3 new tools

3 New Tools
for
problem solving
By Vidyut Bapat
www.sigmaguru.com
Weight Variation Before and After

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Sigmaguru@gmail.com Page 1

There is no quality related problem
that cannot be solved.

Shainin DOE tools are powerful enough to do just that.

SigmaGuru has even improved these amazing tools.

We even devise new tools
when existing tools don’t work.

But we will make sure
your most difficult /chronic problems are solved.

Here I will be presenting 3 such new tools
and corresponding 3 cases.

- Vidyut Bapat


3 new problem solving tools

1. Trace Diagram
2. Surrogate Parameter
3. DAM - Data to Money


Trace Diagram
With
Case of
Reducing Blankets Rejection
from 8% to 0%


Surrogate Parameter
With
Case of
Reducing Customer Rejects
from 2500 ppm to 0 ppm


DAM - Data to Money
With
Case of
Reducing Slippers Rejection
from 3% to 0.4%


First new tool
from
SigmaGuru

Trace Diagram


Trace Diagram

• A new Tool for Mura (variation) Reduction
• Trace Diagrams are a graphical means of performing computations in
linear and multilinear algebra.


Let us start with

Flow Chart
which is One of basic 7 tools
for Quality Improvement.

We will use this
to develop
Trace Diagram ( TD )


Typically
A Flow Chart implies
single material flow

However
Pressing
in reality,

Sintering
flow is much
more complex.
Grinding


The flow is more like this in the Gangetic delta.


Or like lightning on a stormy night


Pressing With TD
We will display
Sintering material flow
in
more detail
Grinding


In our example,
each process is carried out by
one or more machines

Press -1 Press -2 2 presses

Sintering and

2 grinding
Grinding - 1 Grinding- 2 machines


This also true for
2 iron ore mines and 2 blast furnaces
and
2 plastic moulding powder vendors
and 2 moulding machines……

Vendor1 Vendor2

Store

M/c 1 M/c 2


In these cases, we will have 4 possible flows

Pressing1 Pressing2

Sintering

Grinding1 Grinding2


Pressing1 Pressing2

Sintering And

Grinding1 Grinding2 4 different varieties
of output

as indicated


A management
with good knowledge of lean sixsigma
will have only 2 of these flows

In this case
Pressing Pressing
We will have
Only 2 different varieties
Of output
Sintering
This is Improvement over
Grinding Grinding previous case


In real life, we can have,
say 10 stages with 50 machines,
with multiple products and
hundreds of flows,
if not managed properly


These
uncontrolled flows
result in
uncontrolled variations,

resulting in
high rejections.


Trace Diagram
helps us
understand and reduce
the number of
different flows
And reduce
rejections dramatically.


A case study

A blanket manufacturer
was facing problems with rejection,
8% of it,
forcing him to sell as scrap.
Obviously losing a lot!

The problem was attributed to
yarn quality
( weight – or count as they prefer to
call in Textile industry ) variation.


A real case study
Flow Chart - Blankets

Carding

Ring frame Implied single flow

Winding

Weaving


Common process In this process,
when managed as a single flow,
8 % of the production was
rejected.
16 bobbins

16 x 8 cops

16 cones

30 looms

Common process

Reality Check
1 common process
16 bobbins generated
16 bobbins every hour
which generated
16 x 8 cops 16 x 8 cops which in
turn generated
16 cones, which were
further processed on
30 different looms.
16 cones

30 looms

Common process

Possible number of flows

= 16 x
16 bobbins 16 x
30

16 x 8 cops
= 7680

16 cones

30 looms

Old Trace – not controlled
( shown for only 4 bobbins )
Part of 7680 flows

Gives 8 % Rejection


No of stations
at each stage Actual Flow
in
Carding - 16 large no of streams
With
Ring frame- 100
large inventory
between stages
Winding- 20

Weaving - 20


A quick study – multivari – showed
that each location
( in 4 x 4 matrix ) is producing
excellent yarn
with very little variation
but
there is a lot of variation between
column to column.

Each bobbin
was further processed
into 8 cops


COPs, in turn were
converted into a cones

( 8 cops go into winding
one cone -
cops from all bobbins are
stores in one place
with no trace and
randomly used to make a
cone )


Each cone was further
processed into about 2
blankets,
on a loom.


A blanket with 4 colors
requires input from 4
different cones
further complicating the
process


The mix up of such yarn
( flows as we see)
in a blanket resulted in
substandard quality.


