personnel arelikely to be skeptical of SPC and hesitant to make the necessary changes.

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STATISTICAL PROCESS CONTROL

Prioritizing quality problems for the company is a good firststep. Then, determine which projects will have the highest returnon investment and therefore should be the initial focus of qualityimprovement programs. Pareto analysis enables us to do all this.

Pareto analysisPareto1 (pronounced “pah-RAY-toe”) analysis uses the Pareto

principle, also called the 80:20 rule, to analyze and display data.Quality expert J.M. Juran applied the principle to quality controland found that 80 percent of problems stem from 20 percent of thepossible causes. The numbers 80 and 20 are not meant to be abso-lutes. The main point, as Juran stated, is that we should focus onthe “vital few” problems (those in the 20-percent category) ratherthan on the “trivial many” to make the most significant improve-ments in product quality.

Pareto charts are the graphical tool used in Pareto analysis. APareto chart is a bar chart that displays the relative importance ofproblems in a format that is very easy to interpret. The most impor-tant problem (for example, the one highest in cost, frequency, orsome other measurement) is represented by the tallest bar, the nextmost important problem is represented by the next tallest bar, andso on. A check sheet is a useful tool for collecting data for Paretocharts.

Check sheetsCheck sheets are relatively simple forms used to collect data.

They include a list of nonconformities2 and a tally of nonconformi-ties. Check sheets should also include the name of the project forwhich data is being collected, the shift when the items were pro-duced, the names of persons collecting the data, dates of datacollection and of production (if known), and the location of datacollection (e.g., in house or at a customer’s).

1 Vilfredo Pareto was a 19th-century Italian economist who studied thedistribution of income in Italy. He found that about 20 percent of the populationcontrolled about 80 percent of the wealth.2 A nonconforming product is one that fails to meet one or more specifications,and a nonconformity is a specific type of failure. A nonconforming productmay be termed defective if it contains one or more defects that render it unfit orunsafe for use. Confusion of these terms has resulted in misunderstandings inproduct liability lawsuits. As a result, many companies have adjusted theirinternal terminology and now use the terms “nonconforming” and“nonconformity” in favor of “defect” and “defective.”

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PARETO ANALYSIS AND CHECK SHEETS

Check sheets aren’t mandatory to construct Pareto charts. How-ever, because check sheets require you to standardize your list anddefinitions of nonconformities, they provide several benefits.

First, people often do not agree on the major categories ofnonconformities. Therefore, developing a list of commonnonconformities (i.e., quality problems) is not as easy as it sounds.A good way to develop this list is to brainstorm with productionpersonnel, management, QC personnel, and, most important, yourcustomers.

Second, people often do not agree on precisely what constitutes“nonconforming.” In other words, how bad does it have to be toget thrown in the scrap or rework pile?

Last, different people often will put a given item in differentcategories. For example, one person may call an item with torngrain a machining defect, another might call it fuzzy grain, andanother may call it reaction wood. Without standard terminologyand definitions, it becomes very difficult to conduct a Paretoanalysis.

To get an idea of the effect on your company of lack of standard-ized terminology and definitions for nonconformities, try a simpleexperiment. Select several items at random and ask differentpeople to examine them and record nonconformities item by item.One experiment at a secondary wood products manufacturerinvolved five quality inspectors. The inspectors did not agree onthe number of items that should be rejected due to quality prob-lems (the scrap/rework rate varied from 34 to 49 percent) nor didthey agree on the reasons for rejecting the products. Had we lookedonly at data collected by inspectors 1, 2, and 3, we would haveconcluded that torn grain and blue stain were the biggest qualityproblems. Had we looked only at data collected by inspectors4 and 5, we would have concluded that dents (handling damage)and reaction wood were the biggest quality problems. Do notunderestimate the importance of developing a standard list ofnonconformities and precise definitions for each.

The following demonstrates how to construct and interpretcheck sheets and Pareto charts.

ExampleThe Quality Improvement Team at a manufacturer of wood compo-nents visited a customer and examined items in the scrap andrework bins. After looking at each item and talking with the cus-tomer, the team agreed on categories of nonconformities anddeveloped precise definitions for each category. They created a

Do notunderestimate...

the importance ofdeveloping a standardlist of nonconformitiesand precise definitionsfor each.

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check sheet, then inspected each item and tallied the number ofoccurrences (frequency) for each cause of nonconformity. Figure 1presents the results.

Nonconformities were sorted from highest to lowest frequency,and the relative frequency for each was determined (Figure 2).For example, “size out-of-specification” was 194 out of 473 non-conformities, and so the relative frequency for size-out-of specifi-cation was:

194/473 = 0.41 = 41%

An optional final step is to calculate cumulative relative fre-quency. Cumulative relative frequency helps the user to readily seethe combined effect of the “vital few” problems. For example, youcould see that the top three quality problems were responsible fornearly 80 percent of the problems overall. To calculate cumulativerelative frequency, add the relative frequency for each category ofnonconformity to the sum of all preceding relative frequencies. Forexample, there were 194 occurrences of size out-of-specification or41 percent (relative frequency) of the total. There were 105 occur-rences of fuzzy grain. Fuzzy grain was therefore responsible for22 percent of the total. Size out-of-specification and fuzzy graincombined (cumulative relative frequency) were responsible for63 percent of the total. Size out-of-specification, fuzzy grain, andmachine tear-out combined were responsible for 76 percent of the

Figure 1.—A sample check sheet.

