31183-Sujit Pawar-Warehouse Layout Modification

Symbiosis Institute of Business Management, Pune

.

Warehouse Layout OptimisationDissertation Report

Sujit N.Pawar

31183

HINDUSTAN COCA-COLA BEVERAGES PVT.LTD. Dissertation Report

Sujit N.Pawar |SIBM |2008-2010 1

Table of Contents

Executive Summary.................................................................................................................................2

1. Introduction.......................................................................................................................................3

Warehouse Operations In HCCBPL ............................................................................................3

Structure Of A Typical Warehouse ............................................................................................3

Activities Carried Out At A Warehouse .....................................................................................4

Warehouse Productivity ............................................................................................................5

2. Warehouse Layout Optimisation ......................................................................................................6

Need............................................................................................................................................6

Current issues in the storage of goods ......................................................................................6

Consequences.............................................................................................................................6

3. Methodology .....................................................................................................................................8

4. Alternate Layout Proposals.............................................................................................................11

Proposal A.................................................................................................................................11

Proposal B.................................................................................................................................12

Comparison of the two layouts ...............................................................................................13

Implications ..............................................................................................................................13

5. Recommendations ..........................................................................................................................14

6. Conclusions ......................................................................................................................................15

7. Annexures........................................................................................................................................16



Executive Summary

Hindustan Coca Cola Beverages Pvt. Ltd. (HCCBPL) is the bottling arm of The Coca

Cola Company in India and is involved in the manufacture and distribution of its beverages

in India. The company buys the respective concentrates from Coca Cola India, manufactures

the various beverages in its factories across India and ensures availability at various outlets.

The distribution activity is the most important of all and a significant contributor to

the cost of the final product. For the distribution purpose, the company has its own

warehouses managed by franchisees across the country. The warehouses are responsible

for receiving the stock coming from the factory, storing it for some duration of time and

dispatching it to the customer. They are also responsible for performing break-bulk or cross-

docking operations if required. Thus a warehouse is an important component of the Supply

Chain network called Route-To-Market in HCCBPL.

Hence the warehouse productivity becomes important parameter of the

effectiveness of overall operations. An optimum warehouse layout is a very important factor

affecting warehouse productivity and is responsible for efficient operation. The project aims

to capture and organize the data in the most effective manner and optimizing warehouse

layout as to enhance the warehouse productivity.



1. Introduction

Since the project is about warehouse, an understanding of typical warehouse

operations becomes essential.

Warehouse Operations In HCCBPL

The country is divided into the following regions for distribution purposes:

1. North2. Central3. South

These regions are further divided into various units under which come the factories and the warehouses. The warehouse is a hub of activity, especially during the peak season with the various distribution managers, Areas Sales Managers and Regional Managers performing their duties.

The factory dispatches the stock as per the forecast to the various warehouses under

its purview. The warehouse stock this till it is sent to the market. The various customers for

a warehouse are the small retailers, key accounts, distributors, etc. The warehouse has its

own fleet of vehicles for servicing the customers.

Structure Of A Typical Warehouse

The warehouse operations are outsourced to a Carrying and Forwarding Agent (C &

FA) who is responsible for carrying out the various activities in the warehouse. The C & FA is

bound by a formal agreement between him and HCCBPL. C & FA provides the manpower

required for running the operations including casual labourers and the office staff. He is

responsible in Toto for them and is in-charge of supervising their activities, monitoring their

attendance, paying salaries and other such typical functions.

The RTM team also has an office at the warehouse. These are HCCBPL employees

and are responsible for the overall smooth functioning of the warehouse. They include a

Distribution Manager who is also the warehouse head and other people handling the

various types of accounts like Pre-Sell and DSD.



Activities Carried Out At A Warehouse

The activities carried out at a warehouse can be broadly classified into the following

five heads:

1. Loading/Unloading: This includes:

a. Unloading of fresh stock from factory vehicles

b. Loading of route vehicles for delivery

c. Unloading empties and unsold cases from Route Vehicles

d. Loading empties into the company factory for dispatch back to the factory

e. Sorting of empties and palletisation according to the SKUs

2. Order Processing: Includes:

a. Collecting orders from Presell / Tel-Sell and key accounts executives and

preparing the order sheet for the day

b. Preparing load sheets for the Presell and key accounts

c. Handing over the collated orders to the shipping department

d. Preparing load sheets for the Ready Van Sales according to the orders of the

salesmen

e. Preparing pick sheets for Ready van Sales, Presell and key accounts Sales

f. Invoice processing of incoming and outgoing vehicles

3. Order Dispatch/Delivery: Includes the complete cycle of vehicles leaving with their

load and returning with the empties and/or unsold goods

4. Cash Processing: Includes the collection of cash from salesmen, accounting and

depositing the cash

5. Others: Miscellaneous activities like housekeeping, office peons, security,

maintenance etc.



