Work Report Final

Table of contents

1. Introduction

1.1 General overview and history of the company 2

1.2 Present size and location of the company 3

1.3.1 Divisions and departments of the firm 5

1.3.2 Divisions and departments in the Shoprite Checkers supermarket group 5

1.3.3 Departments within the Brackenfell DC 5

1.4 Projects undertaken 6

1.4.1 Project 1: Foods receiving scheduling project 7

1.4.2 Project 2: Modelling of truck and container load handling 7

1.4.3 Project 3: Manual transport data comparisons 7

1.4.4 Project 4: Capturing packaging data for picking orders 8

1.4.5 Project 5: Local dispatch upgrade project 8

1.4.6 Project 6: Transport logistics daily report macro 8

1.4.7 Project 7: Weekly average sales macro for Centurion DC 9

1.4.8 Project 8: Silver Dollar chicken portion project 9

2. Descriptive part

2.1 Technical

2.1.1 Project 1: Foods receiving scheduling project 10

2.2.2 Project 2: Modelling of truck and container load handling 13

2.2.3 Project 3: Manual transport data comparisons 16

2.2.4 Project 4: Capturing packaging data for picking orders 16

2.2.5 Project 5: Local dispatch upgrade project 18

2.2.6 Project 6: Transport logistics daily report macro 23

2.2.7 Project 7: Weekly average sales macro for Centurion DC 26

2.2.8 Project 8: Silver Dollar chicken portion project 29

3. Summary of results with conclusions and recommendations 32

4. Abbreviations and References 35

5. Appendices 36

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1. Introduction

1.1) General Overview and History of the Company

In 1979 the Shoprite Group of Companies was started by purchasing 8 supermarkets in

the Cape region for R1 million. Shoprite has since become known for their innovative

expansion strategies and diverse acquisitions over 31 years. Acquiring the likes of

Grand Bazaars, Checkers, OK Bazaars Group, Sentra, Foodworld and Computicket

made the company show immense growth.

In 1986 Shoprite group was listed on the JSE Securities Exchange South Africa with a

market capitalization of R29 million, having 33 outlets. Although the group continued

growing locally it was decided to expand the business internationally in 1990 when the

first store in Namibia was opened. Shoprite Group has since also been listed on the

Namibian Stock Exchange and on the Lusaka Stock Exchange (LuSE) in Zambia.

The largest acquisition to date came in 1991 when Checkers, a national supermarket

chain, was bought and caused instantaneous growth of 600% leaving the Shoprite

Group with 241 outlets and more than 22500 staff members.

Shoprite entered the franchising field in 1995 by acquiring the central buying

organization then known as Sentra. They acted as a buying group for 550 owner-

managed supermarket members which made it easier for Shoprite to compete in

convenience driven smaller markets.

Having already opened outlets in a few African countries such as Mozambique, Zambia,

Mauritius, Namibia, Egypt, Zimbabwe and Uganda they started implementing the new

format called Usave and it was to be implemented in Ghana and Angola in 2003.

The 2008 Global Powers of Retail report from Deloite Touche Tohmatsu ranked

Shoprite as 132nd on the list of the world’s top 250 retailers for 2006.

In 2010 the Shoprite brand was voted SA’s number 1 supermarket in the Sunday Times

Top Brand Survey for the third year in a row. The Shoprite Group ranked third for its

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share price and dividend performance over the past five years and was the top

performing retailer in the 2010 Sunday Times survey of Top Companies listed on the

JSE. The Group achieved compound annual growth of 46.35% over the period 2005 to

2010, thus an investment of R10 000 in 2005 would have yielded R67 126 in 2010.

1.2) Present size and location of the company

The Shoprite group of companies is currently the largest retail group in Africa having

outlets in countries such as South Africa, Namibia, Mozambique, Zambia, Mauritius,

Egypt, Zimbabwe and Uganda. The company has 1166 corporate and 270 franchise

outlets and has become an empire stretching 16 countries on the African continent.

The Shoprite Group currently employ more than 83 000 people and is the leader in job

creation for retail companies in South Africa.

