Six Sigma for Process Improvement & Reduction in Pastry Variations (1)

8/3/2019 Six Sigma for Process Improvement & Reduction in Pastry Variations (1)

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Use of Six Sigma Tools for

Process Improvement &Reduce Pastry Variations

Project At,

Submitted By,

Mr. Prasun Awasthi

Mr. Shamoil Lokhandwala

Under the Guidance of

Dr. Neil Sequiera

Rizvi Institute ofManagement Studies &

Research


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Off Carter Road, Bandra (W), Mumbai 50.

MMS (Operations) 2007 2009

CERTIFICATE

This is to certify that the project entitled,

Use of Six Sigma Tools to Reduce Pastry Variations &

Process Improvement

submitted by,

Mr. Prasun Awasthi

Mr. Shamoil Lokhandwala

as a part of their summer internship at Monginis Foods Pvt. Ltd, is approved for the

MMS (Operations) 2007-09 course of the University of Mumbai atRizvi Institute of Management Studies & Research.

________________ _________________

Prof: Kalim Khan Dr. Neil Sequiera

(Director RIMSR) (Internal Guide)

__________________

Mr. Yusuf Patanwala

(External Guide)

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Acknowledgement

Having completed this project, it is time for us to express our

deep sense of gratitude to all those who have helped us in the

successful collection of data for the project report.

First we would like to take this opportunity to thank our project

guides,

Mr. Kumail KhorakiwalaChairman & Joint Managing Director

Monginis Foods Pvt. Ltd.

Mr. Yusuf PatanwalaAsst. Manager (Production Development)

Monginis Foods Pvt. Ltd.

Mr. Jayesh Vaidya

Production ManagerMonginis Foods Pvt. Ltd.

Dr. Neil SequieraProfessor, R.I.M.S.R.

Vice President (HR) ELBEE Express

for being a constant source of inspiration and media of help and

support during the entire internship period. We would like to express

our gratitude towards them for their invaluable advice and guidance.

We also sincerely thank the staff and workers of Monginis Foods

Pvt. Ltd for their kind help and co-operation during the entire period.

We would also like to thank our classmates and colleagues who

encouraged us fruitfully in our work.

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Thanks to all for their help and co-operation.

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Table of Contents

--- Abstract 6

Chapter 1. Overview about MONGINIS FOODS PVT. LTD 7

Chapter 2.Objective of the Project 8

Chapter 3.Methodology 9 - 10

Chapter 4.Pastry Making Process An Overview 11 - 17

Chapter 5.Identification & Grouping of Causes 18 - 23

5.1 Fishbone Diagram

5.2 Pareto Analysis

Chapter 6.Root Cause Analysis Defects Related to Sponge 24 -

70

6.1 Man

24 - 29

i. Trial 1 Effect of Depositing Pattern

ii. Trial 2 Effect of Hand Leveling

iii. Trial 3 Effect of Handling Process

6.2 Depositi

ng Operation 30 - 41i. X- & R Chart (To Check Process Capability)

ii. ANOVA 1 Density of Batter

iii. ANOVA 2 Type of Unifiller Machine

iv. X- & R Chart (For Improved Process Capability)

6.3 Equipme

nts 42 - 54

i. P- Chart

1. Oven-wise

2. Deck-wise

3. Mould-wise

4. Trial (To Check Baking Loss)

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6.4 Simulati

on 55 - 70

i. Need for Simulation

ii. Time Study (Mixing Depositing Baking)

iii. Sample Simulation

Chapter 7. Internal Logistics 71 - 80

7.1 Flow of Moulds

7.2 Flow of Sponges Critical to Quality

i. Present Scenario PUSH-PULL System

ii. Just in Time PULL System

iii. Pivot Table (For Real Time Inventory)

Chapter 8. Unbalanced Line 81 - 88

8.1 Fishbone Diagram

8.2 Pareto Analysis

8.3 Time Study (For Pastry Production Line)

Chapter 9. Future Scope 89 - 90

--- Annexure 91 - 93

--- References 94

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ABSTRACT

Monginis Foods Pvt. Ltd. is the largest celebration cakes & pastry

manufacturing company in India. The product portfolio consists of total

of about 160 products. Due to high scalability of operation and semi

automatic nature of process, production is subjected to manual

operation causing variation in pastries and thereby leading to error.

While analyzing the root cause for such errors, we have studied entire

pastry making process in great detail. This gave us an opportunity to

define problems, to analyze causes of error, to trim down causes with

the help of Six Sigma tools. Outcome of this study has resulted in

development of simulation software for baking. Additionally thissoftware will streamline the production process with also help in the

application of Just-in-Time principle.

We hope that, this project will help company to achieve a higher sigma

level for pastry production.

We believe this report will achieve its stated objectives.

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1. MONGINIS FOODS PVT

LTD.

Once upon a time, as they say in fairy tales, more than a century ago,

Monginis was a spacious boulangerie right in the heart of the Fort area

of Bombay, as the city used to be then called. Signor Mongini and his

brother, expatriates of Italian descent, were the presiding deities at

this boulangerie premiere with glass frontage and display stands

patronized by the European expats as well as the more westernized

among the locals citizens.

Old timers still swear by the pastry sold at Monginis situated at thespot where the Akbarallys Flora Fountain Department Store now

stands.

Come Independence and Monginis continued to prosper. But as the

sixties dawned, the Monginis brothers decided to close shop and return

home.

This dovetailed perfectly with the enterprising Khorakiwala Familys

business plans. Sensing a new and profitable opportunity, they bought

the Monginis bakery and brand, lock, stock and barrel.

Within a decade, the Monginis expansion plan based on the franchising

business model was evolved and fine-tuned. A nationwide network of

Monginis shops began to emerge gradually.

Today, Monginis own a sprawling headquarters and state-of-the-art

manufacturing facilities in a North-western Mumbai suburb where an

ever-expanding range of cakes and bakery products, both packaged

and oven-fresh, roll of the conveyor belt and are whisked away to themany Monginis shops awaiting fresh supplies of Celebration Cakes,

Cookies, Specialty Breads, Chocolates, Snack Foods and Savouries.

The product portfolio consists of about 160 products and it has about

183 outlets / shops all over Mumbai. Monginis has 9 bakeries all over

India which makes it the largest organized player in the bakery

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industry in India. Monginis Andheri plant is HACCP (Hazard Analysis

& Critical Control Point) certified which meets stringent quality &

hygiene parameters. Monginis has made a name for itself in site

delivery and accessorized carry-out catering, with telephone and

internet ordering options. Over the years, Monginis food ltd. has

established itself as the unchallenged leader in the Cake & Pastries

and other bakery products.

2. OBJECTIVE OF PROJECT

On an average Monginis produces 25,000 pastries of different types

daily. Since production is done on a mass scale and is semi-automatic

in nature, there are variations with respect to size & shape. This

ultimately results in rejection. Hence the objectives for the above

project are as follows:

1. To study the entire pastry making process in detail.

2. Identify & group the causes in every process & sub processes.

3. Measure & analyses the defects & its sources with the help of six

sigma tools.

4. Eliminate or reduce the root causes of pastry variation by stream

lining the operations. Thereby reducing the overall cycle time &

eliminate work in progress inventory through simulation.

5. Identifying the causes of unbalanced production line. Thereby

eliminating unproductive time & processes.

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3. METHODOLOGY

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1) Use of fish-bonediagram (cause effect diagram) to identify &

group the causes as follows:

2) Use of pareto diagram to quantify the significance of each

cause to segregate the vital few from trivial many.

3) Use of Control Charts to quantify the defects.

a. For Variable data X & R Charts

b. For Attribute data P Chart.

4) Formulation of hypothesis & use of ANOVA for decision making.

5) Simulate the entire baking process subject to different

constraints so as to reduce idle time.

6) Use of Gantt chart to identify the daily production requirement of

sponges on different production lines at a given point of time to

reduce the WIP inventory.

7) Study of the pastry production line to stream line the entire

production with respect to availability of man, material &

equipments.

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4. PASTRY MAKING PROCESS

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Monginis has a wide variety of fresh cream & butter cream pastries. On

an average it produces more than 25000 pastries daily. The pastries

consist of 25 basic varieties and they vary in size, shape, flavour &

type (veg, non-veg).

