Use of Six Sigma Tools for Process Improvement & Reduce Pastry Variations

Use of Six Sigma Tools for Process Improvement & Reduce Pastry Variations Project At,

CERTIFICATE This is to certify that the project entitled,

Use of Six Sigma Tools to Reduce Pastry Variations & Process Improvement

as a part of their summer internship at Monginis Foods Pvt. Ltd, is approved for the MBA (Operations) course of the University of XYZ at

________________ Prof: XYZ (Director – RIMSR)

_________________ Dr. XYZ (Internal Guide)

__________________ Mr. XYZ (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, XYZ 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.

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 Depositing Operation

30 - 41

i. 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 Equipments

42 - 54

i. P- Chart 1. Oven-wise 2. Deck-wise 3. Mould-wise 4. Trial (To Check Baking Loss) 6.4 Simulation

55 - 70

i. Need for Simulation ii. Time Study (Mixing – Depositing – Baking) iii. Sample Simulation

4

Chapter 7.

Internal Logistics

71 - 80

7.1 Flow of Moulds 7.2 Flow of Sponges – Critical to Quality

Chapter 8.

i.

Present Scenario – ‘PUSH-PULL’ System

ii.

Just in Time – PULL System

iii.

Pivot Table – (For Real Time Inventory)

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

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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 this software will streamline the production process with also help in the application of Justin-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 the spot 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 Family’s 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 the many 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 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.

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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-bone’ diagram (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 BATCH

Chocolate Eggless

53 kg

White Eggless

52 kg

White

45 kg

Chocolate

45 kg

Dark Chocolate (Black)

39 kg

3. MIXING

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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 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 the leveling 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 & depanning. Each trolley can accommodate 60 ladi moulds.

6. DE-PANNING a. Cooling of Sponges: The moulds are cooled (at ambient temperature 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 sponge’s edges / corner. This leads to excess side cutting, resulting in increased wastage as well as variation in size of the pastries. 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

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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 sides of 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 the production 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 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

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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 unevenness by 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 pcs even if all other cuts are accurate.

1 2 3 4

1

2

3

4

5 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

MAN

DEPOSITING (OPERATION) Sponge

Leveling Handling

Cutting Pastry

New m/c Variation in Stroke of Depositing m/c

R.M. Mix

Unifiller m/c

Density Panning / Depanning

Temp.

Creaming

VARIATION IN PASTRY

Uneven Surface of Ovens Moulds

Unbalanced Line

Sponge

Variation in Temp. zone in oven

Work in Process Inventory Mixture

EQUIPMENT

PROCESS

Fish bone diagram which is a cause effect diagram helps us to organize existing theories about the causes & to develop new ones. It cannot identify 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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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 same were identified which were density & variation in

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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 line may 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 pilled up 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

40.00

100%

80% 30.00

72.5%

Vital Few

60%

t n rc e P

20.00

40%

S P IV T C E D F O N

Trivial Many

10.00

15.00

20%

14.00

5.00

4.00

0.00 2.00

1.00

3.00

4.00

2.00

0%

5.00

CAUSES OF DAMAGE

We have used the Pareto diagram to prioritize the causes that we have identifies with the help of fish bone diagram. The observations were 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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The reasons for the same are given below.

Causes 1. Corner Damage 2. Sponge Layer - Thin / Thick 3. Cutting - Big / Small 4. Unsynchronized Conveyor 5. Miscellaneous

No. of Pastries

Cumulative %

14 15

% of Total Damaged 35 % 37.5%

5 4

12.5% 10%

85% 95%

2

5%

100%

35% 72.5%

As seen from the above table, corner damage & damage due to sponge 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 saving the resources.

In spite of having these measures, we analyzed unevenness in sponge as root cause. hence we studied entire baking process 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.

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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 the centre of moulds as in (B)

(B)

(A)

Factors that were kept constant during the trial are:1) Surface of oven was even.

2) New moulds were used.

3) Proper handling process.

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4) No hand leveling.

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 in reduction 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. Serial numbers were provided on the trays for easy identification. (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 of leveled 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. Serial numbers were provided on the trays for easy identification.

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.

2. New moulds were used.

3. Hand leveling was done.

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

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5. Same batter was used.