The company is mainly structured around a centralized distribution network that is

highly dependent on the efficient operation of the distribution centres around South

Africa. With the head office stationed in Brackenfell in the Western Cape all strategic

decisions are made in one location. These strategic operational decisions then filters

down into the sourcing and buying of stock, management of the distribution centres and

finally ends up in store locations globally.

The main distribution centres are in Cape Town (Brackenfell), Johannesburg

(Centurion) and Durban (Congela). Smaller warehouses are used throughout South

Africa but they are limited in functionality in that they are used for specific purposes

such as cold storage, bulk goods replenishments and container inventory received

through shipping ports at Cape Town harbour and Durban harbour. In the Western

Cape the smaller warehouses are located in Montague Gardens, Blackheath, Bolt

Avenue and Epping.

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Vocational work was conducted as part of the national team, located in the Shoprite Checkers

Brackenfell DC. The facility on the corner of Old Paarl road and Kruisfontein road has a floor

area of 50 000 m2. Construction of a new flank of the DC is currently in progress, this will be

used as a flow DC for fast moving goods and will be approximately 38 000 m2 in floor area. The

national team is responsible for various tasks but focuses primarily on improving and

maintaining standard operations within the distribution network including development of all new

distribution facilities.

Figure 1.1 Aerial view of Brackenfell DC, Freshmark and the new extension construction on the

right.

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1.3.1) Divisions of the Shoprite group of companies

Shoprite Holdings Ltd has various divisions (brands) with the largest of them being:

Shoprite

Checkers

OK Furniture

Hungry Lion

Usave

House & Home

Checkers Hyper

OK Foods

Medi+Rite

OK MiniMark

OK Grocer

Computicket

1.3.2) Divisions and departments in the Shoprite Checkers supermarket group

The Shoprite Checkers supermarket group is geographically divided into head office

(Brackenfell), main distribution centers (Brackenfell, Durban and Johannesburg), various

warehouses in South Africa and international store locations. Departments within the group

include replenishment (buyers), information technology, human resources, accounting,

marketing, financial, national team, DC management, logistics and transport.

1.3.3) Departments within the Brackenfell DC

Focusing on the Brackenfell DC it is important to note that this facility is a large scale operation

that is operational 24 hours a day, 7 days a week. This division of the group has all of the

departments that head office has except for marketing and replenishment. Operational (“on the

floor”) departments include planning, tower picking, mezzanine, foods receiving, non-foods

receiving, local dispatch, country dispatch, transport, logistics, systems, reclamation, IBI and

exports. All of the departments work from one WSM (Warehouse management system) that

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makes the interrelatedness of departments less visible to staff on the floor but in turn making

scheduling and delivery easier to accomplish.

Figure 1.2 Departmental representation of Brackenfell DC

1.4) Projects undertaken

All work was conducted as part of the national team. The national team consists of industrial

engineers, financial managers, supply chain specialists, project officers, supplier relationship

managers, divisional personnel manager, fleet manager and a secretary. All available work from

the industrial engineers that had very little responsibility or long term outcomes were undertaken

during this period. Projects ranged from basic data collection routine updates of databases to in

depth programming in Excel and Visual Basic. The aim of the work period was to support the

national team in available projects and to add value to the current operational affairs of the

company. All projects undertaken were derived from an initial problem and only then were

different means of action proposed as solution to the problems.

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Brackenfell DC

Operations "On the Floor"

Receiving

Foods

Non-foods

Despatch

Local

Country

Picking

Tower

Full Case

Inner Case

Production

Reclamation

Mezzanine

Exports

System

Inventory

Accounting

Logistics

Transport

Scheduling

Development

Admin

1.4.1) Project 1: Foods receiving scheduling project

Receiving goods at Foods Receiving at certain times took longer than estimated. The

scheduled times for unloading trucks or containers and the actual times varied

significantly. Trucks booked for a specific unloading time could only unload more than

five hours later than scheduled, making the planning and execution of receiving goods

nearly impossible to manage.

Observation of the department was crucial to identify possible problems and then data

had to be collected for the actual deliveries. Analysis of the relevant data had to provide

evidence of faulty procedures and if possible also confirm the corrective actions as a

viable solution.

