Advanced Innovation Group provide Six Sigma Project for Reducing Breakage Percentage

Six Sigma Black Belt Project onSix Sigma Black Belt Project onreduction in breakage in biscuits

LOCATION : NOIDAPROCESS : BISCUITS MAKINGBLACK BELT : Dipti Nayak

Understanding VOC

Customer Customer Comments Customer (CTQ's)

Champion – General Manager

“During the last 8 months, rejection in biscuits due to breakage is around 297 kg against the 7064 kg of biscuits produced on monthly basis. Average of 8 months rejection breakage is around 4.25%, rejection should be around 2.0% of total biscuits processed in kgs”

Reduce rejection from 4.25% to 2.0 % in sweet biscuits

Operations Manager “ Due to high rejection rate of 4.25%, target of productivity is not met which is set at 98%

Increase the productivity from 95.75% to 98%

Project Charterd

Define

Project Leader: Dipti Nayak

Team Members

Business Case: ABC Limited is one the leading biscuit manufacturing company with its location worldwide. Its Manufacturing facility in Noida, India is manufacturing sweet and namkeen biscuits for market leading brands, Noida unit is facing lot of rejection due to breakage in biscuits due to which the scrap /rejection has increased and needs immediate attention as it is causing loss to the A-G biscuits. Currently it is operating under 4.25% rejection against the estimated target of 2.0%. Sweet biscuits monthly production is around 7064 kg and around 297 Kg are rejected to due breakage.

Stakeholders Business Leader

Champion Vice President

Sponsor General Manager

MBB Pranay Kumar

BB Dipti Nayak

Team Member Operations Manager, supervisor, Key operators, quality executives, Engineering manager

Problem Statement: During the period from Mar’2012 to Oct’2012 the average rejection rate is 4.25% of the sweet biscuits manufactured

Goal Statement: To reduce rejection due to breakage in sweet biscuits at Noida location from 4.25% to 2.0% by Mar’2013

Project In Scope: 1. Sweet biscuits manufactured in Noida location Project Out of Scope: 1. Everything beyond as mentioned above is out of scope for this project

Timelines/Milestones/Phases Start Date End Date

Start date: 05th Nov 2012 -DEFINE 05th Nov 2012 19th Nov, 2012

MEASURE 20th Nov, 2012 30th Nov, 2012ANALYZE 03rd Dec,2012 29th Dec, 2012 IMPROVE 02nd Jan, 2013 31st Jan,2013CONTROL 01st Feb,2013 30th Apr, 2013

ARMI and Communication PlanKey Stakeholders ARMI Worksheet

Define Measure Analyze Improve Control

GM Finance, Engg I I I I ISponsor - VP I I I I I

Champion - GM I & A I & A I & A I & A I & AMBB A & I A & I A & I A & I A & I

Operation Manager I & M I & M I & M I & M I & MAshish R & M R & M R & M R & M R & M

Team Members M M M M M

A – Approval of team decisions I.e., sponsor, business leader, MBB.R – Resource to the team, one whose expertise, skills, may be needed on an ad-hoc basis.M – Member of team – whose expertise will be needed on a regular basis.I – Interested party, one who will need to be kept informed on direction, findings.

Communication Plan

Information Or Activity Target Audience Information Channel Who When

Project Status Sponsor, Champion, BB, Members

E-mails Dipti Nayak BI-Weekly

Tollgate Review BB,MBB, Champion E-mails or Meetings Dipti Nayak As per Project Plan

Project Deliverables or Activities Members, MBB Emails, Meetings Dipti Nayak Weekly

dDefine

Packaging

MaterialsMulti solutions

SugarMawana

MaidaKalkaji, Bikaji, Panwar

PARLEPacked BiscuitsGheeAmul, Gopal, Madhusudan

CustomersOutputProcessInputs Suppliers

SIPOC

START Mixing Dough making

Fermentation Baking cooling

packingEND

dDefine

CTQ Tree

Reduce %age rejection

Reduce %age rejection CTQs

%breakage per Month(Project Y Metric)

%breakage per Month(Project Y Metric)

<2.0% breakage per Month(Target)

<2.0% breakage per Month(Target)

2.0 % breakage per Month(Upper Specification Limit)2.0 % breakage per Month(Upper Specification Limit)