3 1

2

6

4

5 7

10 8

9

11

12 13

Fig: Top View of Oven Deck with ‘ladi’

OBSERVATIONS: Out of 26 which were handled properly only 2 sponges were found with slight variations. Whereas in case of usual handling the no. of sponges with variations were 5. The identification number for these 5 sponges is 1,2,3,5 & 10.

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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 process capability we have used Statistical Tools like X & R Charts & the ANOVA technique.

 Variable Control Charts X- & R Chart (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 doesn’t 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.

Sample 1 2 3 4 No. 1 1784 1698 1781 1748 2 1642 1732 1722 1728 3 1732 1818 1894 1786 4 1714 1766 1732 1710

5 1760 1740 1904 1776 TOTAL

Mean (X) 1754.2 1712.8 1826.8 1739.6 7033.4

Range (R) 86 98 172 66 422

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 Chart)

(A2 = 0.577 for n=5; from Statistical Table for X & R

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 Statistical Table for X & R Chart) LCL = D3 x R- = 0.0 CV = R- = 105.5

Control Chart: weight in grams 1,900 weight in grams UCL = 1880.0589

1,850

U Spec = 1730.0000 Average = 1758.3500

1,800

n a e M

L Spec = 1670.0000 LCL = 1636.6411

1,750

1,700

1,650

1,600 1.00

2.00

3.00

Sigma level:

4.00

6

Fig: CONTROL CHART FOR PROCESS MEAN & CONTROL CHART FOR PROCESS VARIABILITY

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Control Chart: weight in grams 400 weight in grams UCL = 340.6593 Average = 105.5000

300

LCL = .0000

e g n a R

200

100

0 1.00

2.00

3.00

Sigma level:

4.00

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 σ Process Capability = 6 σ = 6 x (R- ÷ d2) Process Statistics Capability 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.

35

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

1 2 3 4 5 6 7 8 9

Chocolate Sponge White Sponge White Eggless Chocolate Eggless Dark Sponge White Sheet Chocolate Sheet Brownie Brownie Eggless

36

Range for Std. Density in gms/cm3 0.65 – 0.70 0.62 – 0.72 0.68 – 0.78 0.75 – 0.85 0.65 – 0.75 0.65 – 0.75 0.67 – 0.75 0.82 – 0.88 0.80 – 0.88

DATA:

Obs.no.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Weight in gm for Density = 0.68 gm/cm3 (Density Outside Range) 1650 1604 1618 1688 1738 1710 1568 1616 1664 1732 1638 1530 1654 1741 1540

Weight in gm for Density = 0.77 gm/cm3 (Std Density) 1691 1708 1704 1680 1660 1781 1640 1622 1718 1709 1688 1664 1651 1680 1740

HYPOTHESIS FORMULATION: Ho : µ1 = µ2; There is no significant difference between weight of the mixture with different density Ha : µ1 ≠ µ2; 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 null hypothesis 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: Descriptives

37

Mass 95% Confidence Interval for Mean 1.00 2.00

N 15 15

Mean 1646.0667 1689.0667

Std. Deviation 68.36715 40.40768

Std. Error 17.65232 10.43322

Lower Bound 1608.2062 1666.6896

Upper Bound 1683.9271 1711.4437

Minimum 1530.00 1622.00

Maximum 1741.00 1781.00

Total

30

1667.5667

59.35381

10.83647

1645.4036

1689.7297

1530.00

1781.00

ANOVA Mass

Between Groups Within Groups Total

Sum of Squares 13867.500 88295.867 102163.367

df 1 28

Mean Square 13867.500 3153.424

F 4.398

Sig. .045

29

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.

38

(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. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Weight in gm for Machine 1 Original Unifiller 1698 1742 1646 1692 1684 1704 1684 1688 1710 1694 1752 1704 1724 1746 1684

Weight in gm for Machine 2 New Unifiller (Indian made) 1834 1854 1852 1484 1376 1722 1922 1906 1940 1718 1694 1850 1484 1530 1540

HYPOTHESIS FORMULATION: Ho : µ1 = µ2; There is no difference in the mass deposited by two different ‘unifiller’ machines of the same type.