1.4.2) Project 2: Modelling of truck and container load handling

Process flow charts were needed for the material handling when unloading a truck or a

container at the receiving departments. All processes had to be shown in terms of flow of

materials, flow of information and the systems used. These models of processes are an

integral part of understanding and improving operations in these departments.

1.4.3) Project 3: Manual transport data comparisons

All store deliveries are scheduled in minutes from when the truck leaves the DC until it

returns to the DC. A tripsheet is loaded onto a subsystem of the warehouse management

system stipulating number of pallets to be delivered and the number to be picked up at a

specific time to a specific store. Each driver fills in a timesheet per trip made containing the

same categories as contained in the tripsheet. On return timesheets are recorded onto a

transport database. Doing random comparisons for these two databases show discrepancies

and possible misconduct on the part of the driver. These databases are in different formats

and needed to be compared manually.

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1.4.4) Project 4: Capturing packaging data for picking orders

Packaging of most products change with time whether it is the outer case, inner case or

single item packaging. New products that enter the inventory had to be added to the

database and the packaging type and form also needed to be recorded. The packaging type

and form determines if a product can be put in the bottom of a specific picking order or at the

top. Keeping this data updated is of high importance because it acts as a safety precaution

so that goods aren’t damaged while in transit.

1.4.5) Project 5: Local dispatch upgrade project

The local dispatch department located inside the DC in the middle of the main loading bay

area has cracks in its walls and has become too small for basic operations. A sufficient

budget has been allocated for upgrading this building and for adding another level for offices

on top of local dispatch. Various departments needed to be considered for relocation to the

new site if necessary. Analysis of all candidates also had to confirm the necessity for

procurement of funds for the construction and relocation.

1.4.6) Project 6: Transport logistics daily report macro

Daily logistics has to construct a plan for the next 24 hours, showing what trucks has to be

loaded at what times and also what stores’ goods has to be loaded for the specific trip. This

report (picking plan) has to be delivered to the shift manager on duty to ensure correct

picking of stock and loading of trucks. The report is constructed manually in Excell by using

the available data from the warehouse management system. The need arose to filter the

data automatically from the WMS by means of a VBA macro into the report needed by the

shift manager. Important factors in this project included user friendliness, formatting and

accuracy.

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1.4.7) Project 7: Weekly average sales macro for Centurion DC

A program was needed to predict weekly average sales and peak values at the Centurion

DC for the specified week/weeks. All predicted values had to be based on previous sales

data from the greater north. Data used as inputs to the program had to be updatable for

reasons of accuracy and changing buyer trends. Users would have to have a high level of

freedom when issuing parameters and constraints to the program. Outputs had to be very

accurate because the outputs would be directly used in determining what suppliers to take

into the new extension of the Centurion DC and when they would be taken in.

1.4.8) Project 8: Silver Dollar chicken portion project

Replenishment wanted to know whether it would be financially rewarding if the suppliers

of chicken portioned their product in an unconventional way. What would the optimal

portion size be should a chicken be portioned into nine pieces? Product cost and retail

price per kilogram had to be analyzed to determine the feasibility of a new portioning

standard. Portion sizes had to be calculated using many constraints and determining

the combination that offers maximum profit per chicken and not the maximum income.

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2) Descriptive part

2.1) Technical

2.1.1) Project 1: Foods receiving scheduling project

The first step in evaluating the problem was to observe the system as a whole and collect all

relevant data at the foods receiving department. This was done by conducting time test for

every order arriving at the DC. For each delivery a new entry would be created. Initial data to

be entered into the entry would be the following:

1. Order number

2. Checker name (employee checking if the order is correct)

3. Number of lines

4. Number of pallets

5. Ti (Number of cases on a pallet stacked one case high)

6. Hi (Maximum number of cases allowed in height on one pallet)

7. Method of creating inventory entry (RF or Manually)

8. Booked time

9. Arrival time

10.Time into loading bay area

Calculating the processing times for every delivery was deemed crucial. To get an accurate

representation of the system it was decided to collect data for five working days. The time tests had to

include the following times:

1. Time when all stock is on the receiving bay floor inside the DC

2. Time order is GRV’d

3. Time when the checker starts checking the stock

4. Time stock is ready for put-away

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Figure 2.1 Time test sheet for foods receiving data collection

Time study in the foods receiving department

The foods receiving department consist of four basic operations namely; scheduling of

booked times (unloading times), offloading of trucks, offloading of containers and the

checking of stock before put-away. This material handling system depends on all of the

four operations functioning correctly and in a timely fashion to succeed. Therefore

pressure on the one element of the system can easily be transferred to another.

