supply chain collection model development and feasibility … · 2016. 10. 19. · supply chain...

1 | P a g e Steven A. Corica, 2016

Supply chain collection model development and feasibility

analysis of coffee grind resource for biofuel production.

Written By: Steven A. Corica,

Submission of Engineering Honours Student Thesis on the 20th day of June, 2016

School of Engineering and Information Technology

Murdoch University Perth, Western Australia

Supervisors: Professor Parisa Arabzadeh Bahri and Dr. Karne de Boer.


Executive Summary As the world’s conventional source of energy is predominantly reliant on depleting non-renewable fuel

sources; the necessity to introduce a cleaner viable fuel alternative to the market to reduce the dependency

of conventional fuel is considered imperative for a sustainable future. An opportunity to utilise the coffee

grinds waste as a feedstock for biodiesel production has been considered as an alternative source of

energy. The objective of the paper is to evaluate whether or not this opportunity can be considered as a

economically viable option.

The paper assesses the Internal Rate of Return (IRR) per year pre-tax over the expected lifespan of the

project as a quantitative measure for economic feasibility for different production, collection and logistic

schemes for major cities within Australia. The available waste coffee residue (WCR) has been calculated

using assumption factors to mathematically model the likely collection of the resource for a specific area.

A sensitivity analysis has scrutinised the key assumption factors to find the affect that differing

independent variables have on the final project outcome.

The yields that have been further analysed throughout the paper represent material acquired from M. Haile

[1] in which 19.73% of oil is extracted from the WCR and a value of 80.4% conversion to biofuel from

the waste coffee oil.

The findings of the paper suggest that all Australian cities aside from Sydney, Melbourne and Brisbane

can be dismissed as feasible locations for the project due to the operational expenditure for collection of

the dispersed resource exceeding potential revenue from the sale of the biofuel and biomass of the

remaining WCR.

Depending on initial investment and model selection, viable IRR can be expected in Sydney, Melbourne

and Brisbane. The feasible IRR values are directly related to the quantity of the resource available within

the city offsetting the operational and production expenditure.


Table of Contents Executive Summary ................................................................................................................................................... 2

Figures ....................................................................................................................................................................... 5

Tables ......................................................................................................................................................................... 6

Biodiesel Extraction Process ................................................................................................................................... 10

Coffee Consumption ............................................................................................................................................... 11

Global Coffee Consumption Study ................................................................................................................. 11

Analysis of Grind Resource Availability.......................................................................................................... 12

Total Revenue Available ................................................................................................................................. 14

Analysis of Resource Collection from Australian Cafés ......................................................................................... 15

Coffee & Café Establishments within Australia ............................................................................................. 17

Market Segmentation of Grind within Australia ........................................................................................... 18

Model Derived Resource Calculation ..................................................................................................................... 20

Collection Model 1 - Soluble Coffee Production Collection .......................................................................... 21

Perth, Western Australia Quantity ................................................................................................................. 26

Collection Model 2 – Approx. collection of grind resource from café and coffee shops in Perth (CM2) ..... 28

Collection Model 3 - The collection of grind resource from homes in a given area (CM3) .......................... 29

Adaption of Collection Models to Other Australian Cities ............................................................................ 30

Supply Chain and Logistics ...................................................................................................................................... 32

1. In-store Supermarket Processing ................................................................................................................... 33

1.1 Daisy Chain Collection – 30km Radius from Perth CBD ........................................................................... 35

1.2 Daisy Chain Collection – 30km Radius from Other Australian Cities ...................................................... 37

1.3 Star Collection Model - 30km Radius from Perth CBD............................................................................. 38

1.4 Star Collection Model - 30km Radius from Other Australian Cities ........................................................ 43

2. WCR Collection from IGA supermarket .......................................................................................................... 43

2.1 Logistics and Collection for Central Process Facility in Perth .................................................................. 44

2.2 Logistics and Collection for Central Process Facility Australian Cities .................................................... 46

2.3 Logistics and Collection for Multiple Process Facilities in Perth ............................................................. 47

2.4 Logistics and Collection for Multiple Process Facilities in Australia........................................................ 50