Warehouse Productivity

The warehouse productivity is measured in terms of the following metrics:

1. Manpower Productivity: it is defined as the number of cases sold per manday and

the benchmark value is 60

2. Space Productivity: it is the number of cases sold per square feet and the number of

days in the period considered. The benchmark is .25

3. Vehicle Productivity: it is the number of cases sold per the available vehicle capacity

and is expressed as a percentage. The benchmark value is 120%.

Apart from these three, following two metrics are considered which impact the

safety of the warehouse. These are:

1. Total Incident Rate

2. Number of Fatalities

The warehouse productivity has a significant impact on the operating cost of the

company. And most of the parameters are dependent on the layout of the warehouse. An

efficient warehouse layout streamlines the operations, helps in easy movement of vehicles,

reduces turnaround time and makes it easier to keep track of the inventory. It also helps in

improving the safety of the personnel working in the warehouse.



2. Warehouse Layout Optimisation

HCCBPL has two warehouses catering to Delhi region-at Lawrence Road (LR) and

Dilshad Garden (DG). The entire Delhi region is covered between these two. The project was

based at Lawrence Road depot which is larger of the two.

Need

The existing depot at LR has about 17,000 square feet of covered warehouse area

which is used to stock the various SKUs. It has five bays for loading and unloading of goods.

The loading of route vehicles usually takes place in the night shift and the vehicles leave on

their routes by 9 am. Some vehicles make multiple trips depending on the routes.

The depot receives stock from factories at Dasna (U.P.), Nazeebabad (H.P.), Pune

(Maharashtra) and Goa. Cross –docking and break-bulk operations take place and the stock

is dispatched as per the requirements.

Current issues in the storage of goods

1. Lack of a dedicated area earmarked for various SKUs

2. No FIFO/FMFO

3. No ABC classification of SKUs for storage

4. No dedicated area for other miscellaneous items like empty pallets, cartons, scrap,

promotional material, etc.

5. Fluctuations in the stock receipt from the plants

Consequences

1. High vehicle turnaround time

2. No streamlined flow of goods

3. High loading/unloading time

4. Safety hazards



This has a direct impact on the warehouse productivity. A better layout would not

only enable a streamlining of storage and handling but also help in increasing the

productivity by reducing the labour time and increasing the effectiveness. Hence, a need for

the project.



3. Methodology

The methodology followed was as follows:

1. Studying the operations at the company warehouse at Lawrence Road in Delhi

This involved studying the complete operations of the warehouse in all the shifts. It

included a stay in the depot during the night shift also as a bulk portion of the loading takes

place at night. A list of activities (already covered in introduction) taking place at a

warehouse was prepared which proved to be helpful in the Manpower Planning module

also. It helped in clearing the understanding about the activities at the depot.

2. Studying the existing layout at Lawrence Road depot

The LR depot does not have any layout as such. The incoming stock is placed

according to the need and the space available. However that does not mean it is completely

random. Slow moving SKUs like cans, for e.g., are placed at the back of the warehouse.

The situation becomes especially grim during the peak season as there is a mismatch

between demand and supply. Also, the stacking norms are not usually followed.

3. Preparing alternate layouts based on various SKUs, their sales and required inventory

levels

Peak month’s (May 09) sales were taken into account for calculating the space

requirements and design the warehouse layout to optimize the space for various SKUs.

The different SKUs were classified into 13 broad categories depending on their

volume, type (RGB or PET), brand, etc. the space requirement was then calculated based in

the inventory norms, percentage contribution to the sales, stacking height, and factors like

aisle factor and space utilization factor.

An ABC classification was done based on the sales of the various SKUs:



1. A Class: These constitute 70% of the sales and hence should be located closest to the

loading/unloading area

2. B Class: Constitute 10-20% of the sales and are placed between the A class and C class

items.