Observing the system on its own and also taking individual factors into account it is

important to note the following:

Initial findings in scheduling of booked times:

1. All unloading time slots are 30 minutes in length, not taking into account the

order size.

2. Long haul orders are booked at any time, not considering travel times to the DC.

3. Loads that can be classified as skim loads are booked in the same timeslot

causing simultaneous skim loads to be handled in the loading bay leaving only

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enough space to unload one truck at a time. These cases are common and can

leave the loading bay saturated with skim pallets for up to 6 hours.

4. Suppliers that take a long time to skim are booked for delivery before 08:00 but

the suppliers’ labour only start work at 08:00. This is then by default a late

delivery putting unnecessary pressure on the load bay later in the shift.

5. By using the number of lines and number of cases on the order we can

determine optimal booking times and also larger time slots to accommodate all

suppliers.

Initial findings in the offloading of trucks:

1. Skim loads in the loading bay is sorted by labour that is remunerated per hour,

the longer they work the more they earn. This is in direct contrast to what has to

be achieved at foods receiving.

2. Skim loads can also be received without checking the complete skimmed order.

As soon as a pallet is skimmed and ready to be checked it could be done and

immediately received, thus clearing the loading bay pallet by pallet instead of

waiting for all pallets to be correct before receiving. These loads can amount to

150 pallets in the loading bay that stay there for 6 or 7 hours before the y are

checked and received.

3. Some orders are received with the correct tihi but from a supplier that doesn’t

use Chep pallets, causing a correct order and load to be restacked proving time

consuming. Suppliers should be motivated to use Chep pallets.

Initial findings in the offloading of containers:

1. Single product orders often fills up to four containers. These products are

handled as one order and thus all of these products have to be on the floor

before it can be received, taking up a lot of space for extended periods of times

(up to 6 hours). Possibility of splitting single order into the amount of containers

received and then receiving a container at a time, making it possible to move the

stock into picks as one container is offloaded.

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Initial findings in the checking of stock before put-away:

1. GRV times on Shop 12 do not coincide with the times the truck enters the loading

bay and the driver hands over the invoice but is done at random when the stock

is inside or outside ready to be checked. This step varies from shift to shift and

the procedure followed differs from day to day.

2. The checker times registered on the barcode guns do not reflect the true checker

times and has no value for statistical analysis. In reality checker times start when

the barcode labels are printed after GRV is done.

3. The checking floor is saturated with orders from a few days (up to 6 days) ago,

the RT’s (reach trucks) have a lot of idle time and normally between 2 and 5 can

be found in front of the mole racking at foods receiving. Drivers aren’t checked on

or there aren’t enough drivers on a specific shift.

4. In order for the RT’s to start clearing a order from the checker floor the cone in

front of the order has to be removed by the checker. Lack of instantaneous

removals of these cones often causes orders to stay on the floor for up to 2 hours

more than is needed.

2.2.2) Project 2: Modelling of truck and container load handling

The goal of this project was mainly to show the flow of goods and information during the

material handling phase at the receiving departments. Identifying all systems and databases

used in these departments was set as a requirement. This project was used to determine

which user interfaces of the system could be improved and could then be used as a blueprint

at other locations. A DC has a very clear input (receiving) side and output (dispatch) side. If

input and output isn’t of the same magnitude and velocity (material rates), operations

becomes increasingly difficult because of highly fluctuating inventory levels. If the processes

for inputs and outputs can be better understood through flow chart modelling it helps

management make better periodic decisions. After observing the processes thoroughly the

following flows charts were created for the receiving departments.

