submitted Six Sigma Project

04/14/20231 04/14/20231

Project Name : Six sigma project on Increase in Productivity By Waste Reduction (Reduce the %Breakage)Project Owner : Ariyam Bhattacharya

DMAIC

04/14/20232

Customer Sample Comments Key Output CharacteristicsImportant to Customer (CTQ's)

VP • The objective is to increase the overall productivity & minimise the waste without compromising quality

• Improvement in productivity• Reduce waste• Quality

Process owner • We have to reduce the total no of breakage and maintain the breakage below 3% .

• Improvement in Productivity• Reduce % of breakage.

DefineMap the Project

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Project Charter Define

Project Leader: Soumen Jana

Team Members

Business Case: “X” has been one of the leading biscuit brands in India for the last 8 decades. “X” is reputed to enjoy the most sales among all biscuit brands in the world. Its biscuits offer a fantastic combination of quality, taste, and nutrition. “X” biscuits are available even in faraway villages. At present the company has a 40 percent share of the Indian market for biscuits and is a multi-million dollar organization.

Stakeholders VP

Sponsor GM

SME Sen Manager

Team Member SME,QA, 2 Associates, Financial controller, GB, BB

Problem Statement: For the period of June 10 to Jan 11 the total productivity was reduced as %breakage is more than 3.

Goal Statement: To improve process productivity by decreasing the total no of breakage. %Breakage should be decreased to below 3.

Project In Scope: 1. Production, Quality, vendors and storeProject Out of Scope:

Timelines/Milestones/Phases Start Date End Date

Start date: 1h Feb 2011 -DEFINE 15th Feb 2011 10th March

MEASURE 11th March 15th AprilANALYZE 17th April 30th MayIMPROVE 5th May 20th JuneCONTROL 25th June 15th Aug 2011

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Key Stakeholders Define Measure Analyze Improve Control

VP I

GM A R A M A

Senior Manager M M M M M

BB M M M M M

GB M M M M M

M1,M2 -- --- M M M

Financial controller I I I I M

Message Audience Media Who When

Communication Plan

Define

ARMI WorksheetARMI & Communication Plan

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Customer Output Process

Input Supplier

COPIS Define

Purchase Raw Materials

Quality Inspection

Issue RMs to production

Mixing of RMs in predefined

condition

Dough Making

Designing & cutting

Vendors Order placed Raw Material Store and Quality

Warehouse RM sampleApproved RMs Production

Warehouse and Quality

Approved RMStart Process Production

Production operator

Raw material charging as

per sequenceHomogeneous

pasteProduction

Production Paste Proper Dough Production

Production Proper Dough Proper shape Production

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Process Map Define

Baking

Empty the baking trey

Divide in 6 groups

FG Store

Production Raw shaped biscuit

Baked biscuit Production

Production Approved Biscuits

Packed biscuit FG store

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Y OperationalDefinition

DefectDefinition

PerformanceStandard

Specification Limit

Opportunity

%Breakage It is defined as the percentage of total no of biscuits broken against total production

%Breakage greater than 3

%Breakage <=3 USL = 3LSL = 0

Daily

Y Data Type UnitDecimal to

be Used

Data Base Container

Existing or new data

base

If New when data base would be

ready.

Plan start date for

DCP

%Breakage Discrete No Upto Two Excel Existing NA 11th March 11

Data Collection Plan

Equipment Used for

measurement

Equipment Calibration Information

ResponsibilityAny

Training need

Operator Information

Measure

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Measurement System Analysis

Rule Rule Description Acceptable Result

A R&R % of Tolerance <10%

B% Contribution (R&R Std deviation)

Smaller than Part-to-part

variance

C Number of distinct categories >4

Overall Gage result

Minitab Descriptive Stats

Insert Minitab session window descriptive stats.

For discrete data use the attribute gage study exhibit from Minitab

Measure

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Measure

Median 4.25

Mean 4.430

Mode 4.969

Std Dev 0.924

Cp 0.541

Cpk -0.516

Z value -1.547

Z Score of the process is really poor, there is immediate need to improve the process capability

Process capability Analysis

04/14/202310

Data Stability test

Take away: Patterns suggest that the variation observed is due to "special causes“. further investigation needs to be done to ascertain the causes of and mixtures.

P value for mixture is less than 0.05. means data is not stable.

Analyze

04/14/202311

Normality Test

Normality: P value = 0.038

Shape: Non-Normal

Measure of central tendency :data is non-normal measure of central tendency will be Median = 4.249

Analyze

04/14/202312

I-MR test of % Breakage Analyze

The red points on the I-MR chart shows that currently the Breakage is out of control in the process and requires an urgent attention.

