SSL project

Operating cost reduction & customer profit optimization By customer segmentation, Service level improvement, Order analysis & direct/indirect delivery decision making 31st August 2009 SSL International Plc Ajay Kanwar

2

Executive Summary

Introduction

The purpose of the report is to provide recommendations and illustrate spreadsheet models built for

operating costs reduction and customer profit optimisation. Key issues addressed for operating costs

are high pick and pack costs per customer and direct delivery to small customers. Key issues

addressed for optimising customer profit are comparison of the performance of SKUs across

customer and improvement of service level. Based upon these key issues, four main problems are

identified and four respective spreadsheet models built which are as follows.

Customer/SKU segmentation

The company is looking for ways to compare SKU performance across customers and SKU

performance within customer’s product portfolio to optimise customer profit. The company also

aims to identify its most important customers.

A customer/SKU segmentation spreadsheet model is developed which identifies most important SKU

per customer and compares SKU performance across customers. The model uses ABC analysis to

segment SKUs per customer into gold, silver and bronze. The developed model is dynamic in nature

and thus can accommodate new SKUs and customers.

Customer/SKU service level

The company is finding ways to improve customer service level to optimise customer profit. It is

looking for means to identify those SKUs per customer which are driving low service level. In

addition, the company aims to know the worthiness of improving the service level of a particular

SKU.

A customer/SKU spreadsheet model is developed which analyses last fiscal year data to identify SKUs

which drive low service level. Using ABC analysis, the SKUs are segmented into three categories

(gold, silver, bronze) which reflect the worthiness of improving the service level of a particular SKU.

3

Customer order analysis

The company is attempting to ‘upgrade’ customer selling units to reduce operating costs. The

company aims to identify SKUs per customer whose selling units can be upgraded and savings

realised through such up gradation.

An order analysis spreadsheet model is developed which analyses last fiscal year data and

recommends selling units for a SKU. The flexible model is developed which helps in realising

different cost savings for different values of transportation costs, pick and pack costs, etc.

Direct/indirect delivery model

SSL is looking for ways to identify small customers which might not be profitable customers. Such

customers could be directed to third party distributors to reduce operating costs.

A direct/indirect delivery model is developed to identify small customers. Pareto analysis is used to

segment the customers based on net sales, net value and gross margin values.

Recommendations and findings

The four spreadsheet models developed can help in reducing operating costs and optimising

customer profit. The customer segmentation model shows that there are 10 gold customers and

190 bronze customers. The service level model shows that almost all the customers are provided

with 95% or more service level. The order analysis model recommends selling units for 33 customers

which can reduce operating costs by more than £50,000 per year. The direct/indirect delivery model

identifies 26 small customers which have low sales, low net value and low gross margin.

4

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

Introduction ........................................................................................................................................ 2

Objective 1: Customer/SKU segmentation ......................................................................................... 2

Objective 2: Customer/SKU service level ........................................................................................... 2

Objective 3: Customer order analysis ................................................................................................. 3

Objective 4: Direct/indirect delivery model ....................................................................................... 3

Recommendations .............................................................................................................................. 3

1.0 Introduction ...................................................................................................................................... 6

2.0Objectives .......................................................................................................................................... 7

3.0 Objective 1: Customer/SKU Segmentation ..................................................................................... 10

3.1 Defining dimensions.................................................................................................................... 10

3.2 Data requirements ...................................................................................................................... 10

3.3 Methodology ............................................................................................................................... 10

3.4 Customer/SKU segmentation model .......................................................................................... 12

3.5 Characteristics of the Model ....................................................................................................... 13

3.6 Application of the model ............................................................................................................ 14

3.7 Limitations of the model ............................................................................................................. 15

4.0 Objective 2: Service Level per SKU per Customer ........................................................................... 16


4.2 Methodology ............................................................................................................................... 17

4.3 Model .......................................................................................................................................... 18


4.5 Application of the model ............................................................................................................ 19

4.6 Limitations of the model ............................................................................................................. 21

5.0 Objective 3: Customer Order Analysis ............................................................................................ 21

5.1 Variables ...................................................................................................................................... 22


5.3 Methodology ............................................................................................................................... 24

5.4 Order Analysis Model .................................................................................................................. 27


5.6 Recommending appropriate selling units per SKU. .................................................................... 29

5

5.7 Limitations & further improvement of the model ...................................................................... 29

6.0 Objective 4: Direct/Indirect delivery ............................................................................................... 30

6.1 Defining dimensions.................................................................................................................... 30

6.2 Data Requirements ..................................................................................................................... 30

6.3 Methodology ............................................................................................................................... 30

6.4 Direct/Indirect delivery model .................................................................................................... 31

6.5 Recommendations ...................................................................................................................... 31

7.0 Findings and conclusion .................................................................................................................. 33

Conclusion ......................................................................................................................................... 34

Appendix 1 (Customer Segmentation) .................................................................................................. 36

Appendix 2(Order type) .................................................................................................................... 36

Appendix 3 (Order Analysis Savings) ..................................................................................................... 37

Appendix 4 ( Direct/Indirect delivery ).............................................................................................. 38

6

1.0 Introduction

SSL International is a focused consumer brand company with the leading global brands

Durex and Scholl as well as a diverse portfolio of locally owned brands such as Medised,

Meltus, etc. During the last fiscal year, SSL handled more than 1350 SKUs and directly

supplied its products to more than 200 domestic customers. However, a large portfolio of

SKUs and domestic customers has increased operating costs and the company is looking for

ways to reduce operating costs. Furthermore, the company is looking for ways to optimise

customer profit and improve its service level to customers by analysing historical data. The

main problems faced in optimising customer profit and reducing operating costs are as

follows.

First, the company finds it difficult to compare the performance of a particular SKU across

customers and determine the relative importance of SKUs for a particular customer.

Comparison of SKU across customers and the realisation of the most important SKUs can

help in optimising customer profit and operating cost reduction. For instance, SSL has

different price files for each customer which means that the price of a product varies across

customers. During a product shortage, the company would like to be able to allocate

products to the most profitable customer. But without comparing the gross margin of the

product across customers, it becomes challenging to find the most profitable customer for

that product. Hence, the comparison of the performance of products across customers can

help in optimising customer profit. Similarly, it is laborious to find out the most important

SKUs for a particular customer without segmentation. Realising the most important SKUs for

each customer would help the company to effectively plan demand. Also, if a product is

lowly ranked in a customer’s product portfolio, then it will be wise to distribute that product

through a third party distributor and save on operating costs. Thus, segmentation of SKUs

per customer can help in reducing operating costs.