Size out of specification

Loose knots Raised grain Dents Stain/rot Fuzzy grain Splits Machine tear-out Burn marks Oil/grease marks

Total

Project Quality Improvement Project Name QIT Shift All Location Customer A Dates January 2002

Rel. Cum Reason Freq. Freq. (%) Freq

194

1843

31105

116144

2473

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Size out of specification

Fuzzy grain Machine tear-out Burn marks Stain/rot Loose knots Splits Raised grain Dents Oil/grease marks

Total

total. The cumulative relative frequency for the least frequentcategory (oil/ grease marks, in this example) should be 100 per-cent, however it is slightly less due to rounding. Figure 2 shows thecheck sheet with the nonconformities arranged in descending orderof frequency and with relative frequency and cumulative relativefrequency calculated.

m. Rel.q. (%)

Project Quality Improvement Project Name QIT Shift All Location Customer A Dates January 2002

Rel. Cum. Rel. Reason Freq. Freq. (%) Freq. (%)

194 41 41

105 22 6361 13 7644 9 8531 7 9218 4 9611 2 984 0.8 98.83 0.6 99.42 0.4 99.8

473 99.8

Figure 2.—A sample check sheet showing nonconformities in descending order as well as relative frequency andcumulative relative frequency.

Figure 3 (page 6) is the Pareto chart for the data in Figure 2. Theleft vertical axis indicates the number (frequency) of each type ofnonconformity. Always plot nonconformities in descending orderof frequency, with the most frequent at the left vertical axis. Theright axis indicates cumulative frequency.

The Pareto chart makes it easy to see that size out-of-specifica-tion, fuzzy grain, and machine tear-out are the major nonconformi-ties. Quality improvement that focuses on these items will give the“biggest bang for the buck.”

Frequency, however, is not the only important consideration.Certain types of nonconformities, even if infrequent, may be verycostly to scrap or rework. Therefore, the Pareto analysis shouldtake into account both cost and frequency.

Though scrap and rework often involve very different costs, it’spossible to calculate an average scrap and rework cost based on the

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percentage of product in each category of nonconformity. Forexample, let’s say we estimate that 10 percent of material with sizeout-of-specification must be scrapped, but the remaining 90 per-cent can be reworked to produce a usable product. Further, let’s saythat scrapping the product represents a loss of approximately $20per item, and reworking costs approximately $11 per item. There-fore, our estimate of the average scrap and rework cost for sizeout-of-specification is:

(scrap cost) x (% scrap) + (rework cost) x (% rework)= scrap & rework cost

($20) x (10%) + ($11) x (90%) = $12

To account for frequency as well as scrap and rework costs,multiply relative frequency by cost to obtain relative cost. Forexample, we already determined that approximately 41 percent ofnonconformities were size out-of-specification. Therefore, therelative cost due to size out-of-specification is:

0.41 x $12 = $4.92

The Pareto analysis...

should take intoaccount both costand frequency.

Fuz

zy g

rain

Siz

e ou

t-of

-spe

c

Bur

n m

arks

Sta

in/r

ot

Loo

se k

nots

Spl

its

Rai

sed

grai

n

Den

ts

Oil

/gre

ase

mar

ks

Fre

quen

cy

Cum

ulat

ive

freq

uenc

y (%

)

200

160

120

80

40

0

100

80

60

40

20

0

Cause of nonconformity

Pareto chart

Figure 3.—Pareto chart for the data in Figure 2.

Mac

hine

tear

-out

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Table 1 shows the relative costs, and Figure 4 shows the corre-sponding Pareto chart.

We can see that size out-of-specification is the primary noncon-formity from the standpoint of frequency (Figure 3) as well asrelative cost to scrap or rework (Figure 4). Therefore, to get the

Rel. Cost Rel. Freq. Cum. Rel.Nonconformity ($) (%) Freq. (%)

Size out-of-spec. 4.92 38 38

Machine tear-out 2.34 18 56

Fuzzy grain 1.76 13 69

Stain/rot 1.75 13 82

Loose knots 1.00 8 90

Burn marks 0.72 6 96

Splits 0.32 2 98

Dents 0.09 0.7 98.7

Raised grain 0.06 0.5 99.2

Oil/grease marks 0.03 0.2 99.4

Total 12.99 99.4

Table 1.—Nonconformities and relative costs.