X =3

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Proposed tests according to problem and factor’s data typeAnalyze

S No. Potential Cause Operational Definition Data Type Test of be performed

1%Breakage from

MachinesPercentage of broken biscuit for each machine against total biscuit produced Cont Correlation & Regression

2 Baking time Time required for baking of biscuits produced in a day Cont Correlation & Regression

3 Baking temp. Temp. required for baking of biscuit produced in a day Cont Correlation & Regression

4 Oven Used for baking of biscuits Discrete 1-sample sign.

5 WAP(maida%) Cont Correlation & Regression

6 Oval heat up time Cont Correlation & Regression

7 Vendor of Maida Discrete 1-sample sign.

8 Moisture %(maida) Moisture present in maida in percentage Cont Correlation & Regression

9 Moisture (Ghee%) Moisture present in ghee in percentage Cont Correlation & Regression

10 Vendor of Ghee Discrete 1-sample sign.

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Regression Analysis: % Breakage versus %A

The regression equation is% Breakage = 3.57 + 0.817 %A

Predictor Coef SE Coef T P%A 0.8170 0.1039 7.87 0.000

S = 0.812521 R-Sq = 23.1% R-Sq(adj) = 22.7%

AnalyzeRegression Test between %Breakage & %Breakage from each machine

Regression Analysis: % Breakage versus %B

The regression equation is% Breakage = 4.04 + 0.305 %B

Predictor Coef SE Coef T P%B 0.3046 0.1446 2.11 0.036

S = 0.916705 R-Sq = 2.1% R-Sq(adj) = 1.6%

Regression Analysis: % Breakage versus %C

The regression equation is% Breakage = 3.73 + 0.855 %C

Predictor Coef SE Coef T P%C 0.8550 0.1332 6.42 0.000

S = 0.845816 R-Sq = 16.7% R-Sq(adj) = 16.3%

Regression Analysis: % Breakage versus %D

The regression equation is% Breakage = 4.36 - 0.328 %D

Predictor Coef SE Coef T P%D -0.3283 0.3136 -1.05 0.296

Regression Analysis: % Breakage versus %E

The regression equation is% Breakage = 3.75 + 0.908 %E

Predictor Coef SE Coef T P%E 0.9079 0.1925 4.72 0.000

S = 0.880239 R-Sq = 9.7% R-Sq(adj) = 9.3%

The regression equation is% Breakage = 3.47 + 0.641 %F

Predictor Coef SE Coef T P%F 0.64115 0.08125 7.89 0.000

S = 0.811917 R-Sq = 23.2% R-Sq(adj) = 22.8%

The Regression test shows that since the P-Value is < 0.05, %Breakage from Machine A,B,C,E,F (X) has impact on Total % Breakage (Y).

04/14/202315

Regression Analysis: % Breakage versus Baking Time

The regression equation is% Breakage = 2.70 + 0.0933 Baking Time

Predictor Coef SE Coef T PBaking Time 0.09331 0.04421 2.11 0.036

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

Regression Test between %Breakage & %Breakage from each machine

Regression Test between %Breakage & Baking timeAnalyze

The Regression test shows that since the P-Value is < 0.05, Baking time has impact on % Breakage (Y).

04/14/202316

Regression Analysis: % Breakage versus Baking Temp

The regression equation is% Breakage = 13.6 - 0.0443 Baking Temp

Predictor Coef SE Coef T PBaking Temp -0.04434 0.02049 -2.16 0.032

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

Regression Test between %Breakage & Baking temp Analyze

The Regression test shows that since the P-Value is < 0.05, Baking temp has a –ve impact on % Breakage (Y).

04/14/202317

Mood Median Test: Ovens vs %Breakage

Mood median test for C2Chi-Square = 9.93 DF = 2 P = 0.007

Subscripts N<= N> Median Q3-Q1% Breakage_Oval C 16 32 4.475 0.837% Breakage_Ovan A 56 55 4.238 1.343% Breakage_Ovan B 32 17 4.028 1.008

Individual 95.0% CIsSubscripts ---------+---------+---------+-------% Breakage_Oval C (-------*-------)% Breakage_Ovan A (----------*--------)% Breakage_Ovan B (---------*-------) ---------+---------+---------+------- 4.00 4.25 4.50

Overall median = 4.246

Regression Test between %Breakage & Oven Analyze

The Moods median test shows that since the P-Value is < 0.05, there is a difference between three ovens data.

Sign Test for Median: % Breakage_Oval C

Sign test of median = 3.000 versus not = 3.000

N Below Equal Above P Median% Breakage_Oval C 48 2 0 46 0.0000 4.475

Sign Test for Median: % Breakage_Ovan A


N Below Equal Above P Median% Breakage_Ovan A 111 12 0 99 0.0000 4.238

Sign Test for Median: % Breakage_Ovan B


N Below Equal Above P Median% Breakage_Ovan B 49 5 0 44 0.0000 4.028

The 1-sample sign test shows that since the P-Value is < 0.05, there is an impact of ovens on %Breakage.

04/14/202318

Regression Analysis: % Breakage versus Oval Heatup Time

The regression equation is% Breakage = 4.44 - 0.0113 Oval Heatup Time

Predictor Coef SE Coef T POval Heatup Time -0.011260 0.008782 -1.28 0.201

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

Regression Test between %Breakage & Oval heat up timeAnalyze

The Regression test shows that since the P-Value is > 0.05, oval heat up time has no impact on % Breakage (Y).