Action –

After understanding the
…16 Bobbins… flow in terms of
Trace diagram ,

the random multiple flows
-7680
16 x 8 cops were reduced

into 16.
16 cones…


Knowing the problem faced,
a procedure was introduced
to use
the same 8 cops
from
one bobbin to make one cone.


Reorganised Flow Chart

All processing stations mapped to 4 virtual cells in this diagram

Carding

Ring frame

Winding

Weaving


When such cones were used to make
blankets there was no rejection!

The rejection
dropped
from 8%
to zero!!


You will find hidden flows in your
process too.

Find them and stop them.

And see the dramatic improvement in
the output.


Convert
Inderminate
into
determinate


This is also the finer understanding
of one of
14 principles of Deming

avoid multiple sources
Internal sources in this case


It has links with

Traceability
as required by ISO9001.

You will also recognise relation with

Cells
in Lean methodology.


Second new tool
from
SigmaGuru

Surrogate Parameter


Surrogate Parameter

A new Quality Improvement Tool


Surrogate means Substitute

Surrogate parameter
is one which substitutes another
which is of real interest to us


Surrogate Parameter Tool
is used when

- there is no simple / clear clue

- there is no known cause and effect


Surrogate Parameter Tool
is used when

- it is very expensive or
dangerous or
time consuming or
very inconvenient

to work or experiment
with the original parameter


In such case,
choose a closely related parameter

with which
it is easy to experiment

The new parameter is called

Surrogate Parameter


Surrogate Parameter

should be closely related
to the one we are interested in
but it need not be necessarily be specified or
defined by the customer

for example – weight of a product


Customer may not be interested in weight
as a parameter to be measured,
but

it is very useful to us
as it can be easily measured.
Change in material , dimensions, process results
into change in weight and
it can give us the vital clue in problem solving.


The choice of the second Surrogate Parameter
is of course

very critical
and
one should be very careful in choosing it.


Let us study one case to understand

The Case
Plastic Moulder

Project

To deliver zero defect products to
customer


Background

Subsidiary of European
Multinational
Had Modern machines

They had Zero Defect Policy
But One customer was reporting
sporadic failures
at 2500 ppm level

This is simply not acceptable.


In spite of attention of
entire organization,
defects persisted.

Ultimately services of
SigmaGuru were called in.


About the problem

The problem was about a weld line
around a hole splitting open when
under pressure during assembly.


About the process

Moulding conditions were not
precisely defined.

Operating personnel free to
twiddle
the knobs
within specified range.


A lot of trials made with no
demonstrable results.

Thousands of units were
required per trial.


They had even posted one person
at client location
to monitor and report failures
as they happen !

No efforts were spared!!


At one stage,
even Raw material quality
( melt flow index) was suspect

and a narrow range of properties
were demanded.

But this too did not result
in any improvement.


Due to all these difficulties
we decided
to use

Surrogate Parameter Tool.


Since the problem was with the
finished product,

weight of the finished product
was chosen as
Surrogate Parameter.


It is very easy to measure weight
compared to predicting
if a part will break or not
at the client!


Comparison of Original and Surrogate

Original Surrogate

Parameter Strength Weight
at particular
location

Method Destructive Non
destructive



Original Surrogate

Time to test 72 hours 1 minute

Cost Heavy negligible



Original Surrogate

Size of trial 5000 30

Ease Difficult Very Easy


Now the problem is transformed
from

Improve breaking strength at
customer
to

Improve weight quality during
moulding


Improving weight quality
means
reduce weight variation

So lets us get started….


Here is initial
snap shot of quality
in terms of
surrogate parameter - weight
Time to time variation

485

480

475

470

465

460

sigma= 1.36%
455
15

17

23

27

31

33

39

43
11

13

19

21

25

29

35

37

41

45
1

3

5

7

9


It is known that
Variation in processes
(inputs and outputs of)

causes defects
Six sigma movement was ignited in Motorola by this discovery.

And it was also known that the
process parameters
( machine settings parameters)
were frequently changed.


Hence it was decided to
check the weight again

after keeping machine
parameters constant

at their known best level.


Now the variation looked like this.
Piece to piece variation weight in g

485

480

475

470

465 sigma=0.24%

460

455
13

17

19

29

35
11

15

21

23

25

27

31

33
1

3

5

7

9

Quite an improvement
over the initial

The results are quite
positive!
At this point,
a small meeting was convened
with –

Production
Quality
Process Personnel.