Fuz

zy g

rain

Siz

e ou

t-of

-spe

c

Mac

hine

tear

-out

Bur

n m

arks

Sta

in/r

ot

Loo

se k

nots

Spl

its

Rai

sed

grai

n

Den

ts

Oil

/gre

ase

mar

ksRel

ativ

e co

st

Cum

ulat

ive

freq

uenc

y (%

)

5

4

3

2

1

0

100

80

60

40

20

0

Cause of nonconformity

Pareto chart

Figure 4.—Pareto chart for the data in Table 1.

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“biggest bang for the buck,” it would be wise to begin the SPCprogram by focusing on problems that lead to size out-of-specification.

ConclusionsWe now know the primary nonconformities and therefore where

to focus initial efforts of an SPC program. We do not yet know,however, the specific processing steps that lead to a given noncon-formity—that is, where and how the problem arises—and thereforewe do not yet know where or what to monitor.

To help us discover the specific steps in the process that lead toa given nonconformity, it is helpful to develop a flowchart for theprocess. Flowcharts are the subject of the next report in this series.

For further informationBrassard, M. and D. Ritter. 1994. The Memory Jogger II: A Pocket

Guide of Tools for Continuous Improvement & Effective Plan-ning (Methuen, MA: Goal/QPC). 164 pp. http://www.goalqpc.com/

Grant, E.L. and R.S. Leavenworth. 1988. Statistical Quality Con-trol, 6th ed. (New York: McGraw Hill). 714 pp.

Ishikawa, K. 1982. Guide to Quality Control (Tokyo, Japan: AsianProductivity Organization). 225 pp.

Montgomery, D.C. 1996. Introduction to Statistical Quality Con-trol, 3rd ed. (New York: John Wiley & Sons). 677 pp.

Walton, M. 1986. The Deming Management Method (New York:Putnam Publishing Group). 262 pp.

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This publication is part of a series, PerformanceExcellence in the Wood Products Industry. The variouspublications address topics under the headings of woodtechnology, marketing and business management,production management, quality and process control,and operations research.

For a complete list of titles in print, contact OSUExtension & Station Communications (address below) orvisit the OSU Wood Products Extension Web site athttp://wood.orst.edu

Ordering informationTo order additional copies of this publication, send the complete

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© 2002 Oregon State University

This publication was produced and distributed in furtherance of the Acts of Congress of May 8 and June 30, 1914.Extension work is a cooperative program of Oregon State University, the U.S. Department of Agriculture, andOregon counties.

Oregon State University Extension Service offers educational programs, activities, and materials—without discrimi-nation based on race, color, religion, sex, sexual orientation, national origin, age, marital status, disability, ordisabled veteran or Vietnam-era veteran status. Oregon State University Extension Service is an Equal OpportunityEmployer.

Published January 2002.

Part 4: Flowcharts S. Leavengood and J. Reeb

EM 8772 • January 2002$2.50PERFORMANCE EXCELLENCE

IN THE WOOD PRODUCTS INDUSTRY

Part 1 in this series introduced the reader to Statistical Process Control, and Part 2provided an overview of how and why SPC works. Part 3 began the step-by-stepprocess of building the practical skills necessary for hands-on implementation ofSPC. It discussed Pareto analysis, a tool to help decide where to focus initial efforts.

Part 4 discusses flowcharts. Part 5 in the series will continue building implemen-tation skills by discussing cause-and-effect diagrams. Future publications in theseries will discuss case histories of wood products firms using SPC, providing real-world evidence of the benefits of SPC and examining pitfalls and successfulapproaches.

What’s the next step in implementing SPC?After achieving top management’s commitment to using SPC, the next step in

beginning an SPC program is to determine where to focus initial efforts to get the“biggest bang for the buck.” In Part 3, we presented Pareto analysis as a tool tolocate the primary causes of nonconformities and therefore where to focus initialefforts. Now we need to know which specific activities in the process cause thenonconformity and which quality characteristic(s) to monitor.

An example will help to clarify the above discussion and the objective of thisreport. The Pareto analysis conducted in Part 3 of this series revealed “size out-of-specification” as the major nonconformity, from the standpoint of both frequencyand relative cost to scrap or rework. We now need to know:• The specific step or steps in the process (e.g., dry kilns, rip and chop, moulding)

responsible for causing size out-of-specification• The quality characteristic (e.g., moisture content, width, thickness, motor amps,

or proportion of nonconforming parts) to measure

Cause-and-effect diagrams are commonly used to identify specific activitiesresponsible for causing nonconformities. However, we have chosen to discussflowcharts first, postponing a discussion of cause-and effect diagrams until Part 5 in

Scott Leavengood, Extension wood products,Washington County; and James E. Reeb,Extension forest products manufacturingspecialist; Oregon State University.

2


this series. Our choice is based on the fact that flowcharts havebeen found to be valuable tools for initiating discussion duringcause-and-effect analysis and for ensuring that everyone under-stands and agrees on what really happens—rather than what’ssupposed to happen—in the manufacturing process.

FlowchartsFlowcharts graphically represent the steps in creating a product

or service. The process of creating a chart is often beneficialbecause personnel may be unaware of all the “nitty-gritty” detailsinvolved in producing the product. Also, people often are surprisedto learn of the differences between the ideal process flow and whatactually occurs in the mill. This is particularly true when the teamdeveloping the chart includes representatives of all departments ofthe plant, not just production personnel.