The entire pastry making process is explained below:

1. RECEIVING OF RAW MATERIAL

The raw materials such as icing sugar, white sugar, super flour,

ordinary flour, chocolate, corn-flour, milk powder etc., required for

making different types of pastries & cakes are procured & stored in the

raw material stores after strict quality inspection.

2. BATCH MAKING

This is the very next process where the batches are made as per

different recipes.

a. Sieving of Flour: The flour is then sieved, so as to ensure

the supply of fine powder for mixing.

b. Metal Detection: The sieved flour is then passed through

a metal detection machine in order to detect the presence

of any minute metal particles in the flour. This is an

important activity with regards to the quality.

The batch size of various flavours is as follows:

BATCH APPROX WEIGTH PER BATCHChocolate Eggless 53 kgWhite Eggless 52 kgWhite 45 kgChocolate 45 kgDark Chocolate (Black) 39 kg

3. MIXING

There are a total of 2 mixers available which run simultaneously

to mix the batches that are already made & provided to the

mixing department. There is a set protocol for mixing of different

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flavours. The eggless mixtures are mixed first. The mixer

containers then under go washing & then used for mixtures

containing egg. The same protocol for mixing is followed

throughout the subsequent processes.

4. DEPOSITING

The batter is then poured into different moulds through a

machine known as uni-filler depositing machine. This machine

works on the principle of volumetric deposition. This machine

consists of a pneumatically operated piston which on pressing of

the pedal drops a mass of the batter from the hopper. Different

types of moulds as per the order are placed below the hopper,

and the stroke of the piston is then set according to the weight

required to be deposited in the mould.

a. Leveling: This activity is carried out immediately after the

depositing operation. The batter that is poured in the

mould is in the lump form and it has to be spread equally

in the mould. Leveling operation is an important operation

because if the moulds are not leveled properly it leads to

slantness in sponges.

b. Placing moulds on the baking conveyor line: After theleveling operation. The moulds are then placed on the

conveyor which leads it to the baking department, wherein

they are loaded in the oven manually. Non-synchronization

between depositing leveling placing the moulds on

conveyor loading in oven activities may lead to

development of work in process inventory.

5. BAKING

This is the core activity of any bakery. There are 4 deck ovens &

1 rotating oven in the baking department. The moulds once

loaded in the ovens are baked for 25 minutes at a temperature

of 190o to 210o C. The baking activity consists of two sub

activities.

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a. Loading the moulds in ovens: This activity includes

loading of moulds (the batter in which is leveled) into oven.

Hence this activity becomes critical with regards to

variation, if not handled properly because improper or

rough handling causes unevenness or slantness in the

surface of sponge.

This activity is also important because, if the no. of moulds

that are loaded per deck per oven is not up to its optimum

capacity, then it may result in underutilization of oven

capacity.

If the time that is required to load the entire oven is not

kept constant, it may disturb the loading & unloading

pattern of ovens.

b. Unloading the moulds from ovens: After the batter is

baked, the moulds are removed from oven & placed in

trolleys for the cooling & de-panning. Each trolley can

accommodate 60 ladi moulds.

6. DE-PANNING

a. Cooling of Sponges: The moulds are cooled (at ambienttemperature with the help of fans) for about 15 to 20

minutes.

b. De-panning: After the sponges have cooled. The trolley is

brought to the de-panning table where the sponges are

removed from the moulds. This is again a critical activity

with regards to the following aspect

i. Moulds if not handled properly, may lead to

deformation of moulds, not only causing variation in

sponge (that will be baked through them later) but

also reducing the mould life.

ii. If Sponge is not de-panned properly, there is a

chance of damaging the sponges edges / corner.

This leads to excess side cutting, resulting in

increased wastage as well as variation in size of the

pastries.

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c. Panning: The moulds once de-panned are re-used in the

next cycle. This activity consists of removing the crumbs of

sponge of previous cycle, spreading of ghee and placing

butter paper. This operation is vital from following two

aspects

i. If the crumbs are not removed properly, it will lead to

damage or spoilage of the sponges that are going to

be baked in subsequent cycles.

ii. If the butter paper that is placed in the moulds is

under size or over size, again it leads to variation in

shape of the sponge. Over sized paper tilts over the

leveled batter in moulds damaging the corners of the

sponge while under sized paper results in the

sticking of batter on the mould surface, which

ultimately causes more crumbs along the inner sidesof the moulds.

7. PASTRY PRODUCTION LINE

The pastry production is done on a conveyor line just like an

assembly line concept, in which different activities are performed

by different workers at different work stations along the

conveyor.

As the production is done on a forecasting basis, the production

department prepares a sheet which contains the details of no. of

pastries to be produced & accordingly no. of ladis that are

required.

After the de-panning is over, the sponges are either directly fed

to the production line or moved to the cold storage. The sponges

that are to be fed to the production line, sometimes are stored in

a temporary storage (racks) in case they are not needed on theproduction line on the spot.

There are 25 different types of pastries that are produced on 4

different production lines. 3 of which are for fresh cream pastries

& 1 for butter cream pastries. (In our project, we are focusing on

fresh cream pastries only). As each type of pastry is made in a

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very customized manner, so it is not possible to explain each and

every process of each pastry. As a part of our study we have

elaborated on some processes which are commonly followed.

a. Slicing: In this activity the worker does the side cutting &

slices the sponge depending upon the type of pastry.(Whether one cream layer or 2 cream layers). This operation

gains importance if the ladi sponge is uneven or slant

because, in such a case to mitigate the effect of slantness,

the worker has to put some extra pieces of sponge in

between two uneven layers to get the required height.

b. Layering: In this operation, the worker spreads

cream/chocolate between the layers of sponge. This operation

also gains importance if the ladi sponge is uneven, because

here again the worker has to compensate for the unevennessby adding some extra cream/chocolate.

c. Creaming: This is also known as a topping operation, wherein

cream/chocolate is put on the topmost layer of the sponge as

well as on its sides.

d. Cooling: After creaming the ladi has to be cut in pieces as

per the pastry size. But since the cream is fresh & semi solid

is nature, it has to be first set to ensure better cutting & also

to maintain its freshness, the ladi is passed through a cooling

tunnel wherein it remains for 450 secs approx at a

temperature of -25o C.

e. Cutting: This is the most critical operation on the entire

pastry line. Because most of the rejection is subjected to this

operation.

From 1 ladi, 30 pastries are made. The worker is required to

make 4 horizontal cuts & 5 vertical cuts. This activity is so

critical, that one improper/inclined horizontal cut may lead to

rejection of 12 pcs even if all other cuts are proper. Similarly

one wrong vertical cut may lead to rejection of 10 pastry pcseven if all other cuts are accurate.

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1 2 3 4

5

1

2

3

4

Fig: Different Cuts for 1 Ladi


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f. Final Finishing: This operation varies from pastry to pastry

as the final decoration varies according to the type & flavour

of pastries.

8. FINISHED GOODS STORAGE

Fresh cream pastries are stored in the cold rooms product-wise,

where in the temperature is maintained at -25o C. Butter cream

pastries are stored at ambient temperature.

9. SORTING

The company caters to more than 180 shops spread across

Mumbai Metropolitan Region. The shops are divided into super

long route, long route, medium route & short route. The products

are sorted shop wise as well as route wise.

10. DESPATCH

This is the final activity of the entire pastry making process in

which the product is loaded into the vans route wise and then

dispatched from the plant in a sequence of super long long

medium short route respectively.

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5. IDENTIFICATION & GROUPING

OF CAUSES

5.1 FISH BONE DIAGRAM

Fish bone diagram which is a cause effect diagram helps us to organize

existing theories about the causes & to develop new ones. It cannotidentify the root cause; it simply represents graphically many causes

(X) that might contribute to observed effect (Y). This graphical

representation helps to focus the search for the root cause and

contributes in better understanding of the problem.

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Leveling

Moulds

Handling

Creaming

Uneven Surface

of Ovens

Panning /

Depanning

Density

Variation in Stroke of

Depositing m/c

Variation in Temp.

zone in oven

Unbalanced

Line

Work in Process

Inventory

Cutting

Sponge

Pastry

Temp.