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Figure 2.3 Flow diagram of foods receiving handling an order in a container

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Figure 2.3 Flow diagram of foods receiving handling an order on a truck

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2.2.3) Project 3: Manual transport data comparisons

Printouts of the two databases were used for the comparison. Formats differed substantially

from the one database the other. Samples for one week were taken at random for the

Centurion DC and then compared by hand. The manual work deemed tedious and time

consuming but no clear discrepancies were found. The maximum time difference between

the two samples was eight minutes. Standard deviation was very small and all deviations

were below problematic levels. After all the scheduled times correlated to the actual driver

time entries the pallets counts were checked. All pallets were accounted for, a clear

indication of accurate systems proving their validity as part of highly scheduled service

delivery.

2.2.4) Project 4: Capturing packaging data for picking orders

Specifying whether an item can be placed at the top of an order picking list depends on

rational reasoning by looking at the items’ weight, packaging and overall feel. Items at the top

of a order picking list gets picked first, ending up at the bottom of the order that is shipped

out of the DC.

There are three types of picking done in the DC namely:

Full case picking

An entire case is picked, usually packaged in boxes or shrink-wrapped, is

loaded into roll cages or onto pallets.

Inner case picking

An inner from within a case is picked, usually packaged in smaller boxes or

shrink-wrapped into smaller quantities. Picking is done into roll cages or plastic

bins.

Single item picking

The tower is specially designed for this type of picking, cases are loaded into

self replenishing racking with the front case opened from which items are

picked. All of these items are picked into plastic bins.

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Figure 2.4 Example of Outer case, Inner case and Single item

The warehouse management system creates the picking lists. Items were classified as top or

bottom depending on the logical conclusion made when analysing the packaging. The type of

packaging had to be captured as well and for this reason it was divided into five basic

categories namely tin (T), bag (S), box (B), glass (G) and plastic (P). Collection of the data

for 4916 products was done manually in the DC by capturing data on printed lists. Locations

of some products change frequently thus the list had to be updated daily in order to print a

new list every day that had the correct location IDs for the products.

Figure 2.5 Packaging database list

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2.2.5) Project 5: Local dispatch upgrade project

This project had a wide scope because there were various departments eligible for

relocation. Moving a department would only encourage productivity if the new location made

operations faster, easier or more effective. Initially each department had to be analyzed in

terms of their basic function and the resources they needed.

Department: Planning

Current Location: 1st Level Tower

Number of Staff in Department:

2 Staff in Office (Day and night shift)

Main Functions of Department:

Plans what should be picked when and by whom for every scheduled bomb during a

specific day. Receives the extraction plan for the day from the shift manager by hand,

prints the picking slips and labels for mezzanine, full cases and the tower. When all

relevant documents are completed the intended department is informed and then is

picked up by hand from the planning office.

Equipment Needed for Operation:

2 x Desktop PC’s

2 x Blueprinters

1 x Assignment Printer

1 x Label Printer

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Possible Changes / Office moves:

Move to the picking tower to reduce walking distances per bomb. Not feasible,

equipment needed is too large for the intended space and needs an enclosed office for

functionality of department.

Department: Systems

Current Location: 2nd Level Tower


2 Staff in Office (Day and night shift)


Attend to faulty systems and machinery, conducts general maintenance and staff

queries. Stores faulty replacement equipment in-office for repairs and quotes to be

done.


2 x Desktop PC’s

1 x Printer


None

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Department: Local Dispatch

Current Location: Middle of large dispatch side of the DC


7 Staff in Office (day shift)

4 Staff in Office (night shift)

17 Staff on floor (day and night shift)


This department is responsible for the handling data logging of all local outgoing loads

at the DC. Checks the pallets, completes the pallet status reports, prints the invoices

and fills in the tripsheets for the drivers. Also receives the GRV’d invoices and signed

tripsheets from the drivers. File the mentioned paperwork as well as status reports for

claim purposes.


3 x Desktop PC’s

2 x Blueprinters

1 x Printer/Copier


Space inside building not sufficient for proper operation, new layout and processing

equipment (filing cabinets etc) needed.

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Department: IBI

Current Location: Outside local dispatch office


3 Staff in Office (day shift)

1 Staff in Office (night shift)

10 Staff on floor (day and night shift)


Complete rechecking of some outgoing loads that are selected at random.


2 x Desktop PC’s

2 x Printer/Copier

1 x Printer (redundancy, not in daily use)


Extend the local dispatch building in order to accommodate IBI in the dispatch building

in a separate office. Only a small area is needed for operations, possible to incorporate

into the ground level local dispatch floor plan. Pigeon-hole filing cabinet and two service

windows are needed for the new office to regulate document flow between office staff

and checking staff.