Second, the company is finding ways to improve customer service levels without increasing

operating costs. The customer service level is defined as the percentage of occasions on

7

which a customer’s order volume is provided on time. However, as mentioned before, some

SKUs are more important for a particular customer than other SKUs. Hence, for a particular

customer, improving the service level for the most important SKUs will be of more

significance than improving the service level for the less important SKUs. Thus, SSL is

looking for ways in which the relative importance of SKUs within a customer’s product

portfolio can be highlighted along with the service level of a particular SKU.

Third, the company is attempting to ‘upgrade’ the selling units to its customers to reduce

operating costs. Customers place their orders in trading units. A certain number of trading

units make a ‘shipper’ which acts as a handling box. Similarly, a certain number of shippers

make a layer of a pallet and a particular number of layers make a pallet. Delivering a full

product pallet is more economical than delivering a product layer as it greatly reduces

picking and packaging costs. Similarly, delivering a product layer is more economical than

delivering a shipper. The company is facing the problem of deciding which product’s selling

units should be upgraded and how much savings can be realised from an upgrade.

Lastly, SSL is looking for ways to identify small customers which might not be profitable

customers. These customers order small product volumes. The pick and pack costs and

transportation costs subjugate any profit made from these customers. If identified, these

customers could be either directed to third party distributors to reduce operating costs or

advised to increase their order volume in order to stay in SSL’s direct delivery portfolio.

The remainder of the report is organised as follows. The next section defines the objectives

of the project based upon the above identified problems. The following four sections deal

with each objective separately. These four sections are further subdivided into data

requirements, methodology, model and application of the model. Findings and conclusion of

the models are presented in the final section.

2.0Objectives

The cognitive map shown below summarises the goals, key issues and actions takes for each

issue. It also shows how each objective is related to the two main goals of operating cost

reduction and customer profit optimisation.

Figure 1. A Cognitive Map showing goals, key issues, options and actions

Goals

Key Issues

Options

Actions

Based upon the above, the main objectives of the project are as follows.

1. Customer/SKU segmentation: SSL International has more than 200 direct delivery

customers. Each customer orders some specific SKUs from SSL’s large range of SKU’s.

The project aims to categorize SKUs for each customer into three segments (gold,

silver and bronze) based upon order volume, gross margin and net sales.

Furthermore, customers will be segmented into three categories based upon these

three dimensions.

2. Customer/SKU service level: Customer service level needs to be improved. The

project aims to provide a model which highlights SKUs (from a customer’s product

portfolio) that lower the overall service level. Furthermore, the significance of an

SKU will be displayed to comprehend the worthiness of improving the service level

of a particular SKU.

3. Customer order analysis: All customers place their orders in TUs (trading units).

However there are other selling units (shipper, layer, and pallet) in which orders can

be placed. It is expected that if a customer upgrades its order to a higher selling unit,

then pick and pack costs will be greatly reduced. The project aims to provide a

spreadsheet tool which analyses customer orders and recommends appropriate

selling units per SKU for each customer indicating the relative cost savings.

4. Direct/Indirect delivery: Direct delivery to small customers is not profitable because

of small order volumes. The project aims to provide a model that identifies such

small customers. These customers will either be delivered to indirectly through

distributors or will be advised to increase their order volume to stay in SSL’s direct

delivery portfolio.

3.0 Objective 1: Customer/SKU Segmentation

3.1 Defining dimensions

The customer/SKU segmentation model will be used commercially and operationally. From

a commercial perspective, the model should be able to identify profitable customers for

each SKU. Hence, net value and gross margin were used as two dimensions. From an

operational perspective, the model should be able to identify the SKU volume for each

customer. Hence, Sales in CU (consumer units) is added as another dimension.

3.2 Data requirements

For the purpose of this project, data from the previous fiscal year has been used. Yearly data

takes product seasonality into account and gives a better picture than monthly data across

the three dimensions of net value, gross margin and sales. The main data requirements are

as follows:

1. Customer list: The list of all domestic customers along with their accounts payable

number was pulled from SAP.

2. Sales in CU, Net value and gross margin per customer per SKU: This data was also

extracted from SAP.

3. A comprehensive list of sold-to-party under each accounts payable number was

created.

3.3 Methodology

The segmentation of SKUs per customer is based upon multi-dimension ABC analysis. ABC

analysis was used because it helps in the selection of a limited number of SKUs that produce

a significant overall effect. However, categories have been named gold, silver and bronze

instead of ABC. Such terms (gold, silver, bronze) are easier to understand and company

management required that they should be used. Also the categories were defined as 80%,

15% and 5% for gold, silver and bronze respectively. These categories are defined based

upon ABC analysis which states that ‘A’ class items contain 80% of total value, ‘B’ class items

contain 15% of total value and ‘C’ class items contain 5% of total value.

11

The following steps were taken to develop the segmentation.

1. First, three separate tables were created for each dimension i.e. Sales in CU,

Net value and gross margin. Each table contained the SKU number,

description and one dimension.

2. As per the dimension value, SKUs were arranged in descending order in all

three tables.

3. A cumulative percentage column was added in each table.

4. SKUs within 0-80% of the cumulative percentage were awarded one point.

SKUs lying between 80-95% were given 2 points and SKUs beyond 95% were

awarded 3 points. (See table 1)

5. A fourth table was created in which all points were added together for each

SKU. Based upon its performance under each dimension, a SKU can score

points between 3 and 9. Thus there could be seven categories. The list of the

seven categories is given in the table below (Table 2). As can be seen from

the table, Gold stands for 1 point, Silver for 2 and Bronze for 3.

Net Value Points

0-80% 1

80-95% 2

95-100% 3

Table 1. Point system for three dimensions.

Sales in CU Points

0-80% 1

80-95% 2

95-100% 3

Gross Margin Points

0-80% 1

80-95% 2

95-100% 3

12

Table 2. Seven main categories based on points

A similar methodology was used for segmenting customers; and using this methodology a

spreadsheet model is created, as described in the following subsection.

3.4 Customer/SKU segmentation model

The model is divided into three workbooks. This is done because excel ran out of memory

when only one workbook was created. One workbook contains customers with shoe

accounts while the second workbook contains the rest of the customers. Out of 236

customers segmented, 110 customers had shoe accounts. Hence, as there are a large

number of shoe accounts, it was used to substructure the model into two workbooks.

The third workbook acts as a dynamic tool which contains data provided and the worksheet

to create SKU segmentation for each customer. The worksheet is VBA automated and is

compatible with excel 2003, as used at SSL international. All SKU segmentation worksheets

were created using this model and were stored in the other two separate workbooks

mentioned above. The figure below shows the relationship between the three workbooks.