R.M. Mix

New m/c

Unifillerm/c

Sponge

Mixture

PROCESS

VARIATIO

IN PASTR

EQUIPMENT

MANDEPOSITING

(OPERATION)


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After understanding the entire pastry making process, cause effect

diagram for variation in pastries (Y) with all possible causes (X) is

shown above.The explanation for the same is given below

Variation in pastry is an effect, whose causes can be grouped in four

major categories, which are

1. MAN

2. MACHINE / EQUIPMENT

3. PROCESS

4. OPERATION (DEPOSITING)

If we analyze each factor in more detail then sub causes for these

causes can be identified and shown with the help of dotted arrows in

the above diagram.

MAN

Most of the operations in pastry making are manual. Hence it is

subjected to skills of the worker. Variations in pastry making may come

from different sub causes where manual operation is done.

a. Leveling

b. Handling

c. Creaming

d. Cutting

e. Panning , de-panning

If we consider the cutting operation in more detail, then it involves

cutting of sponge & cutting of pastry leading to variations.

DEPOSITING

In depositing operation, variations in pastry came from variation in

sponge. In this operation it is observed that weight of the batter

deposited in mould is not constant. Hence possible reasons for the

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same were identified which were density & variation in stroke of two

different unifiller machines. In more detail density is the function of

raw material mixture i.e. content & temperature.

Some times due to negligence of worker, the no. of stroke to be

deposited in a particular mould may increase or decrease. There by

leading to variation in weight.

MACHINE / EQUIPMENT

Ovens play a vital role as the baking operation is the heart of the

system. Each oven has four decks. But some of these decks have

uneven surfaces which lead to variation in sponge, thereby leading to

variation in pastry.

PROCESS

After sponge is made, the entire pastry making process takes place on

a pastry production line. As the entire process is done on a conveyor

line, it has to be highly synchronized. If the line is unbalanced it leads

to many concerns like productivity, hygiene issues, speed of operation,

and generation of WIP etc. As explained in earlier part, cutting

operation is the most critical operation on the line. Unbalanced linemay cause the worker to expedite the cutting process thereby making

a wrong cut leading to rejection. Hence it becomes necessary to

identify the causes of unbalanced line to eliminate or minimize them to

the maximum extent.

If we refer the fish bone diagram, the cause process shows one sub

cause as Work in process inventory. This includes inventory of sponge

due to mismatch between demand at production line & supply from

baking section. Work in process of batter means moulds that get pilledup before being put into the oven. So it becomes critical to stream line

the entire process from mixing to depositing to baking to production

line, so that the entire WIP inventory of sponge & batter can be

completely eliminated. The importance of each cause, its analysis and

the possible ways to eliminate or minimize this causes are discussed in

the subsequent part of the report.

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5.2 PARETO ANALYSIS

2.00 1.00 3.00 4.00 5.00

CAUSES OF DAMAGE

0.00

10.00

20.00

30.00

40.00

NOOFDEFECTIVEPCS

0%

20%

40%

60%

80%

100%

Percent

15.00 14.00

5.00 4.002.00

We have used the Pareto diagram to prioritize the causes that we

have identifies with the help of fish bone diagram. The observationswere taken on black forest pastry (which is one of the highest selling

pastries) for 10 ladis i.e. 360 pastries. Out of these 360 pastries 40

pastries were damaged or rejected.

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72.5%Vital Few

Trivial Many


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The reasons for the same are given below.

Causes No. of Pastries % of Total

Damaged

Cumulative %

1. Corner Damage 14 35 % 35%2. Sponge Layer -

Thin / Thick

15 37.5% 72.5%

3. Cutting - Big /

Small

5 12.5% 85%

4. Unsynchronized

Conveyor

4 10% 95%

5. Miscellaneous 2 5% 100%

As seen from the above table, corner damage & damage due tosponge layer which account to 72.5% of the total causes for the

damage of pastries. This means that these are the variations which

have directly come from the baking process. Hence it becomes critical

to analyze the entire baking process in detail so that the vital causes

can be targeted.

It is not that the baking is the only reason due to which the rejections

are happening. Since it is a manual operation, 100% accuracy is not

attainable. But still it can be improved.

For e.g. On pastry production line if there is unevenness identified with

the help of template, then the packing is provided (sponge slices are

adjusted in between) to compensate for the uneven height. Similarly it

is observed that pastry made from sponges with corner damage have

more chances of that particular corner pastry to be rejected. But the

problem is, it is rejected only on the last stage (where the costliest

resources like cream, decorative accessories and time have been

invested). Hence by cutting the damaged corner by the sponge slicing

worker, before it gets on the production line can help a lot in savingthe resources.

In spite of having these measures, we analyzed unevenness in sponge

as root cause. hence we studied entire baking process

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Miscellaneous causes include damage due to handling & worker

negligence. Although the frequency of occurrence of this cause is less,

but when it happens it leads to substantial rejection of pastries.

Cause no 1 & 2 are mainly associated with variations in sponge. While

cause 3 & 4 are due to skill of worker & unbalanced line. So now it is

clear that, we have to study entire baking process & causes related to

unbalanced line and subsequently make improvement in process by

streamlining the operation.

With the help of this diagram we have segregated the vital few causes

from trivial many.

There are some other reasons besides the baking operation in which

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6. ROOT CAUSE ANALYSIS

DEFECTS RELATED TO SPONGE

6.1 MAN

TRIAL (I) Effect of Depositing Pattern

Date: 09-05-2008 ; Time: 3.10 PM

AIM: To Study whether there is any significant difference in sponge

variations on account of type of depositing.

PROCEDURE:

1. 52 trays were put in oven for equal time of 25 min @ 210 C.

Serial numbers were provided on the trays for easy identification.

2. The operations were conducted with different combination of

stroke of depositing machine placed at different places.

a. 26 moulds with 6 strokes of depositing dropped at six

different places in moulds as in (A)

b. 26 moulds with 6 strokes of depositing dropped at thecentre of moulds as in (B)

Factors that were kept constant during the trial are:-

1) Surface of oven was even.

2) New moulds were used.

3) Proper handling process.

4) No hand leveling.

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(B)(A)


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5) Same mixture was used.

OBSERVATIONS: In type B depositing, out of 26 trays 17 were

found with variations (uneven & slant), where as in type A depositing

only 9 sponges were found with variations.

COMMENT: It has to be noted from above experiment that, there is

significant effect of the way in which the batter is deposited in the

mould. As seen in type A 35% sponges were found with variations &

in type B 65%. So it can be concluded that, instead of using type

B method of deposition which is usually practiced, type A method

should be used to reduce variations in sponges.

Still 35% of variations in type A method cannot be neglected. Hence

to study significance of leveling operation we have conducted another

trial to verify the whether hand leveling plays an important role inreduction of variations in sponges.

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TRIAL (II) Effect of Hand Leveling

Date: 11-05-2008 ; Time: 11.45 AM

AIM:To study whether there is any significant difference in the sponge

variations on account of hand leveling.

PROCEDURE:

1) 52 trays were put in oven for equal time of 25 min @ 210 C.


(All 52 moulds were deposited with Type A method of

deposition.)

2) 26 moulds were hand leveled properly, while other 26 were

loaded in oven without leveling.

Factors that were kept constant during the trial are:-

1) Surface of oven was even.

2) New moulds were used.

3) Proper handling process.

4) Same mixture was used.

OBSERVATIONS: In each case variations in not leveled tray were

found to be more than that of leveled ones. i.e. with same pattern of

depositing variations in not leveled 26 trays were 7. While in case ofleveled moulds there were only 3 uneven or slant sponges out of 26.

COMMENT: It has to be noted from above experiment that, there is

significant effect of hand leveling operation. Which means 26% of total

sponges with variations can be brought down to only 10% if we follow

the hand leveling process with type A method of deposition.Hence

the pattern of depositing and hand leveling process in isolation is not

as much effective as it is, if done in combination.

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TRIAL (III) Effect of Handling

Date: 11-05-2008 ; Time: 3.25 AM

AIM:To study whether there is any significant difference in the sponge

variations on account of handling process.

PROCEDURE:

1) 52 trays were put in oven for equal time of 25 min @ 210o C.


2) 26 moulds were kept in first 2 decks of oven with usually

handling process. While 26 moulds in remaining 2 decks were

loaded with proper handling process.

3) The layout showing position of moulds in each deck is given

below.