3. C Class: They constitute less than 10% of the total sales and are hence placed towards the

rear of the warehouse

A sample table of the classification is shown below:

Annexure 2 shows the complete table for all the SKUs.

The final floor pallet positions were then decided for each SKU coming under each of these broad categories taking into account the following factors:

Inventory Norms

o Supply Lead timeo Batch Size

Stacking Height

Movement Space

o Staging Areao Aisles



Calculations

Peak Month Inventory = Peak Month Sales X Inventory Norm (Days) ------------------------------------------------------------

No .of days in the month

Required Pallet Positions = Peak Month Inventory -------------------------------

Utilization Factor

Floor Pallet Positions = Required Pallet Positions -------------------------------------

Stacking Height

Sample layouts were then prepared in Microsoft Excel from the data generated from

the above activity.

Fig.: Calculations for warehouse space

The sample layouts were prepared based on the calculations. After various iterations

and discussions with the concerned people, two proposals were finalized and accepted.



4. Alternate Layout Proposals

Proposal A



Proposal B



Comparison of the two layouts

As can be easily seen from the comparison, layout B results in a storage capacity

improvement of 11% over Layout A.

Implications

1. Faster loading/unloading time due to storage based on ABC classification of SKUs

2. Improved productivity of casual labour and forklifts

3. Improved efficiency and effectiveness of operations

4. Streamlined flow of men, machine and material

5. Improved safety and hygiene

6. Storage as per norms

Hence due care should be taken while implementing the layout.



5. Recommendations

1. Layout Proposal B is recommended for implementation

i. Higher number of cases stored

ii. Use of covered area for storage only

2. Numbering the dock doors

3. Marking the aisles with paint

4. Damaged and BBD stock to be stored outside the covered warehouse area

5. Identification of stacks with overhanging square displays

6. One more entry/exit point would enable a smooth flow of route vehicles



6. Conclusions

The warehouse is a complex hub of multiple activities occurring simultaneously. It is

very difficult to manage, especially during the peak season. Also, it should be the first one to

target for optimization of a Supply Chain.

The layout if implemented would go a long way in streamlining the operations of the

LR depot. However, due care has to be taken as people are resistant to change, especially if

the current scenario is apparently working well. The C & FA would have to be taken into

confidence as he is the one who is ultimately the executor of the plan. It is suggested to

implement the layout step-by-step during the lean season.



7. Annexures

1. Calculation of space required for different SKUs

Sr.No.

SKU

Peak Month(M

ay 09) Sales

% Contribution

Inventory Norm(Days)

Peak Month

Inventory(Cases)

Cases/Pallet

Peak Month

Inventory(Pallets)

Utilisation Factor

Pallet Positions

Reqd

Stacking

Height

Floor Pallet

Positions

Area Reqd(Sq. m)

(including aisle

factor)