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Department: Exports

Current Location: 3rd Level Tower - Office

Export Dispatch - All other operations


7 Staff total, all computer literate and works on the system (Day and night shift)


Responsible for all exports to Africa, has an extraction plan for a single country per day.

Exports do some cross docking and also some shop orders for Southern Namibia and

Lesotho.


2 x Desktop PC’s

1 x Printer


Office and other operations are in two complete different locations inside the DC. This

department’s office needs relocation to the export dispatch area; this will increase

productivity and also keep export staff from using the foods receiving office to print

invoices etc. Data cabling and shelving are needed for the relocation to take place.

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2.2.6) Project 6: Transport logistics daily report macro

Logistics staff manually creates a report on daily basis that is used in the planning of bombs.

A bomb is one round of picking where orders for different stores are picked at the same time.

Using the data extracted from the warehouse management system in the form of a daily

report, it is possible for logistics to plan every bomb for the next 24 hours. Creating this report

consists of logical reasoning, formatting of data and making decisions based on previous

experience.

A macro written in VBA would be able to create the report automatically. All that the user

wanted to do was copy and paste the WMS report into a sheet in Excel and then select the

day of the week that the extraction report was intended for. Output of the program would be

the complete report exactly matching the format of the manual report previously used.

Figure 2.6 WMS data received

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A macro was written to do the following to the WMS data in order to create the report:

1. Remove all empty rows creating a thick border where the empty rows were to show

individual bombs to be extracted

2. Remove unnecessary columns

3. Calculate and fill in the Route number

Monday first truck – 201

Monday second truck – 202 (adding one for every new truck on list)

Tuesday first truck – 301

Tuesday second truck – 302 (adding one for every new truck on list)

4. Colour coding for liquor stores and OK’s

All names of liquor stores en with LC or LS and have to be filled in green

All names of OK stores end with OK and have to be filled in blue

This part of the program should not be case sensitive because entries vary

The cells in the STG and DOR columns had to be filled for all liquor stores

5. Calculate release percentage values automatically for both foods and non-foods

6. Fill all non empty cells in top row with yellow

7. Create thick border around all non empty cells and for the non foods columns

8. Create extra bordered column the length of the report in column W for notes to be

added manually

9. Active buttons on sheet 1 enable the user to select the day intended for the

extractions and automatically perform all above mentioned actions

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Figure 2.7 Sheet 1 containing buttons for day selection

Figure 2.8 Raw Data now containing final report

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2.2.7 Project 7: Weekly average sales macro for Centurion DC

Forecasts about future sales had to be made by using past dated sales figures for the

greater north stores. Data used was specified as highly confidential and the specific

usages of the analysis were only partially disclosed. For these reasons focus was

shifted to the functionality of the program that had to be created in Excel using VBA.

Users could update the sales data by copying and pasting new data into the existing

data table. Columns had to be available for 53 weeks of sales thus being able to make

forecasts by evaluating the previous year’s sales.

Figure 2.9 All data available for forecast pasted into All Data

Setting the parameters for each forecast was the second most important attribute of the

program after accuracy of forecasts. Users should be able to specify the some or all of

the following for each forecast:

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Supplier

When a specific supplier name is entered or selected from the drop down list

containing only suppliers pasted into All Data, only forecasts on products from the

specified supplier is made. If this entry is omitted, all available suppliers are filtered

into the forecast summary.

Family group

There are eleven product family groups ranging from Health and Beauty to Non

Perishable Foods. Forecasting is based on only the specified family group of

products except when this field entry is omitted. When the field is left out all available

family groups are taken into account for the forecast.

Weeks to be included in forecast

Weeks were numbered, week 1 being the first week of the year to week 53 being the

last week in December. Filtering was done to accommodate for entries Week From

being smaller than Week To and vice versa. When the Week From field was left

empty it was set to a default value of 1 and the same was applicable for the Week To

field with a default value of 53.