Ha : µ1 ≠ µ2; 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 Weight in kg

39

95% Confidence Interval for Mean 1.00 2.00

N 15 15

Mean 1723.4667 1713.7333

Std. Deviation 77.16575 186.58566

Std. Error 19.92411 48.17621

Lower Bound 1680.7337 1610.4056

Upper Bound 1766.1996 1817.0610

Minimum 1646.00 1376.00

Maximum 1984.00 1940.00

Total

30

1718.6000

140.37796

25.62939

1666.1820

1771.0180

1376.00

1984.00

ANOVA Weight in kg

Between Groups Within Groups

Sum of Squares 710.533 570762.66

Total

571473.20

df 1 28

Mean Square 710.533 20384.381

F

Sig. .035

.853

29

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.

40

(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.

Sample 1 2 3 4 No. 1 1484 1508 1496 1507 2 1512 1528 1529 1506 3 1493 1487 1513 1528 4 1505 1530 1515 1501

5 1510 1510 1496 1515 TOTAL

Mean (X) 1501 1517 1502 1512 6032

Range (R) 26 23 41 29 119

PROCEDURE:

1) Calculate grand mean and mean range for above data Grand 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 Chart)

(A2 = 0.577 for n=5; from Statistical Table for X & R

LCL = X-- - A2 R- = 1474.32 CV = X-- = 1508.65

3) Statistically Derived Limits for R- Chart:

41

UCL = D4 x R- = 96.06 (D4 = 2.114 & D3 = 0.0 for n=5; from Statistical Table for X & R Chart) LCL = D3 x R- = 0.0 CV = R- = 29.75

Control Chart: weight in grams

weight in grams 1,540

UCL = 1542.9708 U Spec = 1530.0000 Average = 1508.6500

1,520

L Spec = 1470.0000

n a e M

LCL = 1474.3292

1,500

1,480

1.00

2.00

3.00

Sigma level:

4.00

6

Fig: CONTROL CHART FOR PROCESS MEAN & CONTROL CHART FOR PROCESS VARIABILITY

42

Control Chart: weight in grams 100 weight in grams UCL = 96.0627 Average = 29.7500

80

LCL = .0000

e g n a R

60

40

20

0 1.00

2.00

3.00

Sigma level:

4.00

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 Capability 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 actual process 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.)

43

6.3 EQUIPMENT / MACHINE WHICH ARE THE FACTORS? In equipment uneven surface of oven, moulds, variation in temperature zone 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, information about the types of non-conformities is collected on the same chart to help determine the causes of variation.

44

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.

45

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)

1 2 3 4

No. of Defective Sponge (x) 13 8 10 5

52 52 52 52

FACTORS KEPT CONSTANT: 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:

46

P = x/n 0.25 0.153 0.192 0.096

Control Chart: no of defective pcs

no of defective pcs UCL = .3305

0.3

Center = .1731 LCL = .0157

g fm c N tin p ro P

0.2

0.1

0.0 1.00

2.00

3.00

Sigma level:

4.00

3

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, so that immediate measures can be taken.

47

CASE (2) (i) – DECKWISE for OVEN 1

DATA: (OVEN 1) Deck No.

Sample Size (n)

1 2 3 4

13 13 13 13

No. of Defective Sponge (x) 2 3 2 6

FACTORS KEPT CONSTANT: 3. Proper handling process 4. Leveling is done.

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:

48

P = x/n 0.1538 0.2307 0.1538 0.4615

Control Chart: no of defective pcs 0.7 no of defective pcs UCL = .6103

0.6

Center = .2500 LCL = .0000

0.5

0.4

g fm c N tin p ro P

0.3

0.2

0.1

0.0 1.00

2.00

3.00

Sigma level:

4.00

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.

49

CASE (2) (ii) – DECKWISE for OVEN 3 DATA: (OVEN 3) Deck No.

Sample Size (n)

1 2 3 4

No. of Defective Sponge (x) 1 2 4 1

13 13 13 13

FACTORS KEPT CONSTANT: 5. Proper handling process 6. Leveling is done.

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:

50

P = x/n 0.0769 0.1538 0.3076 0.0769

Control Chart: no of defective pcs 0.5 no of defective pcs UCL = .4541 Center = .1538

0.4

LCL = .0000

0.3

g fm c N tin p ro P

0.2

0.1

0.0 1.00

2.00

3.00

Sigma level:

4.00

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 Oven No.

Sample Size (n)

1 2 3 4 NEW MOULDS Oven No.