In addition to understanding processing steps, flowcharts pro-vide other benefits. If detail is sufficient, flowcharts can help toreveal non-value-added activities such as inspection, rework,redundant steps, movement, unnecessary processing loops, andbottlenecks. From the standpoint of SPC, flowcharts also help toreveal the stages in the process where data may be collected.Flowcharts are also excellent tools for training new hires.Brassard and Ritter (1994) list six steps to flowchart development.1. Determine the start and stop points the chart will cover.2. List the major steps (inputs, decisions made, activities, inspec-

tion, delays, and outputs) in the process.3. Put the steps in the proper order.4. Draw the flowchart.5. Test the flowchart for accuracy and completeness.6. Look for opportunities to improve the process (i.e., reduce non-

value-added activities).

Developing a flowchart: An exampleWe will demonstrate flowchart development using a secondary

wood products manufacturer as an example.

BackgroundXYZ Forest Products Inc. produces wooden handles for push

brooms. Their customers produce finished brooms by adding arubber grip to the top of the handle, inserting a threaded metalferrule to the bottom of the handle, and attaching the broom head.

Flowcharts canreveal…non-value-addedactivities such asinspection, rework,redundant steps, andbottlenecks.

3

FLOWCHARTS

Last year, business began to fall off for XYZ; orders dropped40 percent in just 6 months. Several customers stated that thecompetition’s quality was better. A few customers had begunasking XYZ to provide documentation of process performance—namely histograms, control charts, and process capability indices(see Part 2 in this series for an overview of these subjects). There-fore, XYZ was inspired to use SPC.

Because customers reported several different quality problems(fuzzy grain, size out-of-spec., warp, etc.), XYZ personnel did notknow precisely how and where to start their quality improvementprogram. They conducted the Pareto analysis, as presented in Part3 in this series, to help them decide where to focus initially. Sizeout-of-specification was found to be the primary quality problem.Following the Pareto analysis, the general manager of XYZ con-vened a team of personnel from engineering, sales, production,quality control, and management to develop a flowchart for theirprocess. We will summarize their activities using the six stepsdescribed above.

Creating the flowchart

Step 1. Determine the start and stop points that the chartwill cover.

Because XYZ had never developed a flowchart for the process,the team decided to chart the process from start to finish. The startpoint was green lumber receiving, and the stop point was finishedproduct storage. The team agreed to create a macro-flowchart; thatis, a chart showing only the general flow of the process withminimal detail. The team decided that once they’d created a cause-and-effect diagram for the problem, and had determined the spe-cific steps in the process most likely responsible for the problem,they would then create a flowchart with a narrower focus and moredetail.

Steps 2 and 3. List the major steps in the process, and putthe steps in the proper order.

The team brainstormed (see Brassard and Ritter for a discussionof brainstorming) to develop the steps involved in the process.Then, they put the steps in the proper sequence. (Brassard andRitter list steps 2 and 3 separately because, in a group setting,people usually name the activities most familiar to them, which

4


generally leads to a list of steps that is out of sequence). In ourexample, the team identified these steps.• Receive rough green lumber; tally.• Sticker lumber.• Move stickered lumber to green storage.• Move lumber to dry kilns.• Kiln dry lumber.• Unsticker, tally, and stack dry lumber.• Move lumber to dry storage.• Move lumber to planer.• Unload and plane lumber.• Crosscut surfaced lumber.• Rip lumber to handle blank widths.• Tally handle blanks.• Shape broom handles from blanks.• Inspect handles with go/no-go gauge; tally and scrap no-go.• Load and move good handles from shaper to taperer.• Taper ferrule end.• Round grip end of handles.• Inspect handles for appearance; tally and send nonconforming to

scrap and rework.• Load and move handles to sander.• Sand handles.• Load and move handles to packaging.• Package.• Move packaged handles to finished product storage.

Note: It is imperative to list what actually happens duringproduction versus the ideal for the process. For example, if lumberleaving the planer goes to storage, as opposed to going directly tothe crosscut saws as listed above, this should be specified.

Step 4. Draw the flowchart.Symbols are used in flowcharting to identify different categories

of activity. For example, ovals may be used to indicate inputs/outputs, and boxes indicate a processing step (Figure 1).

It is important to maintain a consistent level of detail in theflowchart. Brassard and Ritter suggest the amount of detail toinclude in a flowchart. Macro-level flowcharts show key actionsteps but no decision boxes. Intermediate-level flowcharts showaction and decision points, and micro-level flowcharts showintricate details.

It is imperative…to list what actuallyhappens duringproduction versus theideal for the process.

5

FLOWCHARTS

Each step in the process should be labeled. Arrows should beused to indicate the flow of steps. To make the chart easier to read,it is helpful when using yes/no decision boxes to have the “yes”boxes branch down and the “no” boxes branch to the left. Thiswill, of course, depend on the amount of space available. Forfuture reference, names of team members, the date, and the pur-pose for creating the chart should be included (Figure 2, page 6).