1 RGB 200 ml 12,852 4.01% 3 1428 63 23 80% 28 3 10 16.00

1.1 Limca RGB 200 ml 5,487 1.71% 3 610 63 10 80% 12 3 4 6.40

1.2 Coca-Cola RGB 200 ml 2,742 0.85% 3 305 63 5 80% 6 3 2 3.20

1.3 Thums Up RGB 200 ml 2,207 0.69% 3 245 63 4 80% 5 3 2 3.20

1.4 Sprite RGB 200 ml 1,273 0.40% 3 141 63 2 80% 3 3 1 1.60

1.5 Fanta Orange RGB 200 ml 1,143 0.36% 3 127 63 2 80% 3 3 1 1.60

1.6 Fanta Apple RGB 200 ml 0 0.00% 3 0 63 0 80% 0 3 0 0.00

2 RGB 300 ml 78,569 24.49% 2.5 7275 45 162 80% 202 3 68 108.80

2.1 Coca-Cola RGB 300 ml 26,794 8.35% 2.5 2481 45 55 80% 69 3 22 35.20

2.2 Limca RGB 300 ml 19,888 6.20% 2.5 1841 45 41 80% 51 3 17 27.20

2.3 Thums Up RGB 300 ml 17,441 5.44% 2.5 1615 45 36 80% 45 3 15 24.00

2.4 Fanta RGB 300 ml 7,372 2.30% 2.5 683 45 15 80% 19 3 7 11.20

2.5 Sprite RGB 300 ml 7,074 2.21% 2.5 655 45 15 80% 18 3 7 11.20

3 Juice RGB 19,023 5.93% 2.5 1761 45 39 80% 49 3 17 27.20

3.1 Maaza Juice RGB 19,023 5.93% 2.5 1761 45 39 80% 49 3 17 27.20

4 Juice PET 17,196 5.36% 2.5 1592 45 35 80% 44 1 44 70.40

4.1 Maaza-250 ml Juice PET 6,449 2.01% 2.5 597 45 13 80% 17 1 16 25.60

4.4 Maaza-600 ml Juice PET 4,440 1.38% 2.5 411 45 9 80% 11 1 12 19.20

4.5 Maaza 1.2 L Juice PET 3,415 1.06% 2.5 316 45 7 80% 9 1 8 12.80

4.3 Minute Maid-400 ml Juice PET 2,292 0.71% 2.5 212 45 5 80% 6 1 6 9.60

4.2 Minute Maid-250 ml Juice PET 569 0.18% 2.5 53 45 1 80% 1 1 1 1.60

4.6 Minute Maid 1L Juice PET 31 0.01% 2.5 3 45 0 80% 0 1 1 1.60

5 Small PET 96,479 30.08% 3 8459 45 188 80% 235 2 120 192.00

5.1 350 ml 3,257 1.02% 4 483 45 11 80% 13 2 9 14.40

5.1.1 Coca-Cola-350 ml PET 1,049 0.33% 4 155 45 3 80% 4 2 2 3.20

5.1.2 Limca-350 ml PET 724 0.23% 4 107 45 2 80% 3 2 1 1.60

5.1.4 Fanta O-350 ml PET 575 0.18% 4 85 45 2 80% 2 2 2 3.20

5.1.3 Sprite-350 ml PET 535 0.17% 4 79 45 2 80% 2 2 2 3.20

5.1.5 Fanta A-350 ml PET 269 0.08% 4 40 45 1 80% 1 2 1 1.60

5.1.6 Thums Up-350 ml PET 105 0.03% 4 16 45 0 80% 0 2 1 1.60

5.2 500 ml 14,756 4.60% 3 1640 45 36 80% 46 2 22 35.20

5.2.1 Coca-Cola-500 ml PET 6,007 1.87% 3 667 45 15 80% 19 2 9 14.40

5.2.2 Diet Coke-500 ml PET 3,102 0.97% 3 345 45 8 80% 10 2 4 6.40

5.2.3 Limca-500 ml PET 2,730 0.85% 3 303 45 7 80% 8 2 4 6.40

5.2.4 Fanta O-500 ml PET 1,513 0.47% 3 168 45 4 80% 5 2 2 3.20

5.2.6 Thums Up-500 ml PET 950 0.30% 3 106 45 2 80% 3 2 2 3.20

5.2.5 Sprite-500 ml PET 454 0.14% 3 50 45 1 80% 1 2 1 1.60

5.3 600 ml PET 78,466 24.46% 2.5 6337 45 141 80% 176 2 89 142.40

5.3.2 Limca-600 ml PET 30,189 9.41% 2 2236 45 50 80% 62 2 31 49.60

5.3.1 Coca-Cola-600 ml PET 19,952 6.22% 2 1478 45 33 80% 41 2 20 32.00



Sr.No.

SKU

Peak Month(M

ay 09) Sales

% Contribution

Inventory Norm(Days)

Peak Month

Inventory(Cases)

Cases/Pallet

Peak Month

Inventory(Pallets)

Utilisation Factor

Pallet Positions

Reqd

Stacking

Height

Floor Pallet

Positions

Area Reqd(Sq. m)

(including aisle

factor)