Figure 2.10 User form called by the options button on All Data

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Automatic filtering of data was done when clicking on the Filter Data button in the user

form and results were printed to Report. Reporting the findings for each product had to

contain four values that were calculated namely:

1. Average value for sales

2. Peak value for sales

3. The peak to average ratio

4. The week of peaked sales

Figure 2.11 Basic form of the report after filtering the data

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2.2.8) Project 8: Silver Dollar chicken portion project

All relevant data for this project was received from Johan Hunter (Western Cape Foods

Buyer). This consisted of available portion sizes and average weights of all chicken

portions of the current poultry suppliers. Included in the data for the project were costs

per kilogram for the different portions and the current selling price per kilogram for the

various cuts and portions of chicken. Sales figures for chicken sold in South Africa per

annum were used to estimate the potential impact on sales profit for this project.

Silver Dollar chickens are portioned into nine pieces instead of the normal eight. All the

relevant equipment for this type of portioning is available as KFC has always used these

portions for their products. The extra portion is cut out from in between the breasts and

is called a keel. This ninth portion is almost the shape of a figure eight and the market

study has been done to make sure there is a market for this portion. Average selling

price per kilo for the keel has also been previously determined by the team of Johan

Hunter.

Portioning equipment can be set to specific portion weights in the form of percentages.

Initiative was taken to develop a proposed optimal solution for the portion sizes as

percentages using Excel Solver. The concept was to maximize sales revenue per

chicken at current prices by setting an objective function equal to all portions from one

chicken with variable weight times the average price per portion. Constraints to the

function were listed as:

1. Percentage of both drums and thighs = 46.11 %

2. Percentage of drums maximum = 17.29 %

3. Percentage of thighs minimum = 28.82 %

4. Percentage of keel, wings and breasts = 53.89 %

5. Percentage of breasts minimum = 28.66 %

6. Percentage of wings maximum = 14.40 %

7. Percentage of keel maximum = 12.07 %

8. Percentage of whole chicken = 100 %

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A feasible solution was obtained using Excel Solver and was named Optimal Solution.

The next step was to compare the normal cut, silver dollar and optimal solution by

calculating the total sales price per chicken with the weight of the chicken being a variable

user input.

The next step included costs as well and the profit per chicken for the individual suppliers

were calculated. For each supplier the profit per chicken was calculated for the normal

portions and for the new optimal portion sizes.

All of the calculations and user inputs were put into one Excel spreadsheet making it user

friendly for the team of Johan Hunter.

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Figure 2.12 Spreadsheet calculation optimal portions (figures have been altered and left out for confidentiality)

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3) Summary of results with conclusions and recommendations

Project 1: Foods receiving scheduling project

After collection of data, observation of operations and data analysis it was concluded

that there are various processes that could be improved in this department. The most

important finding was that the scheduled times and length of the timeslot was done by

replenishment and did not take physical operational factors into account. Skimming of

orders by suppliers that took place in the unloading bay area interfered with basic

operations and scheduling but was considered immanent. Whilst skimming could not be

stopped or removed it could be better managed and controlled by the DC staff.

Project 2: Modelling of truck and container load handling

The nature of the project was to model the processes and not to improve them. The scope

only entailed the graphical representations as outputs. Both processes were graphically

represented in a accurate manner, concluding the project. No recommendations was made

because it wasn’t deemed necessary. Refer to figures 2.2.1 and 2.2.2 for the results of this

project.

Project 3: Manual transport data comparisons

Comparing all the data didn’t show any inconsistencies, thus the data were considered equal

in every way except for the different formats. No further conclusion or result were found

during this project.

Project 4: Capturing packaging data for picking orders

All 4916 products were checked for packaging type and picking list placement. The end

result of this project was the final update of the database.

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Project 5: Local dispatch upgrade project

It was concluded that there were three departments that had to have their offices moved or

rebuilt namely Local dispatch, IBI and Exports. Local dispatch and IBI would have to stay in

their current locations but had to get new offices. Current offices were considered too small,

nonexistent or structurally unsafe. Floor plans should be drawn up for the new offices that

are needed and the new building should house both departments with separate entrances

and service counters.