Category Points

Gold Gold Gold 3

Gold Gold Silver 4

Gold Gold Bronze/ Gold Silver Silver 5

Gold Silver Bronze/ Silver Silver Silver 6

Gold Bronze Bronze/ Silver Silver

Bronze

7

Silver Bronze Bronze 8

Bronze Bronze Bronze 9

13

Figure 2. Diagrammatic representation of relationship between three excel workbooks

3.5 Characteristics of the Model

The model is built keeping in mind its commercial and operational usage. Key aspects of the

model are:

1. The model is dynamic in nature. New worksheets for each customer

can be developed to represent the present scenario. Also, new customers can

be added in the future.

2. The model lets users compare the SKU performance across customers.

A dynamic graph is built which shows SKU performance across Sales in CU,

Net value and Gross Margin.

3. The most important SKUs for a particular customer can be identified.

Furthermore, the SKU category graph gives the frequency of SKUs across the

seven categories. (See table 2)

4. The model is user friendly as it contains VBA automated controls

which let the user switch between sheets easily. Also, the three option

buttons change the graphic presentation of Sales in CU, Net value and gross

margin. The figure below shows the main controls which make the model

user friendly.

14

Figure 3. ‘ Customer segmentation’ workbook snapshot reflecting user friendly buttons

3.6 Application of the model

Boots Category Points No. Of SKU's

Total SKU's

GGG 3 47 195

GGS 4 23

GGB/GSS 5 25

GSB/SSS 6 20

GBB/SSB 7 20

SBB 8 12

BBB 9 48

Material Description Sales Net Value Gross margin Points category

00400129 Derbac-M Liquid 200mlx 6 UK 54648 £169,408.80 £114,908.40 3 Gold

00400301 W/WardsGW A&SFree

150mlx12 167028 £128,582.50 £65,930.18 3 Gold

00400313 Boots T/Headache Relief 24x12 105720 £112,919.71 £76,160.81 3 Gold

00400410 Paramol Caplet 12 x12 120744 £98,370.45 £70,925.53 3 Gold

00400420 Paramol Caplet 32 x6 399888 £623,556.61 £399,059.85 3 Gold

00400812 Ashton+Parsons Infant Pdrs20X6 312276 £265,434.60 £137,401.44 3 Gold

00400818 Anbesol Liquid 6.5ml x12 184800 £151,536.00 £101,455.16 3 Gold

00500790 Meltus Adult Chesty 100mlx12 163728 £148,992.48 £89,444.64 3 Gold

00500874 Medised for Children 100mlx12 160704 £159,276.16 £94,929.95 3 Gold

00601061 Syndol Caplet 20 x 1 161784 £223,724.02 £150,014.97 3 Gold

Figure 4. SKU segmentation model for the customer ‘Boots’

15

The figure above shows a part of the model built for customer ‘Boots’. The figure shows the

main columns of the model to give a better understanding. The model transforms data with

the goal of highlighting useful information and supporting decision making at the individual

customer level. The model can be useful in the following ways:

1. It helps in identifying the most important SKUs for a particular customer on the basis

of Sales, net value and gross margin. Identifying the most important SKU can be

helpful in ways such as providing 100% service level to a customer for a particular

SKU. For example, the materials shown above are all important materials for

customer ‘Boots’ and hence 100% service level should be provided for these

materials.

2. It helps in identifying the least important SKUs for a particular customer. Such a

finding can support decision making, such as finding ways to move a SKU up in the

customer list or distributing the SKU through third party distributors.

3. It helps in measuring the performance of a SKU across customers. Such a finding can

help in decision making, such as whether a SKU should be withdrawn as it is not

performing well across all customers.

4. In case of shortages, products can be allocated to the most profitable customer by

looking at the gross margin of a product across all customers.

5. Direct/indirect delivery of a SKU can be decided through this model. If a SKU is not

performing well across three dimensions then such a finding can aid in making a

decision upon indirect delivery through third party distributors.

6. The model can aid in targeting customers for a new product/SKU. The performance

of similar SKUs can be examined across customers and it can help in pointing out

appropriate customers for the new product/SKU. Furthermore, product

cannibalisation can be determined by introduction of new products/SKUs.

3.7 Limitations of the model The customer/SKU segmentation model has been developed using the last fiscal year data.

The model can be used only with the SAP data extracted from ‘SAPBW_download’. In other

words, the data has to be extracted from SAP in one particular way so that all relevant

variables fall into the same columns.

16

The model works inappropriately for small number of SKUs as it does not give the proper

segmentation of the SKU’s. For example, if there are 2 SKUs for a customer and one SKU

accounts for 85% of sales and the other for 15% of sales, then first SKU is shown in Silver

category and the second one in Bronze category. This is because the model categorises

based upon the cumulative percentage column. If the cumulative percentage is less than

80%, the SKU falls into gold category, if it lies between 80 and 95% it falls into silver

category and beyond 95% falls into bronze category.

4.0 Objective 2: Service Level per SKU per

Customer

SSL aims to improve its customer service level in a consistent and cost effective way. To

improve upon a customer service level, the focus has been shifted from overall customer

service level to analysing service level of each SKU per customer. Such evaluation will help

to look upon those SKUs which drive low service level. However, a particular product might

not be of significance in a customer’s product portfolio and improving the service level of

such products will increase costs more than value. Hence, product segmentation per

customer becomes important and identifies significant products to focus on.


The projects aim was to develop a model which shows service level per SKU per customer.

Hence, a SAP query was written to pull out large amounts of data per customer. The

following data was extracted from SAP:

1. Customer list: The list of all domestic customers along with their accounts payable

number was pulled from SAP.

2. Customer orders for the past one year, which contains the following columns:

a. Document number: Document number is used to differentiate between orders.

b. SATY (Order Type): There could be many types of orders such as invoices,

consignment, return goods order, etc. Hence, order type helps to differentiate

actual orders which lead to product delivery from the various other types of

orders.

17

c. Required delivery date: The date on which the customer requires delivery.

d. Material and description: Product code along with the description of the product.

e. Selling units: Type of selling unit such as consumer unit (CU) or trading unit (TU)

f. Order quantity: The quantity ordered by the customer.

g. Confirmed quantity: The quantity delivered by SSL

h. Delivery date: The date on which the product is delivered.

i. Rj: Any product/order rejected because of various reasons.