3. WCR Collection from IGA Supermarket by Waste Management Partners ................................................... 52

Cost Analysis ............................................................................................................................................................ 53

Nestle, Gympie Cost Analysis of Proposed Model ......................................................................................... 53

In-store Supermarket Processing ................................................................................................................... 58

WCR Collection from IGA Supermarket by Waste Management Partners ................................................... 68

Sensitivity Analysis .................................................................................................................................................. 72


Sensitivity Analysis for Collection Model 1 .................................................................................................... 72

Sensitivity Analysis for Collection Model 2 .................................................................................................... 74

Conclusion ............................................................................................................................................................... 77

References ............................................................................................................................................................... 78

Appendix 1 ............................................................................................................................................................... 81


Figures

Figure 1 – General Flow Chart of Biodiesel Process from WCR. M. Haile [1] .......................................................... 10

Figure 2 – Consumption of Global Coffee (1964-2012) ........................................................................................... 12

Figure 3 – Coffee Shop Establishments in Australia (%) .......................................................................................... 17

Figure 4 – Types of Coffee Imported Into Australia from 2007 to 2011 .................................................................. 20

Figure 5 – Flow Diagram of Spent Coffee Ground Waste to Fluidised Bed Boiler ................................................... 23

Figure 6 – Perth and Greater Metro Area IGA Locations ......................................................................................... 34

Figure 7 – Block Diagram Daisy Chain Logistics Model ........................................................................................ 35

Figure 8 – IGA Supermarkets Located within 30km Radius of Perth CBD ............................................................... 36

Figure 9 – 30km Radius from Parramatta, Sydney (Highlighted Pink Region Sydney Boundary) ......................... 38

Figure 10 – Block Diagram Star Collection Model.................................................................................................... 39

Figure 11 – Biofuel Available From IGA Supermarkets in Perth Over a Year ........................................................... 40

Figure 12 – Frequency of Collection When 200L Available at IGA Supermarket (In-store Processing Model) ....... 40



Figure 15 – Gross Profit Per Year (1st Year) Comparing Sale Price of Coffee Oil ...................................................... 55

Figure 16 – Gross Profit Over Lifespan of Project Comparing Sale Price of Coffee Oil ............................................ 56

Figure 17 – Interest Return Per Year Pre-Tax Against Varying Capital Expenditure for Nestle Model .................... 58

Figure 18 – Gross Loss per Year for Instore Processing Model in Australia ............................................................. 61

Figure 19 – Gross Profit Over A Year Period Comparing Different Collection Periods ............................................ 65

Figure 20 – Internal Rate of Return Varying Capital Expenditure for Central Processing Model ............................ 66

Figure 21 – Gross Profit Available for Waste Management Partner Collection Model ........................................... 70

Figure 22 – Internal Rate of Return per Year Pre-Tax for Waste Management Partner Collection Model ............. 71

Figure 23 – IRR Sensitivity Analysis Comparing Operating Hours of Nestle ............................................................ 74

Figure 24 – IRR Sensitivity Analysis Comparing Multiplication Factor for Likelihood Collection of WCR ................ 76


Tables Table 1 – Global Coffee Consumption, WCR Collection and Potential Maximum Revenue ............................................. 15

Table 2 – Total WCR Resource Available From Cafes and Coffee Shops in Australia ....................................................... 17

Table 3 – Potential Revenue of Biofuel for Café and Coffee Shops within Australia ........................................................ 18

Table 4 – Market Segmentation of Coffee within Australia ............................................................................................. 19

Table 5 – Nestle Gympie Assumed Current Energy Requirements .................................................................................. 24

Table 6 – Nestle Gympie Proposed Energy Model ........................................................................................................... 26

Table 7 – Model 2 Calculations for Perth ......................................................................................................................... 29

Table 8 – Model 3 Calculations for Perth ......................................................................................................................... 30

Table 9 – Resources Available from WCR Collection Over A Year Period in Australia ...................................................... 30

Table 10 – Derived WCR Collection Model Calculations Over A Year Period in Australia ................................................ 31

Table 11 – Resources Available from WCR Collection Over A Year Period in Australia .................................................... 32