Any Month % breakage greater than 2.0%(Defect Definition)

Any Month % breakage greater than 2.0%(Defect Definition)

Project YProject Y = (breakage in Kgs/per month) * 100

Production of biscuits in Kgs/per month

mMEASURE

Data Collection Planm

MEASURE

Project-Y Operational Definition Defect Def Performance StdSpecification Limit

OpportunityLSL USL

%age breakage in a month

% breakage is the percentage calculated based upon the breakage in Kgs (say n) to

Production in kgs in a Month (say N)

% breakage in a Month greater

than 2.0%Less than 2.0% - 2.0%

Daily breakage data compiled

on Monthly basis

Project-Y Data Type Unit of Measure Formula to be used

Plan to collect Data Plan to sample

What Database or Container will be

used to record this data?

Is this an existing

database or new?

If new, When will the database be ready for use?

When is the planned start date for data collection?

%age breakage in a month Continuous Percentage

breakage in Kgs

(n/N)*100n is breakage in Kgs per Month, N is the production in Kgs

per month

Excel is being used to record the data

and this data is located in

H:Drive /Six Sigma project file

yes - - Mar’2012 to Oct’2012

Validate Measurement System – Gage R & R

mMEASURE

Scope : The Scope of this measurement system is limited to the recoding of breakages in Kg.

Purpose : The purpose of this study is to repeatability and reproducibility of the measurement system.

Procedure : 1) 10 Data samples were collected randomly of 10 days

2) These 10 days breakage were checked by 3 operators on the same weighing machine.

3) After data collection these results were analyzed in Minitab software

Validate Measurement System – R & R

mMEASURE

KPI Data Type

%breakage in Kgs Continuous

Inference: Graphical summary on R & R suggests that there is no significant variation between appraisers.

Part-to-PartReprodRepeatGage R&R

100

50

0

Per

cent

% Contribution

% Study Var

10 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 1

0.010

0.005

0.000

P/ N

Sam

ple

Ran

ge UCL=0.01158

LCL=0

Operator 1 Operator 2 Operator 3

_R=0.0045

10 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 110 9 8 7 6 5 4 3 2 1

16

12

8

P/ N

Sam

ple

Mea

n

__X=11.59UCL=11.59LCL=11.58

Operator 1 Operator 2 Operator 3

10987654321

16

12

8

P/ N

Operator 3Operator 2Operator 1

16

12

8

Operator

10987654321

16

12

8

P/ N

Ave

rage

Operator 1

Operator 2

Operator 3

Operator

Gage name: Date of study:

Reported by: Tolerance: Misc:

Components of Variation

R Chart by Operator

Xbar Chart by Operator

Data by P/ N

Data by Operator

P/ N * Operator Interaction

Gage R&R (Xbar/ R) for Data

Validate Measurement System – R & Rm

MEASURE

KPI Data Type

%breakage in Kgs Continuous

Inference : As gage R & R is less than 10% of the study variation, we concludes that data can be trusted and good to go.

Gage R&R Study - XBar/R Method

%ContributionSource VarComp (of VarComp)Total Gage R&R 0.00001 0.00 Repeatability 0.00001 0.00 Reproducibility 0.00000 0.00Part-To-Part 8.03352 100.00Total Variation 8.03353 100.00

Study Var %Study VarSource StdDev (SD) (6 * SD) (%SV)Total Gage R&R 0.00268 0.0161 0.09 Repeatability 0.00266 0.0159 0.09 Reproducibility 0.00034 0.0020 0.01Part-To-Part 2.83435 17.0061 100.00Total Variation 2.83435 17.0061 100.00

Number of Distinct Categories = 1491

Graphical summary - Y

mMEASURE

Inference : The summary above represents that data (our project Y) in non-normal in nature as p value is 0.038 which is less than 0.05. the measure of central tendency will be median