A) Existing Handling Process: As a part of our trial, we came

across some practices that the workers have adopted, such as

loading the moulds in the deck by pushing back one mould with

the help of another mould. Since the oven is approx 7 feet deep,

hence the worker has to push the moulds. But when he is in

hurry, severity of pushing of moulds increases, which leads to

nullification of the hand leveling effect.

B) Proper Handling Process: Properly handling process is that

although pushing of the mould due to depth of the deck is

inevitable, its severity & frequency can be reduced if some extra

care is taken while loading the moulds into the oven, even if it

takes some more time. What do we mean by extra care is

putting trays into oven carefully so that enough care should be

taken to maintain even level of batter in moulds

Time taken for loading with existing method is = approx. 3 mins

Time taken for loading with proper method is = approx 4 mins

This same time with proper handling process is considered for

simulation purpose

FACTORS KEPT CONSTANT:

1. Surface of oven was even.

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2. New moulds were used.

3. Hand leveling was done.

4. Directly fed into oven without piling up of the moulds.

5. Same batter was used.

OBSERVATIONS: Out of 26 which were handled properly only 2

sponges were found with slight variations. Whereas in case of usual

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Fi : To View of Oven Deck with ladi

1 2

3

4 5

6

7

8 9

10

11

12

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handling the no. of sponges with variations were 5. The identification

number for these 5 sponges is 1,2,3,5 & 10.

COMMENT:

1. There is an impact of usual handling process versus proper

handling process.

2. Referring to the above diagram, sponges with number

1,2,3,5& 10 & their positions we can say that sponges no. 1-

2-3 which were had to be loaded at the most furthest position

in the oven. Similarly for sponge 5 & 10 along with the above

mentioned sponges, the impact of severity of pushing activity

which is a function of usual handling process are found to be

defective.

Hence we can conclude that if the handling is proper & coupled with

proper pattern of loading of the moulds in the deck, the no. of

defective sponges can be reduced.

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6.2 DEPOSITING OPERATION

For depositing operation, as explained earlier in the fish bone diagram,

density & variation in stroke of unifiller machine lead to variation in

weight. Hence to quantify the defects and check its effect on processcapability we have used Statistical Tools like X & R Charts & the

ANOVA technique.

Variable Control Charts

X- & RChart (Also called as average & range chart. )

Description:

The X chart & R chart is a pair of control charts to study variable data.

It is especially useful for a data that doesnt form a normal distribution

although it can be used with normal data as well. Data are sub

grouped, and averages & ranges for each sub group are plotted on

separate charts.

Use:

When you have variable data, and

When data are generated frequently, and

When you want to detect small process changes

Analysis:

Check X- & R chart with upper control limit & lower control limit

Process is said to be out of control if any of the points lie outside

the limits.

Process capability can be calculated from the same.

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(i) X- & R Chart to Check Process Capability

AIM: To check the process capability of depositing operation.

METHODOLOGY: Plotting of X & R chart & calculation of process

capability with the help of SPSS software.

DATA:

We have randomly selected 4 samples of sample size 5.

Sampl

e No.

1 2 3 4 5 Mean

(X)

Range

(R)1 1784 1698 1781 1748 1760 1754.2 862 1642 1732 1722 1728 1740 1712.8 983 1732 1818 1894 1786 1904 1826.8 172

4 1714 1766 1732 1710 1776 1739.6 66TOTAL 7033.4 422

PROCEDURE:

1) Calculate grand mean and mean range for above data

Grand Mean (X--) = 7033.4 4 = 1758.35

Mean Range (R-) = 422 4 = 105.5,

Sigma level selected = 6

2) Statistically Derived Limits for X Chart:

UCL = X-- + A2 R- = 1880.05 (A2 = 0.577 for n=5; from

Statistical Table for X & R Chart)

LCL = X-- - A2 R- = 1636.64

CV = X-- = 1758.35

3) Statistically Derived Limits for R- Chart:

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UCL = D4 x R- = 340.65 (D4 = 2.114 & D3 = 0.0 for n=5; from


LCL = D3 x R- = 0.0

CV = R

-

= 105.5

1.00 2.00 3.00 4.00

1,600

1,650

1,700

1,750

1,800

1,850

1,900

Mean

weight ingrams

UCL =1880.0589

U Spec =1730.0000

Average =1758.3500

L Spec =1670.0000

LCL =1636.6411

Sigma level: 6

Control Chart: weight in grams

Fig: CONTROL CHART FOR PROCESS MEAN & CONTROL CHART FOR

PROCESS VARIABILITY

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1.00 2.00 3.00 4.00

0

100

200

300

400

Range

weight ingrams

UCL =

340.6593Average =105.5000

LCL = .0000

Sigma level: 6


Interpretation: If we analyze X & R chart we can say that process is

in control as the mean & range for all samples lie between the upper &

lower control limit. But in sample 3 the mean value comes out to be

1826.8 & 172 is the range for that sample. Where we desire to have an

accuracy of weight 1750 gms.

So for better decision making we have to check the process capability

which is given by the formula,

Process Capability Index Cp = Desired Tolerance Limit 6

Process Capability = 6 = 6 x (R- d2)

Process StatisticsCapability

Indices

CP(a).220

*The normal distribution is assumed. LSL = 1670 and USL = 1730. (Tolerance Limit)

*The estimated capability sigma is based on the mean of the sample group ranges.

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Comment: Process is said to be capable if Cp 1. But the actual

process capability is 0.220 which is very lower than what is desired at

6 sigma level. Hence there is need to study to the process of

depositing to identify the causes for variations.

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ANOVA is better known as Analysis of Variance that enables us to test

for the significance of the differences among more than 2 sample

means. Using this technique we can make inference about whether our

samples are drawn from the population having the same mean. In

ANOVA we have two estimates one from population variance from

variance among the sample means, while second is from variance

within sample means.

By comparing these 2 estimates at a given significance level, we check

if these two estimates are equal or not. If they are equal then we

accept the NULL HYPOTHESIS.

(ii) CASE (1) Effect of Density of Batter

AIM:To check whether the density of the batter affects the deposition

volume. (By keeping other factors constant i.e. using same unifiller

machine.)

According to the data taken from Q.C Dept. for density of mixtures.

Sr. No. Mixture Range for Std. Density

in gms/cm31 Chocolate Sponge 0.65 0.702 White Sponge 0.62 0.723 White Eggless 0.68 0.784 Chocolate Eggless 0.75 0.855 Dark Sponge 0.65 0.756 White Sheet 0.65 0.757 Chocolate Sheet 0.67 0.758 Brownie 0.82 0.889 Brownie Eggless 0.80 0.88

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DATA:

Obs.n

o.

Weight in gm for Density

= 0.68 gm/cm3

Weight in gm for Density

= 0.77 gm/cm3(Density Outside Range) (Std Density)

1 1650 16912 1604 17083 1618 17044 1688 16805 1738 16606 1710 17817 1568 16408 1616 16229 1664 171810 1732 1709

11 1638 168812 1530 166413 1654 165114 1741 168015 1540 1740

HYPOTHESIS FORMULATION:

Ho : 1 = 2; There is no significant difference between weight of the

mixture with different density

Ha : 1 2;There is a significant difference between weight of

mixture with different density.

SIGNIFICANCE LEVEL:- There is no single standard or universal level

of significance for testing the hypothesis. It is possible to test the

hypothesis at any level of significance, but we should remember that

our choice of minimum standard for an acceptable probability, or

significance level is also the risk. We assume of rejecting a nullhypothesis when it is true. The higher significance we use for testing

the hypothesis the higher probability of rejecting null hypothesis when

it is true.

Here we have selected 10% significance level i.e. 0.10

SPSS OUTPUT:

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Descriptives

Mass

N Mean

Std.

Deviation Std. Error

95% Confidence Interval for

Mean

Minimum MaximumLower Bound Upper Bound1.00

15

1646.066

7 68.36715 17.65232 1608.2062 1683.9271 1530.00 1741.002.00

151689.066

740.40768 10.43322 1666.6896 1711.4437 1622.00 1781.00

Total30

1667.566

759.35381 10.83647 1645.4036 1689.7297 1530.00 1781.00

ANOVA

Mass

Sum of

Squares df

Mean

Square F Sig.

Between

Groups

13867.50

01 13867.500 4.398 .045

Within Groups 88295.86

728 3153.424

Total 102163.3

6729

INTERPRETATION: As the actual significance level (0.045) calculated

from the data is less than the desired significance level (0.10), we

reject null hypothesis.