15 15 15 15

Sample Size (n)

1 2 3 4

15 15 15 15

No. of Defective Sponge (x) 4 6 5 4

P = x/n

No. of Defective Sponge (x) 1 2 1 3

P = x/n

FACTORS KEPT CONSTANT: 1. Proper handling process 2. Leveling is done.

3. Oven deck was leveled.

PROCEDURE: Calculation for both data are as per above procedures.

SPSS OUTPUT:

52

0.266 0.40 0.333 0.266

0.066 0.133 0.066 0.2307

Control Chart: no of defective pcs 0.7 no of defective pcs UCL = .6770

0.6

Center = .3167 LCL = .0000

0.5

0.4

g fm c N tin p ro P

0.3

0.2

0.1

0.0 1.00

2.00

3.00

Sigma level:

4.00

3

Fig: P – Chart for Old Moulds Control Chart: no of defective pcs 0.4 no of defective pcs UCL = .3653 Center = .1167 LCL = .0000

0.3

g fm c N tin p ro P

0.2

0.1

0.0 1.00

2.00

3.00

Sigma level:

4.00

3

Fig: P – Chart for New Moulds.

53

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.

54

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

OBSERVATIONS:

55

1

3

2

13.93% 13.58 %

13.26% 6 12.96

4

5

13.09%

12.7%

7 11.96%

9

8

10 10.16%

10.33%

10.84

11 10.60% 12 9.86%

13 10.05%

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

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 more where distance of the mould is less from the burner and it decreased gradually towards the lid.

56

6.4 SIMULATION (i) Need For Simulation As explained earlier in operational process flow chart, the baking process 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.

57

58

59

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

4

Idle 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

4

Loading 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 will get over baked for another 5 mins. Due to such random assignments, which are noticed in subsequent observations leads to

60

• •

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 worker’s ignorance only 11 moulds are loaded per 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. of baking cycles.

Hence there was a need to synchronize the mixing-depositing-baking activity which would completely eliminate the generation of WIP.

61

(ii) Time Study for mixing – depositing – baking activities is given 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.

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.

62

(i.e During loading of oven 4 & unloading of oven 1, travelling time is maximum = 1 min) Total time for baking = 35 mins.

OVEN OVEN11

OVEN OVEN22

From

OVEN OVEN 33

OVEN OVEN 44

Conveyor Belt

Depositor Fig: Layout of Ovens in Baking Area

During simulation the chronological sequence of type of mixture, type of 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

63

3. ½ kg Round 4. 1 kg Heart 5. ½ 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 BATCH

Chocolate Eggless

53 kg

White Eggless

52 kg

White

45 kg

Chocolate

45 kg

Dark Chocolate (Black)

39 kg

Table B

64

TYPE OF MOULD

WEIGHT TO BE DEPOSITED

LADI (eggless)

1.75 kg

LADI (sponge)

1.50 kg

½ kg Round / Heart / Square

0.285 kg

1 kg Round / Heart / Square

0.475 kg

Special Shapes

Varies as per shape

65

Now for making a prototype, a sample order date 20-05-2008 is taken which is as given below

MIXTURE Chocolate Egg. Chocolate White Egg. White Black

LADI Pcs 250 200 60 90 60

1 KG RD Pcs

1/2 RD Pcs

1/2 1 HRT HRT Pcs Pcs

1/2 SQ Pcs

100 100

200

150

150 700

300 450

300

Total kg 670.2 5 746.5 105 220.5 90

Batch Size kg

No. Mixings

53 45 52 45 39

12.64 ~ 13 16.58 ~ 17 2.01 ~ 2 4.9 ~ 5 2.30 ~ 3

Sample Calculations: (For Chocolate Eggless) 250(ladi)*1.75 + 150(½ kg round)*0.285 + 100(1kg heart)*0.475 + 300(½ kg heart)*0.285 + 200(½ 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 elapsed between consecutive loading of each oven).

Hence we can say that mixing is a very flexible process and can be easily synchronized the subsequent activities.

66

of

(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 Ladi mould

1 kg ½ kg 1 kg Heart Round Round

½ kg Heart ½ kg Square

No. of pcs 1 in 1 mould

2

3

3

2

3

Constraints: Maximum number of moulds available of each type

Ladi

1 kg Round

½ kg Round

1 kg Heart

½ kg Heart

½ 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

½ kg Round

1 kg Heart

½ kg Heart

½ 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

67

if the ladi mould is used in baking cycle 1, now it cannot be used again before backing cycle 3.