765432

Median

Mean

4.404.354.304.254.204.154.10

1st Quartile 3.7242Median 4.24573rd Quartile 4.8933Maximum 7.4933

4.1225 4.3752

4.1513 4.4089

0.8432 1.0228

A-Squared 0.80P-Value 0.038

Mean 4.2488StDev 0.9243Variance 0.8543Skewness 0.091904Kurtosis 0.612410N 208

Minimum 1.8074

Anderson-Darling Normality Test

95% Confidence I nterval for Mean

95% Confidence I nterval for Median

95% Confidence I nterval for StDev95% Confidence I ntervals

Summary for C1

mMEASURE

Stability analysis

Inference : Stability analysis done by run chart, p-value for mixture, oscillation are more than 0.5, suggests that there is data is stable from mixture and trend point of view, analysis needs to be carried out for existence of trends and clustering. Data is not stable as p-value for clustering and trends are below 0.05

200180160140120100806040201

20.0

17.5

15.0

12.5

10.0

7.5

5.0

Observation

Tota

l

Number of runs about median: 89Expected number of runs: 105.0Longest run about median: 7Approx P-Value for Clustering: 0.013Approx P-Value for Mixtures: 0.987

Number of runs up or down: 126Expected number of runs: 138.3Longest run up or down: 5Approx P-Value for Trends: 0.021Approx P-Value for Oscillation: 0.979

Run Chart of Total

Process Sigma Level

TOTAL OPPUTUNITIES 56515

TOTAL FAILED 2377

DPMO(TOTAL FAILED) X 106)/TOTAL OPPURTUNITIES

42059

CURRENT SIGMA LEVEL 3.22

Inference : Current process sigma level is 3.22

mMEASURE

Cause and Effect diagram from breakage

Inference : brainstorming session has identified 11 contributors (Xs) that can affect the performance of Y, these 11 contributing Xs will be analyzed using proper tools to identify & conclude whether they have significant impact on Y.

aAnalyze

producedof 7064breakge outsuffersbiscuits297 Kgs of

Environment

Measurements

Methods

Material

Machines

Personnel

N/Y(7th to 10th )

Vendor for Ghree

Vendor for Maida

Oven Type

Ghee %

Maida %

WAP(maida) %

Oven Heat up time

Mixing Time

Baking Temperature

Baking Time

Cause-and-Effect Diagram

Operator qualification

Data collection plan for Xs that contributes to printing defects

Measure Type Operation definition

How it is measured/collected

Plan to sample

Test to be performed

% Breakage Y (Kgs of biscuits broken (n) x 100) / No. of Kgs of biscuit produced (N))

Formula( n*100)/NData for n, N is collected individually

Mar’12 to Oct’12 Run chart

Baking Time X Amount of time the contents are Baked in baking machine Timer Mar’12 to

Oct’12Regression Analysis

Baking Temperature X Temperature at which Baking is done Temperature meter Mar’12 to

Oct’12Regression Analysis

Oven Type X There are three Ovens Oven A, Oven B, Oven C in which the Baking is done Oven Identification Mar’12 to

Oct’12Mood’s Median Test

Mixing Time X Amount of Time required to mix the contents Timer

Mar’12 to Oct’12 Regression

Analysis

Oven Heat-up Time X

Pre heating time before the contents are placed for baking at certain temperature

TimerMar’12 to Oct’12 Regression

Analysis

aAnalyze

Data collection plan for Xs that contributes to Stitching defects

Measure Type Operation definition How it is measured/collected

Plan to sample

Test to be performed

WAP Maida(%) X

Water absorption percentage, is the total amount of moisture in the contents

Mo = weight w/o moistureW= weight w/moisture% = (W-Mo)*100/W

Mar’12 to Oct’12

Mood’s Median Test

Vendor for Maida X Different suppliers which supply Maida

Supplier Name on packet



% Maida X %age of Maida in total contents, weight of Maida = M M*100/W Mar’12 to

Oct’12Mood’s Median Test

% Ghee X %age of Ghee in total contents, weight of Ghee = G

G*100/W Mar’12 to Oct’12


Vendor for Ghee X Different suppliers which supply Ghee

Supplier Name on packet



Operator qualification X Qualification of operators

10th Pass (Yes)10th Failed (No)


Mann-Whitney Test

aAnalyze

Hypothesis Testing :

Inference :P-Value observed is 0.036 which is less than 0.05, which suggests that we have sufficient evidence to reject null-hypothesis. Baking Time does significantly impacts breakage.