CONCLUSION: We accept the alternate hypothesis i.e. there is a

significant difference in weight of a mixture with different density. i.e.

the weight of the mixture depends upon the density of the material.

COMMENT: Hence we can say that higher the frequency of mixture

having standard density, lower is the variation in mass deposited by

unifiller.

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(iii) CASE (2) Effect of Type of Unifiller Machine

AIM:To check whether the type of unifiller machine used for

deposition has an impact on the mass deposited.

DATA:

Obs

No.

Weight in gm for

Machine 1

Weight in gm for

Machine 2

Original Unifiller

New Unifiller (Indian

made)1 1698 18342 1742 18543 1646 1852

4 1692 14845 1684 13766 1704 17227 1684 19228 1688 19069 1710 194010 1694 171811 1752 169412 1704 185013 1724 148414 1746 153015 1684 1540

HYPOTHESIS FORMULATION:

Ho : 1 = 2; There is no difference in the mass deposited by two

different unifiller machines of the same type.

Ha : 1 2; There is significant difference in the mass deposited by

two different unifiller machines of the same type.

SIGNIFICANCE LEVEL: We have selected 10% significance level i.e.

0.10

SPSS OUTPUT:

DESCRIPTIVES

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Weight in kg

N Mean

Std.

Deviation Std. Error

95% Confidence Interval for

Mean

Minimum MaximumLower Bound Upper Bound

1.0015

1723.466

777.16575 19.92411 1680.7337 1766.1996 1646.00 1984.00

2.0015

1713.7333

186.58566 48.17621 1610.4056 1817.0610 1376.00 1940.00

Total30

1718.600

0140.37796 25.62939 1666.1820 1771.0180 1376.00 1984.00

ANOVAWeight in kg

Sum of

Squares df

Mean

Square F Sig.

Between

Groups710.533 1 710.533 .035 .853

Within Groups 570762.6

6

28 20384.381

Total 571473.2

029

INTERPRETATION: As the actual significance level (0.853) calculated

from the data is greater than the desired significance level (0.10), we

accept null hypothesis.

CONCLUSION:There is no significant difference between the masses

deposited by two different unifiller machines of the same type. But std

deviation in machine 1 is more than std deviation of machine 2, which

means that machine 2 is giving more consistent readings.

COMMENT: Although with the given sample of reading we are

accepting the null hypothesis, the readings for the standard deviation

indicate that with machine 1, std. deviation is significantly lower than

that with machine 2. Similarly if we compare their std. deviation in first

case it is 78 while in second case it is 177. This means that with

original unifiller machine (if set properly) can give consistent reading

with less std. deviation.

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(iv) X & R Chart for Improved Process Capability

AIM: To check the process capability of depositing operation with

original unifiller

machine & mixture with std. density.

METHODOLOGY: Plotting of X & R chart & calculation of process

capability with the help of SPSS software.

DATA:

We have randomly selected 4 samples of sample size 5.

Sampl

e No.

1 2 3 4 5 Mean

(X)

Range

(R)1 1484 1508 1496 1507 1510 1501 26

2 1512 1528 1529 1506 1510 1517 233 1493 1487 1513 1528 1496 1502 414 1505 1530 1515 1501 1515 1512 29

TOTAL 6032 119

PROCEDURE:

1) Calculate grand mean and mean range for above

dataGrand Mean (X--) = 6032 4 = 1508.65

Mean Range (R-) = 119 4 = 29.75

Sigma level selected = 6

2) Statistically Derived Limits for X Chart:

UCL = X-- + A2 R- = 1542.97 (A2 = 0.577 for n=5; from


LCL = X-- - A2 R- = 1474.32

CV = X-- = 1508.65

3) Statistically Derived Limits for R- Chart:

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UCL = D4 x R- = 96.06 (D4 = 2.114 & D3 = 0.0 for n=5; from


LCL = D3 x R

-

= 0.0CV = R- = 29.75

1.00 2.00 3.00 4.00

1,480

1,500

1,520

1,540

Mean

weight ingrams

UCL =1542.9708

U Spec =1530.0000

Average =1508.6500

L Spec =

1470.0000LCL =1474.3292

Sigma level: 6


Fig: CONTROL CHART FOR PROCESS MEAN & CONTROL CHART FOR

PROCESS VARIABILITY

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1.00 2.00 3.00 4.00

0

20

40

60

80

100

Range

weight ingrams

UCL = 96.0627

Average =29.7500

LCL = .0000

Sigma level: 6


Interpretation: If we analyze X & R chart we can say that process is

in control as the mean & range for all samples lie between the upper &

lower control limit. Here we desire to have an accuracy of weight 1500

gms.

Process Statistics

Capabilit

y Indices

CP(a).782

The normal distribution is assumed. LSL = 1470 and USL = 1530.

The estimated capability sigma is based on the mean of the sample

group ranges.

COMMENT: Process is said to be capable if Cp 1. But the actualprocess capability is 0.782 which is nearer to what is desired at 6

sigma level. Hence we can say that the factors that were considered

and corrected have a huge impact on the process capability. (As the

process capability increased from 0.22 to 0.782, with original unifiller

machine & batter of standard density used.)

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6.3 EQUIPMENT / MACHINE

WHICH ARE THE FACTORS?

In equipment uneven surface of oven, moulds, variation in temperaturezone in oven are considered as the main causes for the variations in

sponge.

HOW DOES IT AFFECT?

a) Uneven surface of oven: Each oven has 4 decks, some of

which have lost their leveling. Due to uneven leveling, the

moulds tilt, causing slantness in sponges.

b) Moulds: Due to rough handling of workers & constant use of

moulds, most of them get deformed, which ultimately cause

deformed sponges. Hence new moulds that are relatively in

better shape are checked against old moulds.

c) Variations in Temperature Zone: Temperature is not uniform

through out the deck of the oven. Hence the variation in baking

loss is also not uniform, leading to variation in sponges.

HOW MUCH?

As the data available is in an attribute form, P- Charts were used for

quantification & analysis. Whereas to study the variation in

temperature zone, trials were taken.

Attribute Control Chart

P Chart (Also called as proportion chart.)

Description:

The p-chart is an attribute control chart used to study the proportion

(fraction or percentage) of non-conforming or defective items. Often,

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information about the types of non-conformities is collected on the

same chart to help determine the causes of variation.

Use:

When counting non-conforming items, and

When sample size varies.

Analysis:

Check P chart with upper control limit & lower control limit

Process is said to be out of control if any of the points lie outside

the limits.

Most desirable points are the points which lie towards the zero or

LCL.

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AIM: To study the effect of uneven surface of oven.a) P-chart plotted oven wise.

b) P-chart plotted deckwise (for ovens which caused maximum

defects in sponge)

METHODOLODY: Plotting of P - chart with the help of SPSS software.

CASE (1) - OVENWISE

DATA:

Oven No. Sample Size (n) No. of Defective

Sponge (x)

P = x/n

1 52 13 0.252 52 8 0.1533 52 10 0.1924 52 5 0.096


1. Proper handling process

2. Leveling is done.

PROCEDURE:

1. C.V. = P- = P no. of sample = 0.691 4 = 0.1731

2. q- = 1 p- = 0.8269

3. UCL = p- + 3 ((p- q-)/n) = 0.3305

4. LCL = p- - 3 ((p- q-)/n) = 0.0157

SPSS OUTPUT:

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1.00 2.00 3.00 4.00

0.0

0.1

0.2

0.3

ProportionNonconforming

no of defectivepcs

UCL = .3305

Center = .1731

LCL = .0157

Sigma level: 3

Control Chart: no of defective pcs

INTERPRETATION:The most desirable points are those which are

closer to lower control limit or zero. In above case for oven no.1 & oven

no. 3, the no. of defect per sample are more as seen in the chart.

CONCLUSION: It is necessary to study these two ovens deck wise, sothat immediate measures can be taken.