Table 12 – Available Resources and Expense Factors (In-store Processing Model) ......................................................... 37

Table 13 – Resource Collection via Daisy Chain Collection Model (In-store Processing Model) ...................................... 38

Table 14 – Total Collection Periods for Biofuel in Perth with Varying Quantities (In-store Processing Model) ............... 42

Table 15 – Total Distance for Resource Collection in Perth with Varying Quantities (In-store Processing Model) ......... 42

Table 16 – Time Required for Biofuel Collection for Varying Quantities in Perth (In-store Processing Model) ............... 43

Table 17 – Distance Travelled And WCR Collection for Daisy Chain Model in Perth for Three Courier Zones ................. 45

Table 18 – Total Time and Distance Travelled for Different Collection Periods in Perth ................................................. 46

Table 19 – WCR Quantity for Different Collection Periods in Australia ............................................................................ 46

Table 20 – Total Time and Distance Travelled for Different Collection Periods in Australia ............................................ 47

Table 21 – Pivot Table of Collection Zones in Perth (Multiple Process Model) ................................................................ 48

Table 22 – Multiple Process Facilities Separated in Zones ............................................................................................... 48

Table 23 – WCR Summary of Perth Multiple Process Zones ............................................................................................ 49

Table 24 – WCR Quantity for Zone Collection in Perth ..................................................................................................... 49

Table 25 – Total Distance for Zone Collection for Different Collection Periods (Multiple Process Model) ...................... 49

Table 26 – Feasible Zone Collection Calculation for Australian Cities .............................................................................. 51

Table 27 – Total Distance Travelled for Zone Collection in Australia ............................................................................... 51

Table 28 – Total Time and Distance for Different Collection Periods in Australia (Multiple Process Model) .................. 52

Table 29 – Assumed Current Vs Proposed Sawdust Requirements for Nestle Energy Model ......................................... 53

Table 30 – Nestle Energy Model Comparisson ................................................................................................................. 54

Table 31 – Operational Expenditure if DME Process Model Implemented on Site .......................................................... 54

Table 32 – Total Gross Profit Value Neglecting Capital Expenditure for DME Process Implemented at Nestle .............. 57

Table 33 – Department of Transport of W.A Freight Guideline Rates .............................................................................. 59

Table 34 – Total Expense Per Year for Daisy Chain Collection Model .............................................................................. 59

Table 35 – Total Revenue Per Year for Daisy Chain Collection Model.............................................................................. 60

Table 36 – Varying Capital Expenditure Multiplied by Quantity of Processes for Each City ............................................ 62

Table 37 – Operational Expenditure of Production Process for Central Process System ................................................. 63

Table 38 – Operational Expenditure For Collecting WCR ................................................................................................. 64

Table 39 – Total Available Revenue for Central Processing Systems ............................................................................... 64

Table 40 – Operational Expenditure of Production Process for Multiple Processing System Model ............................... 67

Table 41 – Resources Available and Revenue for Multiple Processing System in Australia ............................................. 67

Table 42 – Gross Profit Available Utilising Waste Management Partners ........................................................................ 69

Table 43 – Fuel Share Available to Waste Management Partners ................................................................................... 69

Table 44 – Green House Gas (GHG) Emission Comparisson of Biodiesel B100 and Equivalent Diesel ............................. 70

Table 45 – Revenue with respect to Differing Operating Hours at Nestle Production Facility ......................................... 73

Table 46 – Gross Profit with Respect to Differing Operating Hours at Nestle Production Facility ................................... 73

Table 47 – Gross Profit with Respect to Sensitivity Analysis of Multiplication Factor for Model 2 .................................. 75


Acknowledgements

Firstly, I would like to express my sincere gratitude to my honours thesis supervisors Professor Parisa

Bahri and Dr. Karne De Boer for their continuous support throughout the project. Their patience,

motivation, knowledge and advice has helped me achieve the highest possible quality of work for which

I am very grateful.

Besides my supervisors, I would like to thank the rest of the Engineering staff at Murdoch University

whom over the past five and a half years have continually supported me and provided me with the

knowledge I have acquired.