Step 5. Test the flowchart for accuracy and completeness.The team should make certain that symbols are used correctly,

process steps are identified clearly, and that process loops areclosed (that is, every path flows to a logical end). Also, if the chartcontains any process boxes with more than one output arrow, theteam may wish to consider adding a decision diamond. As a finalcheck, someone outside the team should be asked to verify thechart’s accuracy and completeness.

Step 6. Look for opportunities to improve the process(reduce non-value-added activities).

This is where the team seeks opportunities to optimize theprocess. An ideal process flowchart should be made and comparedto the actual process flowchart. The team should then examine thenon-value-added activities, which might include the following.• Unnecessary redundancy. (Two machines performing the same

operation might be necessary redundancy if they increasethroughput without creating bottlenecks; multiple inspectionpoints for the same quality characteristic are often unnecessaryredundancy.)

• Inspection• Delay• Many movements (for example, movement to a staging area,

then to storage, then to another holding area, and then toproduction).

Montgomery suggests several ways to eliminate non-value-added activities.• Rearrange the sequence of worksteps.• Rearrange the physical location of the operator in the system.• Change work methods.• Change the type of equipment used in the process.• Redesign forms and documents for more efficient use.• Improve operator training.

Figure 1.—Flowchart symbols.

Inputsandoutputs

Processing

Decision

Storage

Delay

Data entry

Movement

Inspection

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• Improve supervision.• Identify more clearly the function of the process to all employ-

ees (flowcharts are good visual aids for explaining the process toemployees).

• Eliminate unnecessary steps.• Consolidate process steps.

A macro-level flowchart (Figure 2) lacks the necessary detail toidentify non-value-added activities. Once XYZ team membershave constructed a cause-and-effect diagram for the defect cate-gory, they will know the step(s) in the process for which they needa more detailed flowchart. Consider, for example, that the teamdetermines shaping through sanding as the processing steps thatdeserve a closer look for size out-of-specification troubles. Theirflowchart for this part of the process may look like the charts inFigures 3 and 4.

DriedlumberPlane

RipCrosscutHandleblanks Shape

Finishedhandles Sand Round Taper

Package

Figure 2.—Sample macro-flowchart.

Macro-flowchartXYZ, Inc.12/17/01

Team membersS. JohnsonB. JonesT. WilliamsB. SimonsenE. FredricksW. Harold

PurposeAddress customerconcerns re: sizeout-of-spec.

Kiln dryStickerGreenlumber

Storage

Storage Unstickerand stack

Storage

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FLOWCHARTS

Delay

Load handleson pallet

Movepallet toshaper 3

Load blanksinto shaper 3

Micro-flowchartXYZ, Inc.01/4/02

Team membersS. JohnsonB. JonesT. WilliamsB. SimonsenE. FredricksW. Harold

PurposeAddress customerconcerns re: sizeout-of-spec.Focus on shapingthrough sanding

Checkshape withgo/no-gogauge.

Shape OK?

Movepallet toshaper 1

Handleblanks


Load handlesinto taperer

Delay

Load blankson pallet

Movepallet

toshaper 2

Load blanksinto shaper 1 Load blanks

into shaper 2

ShapeShape

Continuedon page 8

Taper

Figure 3.—Sample micro-flowchart, part 1.

Fromripsaws

Shape


Inspect.

Yes?

No?

Tally

Tochipper

Scrap

8


Potential areas for improve-ment are revealed in Figure 3.Notice the delay at the tapermachine. Three shapers feedone taper machine whichappears to lead to a bottle-neck. More detailed data(downtime, throughput, costs,etc.) would need to be col-lected to determine a solution.

Another area to examine isthe two inspection points, onebefore the taper machine andthe other before the sander.Handles are inspected forconformance to size specifica-tions at the infeed to the tapermachine and are checked forappearance at the infeed of thesander. The team mightaddress numerous questions,including:

1. Are both inspection pointsnecessary? Could theproduct be inspected forboth size and appearancebefore the taper machine?

2. Could appearance bechecked earlier in theprocess? It probably isn’tcost effective to check forconformance to appearancespecifications after signifi-cant value has been addedto the product.

3. If there is a problem withconformance to size specifi-cations before the tapermachine, can it be deter-mined which of the shapersis the likely source of theproblem? Are size data fedback to the operators?

Round


Inspect.Free from nonconformities?

Yes?

Move palletto sander dept.

Inspect.

Yes?

No?

Load handlesinto sander

Tally


Move palletto packaging

No?

Sand

TallyTally

Scrap Rework

Tochipper

Topatchline

Finishedhandles

Reworkable?

Figure 4.—Sample micro-flow chart,part 2.

Frompage 7

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FLOWCHARTS

4. Can the handles be checked with calipers instead of go/no-gogauges? Much more information is obtained using measurementdata than go/no-go information. For example, a go/no-gaugemight reveal that handles are “small” after they go out of speci-fication. Charting data obtained with calipers, on the other hand,would enable the operator to detect trends and make correctionsbefore the product went out-of-spec.