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CASE (2) (i) DECKWISE for OVEN 1

DATA: (OVEN 1)

Deck No. Sample Size (n) No. of Defective

Sponge (x)

P = x/n

1 13 2 0.15382 13 3 0.23073 13 2 0.15384 13 6 0.4615

FACTORS KEPT CONSTANT:3. Proper handling process


PROCEDURE:

5. C.V. = P- = P no. of sample = 0.9998 4 = 0.2500

6. q- = 1 p- = 0.7500

7. UCL = p- + 3 ((p- q-)/n) = 0.6103

8. LCL = p- - 3 ((p- q-)/n) = -0.110 ~ 0.000 (As defects cant be

ve)

SPSS OUTPUT:

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1.00 2.00 3.00 4.00

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


no of defectivepcs

UCL = .6103

Center = .2500

LCL = .0000

Sigma level: 3


INTERPRETATION: In above case for deck no.4 the no. of defect per

sample are more.

CONCLUSION: It is recommended to level deck 4 of oven 1.

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CASE (2) (ii) DECKWISE for OVEN 3

DATA: (OVEN 3)

Deck No. Sample Size (n) No. of Defective

Sponge (x)

P = x/n

1 13 1 0.07692 13 2 0.15383 13 4 0.30764 13 1 0.0769




PROCEDURE:9. C.V. = P- = P no. of sample = 0.6152 4 = 0.1538

10.q- = 1 p- = 0.8462

11.UCL = p- + 3 ((p- q-)/n) = 0.4539

12.LCL = p- - 3 ((p- q-)/n) = -0.1463 ~ 0.000 (As defects cant be ve)

SPSS OUTPUT:

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1.00 2.00 3.00 4.00

0.0

0.1

0.2

0.3

0.4

0.5


no of defectivepcs

UCL = .4541

Center = .1538

LCL = .0000

Sigma level: 3


INTERPRETATION: In above case for oven 3 - deck no.3 the no. of

defect per sample are more.

CONCLUSION: As all other factor leading to slantness are already

nullified. It is recommended to level deck 3 of oven 3.

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Case (3) Mould-wise

AIM: To study the effect of old & new moulds in slantness in sponge.

METHODOLODY: Plotting of P - chart with the help of SPSS software.

DATA:

OLD MOULDS


Sponge (x)

P = x/n

1 15 4 0.2662 15 6 0.403 15 5 0.333

4 15 4 0.266

NEW MOULDS


Sponge (x)

P = x/n

1 15 1 0.0662 15 2 0.1333 15 1 0.0664 15 3 0.2307




3. Oven deck was leveled.

PROCEDURE:

Calculation for both data are as per above procedures.

SPSS OUTPUT:

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1.00 2.00 3.00 4.00

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


no of defectivepcs

UCL = .6770

Center = .3167

LCL = .0000

Sigma level: 3


Fig: P Chart for Old Moulds

1.00 2.00 3.00 4.00

0.0

0.1

0.2

0.3

0.4


no of defectivepcs

UCL = .3653

Center = .1167

LCL = .0000

Sigma level: 3


Fig: P Chart for New

Moulds.

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INTERPRETATION: As seen from the output, it is clear that the

minimum number of defective sponges were found in the new moulds.

The maximum no. of defective sponges is 3 in case of new moulds

while the minimum no. of defective sponges in old moulds is 4.

CONCLUSION: It is better to use new moulds for fewer variations in

sponges.

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CASE (4) TRIAL to Check Baking Loss

Date: 24-06-2008 ; Time: 11.25 AM

AIM:To study the baking loss due to temperature variation in decks of

ovens.

PROCEDURE:

1. Weight of empty moulds was recorded before deposition of

batter.

2. Weight of batter filled moulds was recorded after deposition of

batter.

3. 13 ladi moulds were baked for equal time of 25 min @ 210o C.

Serial numbers were provided on the moulds for easy

identification & their position in the deck was also noted.4. Weight of the moulds was taken after baking.

DATA:

Identification.

No

Weight in

gm Before

Baking

Weight in

gm After

Baking

Difference % Baking

Loss

1 1810 1564 245 13.58

2 1840 1596 244 13.26

3 1795 1560 235 13.09

4 1850 1615 235 12.7

5 1545 1588 257 13.93

6 1720 1497 223 12.96

7 1990 1782 238 11.96

8 1910 1703 207 10.84

9 1820 1632 188 10.33

10 1860 1671 189 10.16

11 1810 1618 192 10.6

12 1825 1645 180 9.86

13 1790 1610 180 10.05

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OBSERVATIONS:

From the above schematic representation, we observe that baking loss

is as high as 13.93% at the rear end of the oven deck while it is as low

as 9.86% near the lid of the deck. Hence we can say that the loss is

59

Fig: % Baking Loss as per position of moulds inside the oven.

13.58 13.26%

13.93%

13.09% 12.7%

12.96

11.96%

10.8410.33%

10.16%

10.60%

9.86%

10.05%

1 23

4 5

6

7

89 10

11

12

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more where distance of the mould is less from the burner and it

decreased gradually towards the lid.

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6.4 SIMULATION

(i) Need For Simulation

As explained earlier in operational process flow chart, the bakingprocess is the core activity of the pastry making process. Since it is a

bottleneck, it results in work in process inventory of the batter-filled

moulds which ultimately results in slantness of the sponge. Also due to

the WIP, the quality of the product gets affected. If the batter is kept in

open for a long time, the required height of the sponge is not achieved

while baking. This WIP gets developed after the leveling operation is

done. Also since most of the moulds are deformed due to improper

handling, batter deposited in it takes the shape of such moulds.

Moreover such moulds are stacked on each other after depositing &

leveling operation, which ultimately results in unevenness in sponge.

It was also observed that a lot of time is lost between consecutive

loadings of every oven. According to the production protocol, the oven

has to be loaded as soon as it is unloaded. But it was observed that the

workers load the oven at random, which lead to a lot of time being

wasted. Hence it was necessary to analyze the loading pattern in

detail.

For that purpose, data for 10 days that were picked at random was

taken & tabulated.

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Table shows the date and the time of loadings and also the number of

loadings that took place that day. It also shows the total time lost

between consecutive loadings for each oven.

Explanation:

Consider the data on 1/4/2008 for oven 1. The first loading took place

at 8.21 AM. That day a total 8 loading cycles took place. The last

loading took place at 2.45 PM. The time between first & second loading

was 7 mins, between second & third was 11 mins so on & so forth.

From the table it can be seen that the average idle time for each oven

is as follows

Oven 1 2 3 4Idle Time in

hrs

1.98 1.95 2.26 2.10

Total idle time is (1.98+1.95+2.26+2.10=8.29 hrs). The shift is of 8

hrs, i.e working hours for four ovens is 32 hours. Therefore it can be

said that out of 32 machine hours a whole 8.5 hrs get wasted everyday

per shift.

The reasons for idle time are as follows

The loading protocol is not followed i.e. (loading of oven 1 should take

place first, then oven 2, then oven 3 & oven 4.) Referring to the table,

on 1/4/2008 the loading of oven 3 took first which is not according to

the protocol.

OVEN 1 2 3 4Loading

Time

8.21 8.31 8.10 8.25

Unloading

Time

8.46 8.56 8.35 8.50

In the above case, oven 3 gets loaded first. If such a thing happens

then at the time of unloading oven 1 can be unloaded till 8.51. But the

unloading time for oven 4 is 8.50. So now either oven 1 will remain idle

for next 5-6 mins until oven 4 gets unloaded or the material in oven 4

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will get over baked for another 5 mins. Due to such random

assignments, which are noticed in subsequent observations leads to

Piling up of WIP for baking

Idle running of ovens that leads to wastage of expensive

resources like fuel.

Over baking of the material leading to quality issues.

Under utilization of actual deck space available in the oven. Not only it

reduces the productivity of the process, but also increases the no. of

baking cycles to be executed for the given order.

For eg. At max 13 ladi moulds can be accommodated in one deck

(taking into account the tolerance for movement of deck lid). But is

observed that due to workers ignorance only 11 moulds are loadedper deck. Which means around 16% under utilization of one oven deck.

i.e for every 6 loadings of a deck we are wasting one deck due to

underutilization.

This effect gets magnified when different types of moulds are to be

loaded in the same deck. Right now there is no calculation as in how

many moulds are to be loaded in case two or more type of moulds are

to be loaded in the same deck. In such a situation the moulds are

loaded according to the whims & fancies of the worker. Which results

in significant underutilization of oven capacity & increase in the no. ofbaking cycles.

Hence there was a need to synchronize the mixing-depositing-baking

activity which would completely eliminate the generation of WIP.