The 4 most critical parameters for
the weld line problem were listed

1. Cycle time
2. Fill time
3. Mould temp
4. Hold time


The team also decided the best and
the worst levels( values)
for these parameters.

Knowing these,
a quick trial was made
with only 30 units
with all parameters
at
their worst and
best levels.


All parameters at their
worst
is called ( 0000) level

All parameters at their
best
is called( 1111) level


All parameters at their worst level
gave this weight variation
0000 Variation

485

480

475

470

465

460
sigma=0.26%

455
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31


And All parameters
at their best level ….
1111 Variation

485

480

475

470

465

460 sigma=0.12%

455
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21


Thus we found sigma dropping

from 1.36 % to 0.12 %
improvement by a factor 10,

within a single working day!


Time to time variation

Piece to piece variation weight in g
485

485
480

480
475

475
470

470
465

465
sigma=0.24%
460

sigma= 1.36% 460
455
11
13

17

25

29

33

41

45
1
3
5
7
9

15

19
21
23

27

31

35
37
39

43
455

11

13

15

17

19

21

23

25

27

29

31

33

35
1

3

5

7

9
0000 Variation
1111 Variation
485

485
480

480
475

475
470

470
465

465
460
sigma=0.26%
460 sigma=0.12%
455
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

455
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21


Weight Variation Before and After

490

485

480

475

470

465

460

455

450

445

440
10

19

25

31

40

46

52
1

4

7

13

16

22

28

34

37

43

49

Going back from
surrogate to original parameter,
trials were made with
now best-known parameters.

Result -
no failures at the customer.


Zero defect
for the critical customer
achieved
within a week.


Conclusion

Wisely chosen Surrogate parameter
yields wonderful results

Zero defect is a reality
and
it can be achieved.


The problem which was evading
solution for one year…

Surrogate Parameter
helped solve in just one week


Third new tool
from
SigmaGuru

DAM


Using DAM

CONVERT
DATA INTO MONEY


This conceptual tool
has 5 steps –

Converting
Data into Information

Information into Knowledge
Knowledge into Insight

Insight into Action

Action into profits



What to do with
5000 defective
slippers every day?



Using first step,
convert available data
into information


An example –




Normally one has
lots of data
sitting in nice files.



Convert it into
useful information

Pareto – slippers rejection in no of pcs



Stagewise rejection
this data is not easily actionable as
there are too many areas.

Shows Focus area

is clearly Pressing



Pressing stage needs improvement
But what needs to be improved?

You need to carry out experiments
to determine


An experiment is carried to understand
which defect is generated at which location in pressing
LOCATION -WISE DEFECTS / Concentration diagram
drawn

All rejects are at the edges

Knowledge into Insight


Knowledge into Intelligence

location -wise
defects

The temp at the edges is low due to
dissipation from the edges

Intelligence into Insight

location -wise defects

Indicates low set temperatures
as possible cause

Insight into Action


Insight into Action
EXPERIMENTS planned with increase in temp

Current temps
at left
E1 / E2 /E3
Are 3 experiments

temp at diff platen locations
increased by 2 dc
As indicated

6 sheets cut
per experiment


Insight into Action

Set vs actual temps in expt 1
EXPERIMENT 1
Actual Temperatures Measured
Platen No Set temp. LEFT 'c CENTRE 'c RIGHT 'c
1 155 155 155 144
2 155 146 157 140
3 150 140 151 147
4 150 140 151 139
5 144 135 149 135
6 162 141 153 146
7 155 116 112 130

Notice deviations on lower side in red.

Insight into Action
RESULTS OF EXPERIMENT NO. 1
DEFECT LOCATIONS

•BLOW HOLES REJECTION = 0.42%
Out of 6 sheets on Press 1.
•COLOUR BLEACH


Action into profits


Action into profits

SUMMARY OF THE EXPERIMENTS

Expt no Total Rej. Pcs Rej. %
1 2 0.40%
2 6 1.20%
3 5 1.00%

EXPT 1 gives best results
Process Will be standardized
And extended over entire production


A
Action into profits
C P
D

Benefits
projected gains from the project in INR
daily production pairs= 80000
1%= 800
selling price per pair = 40
gain per day = 32000

gain per year = 9600000

gain for 2 % reduction in rej = 19200000

in mn INR= 19


This is how

DAM
HELPS CONVERT
DATA INTO MONEY


Contact
sigmaguru at gmail.com

or
Vidyut Bapat at
+91 94235 07631

to get help in solving
your most difficult problems


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