Let’s examine one more potential area for improvement. Noticeall the movements in Figure 3. This company probably has a fleetof forklifts. Product is loaded on pallets, moved, and unloadedmany times. How might throughput increase if the process flowwere improved by, for example, using just in time (JIT) or leanmanufacturing techniques such as work cells, which are groups ofmachines dedicated to producing a particular product or part.

That question can be addressed by creating another type offlowchart known as a value stream map. These maps track the flowof value and information from customer order all the way back tofirst-tier suppliers. Value stream maps add a dimension—time—that flowcharts don’t cover. By tracking process cycle times,equipment uptimes, and inventories, companies can estimate theamount of time they spend doing things the customer would not bewilling to pay for (movement, queues, delays due to large batches,problems related to the scheduling system, rework, etc.) versustime spent altering the product in ways the customer will pay for(generally, those are process cycle times). The current value streammap is used to redesign the process to reduce non-value-addedtime (thus eliminating waste) and reduce customer lead time.

A detailed discussion of value stream mapping is beyond thescope of this report. For more information, see Rother and Shook.

ConclusionWe now have graphical representations of the steps involved in

creating the product. In the process of creating the chart, we havehad the opportunity to increase company personnel’s understand-ing of “how we do things around here” and perhaps also to stream-line the process and reduce non-value-added steps. We now alsohave a valuable tool for initiating discussion during cause-and-effect analysis, the next step in beginning an SPC program.

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For further informationBrassard, M. and D. Ritter. 1994. The Memory Jogger II: A Pocket

Guide of Tools for Continuous Improvement & Effective Plan-ning (Methuen, MA: Goal/QPC). 164 pp. http://www.goalqpc.com/

Grant, E.L. and R.S. Leavenworth. 1988. Statistical Quality Con-trol, 6th ed. (New York: McGraw Hill). 714 pp.

Ishikawa, K. 1982. Guide to Quality Control (Tokyo, Japan: AsianProductivity Organization). 225 pp.

Montgomery, D.C. 1996. Introduction to Statistical Quality Con-trol, 3rd ed. (New York: John Wiley & Sons). 677 pp.

Rother, M. and J. Shook. 1999. Learning to See: Value StreamMapping to Add Value and Eliminate Muda, v. 1.2 (Brookline,MA: The Lean Enterprise Institute). 102 pp. http://www.lean.org

Walton, M. 1986. The Deming Management Method (New York:Putnam Publishing Group). 262 pp.

11

FLOWCHARTS

This publication is part of a series, PerformanceExcellence in the Wood Products Industry. The variouspublications address topics under the headings of woodtechnology, marketing and business management,production management, quality and process control,and operations research.

For a complete list of titles in print, contact OSUExtension & Station Communications (address below) orvisit the OSU Wood Products Extension Web site athttp://wood.orst.edu

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© 2002 Oregon State University

This publication was produced and distributed in furtherance of the Acts of Congress of May 8 and June 30, 1914.Extension work is a cooperative program of Oregon State University, the U.S. Department of Agriculture, andOregon counties.

Oregon State University Extension Service offers educational programs, activities, and materials—without discrimi-nation based on race, color, religion, sex, sexual orientation, national origin, age, marital status, disability, ordisabled veteran or Vietnam-era veteran status. Oregon State University Extension Service is an Equal OpportunityEmployer.

Published January 2002.

Part 5: Cause-and-Effect Diagrams Scott Leavengood and James E. Reeb






Our focus for the first four publications in this series has been on introducing you to Statistical Process Control (SPC)—what it is, how and why it works, and then discussing some hands-on tools for determining where to focus initial efforts to use SPC in your company. Experience has shown that SPC is most effective when focused on a few key areas as opposed to the shotgun approach of measuring anything and everything. With that in mind, we presented check sheets and Pareto charts (Part 3) in the context of project selection. These tools help reveal the most frequent and costly quality problems. Flowcharts (Part 4) help to build consensus on the actual steps involved in a process, which in turn helps define precisely where quality problems might be occurring and what quality characteristics to monitor to help solve the problems.

In Part 5, we now turn our attention to cause-and-effect diagrams (CE diagrams). CE diagrams are designed to help quality improvement teams identify the root causes of problems. In Part 6, we will continue this concept of root cause analysis with a brief introduction to a more advanced set of statistical tools: Design of Ex-periments.

It is important, however, that we do not lose sight of our primary goal: improving quality and in so doing, improving customer satisfaction and the profitability of the company.

We’ve identified the problem; now how can we solve it?In previous publications in this series, we have identified the overarching qual-

ity problem we need to focus on and developed a flowchart identifying the specific steps in the process where problems may occur. We now need to narrow our focus so that we know what is causing the problem—and therefore how it can be solved.