Nomenclature: For Moulds:

Type Moulds

of Ladi

Nomenclature L

1 kg ½ kg 1 kg ½ kg ½ kg Special Round Round Heart Heart Square Shapes R

RI

H

HI

S

SP

For Mixture:

Type Mixture

of Chocolate Eggless

Nomenclature C.E

White Eggless

White

Chocolate

Black

W.E

W

C

B

For Activities:

Type of Activity

Loading Starts

Baking Starts

Unloading Starts

Unloading Ends

Nomenclature

L.S

B.S

U.L.S

U.L.E

68

69

70

71

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 the sponge.)

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.

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 ½ round for the same mixture.

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Referring table A, in cycle 2 there is a changeover of mould in deck 4 of oven 1.

Calculation for approx no. of moulds of ½ round that can accommodated in deck 4 is given below: Sample Calculation: Maximum ladi moulds per deck = 13.

4’th deck of oven 1 is loaded with 3 ladis i.e approx 25% of total deck capacity. Hence 75% of ½ round moulds can be accommodated in that deck. For ½ 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 ½ square due to capacity constraint. All the three protocols are to be strictly adhered.

Hence chocolate eggless mixture gets over in the 3’rd cycle. In the same cycle we are loading 60 ladis of white eggless & 90 ladis of white mixture. As explained earlier 208 moulds 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 3’rd cycle. After this loading is over we can now move to the ½ kg round as per our mould protocol. But since only 140 moulds are available of ½ kg heart and the same has to be re-used in the immediate alternate cycle.

Hence we are loading it before the 1 kg & ½ 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 – cooling operations. 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 – depanning. Consider a hypothetical example where in we have to produce 750 ladis & only 250 moulds are available. Then the same moulds have to be used thrice. More efficient

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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. The same 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.

Sponges from Baking Dept.

Fig: Movement of Sponges from Baking Department 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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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 main problems 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. The sponges 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-intime basis. This will completely eliminate the activity of storing sponge at ambient temperature or in cold room. Thereby reducing the chances of damage during material handling.

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(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 of what 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 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

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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 same thing 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 now has only one depositing machine. Hence it is recommended that they need to increase the no. of depositing machines.

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8. UNBALANCED LINE – LINE STOPPAGE Unbalanced line is the major cause identified with the help of fishbone diagram, so it becomes necessary to study why a line stops? Why unproductive time is there? Is worker to task assignment is proper? So in this part of the project we have identified and grouped causes for unbalanced line and line stoppages, which are shown in the diagram in the diagram given below.

(i) FISH BONE DIAGRAM

MATERIAL Basic Material is not available. (Cream / Sponge)

Ancillary Material.

(Piping / Mounting)

CHANGEOVER Cleaning of Tools / Equipments

Change of flavour (Cream / Sponge)

UNBALANCED LINE Fatigue allowance for workers

Trays are not available Receiving Window person not available

Unofficial leaving of line

WORK STATION

PACKING STATION

This fishbone diagram shows some major causes and their sub causes for unbalanced line which includes causes related to work station, packing station, change over and material related cause explained with the help of “fishbone diagram”

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(ii) PARETO ANALYSIS These causes then quantified by collecting data from pastry production line for a period of 13 hours (i.e.790 minutes) taken over a period of two days this data then tabulated as follows:

Case No. 1 2 3 4 5 6 7 8 9

Reasons Material Not Available Change of Pastry Type Piping Preparation Change of Tools Cleaning Tools Opening Cupli Worker leaving the line Trays Not Available (FGS) Receiving window Busy

Avg Time in Minutes 3 8 2 2 1 4 10

Frequency 3 5 5 4 6 2 3

Total Time in Minutes 9 40 10 8 6 8 30

6

3

18

6

3

18

TOTAL

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This table shows reasons for line stoppages or unproductive time on line, which shows average time for the same & also its frequency. This ultimately helps in plotting the ‘pareto’ diagram. A total of 147 out of 790 minutes over a period of 2 days gets wasted on a line for the above mentioned reasons.