aAnalyze

Regression Analysis: Project Y versus Baking Time

The regression equation isProject Y = 2.70 + 0.0933 Baking Time

Predictor Coef SE Coef T PConstant 2.6962 0.7383 3.65 0.000Baking Time 0.09331 0.04421 2.11 0.036

S = 0.916668 R-Sq = 2.1% R-Sq(adj) = 1.6%

Analysis of Variance

Source DF SS MS F PRegression 1 3.7436 3.7436 4.46 0.036Residual Error 206 173.0977 0.8403Total 207 176.8413


Inference :P-Value observed is 0.032 which is less than 0.05, which suggests that we have sufficient evidence to reject null-hypothesis. Baking Temperature does significantly impacts breakage.

aAnalyze

Regression Analysis: Project Y versus Baking Temper

The regression equation isProject Y = 13.6 - 0.0443 Baking Temper

Predictor Coef SE Coef T PConstant 13.646 4.343 3.14 0.002Baking Temper -0.04434 0.02049 -2.16 0.032

S = 0.916172 R-Sq = 2.2% R-Sq(adj) = 1.7%




Inference :P-Value observed is 0.007 which is less than 0.05, which suggests that we have sufficient evidence to reject null-hypothesis. Oven Type does significantly impacts breakage.

aAnalyze

Mood Median Test: Project Y versus OVEN

Mood median test for Project YChi-Square = 9.93 DF = 2 P = 0.007

Individual 95.0% CIsOVEN N<= N> Median Q3-Q1 ---------+---------+---------+-------OVEN A 56 55 4.238 1.343 (----------*--------)OVEN B 32 17 4.028 1.008 (---------*-------)OVEN C 16 32 4.475 0.837 (-------*-------) ---------+---------+---------+------- 4.00 4.25 4.50

Overall median = 4.246


Inference :P-Value observed is 0.000 which is less than 0.05, which suggests that we have sufficient evidence to reject null-hypothesis. Mixing Time does significantly impacts breakage.

aAnalyze

Regression Analysis: Project Y versus Mixing TIme

The regression equation isProject Y = 7.09 - 0.338 Mixing TIme

Predictor Coef SE Coef T PConstant 7.0874 0.5901 12.01 0.000Mixing TIme -0.33816 0.06992 -4.84 0.000

S = 0.878025 R-Sq = 10.2% R-Sq(adj) = 9.8%




Inference :P Value observed is 0.201, which is greater than 0.05, which suggests that we do not have sufficient evidence to reject null hypothesis. Oven heat up time does not significantly impacts breakage

aAnalyze

Regression Analysis: Project Y versus OVEN Heatup Time

The regression equation isProject Y = 4.44 - 0.0113 OVEN Heatup Time

Predictor Coef SE Coef T PConstant 4.4407 0.1628 27.28 0.000OVEN Heatup Time -0.011260 0.008782 -1.28 0.201

S = 0.922852 R-Sq = 0.8% R-Sq(adj) = 0.3%




Inference :P Value observed is 0.813, which is greater than 0.05, which suggests that we do not have sufficient evidence to reject null hypothesis. WAP (Maida)% does not significantly impacts breakage

aAnalyze

Mood Median Test: Project Y versus WAP(maida) %


Individual 95.0% CIsWAP(maida) % N<= N> Median Q3-Q1 ----+---------+---------+---------+--7.0 11 6 4.17 0.87 (-------*----------------)7.1 17 18 4.34 1.41 (----------------*--------)7.2 27 25 4.21 1.12 (---------*-------)7.3 24 27 4.30 1.48 (----------------*--------)7.4 9 8 4.17 1.07 (-----------*---------------------)7.5 16 20 4.47 1.14 (------------*-----) ----+---------+---------+---------+-- 3.90 4.20 4.50 4.80



Inference :P-Value observed is 0.003 which is less than 0.05, which suggests that we have sufficient evidence to reject null-hypothesis. Ghee (%age) does significantly impacts breakage.

aAnalyze

Mood Median Test: Project Y versus Moisture(Ghee) %


Individual 95.0% CIsMoisture(Ghee) % N<= N> Median Q3-Q1 --+---------+---------+---------+----11 15 2 3.48 1.25 (-----------*----)12 52 48 4.21 1.34 (-*--)13 9 10 4.26 1.26 (----*----------)14 27 44 4.47 0.99 (--*-)15 1 0 3.51 * --+---------+---------+---------+---- 2.80 3.50 4.20 4.90