4.2 Methodology

The raw data provided was first cleaned. The following data rows were removed:

1. Orders with order type OR, SO and KB only were taken into account as these order

types reflect the actual delivery. Hence all other order types were removed. (See

appendix for full list and explanation)

2. Product orders which are cancelled for any reason were removed. The reasons for

cancellation could be many, such as the customer’s packing specifications not being

met. However, as these products are actually delivered on time, ideally they should

be counted in the on time delivery statistics. However, because these products were

later ordered again these rows were removed to avoid double counting the delivery.

After data cleaning, the methodology used is as follows:

1. First the list of unique SKUs ordered by the customer in a year is created.

2. The quantity ordered by the customer for each SKU in a year is calculated.

3. The quantity delivered on time in a year is calculated. To find such orders, document

numbers and delivery dates were used. If a product with the same document

number appears twice with two different delivery dates, it means that the product

was not delivered on the required delivery date.

4. The service level (in percentage) was calculated by dividing the quantity delivered on

time by the order quantity.

5. As per order quantity, SKUs were arranged in descending order.

6. A cumulative percentage column was added to the table.

18

7. SKUs within 0-80% of the cumulative percentage were counted in the Gold category.

SKUs lying between 80-95% were counted in the silver category and SKUs beyond

95% were counted in the bronze category.

8. The frequency of SKUs per category (gold, silver, bronze) was also calculated. Such

information shows the number of SKUs which are of importance to a customer.

The segmentation of SKUs follows the same ABC analysis which was used for Customer/SKU

segmentation. However, it should be noted that this segmentation uses trading units as the

selling unit whereas the customer/SKU segmentation uses consumer units as the selling

unit. In other words, the SKU category based upon order volume can vary across the two

models. Consumer units were not taken as selling units for this model as data inconsistency

was found in converting trading units to selling units. The SAP conversion and product

passport conversion differed for some products.

4.3 Service Level Model

The service level model is produced for domestic customers with accounts other than shoe

accounts. Customers with shoe accounts were not considered because their order volume

and order frequency is small.

The model is divided into two workbooks. While one workbook contains the VBA automated

model which develops the service level worksheet for the desired customer, the other

contains the service level worksheets developed through this model. In other words, one

workbook acts as the dynamic model whereas the other workbook acts as the database for

the developed worksheets. The main reason for developing separate workbooks is that

excel runs out of memory if one dynamic sheet is created. In other words, excel cannot

handle many dynamic sheets. This is the same issue that was faced when developing the

model to satisfy objective 1. The figure below shows the relationship between the two

workbooks.

19

Figure 5. Diagrammatic representation of the relationship between the two workbooks.

4.4 Characteristics of the Model

The main characteristics of the model are as follows:

1. The VBA automated workbook makes the model dynamic in nature. Hence, the

model is capable of handling new SKUs and customers along with new data and can

be updated in the future.

2. A macro-enabled button is provided on the ‘customer’ sheet which provides easy

access to the required customer sheet.

3. A list of all customers with their service level and category is provided to give an

overall view of the service level of all customers.

4.5 Application of the model

The model addresses the primary objective of finding the service level of each SKU per

customer. A portion of the model for ‘Boots’, a key customer, is show below.

20

BOOTS

Service

level= 93.31%

Category No. of SKU's

No. Of

SKU's 201

Gold 67

Silver 61

Sum= 2962107 2763914

Bronze 73

Material Product

Order

Qty(TU's)

On time

delivery

Order

%

Cumulative

order % Category

Service level

%

601062 Syndol Caplet 30 x 1 654696 627480 22.10% 22.10% Gold 95.84%



400812 Ashton+Parsons Infant Pdrs20X6 116982 54786 3.95% 36.60% Gold 46.83%

400420 Paramol Caplet 32 x6 75168 66960 2.54% 39.14% Gold 89.08%

10022943 DrxFetherlite12pkx6UK 69696 62976 2.35% 41.49% Gold 90.36%

10022942 DrxExtra Safe12pkx6UK 55760 51920 1.88% 43.38% Gold 93.11%

10022941 DrxElite12pkx6UK 47460 42108 1.60% 44.98% Gold 88.72%

10014733 Crckd HeelRepCrm 60mlx6UK 41076 34776 1.39% 46.37% Gold 84.66%

Figure 6. A part of the model showing the service level of SKUs for the customer ‘Boots’

The model can be used for the following purposes:

1. The model can be used to look at the service level of a SKU per customer. In other

words, the on time delivery of the SKU in a year can be determined for each

customer.

2. The model can be used to look upon the SKUs which drive a low service level for the

customer. The database model highlights the bottom 10 SKUs as per the service

level. Hence, the company should focus upon ways to improve the service level of

these SKUs to improve the overall service level of the customer.

3. The category of each SKU is shown in the model which shows the importance of the

SKU for that customer. Such information can help in deciding whether it is worth

improving the service level of the SKU for that particular customer. For instance, the

table above shows that material no. 400812 is of high importance for Boots as it falls

in the gold category and its service level is very low. Hence, SSL should look at ways

of improving the service level of this material to Boots.

21

4. The number of SKUs in a category is shown. Such information highlights the number

of SKU’s which drive high volume.

4.6 Limitations of the model The model developed can be used only with the data extracted from SAP in a particular way.

In other words, the columns of the relevant variables should remain same.

The data used should contain the order quantities in TUs only. If any other selling unit is

used, the model considers it as in TU and categorises accordingly.

The bottom 10 service levels were found using Excel 2007 conditional formatting tool. As

such tool is not present in excel 2003, the bottom 10 service levels have to be looked into by

the user when new data is used.

5.0 Objective 3: Customer Order Analysis

SSL receives orders from its customers in trading units which are picked and packed at

Stakehill distribution centre. All orders are delivered on pallets as per customer order

specifications. Each customer orders different volumes for different products. Some orders

are close to a whole pallet, such as 80% of a pallet. However, if these products were to be

ordered in full pallets then it would greatly reduce picking and packing costs. Similarly, if

those orders which are close to a whole layer were to be ordered in full layers, then again

pick and pack costs would be greatly reduced. The same analogy can be applied to

upgrading selling units from a trading unit to a shipper. Altogether, operating costs can be

reduced by upgrading selling units to shipper, layer or pallet. Elevating selling units can

reduce SSL’s operation costs as:

1. It will reduce material handling.

2. It will make pallets more economical.

3. It can result in more stackable pallets thereby reducing packaging and transportation

costs.

4. Transportation costs will reduce as more volume is delivered in fewer deliveries.

A diagrammatic presentation of the type of selling units is shown below to give a better

22

understanding of the relationship among them.