04/14/202319

Regression Test between %Breakage & Wap (maida%)Analyze

Regression Analysis: % Breakage versus WAP(maida) %

The regression equation is% Breakage = 1.66 + 0.357 WAP(maida) %

Predictor Coef SE Coef T PWAP(maida) % 0.3570 0.4257 0.84 0.403

S = 0.924950 R-Sq = 0.3% R-Sq(adj) = 0.0%

The Regression test shows that since the P-Value is > 0.05, Wap (Maida%) has no impact on % Breakage (Y).

04/14/202320

Regression Test between %Breakage & Maida vendorAnalyze

Mood Median Test: Maida vendors


Subscripts N<= N> Median Q3-Q1% Breakage_Bikajee 55 57 4.270 1.143% Breakage_Kalkaji 26 19 4.097 1.298% Breakage_Panwar 23 28 4.413 1.156

Individual 95.0% CIsSubscripts -------+---------+---------+---------% Breakage_Bikajee (--------*-------)% Breakage_Kalkaji (----------*------------)% Breakage_Panwar (-------------*------) -------+---------+---------+--------- 4.00 4.25 4.50


The Moods median test shows that since the P-Value is >0.05, there is no difference between data of Maida vendors

The 1-sample sign test shows that since the P-Value is < 0.05, there is an impact of Maida vendors on %Breakage.

Sign Test for Median: % Breakage_Kalkaji


N Below Equal Above P Median% Breakage_Kalkaji 45 5 0 40 0.0000 4.097

Sign Test for Median: % Breakage_Panwar


N Below Equal Above P Median% Breakage_Panwar 51 4 0 47 0.0000 4.413

04/14/202321

Regression Test between %Breakage & Moisture % in MaidaAnalyze

Regression Analysis: % Breakage versus Moisture(Maida) %

The regression equation is% Breakage = 4.35 - 0.0078 Moisture(Maida) %

Predictor Coef SE Coef T PMoisture(Maida) % -0.00784 0.06066 -0.13 0.897

S = 0.926490 R-Sq = 0.0% R-Sq(adj) = 0.0%

The Regression test shows that since the P-Value is > 0.05, Moisture% (Maida) has no impact on % Breakage (Y).

04/14/202322

Regression Test between %Breakage & Moisture % in GheeAnalyze

Regression Analysis: % Breakage versus Moisture(Ghee) %

The regression equation is% Breakage = 1.15 + 0.243 Moisture(Ghee) %

Predictor Coef SE Coef T PMoisture(Ghee) % 0.24349 0.05938 4.10 0.000

S = 0.890878 R-Sq = 7.5% R-Sq(adj) = 7.1%

The Regression test shows that since the P-Value is < 0.05, Moisture% (Ghee) has an impact on % Breakage (Y).

04/14/202323

Regression Test between %Breakage & Vendor for GheeAnalyze

Sign Test for Median: % Breakage_Amul


N Below Equal Above P Median% Breakage_Amul 78 7 0 71 0.0000 4.086

Sign Test for Median: % Breakage_Gopal


N Below Equal Above P Median% Breakage_Gopal 58 6 0 52 0.0000 4.402

Sign Test for Median: % Breakage_Madhusudan


N Below Equal Above P Median% Breakage_Madhusudan 71 6 0 65 0.0000 4.400

Sign Test for Median: % Breakage_MD


N Below Equal Above P Median% Breakage_MD 1 0 0 1 1.0000 4.554

Mood Median Test: C2 versus Subscripts


Subscripts N<= N> Median Q3-Q1% Breakage_Amul 46 32 4.086 1.089% Breakage_Gopal 26 32 4.402 1.186% Breakage_Madhusudan 32 39 4.400 1.156% Breakage_MD 0 1 4.554 *

Individual 95.0% CIsSubscripts --------+---------+---------+--------% Breakage_Amul (----------*-------)% Breakage_Gopal (-----------*----------)% Breakage_Madhusudan (---------*-------)% Breakage_MD --------+---------+---------+-------- 4.00 4.25 4.50


The Moods median test shows that since the P-Value is >0.05, there is no difference between data of Ghee vendors

The 1-sample sign test shows that since the P-Value is < 0.05, there is an impact of Ghee vendors on %Breakage.

04/14/202324

S No. Potential Cause Data Type Test of be performed Impact

1 %Breakage from Machines Cont Correlation & Regression Significant relationship of machines except D on Y

2 Baking time Cont Correlation & Regression Significant relationship on Y

3 Baking temp. Cont Correlation & Regression Significant relationship on Y

4 Oven Discrete 1-sample sign. Significant relationship on Y

5 Oval heat up time Cont Correlation & Regression No significant relationship on Y

6 WAP(maida%) Cont Correlation & Regression No significant relationship on Y

7 Vendor of Maida Discrete 1-sample sign. Significant relationship of Maida vendors on Y

8 Moisture (maida%) Cont Correlation & Regression No Significant relationship on Y

9 Moisture (Ghee%) Cont Correlation & Regression Significant relationship on Y

10 Vendor of Ghee Discrete 1-sample sign. Significant relationship of Ghee vendors except MD on Y

Summary of Statistical AnalysisSummary of Statistical Analysis Analyze

The factors highlighted in red are the vital X’s

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