Inference :P Value observed is 0.446, which is greater than 0.05, which suggests that we do not have sufficient evidence to reject null hypothesis. Vendor of Maida does not significantly impacts breakage

aAnalyze

Mood Median Test: Project Y versus Vendor for Maida


Vendor Individual 95.0% CIsfor Maida N<= N> Median Q3-Q1 -------+---------+---------+---------Bikajee 55 57 4.270 1.143 (--------*-------)Kalkaji 26 19 4.097 1.298 (----------*------------)Panwar 23 28 4.413 1.156 (-------------*------) -------+---------+---------+--------- 4.00 4.25 4.50



Inference : P-Value observed is 0.003 which is less than 0.05, which suggests that we have sufficient evidence to reject null-hypothesis. % of Ghee does significantly impacts breakage.

aAnalyze

Mood Median Test: Project Y versus Ghee%


Individual 95.0% CIsMoisture(Ghee) % N<= N> Median Q3-Q1 --+---------+---------+---------+----11 15 2 3.48 1.25 (-----------*----)12 52 48 4.21 1.34 (-*--)13 9 10 4.26 1.26 (----*----------)14 27 44 4.47 0.99 (--*-)15 1 0 3.51 * --+---------+---------+---------+---- 2.80 3.50 4.20 4.90



Inference : P Value observed is 0.148, which is greater than 0.05, which suggests that we do not have sufficient evidence to reject null hypothesis. Vendor for Ghee does not significantly impacts breakage

aAnalyze

Mood Median Test: Project Y versus Vendor for Ghee


Vendor for Individual 95.0% CIsGhree N<= N> Median Q3-Q1 --------+---------+---------+--------Amul 46 32 4.086 1.089 (----------*-------)Gopal 26 32 4.402 1.186 (-----------*----------)Madhusudan 32 39 4.400 1.156 (---------*-------)MD 0 1 4.554 * --------+---------+---------+-------- 4.00 4.25 4.50



Inference :: P Value observed is 0.5641, which is greater than 0.05, which suggests that we do not have sufficient evidence to reject null hypothesis. Operator qualification does not significantly impacts breakage

aAnalyze

Mann-Whitney Test and CI: No, Yes

N MedianNo 96 3.9150Yes 112 4.4698

Point estimate for ETA1-ETA2 is -0.564195.0 Percent CI for ETA1-ETA2 is (-0.8246,-0.3031)W = 8136.0Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0000The test is significant at 0.0000 (adjusted for ties)

Measurable Type Data Type Test Performed P-Value Inference

% Breakage Y Continuous Run chart N/A N/A

Baking Time X ContinuousRegression Analysis

0.036 Statistically, X has significant impact on Y, as P Value is less than 0.05

Baking Temperature

X ContinuousRegression Analysis


Oven Type X DiscreteMood’s Median Test


Mixing Time X ContinuousRegression Analysis


Oven Heat-up Time

X ContinuousRegression Analysis

0.201 Statistically, X has no significant on Y, as P value is greater than 0.05

Summary on Findings on Xs contributing to Printing Defects aAnalyze

Measurable Type

Data Type

Test Performed P-Value Inference

WAP Maida(%) Y DiscreteMood’s Median Test

N/A N/A

Vendor for Maida

X DiscreteMood’s Median Test


% Maida X DiscreteMood’s Median Test


% Ghee X DiscreteMood’s Median Test


Vendor for Ghee

X DiscreteMood’s Median Test


Operator qualification

X DiscreteMann-Whitney Test


Summary on Findings on Xs contributing to Stitching Defects aAnalyze

Box – plot on Baking Time, Baking Temperaturei

Improve

A5A4A3A2A1

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3

2

Baking Temperature Category

Pro

ject

Y

Boxplot of Project Y

DCBA

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Baking Time category

Pro

ject

Y


DCBA

DCBA

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DCBA

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A1

Baking Time category

Pro

ject

Y

A2 A3

A4 A5


Panel variable: Baking Temperature Category

Baking Time and temperature data have been categorized to facilitate to draw them in box plot .

Box plot of Baking time and Temperature does not produce any concrete information on specific contributors.

