Case Study Scenario - Global Trading PLCGlobal Trading PLC is.docx
SSL project
1. 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. 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. 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. 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
4.1 Data requirements......................................................................................................................16
4.2 Methodology...............................................................................................................................17
4.3 Model..........................................................................................................................................18
4.4 Characteristics of the Model.......................................................................................................19
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
5.2 Data requirements......................................................................................................................23
5.3 Methodology...............................................................................................................................24
5.4 Order Analysis Model..................................................................................................................27
5.5 Characteristics of the Model.......................................................................................................27
5.6 Recommending appropriate selling units per SKU. ....................................................................29
6. 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. 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.
8. Figure 1. A Cognitive Map showing goals, key issues, options and actions
Goals
Key Issues
Options
Actions
9. 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.
10. 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. 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. 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. 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.
15. 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. 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.
4.1 Data requirements
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. 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. 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.
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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. 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%
601061 Syndol Caplet 20 x 1 169344 163296 5.72% 27.82% Gold 96.43%
601060 Syndol Caplet 10 x 1 143208 143208 4.83% 32.65% Gold 100.00%
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. 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. 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 UnitTrading Unit
23. 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.
5.2 Data requirements
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. 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. 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. 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. 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
aterial Description
No. Of
Orders
Qty
Ordered
Average
Qty
ordered
Number
of
orders
in
shipper
fraction
Shipper
Savings
Layer
fraction
Layer
Savings
Pall
et
fra
ctio
n
Pallet
savings Recommended Orders
00874
Medised for Children
100mlx12 62 13356 215 54
£
476.71 Pallet
17 Tiger Balm Extra Strong x6 39 12720 326
91 Durex Avanti 5 x6 UK 14 6742 482 1 £ 0.05 1 £ 0.60 12
£
122.50 Pallet
15789 Drx Play Feel 50ml x 6 UK 61 25056 411 42 £ 286.37 Pallet
31858
Drx Play Pina Colada
50mlx6 UK 1 912 912
28. 28
23105
Drx Pleasurepack 9pk+3x6
UK 40 16200 405 17
£
118.63
30722 Tingle Bells 4 2510 628 2 £ 0.23 Pallet
31757
PFImplusePackBoots08x6-
GB 9 3322 369 4 £ 2.28
36009
Deo-ActivFreshWipesx5-
GB 5 5640 1128 1
£
1.38
22457
DrxVibRingGen3
1pouchx6 UK 72 15264 212 39 £158.29 1
£
51.31 Layer
16 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
Volume High Value High Margin
500951 Mr. A V Edwards (Territory 21)
High
Volume High Value High Margin
131908 Mr. D. Mills (Territory 17)
High
Volume High Value High Margin
131907 Stephen Brown(Territory 25)
High
Volume High Value High Margin