5.3.5 Sprite-600 ml PET 10,182 3.17% 2.5 943 45 21 80% 26 2 14 22.40

5.3.3 Fanta O-600 ml PET 9,214 2.87% 2.5 853 45 19 80% 24 2 11 17.60

5.3.6 Thums Up-600 ml PET 8,622 2.69% 2.5 798 45 18 80% 22 2 12 19.20

5.3.4 Fanta A-600 ml PET 307 0.10% 2.5 28 45 1 80% 1 2 1 1.60

6 Large PET 55,710 17.37% 3 4318 36 120 80% 150 2 75 120.00

6.3 Limca-1L PET 1,138 0.35% 3 126 36 4 80% 4 2 2 3.20

6.1 Coca-Cola-1L PET 749 0.23% 3 83 36 2 80% 3 2 2 3.20

6.2 Fanta-1L PET 545 0.17% 3 61 36 2 80% 2 2 2 3.20

6.5 Thums Up-1L PET 302 0.09% 3 34 36 1 80% 1 2 1 1.60

6.4 Sprite-1L PET 145 0.05% 3 16 36 0 80% 1 2 1 1.60

6.10 Coca-Cola-2L/2.25L PET 20,593 6.42% 2 1525 36 42 80% 53 2 26 41.60

6.8 Limca-2L/2.25L PET 17,609 5.49% 2 1304 36 36 80% 45 2 22 35.20

6.6 Thums Up-2L/2.25L 3,353 1.05% 2 248 36 7 80% 9 2 4 6.40

6.7 Sprite-2L/2.25L PET 2,940 0.92% 2 218 36 6 80% 8 2 3 4.80

6.9 Fanta-2L/2.25L PET 9,474 2.95% 2 702 36 19 80% 24 2 12 19.20

7 Schweppes 1,615 0.50% 3 179 45 4 80% 5 3 2 3.20

7.4 Schweppes Tonic Water 1,414 0.44% 3 157 45 3 80% 4 3 1 1.60

7.2 Schweppes Gingerale 144 0.04% 3 16 45 0 80% 0 3 1 1.60

7.1 Schweppes Bitter Lemon 21 0.01% 3 2 45 0 80% 0 3 0 0.00

7.3 Schweppes Soda 36 0.01% 3 4 45 0 80% 0 3 0 0.00

8 Soda RGB 3,460 1.08% 3 384 45 9 80% 11 3 4 6.40

8.1 Kinley Soda-RGB 3,460 1.08% 3 384 45 9 80% 11 3 4 6.40

9 Soda PET 700 0.22% 3 78 45 2 80% 2 2 2 3.20

9.1 Kinley Soda-600 ml PET 700 0.22% 3 78 45 2 80% 2 2 2 3.20

9.2 Kinley Soda-500 ml PET 0 0.00% 3 0 45 0 80% 0 2 0 0.00

10 Water PET 11,861 3.70% 2 879 45 20 80% 24 1 24 38.40

10.1 Bonaqua-1L PET 11,154 3.48% 2 826 45 18 80% 23 1 22 35.20

10.2 Bonaqua-500 ml PET 707 0.22% 2 52 45 1 80% 1 1 2 3.20

10.3 Kinley water-1L PET 0 0.00% 2 0 45 0 80% 0 1 0 0.00

11 Cans-330 ml 15,275 4.76% 10 5657 110 51 80% 63 3 21 33.60

11.1 Diet Coke (5+1)330ml Can 5,122 1.60% 10 1897 110 17 80% 22 3 7

11.2 Coca-Cola Can 4,209 1.31% 10 1559 110 14 80% 18 3 5 8.00

11.3 Diet Coke Can 3,029 0.94% 10 1122 110 10 80% 12 3 4 6.40

11.4 Sprite Can 1,519 0.47% 10 563 110 5 80% 6 3 2 3.20

11.5 Fanta Can 1,033 0.32% 10 383 110 3 80% 4 3 2 3.20

11.6 Thums Up Can 363 0.11% 10 134 110 1 80% 2 3 1 1.60

12 Canisters 753 0.23% 5 139 6 23 80% 29 2 15 24.00

12.1 Coca-Cola CnstrPOS18 Postmix 18L

434 0.14% 5 80 6 13 80% 17 2 8 12.80

12.2Fanta O CnstrPOS18 Postmix

18L135 0.04% 5 25 6 4 80% 5 2 3 4.80

12.3 Sprite CnstrPOS18 Postmix 18L 113 0.04% 5 21 6 3 80% 4 2 2 3.20

12.4 Limca CnstrPOS18 Postmix 18L 71 0.02% 5 13 6 2 80% 3 2 2 3.20

13 Maaza Tetra 7,264 2.26% 5 1345 180 7 80% 9 2 5 8.00