Box plot of time versus temperature shows that with Baking time category A and Temperature category A3 there is some scope of improvement

Box Plot on Oven Type and Mixing Time iImprove

OVEN COVEN BOVEN A

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OVEN

Pro

ject

Y_1

Boxplot of Project Y_1

10987

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Mixing Time

Pro

ject

Y_1


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OVEN A

Mixing Time

Pro

ject

Y_1

OVEN B

OVEN C

Boxplot of Project Y_ 1

Panel variable: OVEN

Box Plot for Oven type and Mixing time has beenPlotted

With oven type it is hard to conclude which Oven plays roleIn reduced breakage, but with Mixing time, “10” time is playing role in breakage

Box plot for Mixing time and Oven type shows that If mixing time is “10” and Oven Type is “C” combination of these two contributes to reduction in breakage

Box Plot on Ghee Percentage iImprove

1514131211

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% Ghee

Pro

ject

Y_1


Box plot on ghee percentage concludes that breakage is low, when the ghee percentage used is 11%

QFD-House of Quality (process requirements-Production Methods)i

Improve

FMEA for Biscuit Manufacturing Processi

Improve

Implementing Solutions and Time Lines – Pilot Lot Runi

Improve

221199177155133111896745231

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Observation

Indiv

idual V

alu

e

_X=2.048

UCL=3.800

LCL=0.295

1 2

221199177155133111896745231

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1

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Observation

Movin

g R

ange

__MR=0.659

UCL=2.154

LCL=0

1 2

1

11111

I-MR Chart of % age breakage by C12

Evaluation of the Improvement in Pilot Lot Run

Process after improvement is marked in dotted circle, shows that improvement is there in the pilot lot. No points is falling beyond control limits suggests that the process is in statistical control

iImprove

Hypothesis Test to validate statistically significant improvement

Inference: Two sample proportion test was conducted to test whether there was statistically significant improvement during pilot run, p value observed is 0.000 which is less than 0.05, thus we can

conclude that there is significant improvement.

iImprove

Two-Sample T-Test and CI: Before, After

Two-sample T for Before vs After

N Mean StDev SE MeanBefore 208 4.249 0.924 0.064After 15 2.048 0.492 0.13

Difference = mu (Before) - mu (After)Estimate for difference: 2.20195% CI for difference: (1.905, 2.497)T-Test of difference = 0 (vs not =): T-Value = 15.48 P-Value = 0.000 DF = 21

Hypothesis Test to validate whether the target is met or not

Inference: Hypothesis test was conducted to check whether pilot results proportion is meeting the target proportion of 0.015, P-value observed is 0.714 which is greater than 0.05 which shows that there is no significant difference in proportion of the pilot lot and the target, which means the target

is met.

iImprove

One-Sample T: After

Test of mu = 2 vs not = 2

Variable N Mean StDev SE Mean 95% CI T PAfter 15 2.048 0.492 0.127 (1.775, 2.320) 0.37 0.714

2512262011761511261017651261

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Observation

Indiv

idual V

alu

e

_X=2.034

UCL=3.322

LCL=0.746

1 2 3

2512262011761511261017651261

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Observation

Movin

g R

ange

__MR=0.484

UCL=1.583

LCL=0

1 2 3

1

11111


Control chart for checking the sustainability over a period of time

Post pilot monitoring of the process over one month

Pilot lot monitoring

Inference: Post pilot monitoring of process shows that process is still in statistical control.

cControl

CONTROL PLAN cControl

IMPROVED PROCESS HANDOVERING – PROJECT CLOSURE cControl

1. Process is being handed over to the process owner with the status of improvement shown in the above control chart

2. Control chart should be in place for monitoring the process

3. Revised Process documents, auditing checklist, control plan handed over to operating personnel with changes that were done during pilot run.

4. Out of control action plan for control chart developed and handed over to operations manager

2512262011761511261017651261

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Observation

Individual Value

_X=2.034

UCL=3.322

LCL=0.746

1 2 3

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1

0

Observation

Moving R

ange

__MR=0.484

UCL=1.583

LCL=0

1 2 3

1

11111


Documents

Advanced Innovation Group provide Six Sigma Project for Reducing Breakage Percentage