I thank my fellow class mates for the stimulating discussions, for the sleepless nights we were working

together before deadlines, and for all the fun we have had over the years.

I would like to thank my first Engineering Manager, Andy Stevenson, whom provided me with an

opportunity to be part of his team, as well as the encouragement for continual improvement.

I would like to thank my friends for understanding my busy schedule and not holding it against me for

missing several events. I deserve and accept the nick name of ‘Digital Steve”.

I would like to thank my family: my parents Charlie and Mary Corica, my sisters Jody and Jessica and

brother in-laws Raymond and Matthew for continual support throughout my studies and my life in

general. Without you I would not be the person I am today.

Last but not the least, I would like to thank my beautiful partner Sarah Elford, for putting up with me over

the past few years and for continually encouraging and supporting me.


Introduction

With society’s increasing concern of the effect that the combustion of fossil fuels has on the environment,

along with the growing demand for energy and the depletion of current fuel reserves; there is a push to

identify alternate viable sources of renewable energy.

An opportunity to utilise the coffee grinds waste as a feedstock for biodiesel production has been

considered as an alternative source of energy. The project objective is to evaluate whether or not this

opportunity can be economically considered as a viable option.

The preliminary objective of this paper is a study of the collection of the dispersed resource itself, with

respect to the quantity that is available. This paper analyses multiple collection models, based on

mathematical models and assumptions to determine the resource available for a specified area.

The collection philosophy is to utilise existing supermarkets based in a given area where population

density and coffee shop establishment density is high. Different logistic models and collection strategies

analysing the cost and investment potential have been evaluated throughout the paper to find an optimised

strategy.

Included in the paper is the development study of implementing the dimethyl ether (DME) process system

at a soluble coffee production and manufacturing facility where the Waste Coffee Residue (WCR) is

readily available. Energy calculations have been undertaken to provide a comparison with the assumed

current model implemented at the facility and the proposed model.

Economic and financial viability has been explored through analysis providing a quantitative measure by

evaluating the Internal Rate of Return (IRR) per year pre-tax over an expected lifespan of the project. As

the initial capital expenditure of the different schemes is unknown, a varying capital expenditure with

respect to IRR has been plotted to determine the feasibility of the project and also to provide break even

values from an initial investment point of view.


Each implementation model has varied risks, in which the accepted IRR percentage has been aligned. For

schemes in which the collection of the dispersed resource is required, the risk significantly increases due

to the reliance on obtaining the WCR in comparison to models where the resource is readily available.

To adjust to this risk, the IRR pre-tax per year percentages for models which rely on collection has been

set for investment feasibility at 15% and the model where the resource is readily available at 9%.

A sensitivity analysis in which key assumptions and computations with respect to the collection model

factors have been altered to assess the effect of the final derived IRR value to evaluate the uncertainty of

the key assumptions made.

The Renewable Energy Certificate (REC) discounts awarded by the Clean Energy Regulator of Australia

have not been taken into account in this paper. This is because the paper focuses not on generating

electricity from the resource, but from the sale of the alternative renewable energy source itself.


Biodiesel Extraction Process

To utilise the WCR as a renewable source of fuel, different production process systems can be applied.

Most commonly, the oil is extracted from the WCR using n-hexane process system as per Figure 1.

FIGURE 1 – GENERAL FLOW CHART OF BIODIESEL PROCESS FROM WCR. M. HAILE [1]

Research and analysis has been undertaken by the stakeholders of this paper to develop a more effective

and efficient process system to replace the traditional hexane solvent extraction method. The process

system which has been identified for optimal performance to produce higher yields under similar

conditions is the use of dimethyl ether (DME) as the extraction solvent. The DME model does pose further

complications, of which under standard conditions, takes the form of vapour.


For the purpose of this paper, the extraction process that will be implemented will not disturb the final

outcome; which is to develop a supply chain collection model for the WCR and undertake an economic

feasibility assessment of the biofuel production system if it were to be implemented in Australia.

The yields that have been further analysed throughout the paper represent material acquired from M. Haile

[1] in which a yield 19.73% of oil is extracted using n-hexane.