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(ii) Time Study for mixing depositing baking activities isgiven below

MIXING

Actual Mixing (for 1 container)= 3.10 mins.

Change over of container from mixers = 90 secs

Total Mixing Operation = 3.10 + 90 secs ~ 5 mins

DEPOSTING

Each container of batter contains 45 to 60 kg depending upon the

flavor. For ease of calculation, each batch is assumed to be 60 kg.

No. of Trays Deposited from each batch/container = 60 kg / 1.75 kg =

34.28 ~ 35 moulds

Deposition of mixer is 1 mould = 6 secs approx.

Total time for depositing total mixer = 35 * 6 = 210 secs ~ 3.5 mins

approx.

Change over time for each container on depositor = 1.5 mins approx.

BAKING

There are 4 deck ovens having 4 decks each.

Loading the moulds in 1 oven = 3.5 mins approx.

Backing = 25 mins approx.

Unloading the moulds from 1 oven = 3.5 mins approx.

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Travelling time of moulds on conveyor depends on the position of the

oven. It is maximum for the oven which is situated farthest away = 1

min max.

(i.e During loading of oven 4 & unloading of oven 1, travelling time is

maximum = 1 min)

Total time for baking = 35 mins.

During simulation the chronological sequence of type of mixture, typeof moulds & oven should be maintained and has to be fixed.

Protocol for mixing is as follows.

1. Chocolate Eggless

2. White Eggless

3. White

4. Chocolate

5. Black

6. Coloured

Protocol for the loading of moulds is:

1. Ladi

2. 1 kg Round

3. kg Round

67

OVEN 1 OVEN 2 OVEN 3 OVEN 4

Conveyor BeltFrom

Depositor

OVEN 1 OVEN 2 OVEN 3 OVEN 4

Fig: Layout of Ovens in Baking Area


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4. 1 kg Heart

5. kg Heart

6. Special Shapes (Bugs Bunny, 1 kg Square, 2 & 3 kg Round

etc)

Protocol for Loading of Ovens is:

1. Oven 1 has to be loaded first

2. Oven 2 has to be loaded second

3. Oven 3 has to be loaded third

4. Oven 4 has to be loaded last

Simulation Process starts with mixing but the weight to be deposited

varies as per the type of shape and the type of mixture. Similarly

weight of the batch also varies as per the type of mixture.

Table A

MIXTURE APPROX WEIGTH PER BATCHChocolate Eggless 53 kgWhite Eggless 52 kgWhite 45 kgChocolate 45 kg

Dark Chocolate (Black) 39 kg

Table B

TYPE OF MOULD WEIGHT TO BE DEPOSITEDLADI (eggless) 1.75 kgLADI (sponge) 1.50 kg kg Round / Heart / Square 0.285 kg

1 kg Round / Heart / Square 0.475 kgSpecial Shapes Varies as per shape

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Now for making a prototype, a sample order date 20-05-2008 is taken

which is as given below

MIXTURE

LAD

I

1 KG

RD

1/2

RD

1

HRT

1/2

HRT

1/2

SQ Total

Batch

Size

No. of

MixingsPcs Pcs Pcs Pcs Pcs Pcs kg kg

Chocolate

Egg. 250 150 100 300 200

670.

25 53 12.64 ~ 13

Chocolate 200 150 700 100 450

746.

5 45 16.58 ~ 17

White Egg. 60 105 52 2.01 ~ 2

White 90 300

220.

5 45 4.9 ~ 5

Black 60 90 39 2.30 ~ 3

Sample Calculations: (For Chocolate Eggless)

250(ladi)*1.75 + 150( kg round)*0.285 + 100(1kg heart)*0.475 +

300( kg heart)*0.285 + 200( kg square)*0.285 = 670.25 kg

Total Mixing = 670.25 53 (Batch Size) = 12.64 ~ 13 Mixings

(Refer Table A & B)

As the mixing operations take 5 minutes for one batch & since we have

2 mixers available i.e we can get 2 mixing in 5 minutes. According to a

thumb rule, to load one oven completely we need 1.75 mixings approx.

Hence for 4 ovens we will need 7 mixings. So what it means is that we

have to start the mixings operations at least 15 to 20 minutes prior to

every baking cycle. (1 baking cycle can be defined as the time elapsedbetween consecutive loading of each oven).

Hence we can say that mixing is a very flexible process and can be

easily synchronized the subsequent activities.

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(iii) SAMPLE SIMULATION

This simulation was done for the BAKING PROCESS for order on

date 20-05-08

Assumptions:

1. All the ovens are in working condition

2. The protocols for mixing, loading of the type moulds in the oven

etc. are strictly adhered.

Table 1.1

Type of

mould

Ladi 1 kg

Round

kg

Round

1 kg

Heart

kg

Heart

kg

SquareNo. of

pcs in 1

mould

1 2 3 2 3 3

Constraints:

Maximum number of moulds available of each type

Ladi 1 kg

Round

kg

Round

1 kg

Heart

kg

Heart

kg Sqaure

250 120 270 80 140 270

Maximum number of moulds that can be loaded in one deck of oven

Ladi 1 kg

Round

kg

Round

1 kg

Heart

kg

Heart

kg Sqaure

13 15 21 12 18 21

After Unloading from oven at least 30 minutes are required to cool, de-

pan & again pan the moulds for re-use. Hence moulds used in one

cycle cannot be re-used in the consecutive cycles but at earliest it can

be used in the next alternate cycle. For example if the ladi mould is

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used in baking cycle 1, now it cannot be used again before backing

cycle 3.

Nomenclature:

For Moulds:

Type of

Moulds

Ladi 1 kg

Round

kg

Round

1 kg

Heart

kg

Heart

kg

Square

Special

Shape

sNomenclat

ure

L R RI H HI S SP

For Mixture:

Type of

Mixture

Chocolate

Eggless

White

Eggless

White Chocolate Black

Nomenclat

ure

C.E W.E W C B

For Activities:

Type of

Activity

Loading

Starts

Baking

Starts

Unloading

Starts

Unloading

EndsNomenclatur

e

L.S B.S U.L.S U.L.E

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Table A shows the deck wise position of the different moulds at a given

point of time of a particular mixture. While table B shows the status of

order baked per cycle. For better understanding of the simulation, both

the tables have to be read simultaneously. (All figures in table A shows

no. of moulds & all figures in table B shows the no. of pieces of thesponge.)

In Table A, decks which cannot be fully loaded either due to any of the

constraints are highlighted and the empty space is shown in

percentage form.

In cycle 1, oven 1 is loaded at 7.00 AM. As the time taken for loading is

5 mins, baking can start at 7.05 AM. The material has to be baked for

25 mins, which means the unloading can be started at 7.30 AM which

will end at 7.35 AM. Now the oven is ready for the next loading, which

will start immediately.

Oven 2 is loaded at 7.05 AM as soon as the loading for oven 1 ends.

Similarly oven 3 & oven 4 can be loaded at 7.10 & 7.15 AM

respectively.

As each oven takes total time of 35 mins for the complete operation,

cycle 1 which is starting with loading of oven 1 will end with unloading

of oven 4 at 7.50 AM as soon in Table A.

At 7.20 AM we can finish with loading operation of oven 4 & unloading

operation for oven 1 starts at 7.30 AM. Hence there is an idle time of

10 mins as all the ovens are fully loaded.

Now in cycle 2, loading for the oven 1-2-3-4 can be started at 7.35

7.40 7.45 7.50 AM respectively. Which means the time between

consecutive loading for each oven is 35 mins. And the time required to

complete one cycle as shown in table A is 50 mins.

Consider cycle 1, we have a order of 250 ladis for chocolate eggless.

The capacity per deck is 13, i.e we can load 13 x 4 (deck/oven) x 4

(oven) = 208 ladis at max in one cycle. Hence in Table B it is denoted

by (O), which means that although we have 250 moulds available,

but we cant bake them at once because of the capacity constraint of

the oven.

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In Cycle 2, the remaining 42 ladis can be loaded. According to the

protocol, 1 kg round should be the next mould, but since there is no

order for it, we move to the next mould i.e round for the same

mixture.

Referring table A, in cycle 2 there is a changeover of mould in deck 4of oven 1. Calculation for approx no. of moulds of round that can

accommodated in deck 4 is given below:

Sample Calculation:

Maximum ladi moulds per deck = 13.