Exports had to be moved from the third floor in the tower to the north east corner of the DC

where they ran operations. The move was of high importance and thus they could be moved

to a temporary location in the area of operations whilst they wait for a small office building to

be completed. All that was needed for the temporary location was a switch to be moved from

the forklift area in the middle of the northern side of the DC to the new location. This was

considered the best option to get a data connection to the required location, because data

cables are too short and a wireless connection could be penetrated from outside the facility

making it a security risk.

Project 6: Transport logistics daily report macro

After running the program in a testing phase, all inconsistencies were corrected and the

program was ready to become operational. Implementation of the program confirmed its

value to the logistics department and signaled the end of a very successful project.

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Project 7: Weekly average sales macro for Centurion DC

Confidentiality of the data used in this project restricts result obtained from the programs

forecasts. The program itself proved to be highly accurate and the flexibility of user inputs

lend itself to various applications within the organization. On completion of this project the

program was used by various departments. The basic structure of the data filtering methods

used in the programming code reliant on user specifications can be used by the organization

as a base for future programs.

Project 8: Silver Dollar chicken portion project

Using the Silver Dollar portioning sizes was financially sound and could be proved by looking

at sales data and current prices. However a solution that yielded larger revenues were found

and by looking at the sheer volume of sales it made sense to change the portion sizes. A

small increase in profit per chicken led to substantial increases in yearly revenue. A viable

proposed solution to portion sizes was found and was mathematically shown to be a better

solution than the Silver Dollar portions.

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4. Abbreviations and References

4.1 Abbreviations

WMS – Warehouse Management System

VBA – Visual Basic

DC – Distribution Centre

4.2 References

Shoprite Supply Chain, PowerPoint Presentation, 25 June 2010, Willem

Oosthuizen

Centralized Distribution, PowerPoint Presentation, 2010, National Team.

About Shoprite, Accessed at:

http://www.shoprite.co.za/pages/127416071/About.asp, 9 December 2010

Skimming of loads at the Shoprite Brackenfell Distribution Centre, 2010,

Cecil Dramat.

Slotting Logic, 2010, Hennie Joubert.

Successful Warehouse Slotting, 2010, Envista.

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http://www.shoprite.co.za/pages/127416071/About.asp

5. Appendices

5.1 Diary of daily work done

Description of Work done at Brackenfell Distribution Centre06-Dec-

10 Meet fellow staff, tour the Brackenfell DC, Start collecting data at Foods Receiving07-Dec-

10 Data collection at Foods Receiving08-Dec-




10 12-Dec-

10 13-Dec-

10 Analysis of data collected, creating userform14-Dec-



10 17-Dec-

10 18-Dec-

10 19-Dec-

10 20-Dec-

10 Creating flow charts for truck and container handling for Hennie Joubert21-Dec-

10 Project for tracking of pallets and roll cages for Stuart Strang22-Dec-

10 Centurion transport data checking for Andre Grove23-Dec-

10 Centurion transport data checking for Andre Grove24-Dec-

10 25-Dec-

10 26-Dec-

10 27-Dec-

10

28-Dec- Collecting picking data for Hennie Joubert

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1029-Dec-

10 Determine which departments to move to the top floor of proposed local despatch 30-Dec-

10 Determine which departments to move to the top floor of proposed local despatch 31-Dec-

10 Write Logistics Transport report macro01-Jan-

11 02-Jan-

11 03-Jan-

11 Write Logistics Transport report macro04-Jan-

11 Collecting picking data for Hennie Joubert 05-Jan-

11 Collecting picking data for Hennie Joubert 06-Jan-

11 Update picking data on system for Hennie Joubert07-Jan-

11 Write weekly average and peak sales macro for Centurion08-Jan-

11 09-Jan-

11 10-Jan-




11 Sort Dimension Data for items in Centurion14-Jan-

11 Meeting and development of 9 piece Chicken Project, Update packaging database15-Jan-

11 16-Jan-

11 17-Jan-

11 Analyze 9 piece Chicken Project - Develop Optimal portion sizes using Solver18-Jan-

11 Analyze 9 piece Chicken Project - Develop Optimal portion sizes using Solver19-Jan-

11 Handover and Implementation of Logistics Transport Macro20-Jan-

11 Handover and implementation of Chicken portioning project21-Jan-

11 Basic database updates and entries

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Documents

Work Report Final