TOTAL 3,20,757 100% 33,495 683 852 30 407 651



2. Analysis of sales data

Sr.No SKU

Peak Month(May 09) Sales

% Contribution

Cumulative

contribution

SKU Classificati

on

Calculated Floor Pallet

Positions

LegendAllotted

Pallet positions

Stacking Height

No of Pallets

Cases/Pallet

CasesStored

1 Limca-600 ml PET 30,189 9.38% 9.38% A 31 Lim600

32 2 64 45 2880

2 Coca-Cola RGB 300 ml 26,794 8.32% 17.70% A 22Coke300

24 3 72 45 3240

3 Coca-Cola-2L/2.25L PET 20,593 6.40% 24.10% A 26 Coke 2.25

26 2 52 36 1872

4 Coca-Cola-600 ml PET 19,952 6.20% 30.30% A 20Coke600

24 2 48 45 2160

5 Limca RGB 300 ml 19,888 6.18% 36.48% A 17Lim300 16 3 48 45 2160

6 Maaza Juice RGB 19,023 5.91% 42.39% A 17 MZ250

17 3 51 45 2295

7 Limca-2L/2.25L PET 17,609 5.47% 47.86% A 22 Lim 2.25 24 2 48 36 1728

8 Thums Up RGB 300 ml 17,441 5.42% 53.27% A 15TU300 16 3 48 45 2160

9 Bonaqua-1L PET 11,154 3.47% 56.74% A 22 Wtr1L

22 1 22 45 990

10 Sprite-600 ml PET 10,182 3.16% 59.90% A 14Spr600 16 2 32 45 1440

11 Fanta-2L/2.25L PET 9,474 2.94% 62.85% A 12 Fnt O 2.25

12 2 24 36 864

12 Fanta O-600 ml PET 9,214 2.86% 65.71% A 11Fnt O600

11 2 22 45 990

13 Thums Up-600 ml PET 8,622 2.68% 68.39% A 12TU600 12 2 24 45 1080

14 Fanta RGB 300 ml 7,372 2.29% 70.68% B 7 Fnt O300

8 3 24 45 1080

15 Maaza Tetra 7,264 2.26% 72.93% B 5MZ200 6 2 12 180 2160

16 Sprite RGB 300 ml 7,074 2.20% 75.13% B 7 Spr300

8 3 24 45 1080

17 Maaza-250 ml Juice PET 6,449 2.00% 77.14% B 16MZ

250P 15 1 15 45 675

18 Coca-Cola-500 ml PET 6,007 1.87% 79.00% B 9 Coke500

9 2 18 45 810

19 Limca RGB 200 ml 5,487 1.70% 80.71% B 4Lim200

4 3 12 63 756

20 Diet Coke (5+1)330ml Can 5,122 1.59% 82.30% B 7D.Co330 7 3 21 110 2310

21 Maaza-600 ml Juice PET 4,440 1.38% 83.68% B 12 MZ600

12 1 12 45 540

22 Coca-Cola Can 4,209 1.31% 84.98% B 5Coke330 5 3 15 110 1650

23 Kinley Soda-RGB 3,460 1.07% 86.06% B 4 Soda300

4 3 12 45 540

24 Maaza 1.2 L Juice PET 3,415 1.06% 87.12% B 8MZ

1.2 L8 1 8 45 360

25 Thums Up-2L/2.25L 3,353 1.04% 88.16% B 4TU

2.25 6 2 12 36 432

26 Diet Coke-500 ml PET 3,102 0.96% 89.13% B 4 D.Co500

4 2 8 45 360

27 Diet Coke Can 3,029 0.94% 90.07% C 4D.Co330 4 3 12 110 1320

28 Sprite-2L/2.25L PET 2,940 0.91% 90.98% C 3 Spr 2.25 3 2 6 36 216

29 Coca-Cola RGB 200 ml 2,742 0.85% 91.83% C 2 Coke200

2 3 6 63 378

30 Limca-500 ml PET 2,730 0.85% 92.68% C 4Lim 500 5 2 10 45 450

31 Minute Maid-400 ml Juice PET 2,292 0.71% 93.39% C 6 Pulpy400

6 1 6 45 270

32 Thums Up RGB 200 ml 2,207 0.69% 94.08% C 2TU200

2 3 6 63 378