Figure 7.Diagrammatic Representation of Consumer unit,Trading Unit, Shipper, Layer & Pallet

5.1 Variables

The main reasons mentioned above for how elevating selling units can reduce operation

costs give an idea of the operation costs to be considered. As mentioned above, elevating

selling units will reduce transportation costs, pick and pack costs and pallet costs. A brief

description of these three costs in relation to SSL is given below.

1. Transportation cost: SSL uses two types of vehicles for delivery.

a) Dedicated vehicles: these are contracted vehicles which are used solely by

SSL for delivery. They cover certain geographical areas for delivery.

b) Network vehicles: These are shared user vehicles which are run by a third

party logistics company. These vehicles are used for areas not covered by

dedicated vehicles and provide a next day delivery pallet service.

Pallet

Layer

Shipper

Consumer Unit Trading Unit

23

The transportation costs vary for both kinds of vehicles. However, the minimum

transportation cost per pallet is £35 and the maximum transportation cost is £65

with an average of £45.

2. Pick and Pack cost: Pick and pack cost is influenced by the following variables:

a) Quantity: The greater the quantity, the greater the packing costs would be.

b) Product: Pick and pack costs vary as per product. Some products are

handpicked while some involve forklifts.

c) Product lines: If there are more product lines ordered then picking costs will

be greater.

d) Customer requirements: Some customers require products to be packed in a

special way which increases packing time. For example, Debenhams requires

Euro price tags to be in place for foot care products. Such specifications

increase packing costs.

3. Pallet costs: Based on customer specifications, there are two types of pallets used

for domestic order delivery:

a) Normal pallets: Normal pallets are standard pallets. All orders are delivered on

normal pallets if the customer does not have any particular specification. Each pallet

costs £3.

b) Chap pallets: These are blue pallets which are considered to be strong pallets. Some

customers require blue pallets to be used. SSL hires blues pallets at a cost of £1.20.

While some customers return pallets, most customers do not as this is not stipulated in the

service level agreement.


A large set of data is required for this objective. The data requirement is as follows:

1. Customer list: The list of all domestic customers along with their accounts

payable number was pulled from SAP.

24

2. Order volume per customer per SKU: Data for the main customers was

collected. A SAP query was written by an IT trainee to collect the required data. The

data was pulled through accounts payable number. The data contains the following

rows:

a) Delivery date: It helps in differentiating between the orders. The same

product appearing twice for one delivery date means the product is

backordered and has appeared twice. Hence, product order duplicity should

be removed.

b) Material no.: The unique product code assigned to each product.

c) Description: Describes the type of product.

d) Order Quantity: The quantity ordered by the customer.

3. Trading Unit conversion file: SAP stores order volume in TU as it is the defined

selling unit. A conversion file was used to convert trading units into a fraction of a

shipper, layer and pallet.

5.3 Methodology Determining the exact relationship between cost savings and the variables mentioned

above is a very complicated and time consuming task. Hence, there are some

assumptions and estimations made to give an approximation of cost savings. These

approximations and assumptions are explained wherever they have been used in the

methodology.

The methodology used is as follows:

1. From the past one year’s data, unique SKUs are extracted.

2. The number of orders placed for each SKU is counted.

3. The average order for each SKU in a year is calculated. Orders for the same

material can vary. However, to get an approximation of the orders placed over the

whole year, an average order for the SKU is calculated. The average order for a SKU

in a year is used to calculate cost savings.

4. All orders are converted into fractions of a shipper, layer and pallet using the

selling unit conversion file.

25

5. The number of orders with a shipper fraction of .5 or more is counted. For

example, if an order converts into 7.5 shippers then the fractional part of the order is

equal to .5; it is counted as a shipper fraction. Similarly, the number of orders with a

layer fraction of .8 or more and pallet fraction of .8 or more is counted in separate

columns. The fractional cut off points parts are decided by the management of SSL

and are used for the recommendations. However, these fractional parts can be

changed as explained in the characteristics of the model.

6. The number of extra TUs required to convert the above mentioned shipper

fraction, layer fraction and pallet fraction into full shippers, layers and pallets for

each order is calculated.

7. In case of a shipper fraction, the packaging box has to be opened and non-

ordered TUs have to be removed. However, if a full shipper is ordered then no TUs

have to be removed which will save time. The time saved in picking would be equal

to the time required to remove the number of TUs. Activity research was carried out

in the warehouse to approximate the time required to remove one TU out of the

shipper. The research showed that it takes approximately 10 seconds to remove a TU

out of a shipper. Hence, the number of TUs removed multiplied by 10 gives the

approximate time savings in seconds.

8. In case of layer fractions and pallet fractions, shippers have to be removed

from a layer or from a pallet. However, if a full layer or full pallet is ordered then no

shipper has to be removed. The time saved would be equal to the time required to

remove one shipper multiplied by the number of shippers removed. Activity research

shows that it takes approximately 10 seconds to remove a shipper from a pallet and

thus 10 seconds/shipper was used to calculate time savings. The calculations below

give an example of the time savings for product code 03711 when 432 TUs are

ordered.

Product Code: 03711 TUs ordered= 432

Conversion of order into shipper and pallet fraction No. Of TUs in a shipper= 12

No. Of shippers ordered (TUs ordered/ No. Of TUs in a shipper)= 36

No. Of shippers in one pallet= 42

No. Of pallet ordered= 0.857

No. Of shippers required to convert into full pallet= 6

26

Time savings= 60 seconds

9. The labour cost for picking and packing is £8.28 per hour. The time savings

multiplied by the labour cost gives the pick and pack cost savings.

10. To calculate savings on transportation costs by ordering a full pallet, first the

number of fraction pallets (with 0.8 or more of a fraction) was counted. Then, the

number of TUs required to convert these fraction pallets into full pallets was

calculated. The counted TUs are those TUs which could have been shipped in the

fraction pallets and hence which would have converted these fraction pallets into

full pallets. But these TUs were shipped separately with other orders.

Transportations savings for these TUs can be realised by dividing these calculated

TUs by the average order placed in a year. In other words, the number of

transportations carried out for these TUs is calculated. This transportation number

multiplied by the cost to transport one pallet gives cost savings by ordering a full

pallet. An average cost of £45 to transport one pallet has been used to calculate cost

savings.

11. A similar analogy can be applied for calculating transportation costs by

ordering a full layer rather than a fraction of a layer and for ordering a full shipper

rather than a fraction of a shipper. An average cost of £15 to transport one layer or

one shipper has been used to calculate cost savings.