4th deck of oven 1 is loaded with 3 ladis i.e approx 25% of total deck

capacity. Hence 75% of round moulds can be accommodated in that

deck. For round, we can load at the most 21 moulds per deck (refer

constraints table). That means, here we can load 75% of 21 i.e 15

moulds in deck 4. All the subsequent calculations for deck capacity are

done in the same manner.)

Referring Table B - At the end of cycle 2, we are able to load only

30/200 moulds of square due to capacity constraint. All the three

protocols are to be strictly adhered. Hence chocolate eggless mixture

gets over in the 3rd cycle. In the same cycle we are loading 60 ladis of

white eggless & 90 ladis of white mixture. As explained earlier 208moulds that we have used in first cycle are now available for re-use.

In fourth cycle we have the order 200 ladis for chocolate mixture, but

cannot load more than 100 as we have already used 150 moulds in the

previous cycle.

In fifth cycle remaining 100 ladis of chocolate mixture can be loaded as

150 ladi moulds are now available that were used in the 3rd cycle.

After this loading is over we can now move to the kg round as per

our mould protocol. But since only 140 moulds are available of kgheart and the same has to be re-used in the immediate alternate cycle.

Hence we are loading it before the 1 kg & kg heart.

All the subsequent loadings take place in the usual manner as

explained above.

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7. INTERNAL LOGISTICS

1.FLOW OF MOULDS Critical to Process

The diagram given below gives us an insight into flow of moulds in the

entire baking process.

Figure: Flow of Moulds during the Baking Process.

It has to be noted that the no. of moulds that are available are fixed, so

it is considered to be a constraint in the baking process/cycle.

Consider the flow of moulds from depositing baking coolingoperations. In all it takes almost 60 minutes for the moulds to reach

the panning table once it leaves it. So with limited no. of moulds

available it becomes very necessary to ensure a continuous &

consistent flow of the moulds from panning depositing baking

cooling de-panning. Consider a hypothetical example where in we

have to produce 750 ladis & only 250 moulds are available. Then the

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same moulds have to be used thrice. More efficient the operation,

lesser will be the cycle time. If we consider the above diagram the time

for all other activities except cooling is constant. But it is observed that

the cooling time for the moulds is not kept constant due to worker

negligence.

It is observed that once the moulds are kept for cooling, are not

attended until they are again required for baking as per mixing

protocol. This means that, by streamlining & simulating the entire

process we can reduce the variation in cooling time.

Consider the same example in which the entire process is simulated in

such a way that, the time between re-use of the mould in kept at 35

minutes so that the mould used in the first cycle are required in the

next alternate cycle. Then the worker has to de-pan it by default. Thesame cycle in detail is explained in the simulation part of the report.

Moreover if the cooling process is made more efficient by use of

artificial cooling, then the cooling time can be significantly decreased,

which will ultimately help in streamlining the process & increasing the

productivity by reducing non-value adding time.

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2.FLOW OF SPONGES Critical to Quality

As we have noted from the study of pastry making process and

simulation, the flow of sponges has to be synchronized with its

requirement at different production lines.

Mixing is presently done on the forecasting basis, in which no. of.

batches for a particular mixture are calculated. Here there is no scope

for fractional mixing which means if you have an order of 100

chocolate eggless sponges and 90 sponges can be produced with the

help of 3 mixing but for remaining 10 sponges another full mixing is

done. This ultimately results in excess production of sponge, which is

supposed to be stored in the cold room. If the sponges remains in an

ambient temperature for more than six hours, it starts to loose its

moisture which reduces the shelf life of the sponge by one whole day.

As it affects the taste of the product it becomes critical to quality.

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Sponges from

Baking Dept.

Fig: Movement of Sponges from Baking


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Linking the supply with baking and requirement at production

line.

It again acts as a bottleneck for the entire process because

requirement at different production line varies with respect to time,shape, weight & flavour. For e.g. It may happen that sponges are

required on the pastry line at 11.00 am, but heart shape for the same

mixture may be required at 3.00 pm. Requirement at production line is

very dynamic while the mixing is done flavour wise. So it is very

difficult to produce requirement of all production line at the same time

of the same mixture.

There are two mainproblems in the present system.

1. Excessive production due to absence of fractional mixing which

to an extent is also responsible for push type system.

2. Mis-match between requirements at production line - mixing

baking.

(i) Present Scenario PUSH - PULL SYSTEM:

Presently a mixing protocol is followed for all the mixings which

means that the baking is also done accordingly. As the production is

based on a forecast, mixing is done in night shift & first shift. Thesponges which are produced in the night shift are stored in the cold

storage and used on the production lines in the first shift, which is

shown in the given diagram.

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Once the sponges stored in the cold room are over, the production line

becomes extremely dependent on baking department. As the

requirement is very dynamic in nature, the company has to keep a

large buffer stock in cold room. Company is presently tackling this

situation by insulating the baking process from the demand at

production line. Hence it becomes inevitable to produce and store

approx 70 % of total sponge requirement of the first shift in night shift.

If enough care is not taken for storing the sponges in the cold room,

sponges become brittle due to loss of moisture.

For sponges more the time they lie in ambient temperature, higher is

the deterioration in quality. Hence there can be two possible ways by

which these problems can be tackled.

1. By taking efficient measures which ensure that sponge will

not be at ambient temperature for more time. So by the

storing the sponge in cold storage and using it whenever

required by following the FIFO principle.

2. By understanding the requirement at different production line

shape wise, weight wise & flavour size. And then simulating

baking & mixing process in such a way that every shape of

every mixture will be produced on just-in-time basis. This will

completely eliminate the activity of storing sponge at ambient

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temperature or in cold room. Thereby reducing the chances of

damage during material handling.

(ii) JUST-IN-TIME Production Pull System

After the simulation is done with the help of software, the production

personnel will have the following information in advance about the

time at which particular sponge will be available, flavour wise & shape

wise. In essence we can say that with this information, demand &

supply for sponge at the production line can be synchronized. For this

purpose we have studied 5 different production lines and its

requirements for sponges at a given point of time. So that integrating

the production line with all back end operations.

Data for requirement of sponges on different lines was collected,

analyzed & tabulated in the form of a pivot table in MS EXCEL, which

facilitates in providing the information ofwhat is required, when it is

required and where is it required?

(iii) PIVOT TABLE (For Real Time Inventory &

Increased Visibility)

The Pivot Table given alongside contains a very comprehensive data

about the requirements of different production lines at different time. It

also contains data as to, which shape, of which flavour, of which type

(normal / eggless) is required at which line and at what time.

For e.g. with reference to table A which gives information on the basis

of different shapes required at different production lines in a given slot

of time.

Let us consider the time slot of 11 am to 1 pm for Butter Cream

Gateaux line on which 90 (normal) bunny shapes are required.

Similarly on butter cream pastry line for a same time slot 30 rectangle

(eggless), 33 rectangle (normal) & 18 sheets (normal) are required.

With the help of this table we will also get to know the total sponge

required in a given slot of time. Different requirements of different

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shapes on different production lines with their cumulative figures are

shown in the same table.

With this table we would not get figures based on a particular flavour

for this purpose we have to refer table B.

Consider the same time slot & the same e.g. of Butter cream gateaux

line. Table B shows total requirement of sponge (flavour wise) in

different time slots on any particular line. It also gives classification of

eggless & normal mixture. So for the same e.g. if we consider 11 am to

1 pm slot, the no. of sponges of bunny shape are 90 as per table A, if

we refer to table B simultaneously, then for the same line in the given

slot of time 90 sponges of chocolate flavour are required. Hence we

can say on butter cream gateaux line within the slot of 11 am to 1 pm

we require 90 bunny shape sponge of chocolate flavour. The samething can be explained for every other production line as well.

With the help of these tables, we can make the entire process highly

responsive & synchronized, because once you know no. of sponges

required at different production line at different time and with the help

of simulation software you also know the time at which it can be made

available. This will also help in giving the real time inventory

information about the sponges. There by increasing the visibility in the

process. So now we can bake the sponges as and when required at

production line. But there is one constraint. The company right nowhas only one depositing machine. Hence it is recommended that they

need to increase the no. of depositing machines.

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8/3/2019 Six

Documents

Six Sigma for Process Improvement & Reduction in Pastry Variations (1)