Continuing our example from Parts 3 and 4, we determined that “size out of spec-ification” for wooden handles was the most frequent and costly quality problem. The flowchart showed that part size/shape was inspected with a “go/no-go” gauge at the infeed to a machine that tapers the handles. The results of go/no-go inspection are either that the shape is acceptable (“go”), in which case the parts were loaded into the tapering machine, or that the shape is not acceptable (“no go”), in which case the parts are scrapped. However, customers are still indicating that the sizes of the handles are not meeting their specifications.

Scott Leavengood, director, Oregon Wood Innovation Center, Oregon State University; and James E. Reeb, Extension forester, Lincoln County, Oregon State University.

EM 8984-E • August 2009

2

StatiStical ProceSS control

In short, our prior efforts have helped us identify what the problem is and where it might be occurring in the process. We still do not know, however, what to do to solve the problem because we do not know what might be causing the problem. Once we identify and confirm a solution, we can take steps to closely monitor the situation such that the solution is maintained over time.

Cause-and-Effect DiagramsA cause-and-effect (CE) diagram is a graphical tool for organizing and dis-

playing interrelationships of various theories of the root cause of a problem. CE diagrams are also commonly referred to as fishbone diagrams (due to their re-semblance to a fish skeleton) or as Ishikawa diagrams in honor of their inventor, Kaoru Ishikawa, a Japanese quality expert.

Like flowcharts, CE diagrams are typically constructed as a team effort; and as with many team efforts, the process is often more important than the end prod-uct. When a team is brought together to study potential causes of a problem, each member of the team is able to share their expertise and experience with the prob-lem. The team approach enables clarification of potential causes and can assist with building consensus for most likely causes. By empowering the team to iden-tify the root cause and its solution, the team gains ownership of the process and is far more motivated to implement and maintain the solution over the long term.

Perhaps most importantly, using a team to develop a CE diagram can help to avoid the all-too-common challenge of pet theories. Pet theories might arise when someone asserts that he or she already knows the cause of a problem. The person(s) presenting this theory may well be right, and if they are in a position of authority, chances are their theory will be the one that gets tested! There are risks, however, in simply tackling the pet theory. If the theory is in fact wrong, time and resources may be wasted, and even if the theory is correct, future team efforts will be stifled, since team members may feel their input to problems is neither needed nor valued. Further, the theory may be only partially correct: It might address a symptom or secondary cause rather than the actual root cause.

CE diagrams, instead, bring the team together to identify and solve core problems.Brassard and Ritter (1994) list two common formats for CE diagrams:

• Dispersion analysis: The diagram is structured according to major cause cat-egories such as machines, methods, materials, operators, and environments.

• Process classification: The diagram is structured according to the steps involved in the production process such as incoming inspection, ripping, sanding, mould-ing, etc.We will discuss the developing a CE diagram via an example.

Developing a cause-and-effect diagramXYZ Forest Products Inc. produces wooden handles for push brooms. Com-

pany representatives visited a customer facility and examined the contents of the scrap and rework bins. Through the use of a check sheet and a Pareto chart, they

3

cauSe-and-effect diagramS

were able to identify “size out of specification” as the most frequent and costly quality problem. A flowchart helped build team consensus on the actual (vs. ideal) steps involved in the manufacturing process and enabled the team to identify points in the process where the problems might occur, as well as where measure-ments were currently being taken.

To be able to address this problem, the team members must now identify the root cause and then determine and test potential solutions. For the long term, they will need a plan to ensure that their solution to the problem becomes standard operating procedure.

CE diagrams are often developed via a brainstorming exercise. Brainstorming can be either a structured or unstructured process. In a structured process, each member of the team takes a turn in presenting an idea. In unstructured brainstorm-ing, people simply present ideas as they come. Either approach may be used, however the advantage of the structured approach is that it elicits ideas from everyone—including more shy members of the team.

The following steps are taken to develop a CE diagram:1. Clearly define the problem (effect): Ensure the problem is clearly stated and

understood by everyone. In the example here, it would be good to ensure that everyone understands specifically what “size out of specification” means. In this case, the team might create a definition such as, “The diameter of the broom handle measured at the bottom tip is either too large or too small to meet our customers’ specifications of ± x inches.” The bottom line for CE diagrams is that there is only one clearly defined effect being examined. The process fo-cuses primarily on the causes—of which there will likely be far more than one.

2. Decide on format: The team should determine if the dispersion analysis or pro-cess classification (described above) is most appropriate for the situation. Either approach is acceptable. The primary concern is which format works best for the group and the problem being explored. For our purposes, we will focus on the dispersion analysis approach.

3. Draw a blank CE diagram: The diagram should look like Figure 1. The effect or problem being studied is entered in the box on the right-hand side. The main backbone is then drawn, followed by angled lines for the various cause catego-ries. In this case, we have entered the common dispersion analysis categories of machine, methods, materials, operator, and environment.