12. Pallet savings are realised by multiplying pallet costs by the number of extra

orders placed for TUs required to convert into full pallets, layers and shippers.

13. During a year, a customer orders various volumes of a product based upon

seasonality and other factors. This means that the fraction of a shipper, layer and

pallet of a product will vary overtime. If such fractions are lower in number as

compared to the number of orders placed, then it is not reasonable to recommend a

customer to upgrade its selling units. For example, if a product is ordered 50 times in

a year and only 5 orders are more than or equal to 0.8 of a pallet, then it is not

reasonable to upgrade the selling units of the product. To overcome this problem, a

cut off for recommending selling units for upgrading is used. For the

recommendation purposes, 50% of the total orders should be a fraction of a shipper,

27

layer or pallet. The 50% cut off was used as it was desired by SSL management.

However, this cut off can be changed at a later date if appropriate, as explained in

the characteristics of the model.

5.4 Order Analysis Model

The order analysis model uses yearly data of customer orders and recommends appropriate

selling units of products based upon the methodology explained above. The model analyses

33 customer orders which are divided into 3 workbooks. The worksheet of each workbook is

dynamic which increases the memory burden on excel and hence the model is divided into 3

excel workbooks. The total cost savings from all the customers are calculated in ‘order

analysis 1’ workbook. The figure below shows the three workbooks created for this

objective. Note that the three workbooks are independent and are not related to each

other.

Figure 8. Diagrammatic representation of 3 excel workbooks

5.5 Characteristics of the Model BOOTS Shipper to consider Layer to consider Pallet to consider Recommended order

Transportation costs

£ 45.00 per pallet 0.5 0.8 1 0.8 1 Pallet cut off 0.5

Pallet cost £ 3.00 1.8 2 1.8 2 Layer cut off 0.5

Picking costs £ 8.28 per hr 2.8 3 2.8 3 Shipper cut off 0.5

Time savings 10 in secs 3.8 4 3.8 4

4.8 5 4.8 5

5.8 6

Savings £557.48 ###### £3,365.44 £ 1,705.70

MMaterial Description No. Of Orders

Qty Ordered

Average Qty ordered

Number of orders in shipper fraction

Shipper Savings

Layer fraction

Layer Savings

Pallet fraction

Pallet savings Recommended Orders

00500874 Medised for Children 100mlx12 62 13356 215 54

£ 476.71 Pallet

04817 Tiger Balm Extra Strong x6 39 12720 326

05191 Durex Avanti 5 x6 UK 14 6742 482 1 £ 0.05 1 £ 0.60 12 £ 122.50 Pallet

10015789 Drx Play Feel 50ml x 6 UK 61 25056 411 42 £ 286.37 Pallet

10031858 Drx Play Pina Colada 50mlx6 UK 1 912 912

28

10023105 Drx Pleasurepack 9pk+3x6 UK 40 16200 405 17

£ 118.63

10030722 Tingle Bells 4 2510 628 2 £ 0.23 Pallet

10031757 PFImplusePackBoots08x6-GB 9 3322 369 4 £ 2.28

10036009 Deo-ActivFreshWipesx5-GB 5 5640 1128 1

£ 1.38

10022457 DrxVibRingGen3 1pouchx6 UK 72 15264 212 39 £158.29 1

£ 51.31 Layer

04816 Tiger Balm Regular x6 21 5340 254 1 £ 0.46 19 £ 98.22 Layer

Figure 9. Snapshot of order analysis model for Boots

The figure above shows the part of the model developed for customer ‘Boots’. The model

developed gives an indication of cost savings. The main characteristics of the model are as

follows:

1. The model is dynamic in nature. The model lets the user input new data

which generates new recommendations.

2. The model is flexible in nature as it lets the user investigate different cost

savings by changing the following inputs:

a) Transportation costs: An average cost of £45 is used for each customer.

However costs may vary for each customer and thus an input cell for

transportation costs is provided.

b) Pallet costs: Pallet costs can vary in the future. An input cell for pallet costs is

provided to accommodate such changes.

c) Hourly picking costs: An hourly picking cost of £ 8.28 is used for

recommendations. However, these could be updated as and when required.

d) Time savings (in secs): The time saved in picking can be changed. For the

purposes of this report, savings of 10 seconds per shipper have been taken.

e) Shipper, Layer and Pallet fractions to consider: The fractions taken for

recommendations are .5 for shipper and 0.8 for layer and pallet. However,

new fractions could be used to evaluate cost savings.

f) Cut offs for recommended orders: The cut off pallet, layer and shipper cut off

of 0.5 has been taken for recommendation purposes. However, these cut offs

can be changed in the future.

By changing these cells, the company can see the variation in the

recommendations and the cost savings associated with such recommendations.

29

3. The conversion file can be changed in the future. The present conversion file

has data inconsistency issues and thus would be changed in future. Care has to be

taken when accommodating such changes in future.

5.6 Recommending appropriate selling units per SKU.

The model was used for recommending selling units for gold and silver category customers.

The category of the customer (gold and silver) is decided by the customer segmentation

model. Bronze category customers were not included because most of them order small

amounts and less frequently. Thus, recommendations for selling units are produced for 33

customers.

Furthermore, the input cells described in the previous section have been fixed for the

recommendations. These values have been agreed upon by the management of SSL for

recommendation purposes. For recommendation purposes, transportation costs were fixed

at £45, pallet costs at £3 and hourly pick and pack costs as £8.28. The pick and pack saving

time was fixed at 10 seconds per trading unit and 10 seconds per shipper. The shipper

fraction to be considered was fixed at 0.5 and the layer and pallet fraction were both fixed

at 0.8. The cut off for the fraction to consider was fixed at 0.5.

Based upon the above fixed values, SSL International can save £50,792 per year from its top

33 customers. The list saving from each customer is provided in the appendix section. The

recommendation for each product for per customer can be looked through the model.

5.7 Limitations & further improvement of the model

The model uses last fiscal year data and hence each sheet contains the formulas for a fixed

number of rows which are decided by the last fiscal year data. If the number of rows exceed

while using the new data, then these formulas have to be pulled down manually. The reason

for not inserting formulas in each row is that it makes the model too heavy and excel ran

out of memory.

The model gives indicative cost savings. Exact savings can be realised by further research on

the following variables:

30

1. Transportation costs: Exact transportation costs should be used to get

accurate savings for transportation. The recommendations provided uses average

transportation costs of £45.

2. Pick and pack costs: Activity based costing can be used to find the exact pick

and pack costs for each SKU. As mentioned before, pick and pack costs depend upon

quantity, product, product lines and customer requirements. A relationship between

these four factors and pick and pack costs can be found using activity based research

to get a more approximate value.