33 Sprite Can 1,519 0.47% 94.55% C 2Spr330 2 3 6 110 660

34 Fanta O-500 ml PET 1,513 0.47% 95.02% C 2 Fnt O500

2 2 4 45 180

35 Schweppes Tonic Water 1,414 0.44% 95.46% C 1SchwTonic 1 3 3 45 135

36 Sprite RGB 200 ml 1,273 0.40% 95.85% C 1 Spr200

1 3 3 63 189



Sr.No

SKUPeak

Month(May 09) Sales

% Contribution

Cumulative

contribution

SKU Classificati

on

Calculated Floor Pallet

Positions

LegendAllotted

Pallet positions

Stacking Height

No of Pallets

Cases/Pallet

CasesStored

37 Fanta Orange RGB 200 ml 1,143 0.36% 96.21% C 1Fnt O200 1 3 3 63 189

38 Limca-1L PET 1,138 0.35% 96.56% C 2 Lim1 L

2 2 4 36 144

39 Coca-Cola-350 ml PET 1,049 0.33% 96.89% C 2Coke350

2 2 4 45 180

40 Fanta Can 1,033 0.32% 97.21% C 2Fnt O330 2 3 6 110 660

41 Thums Up-500 ml PET 950 0.30% 97.50% C 2TU500

2 2 4 45 180

42 Coca-Cola-1L PET 749 0.23% 97.74% C 2Coke1 L 2 2 4 36 144

43 Limca-350 ml PET 724 0.22% 97.96% C 1 Lim350

1 2 2 45 90

44 Bonaqua-500 ml PET 707 0.22% 98.18% C 2Wtr500 2 1 2 45 90

45 Kinley Soda-600 ml PET 700 0.22% 98.40% C 2 Soda600

3 2 6 45 270

46 Fanta O-350 ml PET 575 0.18% 98.58% C 2Fnt O350

2 2 4 45 180

47 Minute Maid-250 ml Juice PET 569 0.18% 98.75% C 1Pulpy250 1 1 1 45 45

48 Fanta-1L PET 545 0.17% 98.92% C 2 Fnt O1 L

2 2 4 36 144

49 Sprite-350 ml PET 535 0.17% 99.09% C 2Spr350 2 2 4 45 180

50 Sprite-500 ml PET 454 0.14% 99.23% C 1 Spr500

2 2 4 45 180

51Coca-Cola CnstrPOS18 Postmix 18L

434 0.13% 99.37% C 8 Coke 8 2 16 6 96

52 Thums Up Can 363 0.11% 99.48% C 1 TU330

1 3 3 110 330

53 Fanta A-600 ml PET 307 0.10% 99.57% C 1Fnt A600

1 2 2 45 90

54 Thums Up-1L PET 302 0.09% 99.67% C 1TU1L 2 2 4 36 144

55 Fanta A-350 ml PET 269 0.08% 99.75% C 1 Fnt A350

2 2 4 45 180

56 Sprite-1L PET 145 0.05% 99.80% C 1Spr1 L 1 2 2 36 72

57 Schweppes Gingerale 144 0.04% 99.84% C 1 SchwGinge

1 3 3 45 135

58Fanta O CnstrPOS18 Postmix 18L

135 0.04% 99.88% C 3 Fnt O 4 2 8 6 48

59 Sprite CnstrPOS18 Postmix 18L 113 0.04% 99.92% C 2 Spr 4 2 8 6 48

60 Thums Up-350 ml PET 105 0.03% 99.95% C 1 TU350

1 2 2 45 90

61 Limca CnstrPOS18 Postmix 18L 71 0.02% 99.97% C 2 Lim 2 2 4 6 24

62 Schweppes Soda 36 0.01% 99.98% C 0SchwSoda

1 3 3 45 135

63 Minute Maid 1L Juice PET 31 0.01% 99.99% C 1Pulpy

1L 1 1 1 45 45

64 Schweppes Bitter Lemon 21 0.01% 100.00% C 0 SchwBt L

1 3 3 45 135

65 Fanta Apple RGB 200 ml 0 0.00% 100.00% C 0Fnt A200 1 3 3 63 189

TOTAL 3,21,895 100% 407 433 928 45225

31183-Sujit Pawar-Warehouse Layout Modification

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