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150.00

100%

120.00

80%

60%

60.00

40%

30.00

20%

ts u M n e im T

t n rc e P

90.00

40.00 30.00 18.00 18.00

10.00

9.00

8.00

8.00

6.00

3

1

4

6

5

0.00 2

7

8

9

0%

Case Number

As observed from the above pareto diagram we can segregate the major causes which are:Case no. 2 : Change over from one pastry to another.

Case no. 7 : Worker leaving the line.

Case no. 8 : Trays not available at the FGS window.

Case no. 9 : The person at the receiving window is busy.

These four causes contribute to about 72% of total reason for unbalanced line. We have also analyzed why these causes occur. The production on the line is the function of baking process, because when first shift starts, the sponges needed at the production line are taken from cold storage. When these sponges are over, they schedule on the production line changes as per the availability of the sponges from baking department.

These all four causes are controllable provided we have the information about the sponges and their availability with regards to quantity & time.

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For cause no. 2 & 7, the worker has to leave his place as, for different pastries different ancillary materials is required, different knives, different cream and many other finishing items are required. Hence if we know in advance the quantity of sponges then accordingly we can schedule the production line and by providing all ancillary & accessory materials for a particular pastry prior to its production. There by reducing these four causes.

(iii) TIME STUDY – (For Pastry Production Line) We have also conducted a time study on the various activities conducted on the pastry production line, which is shown alongside.

This data shows the average time required per activity & the number of workers carrying out that particular activity on the line. It helps us to determine the average time per pastry which can be used to calculate the time required to complete a production order. This not only helps in scheduling the operation but also help in controlling the causes leading to unbalanced line.

This time study data will be beneficial to the company in the implementation of their future projects that are in the pipeline. One of these projects is the single piece flow strategy on the production line, which is a part of lean production system.

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9. FUTURE SCOPE As a part of our study we have observed that every department i.e. the mixing – baking – production is working to its optimum level. But individual optimization does not serve our purpose, which is to produce quality products in the most cost effective manner with optimum utilization of resources. As all these process are highly dependent on each other. Optimality of one process may act as a bottleneck for another process. (Eg. Optimality of mixing leads to generation of work in process inventory.) Hence it’s not about local optimization but it’s always about global optimization. i.e optimum use of each department in such a way that overall optimization is attained through interlinking all the departments & processes.

HOW A PULL SYSTEM WILL WORK IN MONGINIS? In a pull system, the company will produce only that quantity which is required at a given point of time. Lean production system works on the same principle in which the main objective is to have zero work in process inventory. If they attain this objective, some of the incidental benefits that the company will accrue are:1. Reduced inventory of moulds.

2. Reduced production cycle time.

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3. Reduced material handling.

4. Reduced manpower requirement.

5. Increase in quality of sponges due to less moisture loss.

6. Reduced inventory holding costs.

7. Due to less or no WIP inventory, more space will be freed up, that can be used for other processes But how will it work?

Once the company has the production order, the production personnel can easily plan the no. of different sponges required at different production line & accordingly with the help of Pivot table, they can plan in advance at what time they have to provide the sponges of which flavour, which type & which shape on which production line.

Once they have this data, and also with the help of simulation software, they can backward calculate how much mixings that has to be made for that particular order and also the batches can be made accordingly. In the existing procedure the batch size is fixed, so is the batch size of mixings. In this way, they will only produce, whatever is required i.e. fractional mixings will be done.

As recommended, the company can use 4 depositing machines which contain batter of different flavours just like a coffee dispensing machine. This will enable the company to customize the depositing operation i.e. whatever shape of whatever flavour that is required can be deposited & loaded in the oven.

Also with the help of simulation software, the company will know exactly how much time that will be required to produce the sponges. So after the baking process sponges can be directly fed to the production line. As the production personnel will now know the exact time that will be taken for every process & sub process, the entire production can be streamlined. This will ultimately result in zero WIP inventory.

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ANNEXURE 1. Diagrams for different Moulds

Fig: Ladi Mould for 1.5 & 1.7 kg.

Fig: 1 kg Heart

Fig: 1 kg Round

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Fig. ½ kg Heart

Fig: ½ kg Square

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Fig: ½ kg Round

2. Control Chart Factors Table

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REFERENCES 1. Statistics for Management – 7’th Edition - By Richard I. Levin, David S. Rubin 2. Statistical Process Control – S.P Mahajan 3. Formulae & Tables – By Board of Directors ICFAI University.

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