6.0 Objective 4: Direct/Indirect delivery

The purpose of the project is to indicate those customers which are small customers from

the SSL’s customer portfolio. Hence, a model is developed to indicate those small customers

6.1 Defining dimensions

A series of meetings were conducted with company’s management to define the

dimensions which should indicate small customers. . Using this model, the management

would like to know those customers which are low in volume, low in margin and low in net

value. Hence net sales, net value and gross margin were used as dimensions.

6.2 Data Requirements

The above mentioned three dimensions were used for customer/SKU segmentation model.

As this objective is an extension of the customer segmentation model, no data was pulled

out from SAP. The model uses the data from customer segmentation model.

6.3 Methodology

The model uses Pareto Analysis principle to divide the customers into two categories.

Pareto analysis is used at it helps in selection of those customers which produce significant

overall effect. Thus, pareto analysis is applied across three dimensions and customers are

categorised using 80-20% rule which forms the basis of pareto analysis.

31

The direct/indirect delivery model is an extension of customer segmentation model as it has

the same defining dimensions. The customer segmentation model categorises customers

into three groups i.e. gold, silver and bronze. The customers lying in the bronze category are

extracted for direct/indirect delivery model. The customers with shoe accounts are not

considered for this objective. The main reason for not considering customers with shoes

accounts is that these customers order in very small amounts (eg. 200 CU’s per year) during

the whole year whereas non shoe accounts customers order in comparatively large amounts

( eg. 1000 CU’s per year).

The methodology used is as follows:

3. First the list of customers with non shoe accounts and in bronze category is

extracted from the customer segmentation model. The list also contains sales in CU,

net value and gross margin for each customer.

2. Three separate tables are made for each dimension i.e. Sales in CU, Net value

and gross margin. Each table contains customer payer number and one dimension.

3. Each table was arranged in decreasing order as per the dimension value.

4. A cumulative percentage column is added to the table.

5. For sales in CU table, customers with cumulative percentage of 80% or less

were categorised as high volume customers whereas others were categorised a low

volume customers. Similar analogy was used to define high sales and low sales

customers for net value table and high margin and low margin customers for gross

margin table. The 80-20% rule is based on pareto analysis principle.

6. A cumulative table is created which shows the category of each customer

across the three dimensions.

6.4 Direct/Indirect delivery model A spreadsheet model is developed for categorising the customers as per the methodology described

above. The model is static in nature as it will not be updated in future. A cumulative frequency of

customers across the categories is shown.

6.5 Recommendations

The model indicates those customers which are low in sales, value and margin. The table

below shows the eight categories possible based upon three dimensions and the

32

abbreviations used for each category. A frequency chart is shown next to indicate the

number of customers which lie in each category.

Abbreviation Sales Category Net value category Gross margin category

HHH High Volume High Value High Margin

HHL High Volume High Value Low Margin

HLH High Volume Low Value High Margin

LHH Low Volume High Value High Margin

HLL High Volume Low Value Low Margin

LHL Low Volume High Value Low Margin

LLH Low Volume Low Value High Margin

LLL Low Volume Low Value Low Margin

Table 3: The eight categories defined as per three dimensions

Figure 10. Chart showing frequency of customers in each category.

The chart shows that there are 26 customers which are low in sales, low in net value and

low in margin. Hence, SSL should focus upon these 26 customers to decide direct/indirect

delivery and save operating costs. The list of customers along with their category across

three dimensions is given in the appendix section.

33

7.0 Findings and conclusion

The four models can be used for various decision making processes. The main findings and

recommendations through the four models are as follows.

Customer/SKU segmentation model: The customer segmentation model shows that there

are 10 customers which fall into gold category across all the three dimensions and there are

astonishing 190 customers which fall into bronze category across net sales, net value and

gross margin. The graph below shows the number of customers falling into each category.

The list of all customers which fall into various categories can be looked through the model.

Also, The SKU category per customer can be looked through the model.

Figure 11. Customer Segmentation bar chart.

2. Customer service level: The histogram below shows the number of customers for

each service level. It shows that most of the customers are provided with a 100%

service level while almost all the customers are provided with more than 95% service

level. The list of all the customers along with their service level is shown in the

appendix. The service level of each SKU per customer can be looked through the

model.

34

Figure 12. Bar graph representing number of customers in each service level

3. Customer Order analysis: The order analysis of top 33 customers using the

management recommended cut off values shows that SSL can save upto £50,000 by

upgrading customer selling units. The savings vary across customers. For example,

the maximum savings of more than £10,000 pounds per year can be realised by

upgrading selling units for Alliance Healthcare, whereas the minimum savings of £1

can be realised by upgrading units for Somerfield Stores. The management should

recommend upgrading selling units to its customers to realise such operating cost

savings. The upgraded selling units for all customers can be searched through the

model.

4. Direct/Indirect delivery model: 26 small customers have been found using the Pareto

Analysis across net value, net sales and gross margin. The company should focus

upon such customers to decide the direct/indirect delivery to such customers. The

list of all customers is provided in the appendix section.

Conclusion

This report has illustrated four models which are built for four objectives. The methodology

used for each model has been explained and the characteristics of each model have been

35

elaborated. The report also provided the application of two models and recommendations

for two objectives.

The models developed can be further improved by linking the models with SAP. This will make the

models more dynamic and will update as and when new data is stored in SAP. However, present

models do give indicative findings which can be used for decision making.

In the end, the project has been able to analyse and recommend ways to reduce operating costs and

optimise customer profit. The models can be used in future with new data which increases the

applicability of the model and success of the project.

36

Appendix

Appendix 1 (Customer Segmentation)

The list below shows yearly sales, net value and gross margin per customer.

The list have been removed because data sensitivity. Will update the list after discussing

with Mathew Baxter.

Appendix 2(Order type) Below is the list of types of orders stored in SAP. This list was used for data cleaning purposes during

the development of the model.

OR - Outgoing Customer Order - customer order received by SSL.

SO - Rush Order - Urgent order received by SSL, normally only used for sample orders when the

customer needs the stock sooner than normal. For instance within 1-2 working days.

CR - Generic Credit to customer - (can be for a number of reasons such as damaged stock, short

delivery etc)

DR - Generic Invoice to customer - normally used to invoice a customer when they have received

more stock than they ordered (normally due to a packing error at Stakehill warehouse)

RK - Invoice correction - (price correction, used when a customer has been charged incorrectly.)