131913 Weldrick
High
Volume High Value High Margin
39. 39
131764 Wilkinsons Of Jersey Ltd
High
Volume High Value High Margin
131512 R J Vending Ltd
High
Volume High Value High Margin
131919 Manor Drug Co.(Nottingham)Ltd.
High
Volume High Value High Margin
131762 Sandra and Michael Barratt
High
Volume High Value High Margin
132194 David Rogers (Territory 31)
High
Volume High Value High Margin
131925 Drayton Services Ltd. (Terr 27)
High
Volume High Value High Margin
134665 E.H. Booth & Co. Ltd.
High
Volume High Value High Margin
131911 Mr Bob Mills
High
Volume High Value Low Margin
131548 Debenhams Retail PLCStore 07
High
Volume High Value High Margin
131929 Norchem Ltd.,
High
Volume High Value High Margin
132112 P I F Medical Supplies Ltd.
High
Volume High Value High Margin
501072 Durham Pharmaceuticals Ltd.
High
Volume High Value High Margin
131753 Lincoln Co-op. Society Ltd.
High
Volume High Value High Margin
132489 W. H. Smith Travel Ltd
High
Volume High Value Low Margin
131551 P & A J Cattee (Wholesale) Ltd
High
Volume High Value High Margin
500920 Centru Ltd.,
High
Volume High Value High Margin
501272 C M White (Territory 20)
High
Volume High Value High Margin
132226 Fielden Vending Limited
High
Volume High Value Low Margin
131766 LoveHoney Ltd.,
High
Volume High Value High Margin
131928 Burrows & Close Wholesale Ltd High Low Value Low Margin
40. 40
Volume
131915 Wilkinsons of Guernsey Limited
High
Volume Low Value Low Margin
131924 Mr Trevor West (Territory 44)
High
Volume Low Value Low Margin
131918 Richard Nicholls(Territory 48)
High
Volume Low Value Low Margin
131906 Peter Jackson (Territory 29)
High
Volume Low Value Low Margin
131510 Ian Rudd (Territory 40)
High
Volume Low Value High Margin
131914 Rob Brome (Territory 45)
High
Volume Low Value Low Margin
132040 Michael Lessons (Territory 36)
High
Volume High Value Low Margin
131770 K. Waterhouse Ltd
Low
Volume High Value High Margin
131916 F Maltby & Sons Ltd
Low
Volume Low Value Low Margin
131701 Trago Mills Ltd
Low
Volume Low Value Low Margin
131749 Williams Medical Supplies Ltd
Low
Volume High Value High Margin
131807 Blacks Leisure PLC
Low
Volume High Value High Margin
211892 Pasante Ltd
Low
Volume Low Value Low Margin
132002 Leeds Trading Co Ltd
Low
Volume Low Value Low Margin
213762 S.N. Prdct Ltd NHS Condoms
Low
Volume Low Value Low Margin
500934 Southern Syringe Services
Low
Volume Low Value Low Margin
131761 Webdirect Limited
Low
Volume Low Value Low Margin
131589 Camden Primary Care Trust
Low
Volume High Value High Margin
501082 Boots Dotcom,
Low
Volume High Value High Margin
41. 41
501381 Creative Conceptions Ltd
Low
Volume Low Value Low Margin
131757 John Lewis plc2
Low
Volume High Value High Margin
500938 Mr Alex Sampson (Territory 37)
Low
Volume High Value High Margin
500940 T & S Stores 2003 Ltd OneStop
Low
Volume Low Value Low Margin
132331 Washroom Essentials Ltd.,
Low
Volume Low Value Low Margin
132118 D Thomas Heart of Wales Riding
Low
Volume High Value High Margin
135146 G & T Vending Ltd
Low
Volume Low Value Low Margin
213199 C.G. Murray & Son Ltd Murrays
Low
Volume Low Value Low Margin
131835 Safedale Ltd
Low
Volume High Value High Margin
132001 Ocado Ltd
Low
Volume Low Value Low Margin
131810 Arcadia Group Brands Ltd
Low
Volume Low Value High Margin
500922 Vendplan Ltd Glenn Norcliffe
Low
Volume Low Value Low Margin
131541 DCS Europe PLC
Low
Volume Low Value High Margin
500941 Eclipse Generics Ltd.,
Low
Volume Low Value Low Margin
501060 R & J Manley VndgServices
Low
Volume High Value High Margin
132398 Manichem Ltd
Low
Volume Low Value Low Margin
214739 Office Holdings Ltd
Low
Volume Low Value Low Margin
131708 Aeromedic Innovations Ltd.,
Low
Volume Low Value Low Margin
501025 Wilsons
Low
Volume Low Value Low Margin
131511 George Twist (Wholesale) Ltd. Low Low Value Low Margin
42. 42
Volume
501091 Oasis Stores Ltd IN ADMIN
Low
Volume Low Value High Margin
501428 Web Agent Ltd
Low
Volume High Value Low Margin
131859 William Lindop Ltd
Low
Volume Low Value Low Margin
132490 A. Algeo Ltd
Low
Volume Low Value Low Margin
500952 National Services Scotland
Low
Volume Low Value Low Margin
215094 Frontier Medical Group
Low
Volume Low Value Low Margin
131517 Credenhill Ltd.
Low
Volume Low Value Low Margin
132417 MyTights.com Limited
Low
Volume Low Value Low Margin
