8D Case Study

Objectives of this workshopDescription of Problem

The rejections of paper manufactured by a company exceeds 30%. The magnitude of the problem has increased recently. Problem is observed at the end of the paper production process where the entire paper produced is tested for its quality.

Rejection36%

Rs18 croreper annum

Acceptedproduct

Others

Product“A”50%

Holes

Thin Spots

Black Spots

Highest contribution to rejections

Most serious

Drawn to scale

1% rejection corresponds to a revenue loss of Rs 50

lakh pa

Scope the problem

HOLES ARE THE MAJOR CONTRIBUTORS TO REJECTION.PROBLEM IS SCOPED TO: REJECTION OF PAPER CAUSED DUETO PRESENCE OF HOLES

The pet theory of the personnel in the plant was: “Roller Number 1 is root cause”

“It picks up fibre or something and leaves a hole”Capex of Rs.2 crore would be required to replace the roller with a larger one.

We take a systematic problem solving approach, that uses:• only data and no opinion• strict logical analysis• experimental confirmation

THE DATA OF %DEFECTS OVER A PERIOD OF TIME INDICATED THAT THE PROBLEM IS OF VARIATION TYPE.

Problem

Is Present in Problem

Is Absent in Problem

1. What

2. Where

3. When

4. Quantity

Is/Is-Not Matrix: Data Collection Format

Problem Present AbsentWhat is the unit with defect

Paper Not applicable

What is the defect on the unit (this is the defect under study)

Holes Not applicable

What other defects are present on the defective unit

None Not applicable

What other defects are absent on the defective unit

Not applicable

Profile, Thickness, GSM, Porosity, etc..

Data Obtained on “What”

Yellow is where data is available in this case

Hole

The Holes in the Paper

Hole

The paper with holes does not have any other

problems

HoleDetector

Problem Present AbsentWhere – on which part of defective unit

Anywhere across machine profile

Not present at any specific location

Hole

M/C

wid

th

RollerNumber

1

Data Obtained on “Where”

View across width of machine

Red: Medium size holeBlue: Large size hole

WIDTH OF MACHINE

The Holes in the PaperWhere is present?

Problem Present AbsentWhere – on which part of defective piece



Hole

M/C

wid

th

RollerNumber

1

Absence of specific location, and presence of random spread suggests

that the holes are not machine related, but related to something that contacts

machine anywhere randomly

Problem Present AbsentWhere - on which part of defective piece



Where – on which product variety Imported RM,

More hardwood.

Indian RM,

Less hardwood.

Where – after which operation, and not before which operation

Cannot measure Cannot measure

Where – on which machine or line Only one m/c available

Only one m/c available

Where – in which plant/ area Not checked Not checked

Data Obtained on “Where”:

Input feed to the process is randomly

distributed

Corroboration: Holes are

correlated with type of input into process

Data obtained in “where” indicates that:Holes do not appear to be correlated with machine

andHoles appear to be correlated with something

randomly distributed across the process.

Problem Present

Absent

When – draw time line and show occurrences of defect

When – in which shift and which part of the shift (hr/min)

When – in which month; in which season

When – in which year Yellow is where data is available in this case

Detailed Problem Specs Sheet for “When”

Problem Present

Absent

When – draw time line and show occurrences of defect

No clear pattern,trend or

periodicity

When – in which shift and which part of the shift (hr/min)

No pattern visible

When – in which month; in which season

When – in which yearYellow is where data is available in this case

Detailed Problem Specs Sheet for “When”

0.00

0.50

1 .00

1 .50

1

Ser ies1

TREND TR-5000 ULMA % REJ ECTION

0

510

15

2025

30

3540

45

5055

60


0

5

10

15

20

25

30

35

40

45

50

55

60


0

5

10

15

20

25

30

35

40

45

50

55

60

Data on “When” – Time line

1

2

3

Today

No clear patternno build ups, no periodicity

None of the recorded machine parameters and none of the

recorded input parameters correlate with the ups and downs in the

rejection percentage on the time line, when compared for the same

grade of material.

Problem Present

Absent

Quantity – How much (how deep/wide…) are the defects

Quantity – How many defective pieces in the batch

Quantity – How many defects on defective pieceYellow is where data is available in this case

Detailed Problem Specs Sheet for “Quantity”

Problem Present AbsentQuantity – How much (how deep/wide…) are the defects

Approx circular holes, which are most common

Not steaks, tears,

Quantity – How many defective pieces in the batch (roll)

Many Not zero

Quantity – How many defects on defective piece

Data Obtained for “Quantity”

Physically search for “mura”

(inconsistency) in input material

Found foreign bodies in head

box input!

Found no contamination in recycle water!

Found no contamination in

input raw materials

Found no contamination in chemicals and other streams

Found foreign bodies in fresh

water!

Hypothesis:Foreign bodies from

fresh water lead to holes

Testing of Hypothesis

Experimental confirmation:• Experiment : Stop fresh water: Rejections should reduce

Roll No Grade Meters Holes small

Holes medium

Holes large

Dark Sp small

Dark sp medium

Dark sp large

Thin sp small

Thin sp medium

Thin sp large

Bobbinsreject

% bobbins reject

234 TR 3 21111 2 70 57 0 0 0 335 142 29 56 With fresh water



244 TR 3 21,676 3 28 15 0 0 0 66 34 4 14 5% No fresh water

Experimental Testing of Hypothesis

Conclusion

Foreign particles from fresh water are primary chronic causes of holes,

resulting in rejections

Machine parts, including “roller number 1” aggravate the problem, and are causes of instability problem (cyclic trend), resulting

in rejections.

Countermeasures

•Reduce contamination in fresh water

This is a sporadic problem of holes in shape of cracks

This is a instability problem.Not to be confused with chronic problem.

Cause for this is different, and known.

Difference between Sporadic and Shift

Sporadic

Shift

We are solving this

Now the real challenge is on!

1. Completing the countermeasures2. Sustaining the gains3. Preventing and immediately correcting

sporadic problems

Should become redundant

Can you meet it?

8D approach makes no assumptions.It starts with data, from scratch on blank slate.

It uses logic to narrow down the area of search for the root cause.

It does not require only technical expertise.It also requires reliable data.

It obtains data by asking a series of questions.The questions are in strict sequence.

Learning’s

8D Case Study

Documents

Transcript of 8D Case Study