RE - Return of goods from Customer - (used when a customer wants to return unwanted goods for

any number of reasons, such as out of date stock, faulty goods)

KB - Consignment order - Order type used to send orders into Consignment warehouse. These order

types have no value.

KE - Consignment Invoice - Used to create an invoice for consignment stock. Information is given to

SSL from the customer to show how much stock has been sold, and the customer is invoiced

accordingly.

KR - Consignment Return - Used to return stock from the customers warehouse, to the consignment

warehouse.

KA - Consignment Pick-up - Used to return stock from Consignment Warehouse, to Stakehill

Warehouse.

37

Appendix 3 (Order Analysis Savings)

The list below shows the saving per customer per year by upgrading the selling units of

products as per the recommendations.

Total

£ 50,792.18

Ac. No. Customer Name Recommended Orders savings

131503 Boots The Chemist 125016 £ 1,705.70

500925 Tesco Stores Ltd. (Supp No. 4

£ 6,850.01

500928 Asda Stores Ltd Ac833031 £ 913.76

131774 J Sainsbury Plc £ 1,981.02

500913 AAH Pharmaceuticals Limited £ 3,193.55

500931 Wm Morrison Supermkt Plc. £ 101.18

131936 Superdrug Stores Plc £ 916.23

500933 Alliance Healthcare (Distbn) £ 10,640.62

500943 Farmlea Foods Ltd. £ 582.31

131736 Phoenix Healthcare Distrbn Ltd

£ 3,504.53

501425 Barclay Pharmaceuticals Ltd £ 2,163.92

131824 Wilkinson Hardware Stores Ltd

£ 638.37

500914 John Lewis Plc £ 249.59

500910 Durbin Plc £ 56.32

131750 Sangers (NI) Ltd. £ 3,142.06

500954 Savers Health & Beauty Ltd £ 23.91

500929 Sants Pharmaceutical Dist £ 2,116.15

500930 Somerfield Stores £ 0.76

131549 Colorama Pharmaceuticals Ltd £ 1,328.23

132352 Poundland Ltd £ 122.04

38

500936 C.W.S Retail £ 148.78

500944 Sigma Pharmaceuticals PLC £ 1,238.98

131758 Day Lewis Medical Ltd. £ 713.41

131745 Mawdsley Brooks & co Ltd £ 1,210.32

131584 Lexon (UK) Limited £ 2,883.48

131754 Sangers (Maidstone) Ltd. £ 1,030.54

132382 Ethigen Ltd., £ 1,691.44

131544 G R & M M Blackledge Plc £ 255.02

132403 Ann Summers Ltd., £ 56.81

500932 Scotmid Co-op Ltd Semichem £ 269.83

131520 Johnson Bros (Belfast) Ltd £ 442.35

131547 Rayburn Trading Co. Ltd £ 94.32

131760 Prinwest Ltd £ 526.63

Appendix 4 ( Direct/Indirect delivery )

The list below shows the customers and their category across the three dimensions.

Ac. No. Customer Name Sales Net Value Margin

132039 Marshall-Banks Vend Services High

Volume High Value Low Margin

132051 Mr Richard Spragg High

Volume High Value High Margin

131927 Tim Martindale High


500951 Mr. A V Edwards (Territory 21) High


131908 Mr. D. Mills (Territory 17) High


131907 Stephen Brown(Territory 25) High


131913 Weldrick High


39

131764 Wilkinsons Of Jersey Ltd High


131512 R J Vending Ltd High


131919 Manor Drug Co.(Nottingham)Ltd. High


131762 Sandra and Michael Barratt High


132194 David Rogers (Territory 31) High


131925 Drayton Services Ltd. (Terr 27) High


134665 E.H. Booth & Co. Ltd. High


131911 Mr Bob Mills High


131548 Debenhams Retail PLCStore 07 High


131929 Norchem Ltd., High


132112 P I F Medical Supplies Ltd. High


501072 Durham Pharmaceuticals Ltd. High


131753 Lincoln Co-op. Society Ltd. High


132489 W. H. Smith Travel Ltd High


131551 P & A J Cattee (Wholesale) Ltd High


500920 Centru Ltd., High


501272 C M White (Territory 20) High


132226 Fielden Vending Limited High


131766 LoveHoney Ltd., High


131928 Burrows & Close Wholesale Ltd High Low Value Low Margin

40

Volume

131915 Wilkinsons of Guernsey Limited High

Volume Low Value Low Margin

131924 Mr Trevor West (Territory 44) High


131918 Richard Nicholls(Territory 48) High


131906 Peter Jackson (Territory 29) High


131510 Ian Rudd (Territory 40) High

Volume Low Value High Margin

131914 Rob Brome (Territory 45) High


132040 Michael Lessons (Territory 36) High


131770 K. Waterhouse Ltd Low


131916 F Maltby & Sons Ltd Low


131701 Trago Mills Ltd Low


131749 Williams Medical Supplies Ltd Low


131807 Blacks Leisure PLC Low


211892 Pasante Ltd Low


132002 Leeds Trading Co Ltd Low


213762 S.N. Prdct Ltd NHS Condoms Low


500934 Southern Syringe Services Low


131761 Webdirect Limited Low


131589 Camden Primary Care Trust Low


501082 Boots Dotcom, Low


41

501381 Creative Conceptions Ltd Low


131757 John Lewis plc2 Low


500938 Mr Alex Sampson (Territory 37) Low


500940 T & S Stores 2003 Ltd OneStop Low


132331 Washroom Essentials Ltd., Low


132118 D Thomas Heart of Wales Riding Low


135146 G & T Vending Ltd Low


213199 C.G. Murray & Son Ltd Murrays Low


131835 Safedale Ltd Low


132001 Ocado Ltd Low


131810 Arcadia Group Brands Ltd Low


500922 Vendplan Ltd Glenn Norcliffe Low


131541 DCS Europe PLC Low


500941 Eclipse Generics Ltd., Low


501060 R & J Manley VndgServices Low


132398 Manichem Ltd Low


214739 Office Holdings Ltd Low


131708 Aeromedic Innovations Ltd., Low


501025 Wilsons Low


131511 George Twist (Wholesale) Ltd. Low Low Value Low Margin

42

Volume

501091 Oasis Stores Ltd IN ADMIN Low


501428 Web Agent Ltd Low


131859 William Lindop Ltd Low


132490 A. Algeo Ltd Low


500952 National Services Scotland Low


215094 Frontier Medical Group Low


131517 Credenhill Ltd. Low


132417 MyTights.com Limited Low


SSL project

Documents

Transcript of SSL project