4. Brainstorm causes: The team can now begin brainstorming potential causes of the problem. It is typical for causes to come in rapid-fire fashion unrelated to categories on the diagram. The meeting facilitator will have to enter the causes in the appropriate place on the diagram. If ideas are slow in coming, however, the facilitator might address each of the categories one at a time with ques-tions such as, “Could our machinery be leading to handle size being outside the specifications?”

5. “Go for the root” (cause): As the team discusses some of the causes, it will become apparent that there are underlying causes for some items. For example,

4


under materials, someone might mention wood moisture content (MC). Within this item, there could be a problem of MC variation within a wood species as well as differences between species. There may also be MC variation due to mixing purchased materials (dried by a vendor) with material dried in-house. In addition, MC could be explored further with regards to the other catego-ries such as incoming inspection failing to check MC (an issue involving both operators and methods) and/or extended storage of the material in areas without temperature and humidity control (related to environment). The basic idea is to ensure that causes are explored in enough depth such that the fundamental or root cause(s) is identified.

Of course, at some point, the process will come to a natural conclusion. This can happen either when the team has exhausted all possibilities, or some consen-sus is reached that the root cause has been identified.

The completed CE diagram might look like the one in Figure 2. Due to space limitations, many of the items listed here are quite cryptic. When working on a flipchart or whiteboard, a team would want to use more detail in describing potential causes. As discussed in Step 5 above, notice that some causes appear in multiple categories. For example, causes related to moisture appear in “materi-als,” “methods,” “environment,” and “operator.” This is to be expected, since the issues themselves are multidisciplinary. Moisture content of wood, for example, is a material property that is influenced by the environment, and proper control requires the right methods as implemented by the operator.

Also notice the secondary branches. For example, under operator, “size checks” is listed, with potential causes including “frequency” (i.e., the operator checks the part size but not often enough) and “skipping” (i.e., the operator doesn’t do the checks at all.)

Figure 1. Blank cause-and-effect diagram

5

cauSe-and-effect diagramS

ConclusionNow that the team has completed the diagram, how do they know which cause

is the root cause? As stated above, the process is as important as the end product. It is not the diagram per se that tells the team what the root cause might be, but rather the discussion while constructing the diagram that will help lead the team to a cause or two worthy of further exploration.

In this case, the fact that “moisture content” appeared in so many places on the diagram might lead us to speculate that the team spent a fair amount of time discussing this issue. That fact, combined with a basic knowledge of wood (i.e., wood shrinks and swells with changes in moisture content) might lead the team to decide to collect data and/or conduct an experiment to verify one or more of the items on the diagram. For example, the team might decide to gather baseline data—measure the moisture content within species and between species and con-struct a histogram. They could then conduct an experiment to examine the impact of changes in moisture-check methods on moisture content variability and verify the effect of these changes by constructing additional histograms. If the changes appear to work, they would then need to ensure that the changes become standard practice (and of course, are followed). If the changes do not seem to work, how-ever, the team might then move to the next most likely cause.

In that regard, it should be noted here that merely reaching consensus on the cause of a problem certainly doesn’t guarantee accuracy. In fact, the team’s deci-sion on the root cause might be wrong. In some situations, more advanced statisti-cal tools may be needed to identify causes and conduct and interpret the results of experiments. Design of experiments (DOE) is a set of statistical methods and tools for ensuring the efficient and effective conduct of experiments. Our next publica-tion in this series will present a brief overview of DOE. Using DOE, however, requires more advanced statistics than are within the scope of this series. We will

Figure 2. Completed cause-and-effect diagram

Size out-of- specification

Environment Operator

Machine Methods Materials

bad bearings machine maint.

setup proced. knife grinding

size checks

moisture content

incoming moisture checks

variation between or within species

moisture content material storage conditions

quality of knives

knives dull

damaged

skipping frequency

setup

moisture checks

skipping

sanding mixing purchased w/in-house

6


merely introduce DOE to give you some familiarity with the topic and to help you decide if you want to pursue formal training in the subject.

For more information

Brassard, M. and D. Ritter. 1994. The Memory Jogger II: A Pocket Guide of Tools for Continuous Improvement & Effective Planning (Methuen, MA: Goal/QPC). http://www.goalqpc.com

Ishikawa, K. 1982. Guide to Quality Control (Tokyo, Japan: Asian Productivity Organization).

© 2009 Oregon State UniversityThis publication was produced and distributed in furtherance of the Acts of Congress of May 8 and June 30, 1914. Extension work is a cooperative program of Oregon State University, the U.S. Department of Agriculture, and Oregon counties.

Oregon State University Extension Service offers educational programs, activities, and materi-als—without discrimination based on race, color, religion, sex, sexual orientation, national origin, age, marital status, disability, or disabled veteran or Vietnam-era veteran status. Oregon State University Extension Service is an Equal Opportunity Employer.Published August 2009.

This publication is part of a series, Performance Excellence in the Wood Products Industry. The various publications address topics under the headings of wood technology, marketing and business management, production management, quality and process control, and operations research.

For a complete list of titles in print, visit the OSU Extension Service catalog at http://extension.oregonstate.edu/catalog


ABOUT THIS SERIES






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