The biofuel development occurs via a two-step process incorporating, “acid catalysed esterification

followed by base catalysed transesterification using catalysts sulfuric acid and sodium hydroxide

respectively” [1]. The conversion value of WCR oil to biodiesel used throughout this paper is 80.4%.

Coffee Consumption

Global Coffee Consumption Study

An important part of any commercial model for an alternative fuel source supply business is the

availability and collection of the energy resource. This paper which focuses on harnessing and extracting

the energy from coffee grind resource requires people around the world to consume coffee.

The aim of this section of the paper is to conduct research and analysis to provide accurate information

on possible collection of the dispersed grind resource itself; so that further models with respect to logistics,

supply chain and Levelized Cost of Energy (LCOE) can be accurately modelled and calculated.

As per data from 2008, a total of 7,358,897 [2] metric tonnes of coffee is consumed globally. This on

average equates to approximately 1.3kg per person per year [2].

The future outlook of coffee consumption globally will continue to grow at a rate of approximately 2%

per year given past data and trends [3].


The measurement “Million Bags” in Figure 2 below indicates the International standard of a bag of coffee

beans weighing 60kg. It is evident as per Figure 2, the consumption from traditional markets has stabilised

whilst continued growth from emerging markets and exporting countries has increased significantly over

the time period.

FIGURE 2 – CONSUMPTION OF GLOBAL COFFEE (1964-2012)

Analysis of Grind Resource Availability

Initially, all major cities included in this section of the report for analysis have data in the form of

approximate coffee consumption based on ‘green bean equivalent’ [2].

Green bean equivalent is an unroasted coffee bean which has the approximate ratio of 1kg of roasted

coffee to 1.19kg of green coffee beans. The conversion as shown in Table 1, (Column D) is 0.8403

multiplied by green bean equivalent (column C).

Experimentation was undertaken to find the relationship of roasted coffee beans and the available

remaining resource once coffee had been produced. The value using an automatic coffee machine, with

weight 700 grams blend of Arabica and Robusta beans indicated a total mass value of 1519 grams waste

resource. The resultant average of coffee beans to grind waste resource was 1 gram in of beans to 2.17

grams out of resource.

Mass balance states that in a model with no external interaction or losses, mass into the system will equal

mass out. With this in mind, it is obvious that water adds moisture to the grind resource and is the reason


for the increase of weight resource from the initial 1 gram. This approximate model 1: 2.17 conversion of

roast bean to resource has been used throughout the initial stage of the report.

With the moisture content of the waste resource now approximately known, the resource can be dried and

then weighed again to accurately identify how much of the weight value was moisture content and how

much of the beans weight was lost through the production of the coffee.

The figure of the final moisture content value after the dehydration of moisture had taken place was

approximately 59%. The final weight of the resource available for biodiesel production from the initial

starting value of 700 grams of coffee beans was 623 grams. To ensure testing throughout the process was

conducted correctly, further research from Haile, Mebrahtu [1] was undertaken which identified the

average moisture content of the beans from coffee production being 57.2% of the resource. This value,

57.2% is very close to the value obtained from experimentation and has been used as an approximation

in Table 1, Column F.

To calculate the oil extracted from the grind weight, the Hexane extraction model figures identified and

published by Haile, Mebrahtu [1] of 19.73% have been utilised to give the project a clear and precise

value. This value has been implemented in the Table 1, Column G.

A conversion figure, after the esterification of waste coffee residue oil to biofuel diesel of 80.4% has been

used to calculate the mass value of biofuel available in terms of ‘millions of kilograms’. This is shown

for each city that has been analysed in Table 1, Column H.

As biodiesel is traded on the commodity market using volumetric units, the figure is then rearranged to

reflect this. As the current mass value of biofuel is in kilograms a conversion is required. Mass is equal

to the multiplication of 𝑉𝑜𝑙𝑢𝑚𝑒 𝑏𝑦 𝐷𝑒𝑛𝑠𝑖𝑡𝑦; and the density of the biofuel is calculated to 891.5𝑘𝑔

𝑚3. With

the density and the mass available of the biofuel now known, a volumetric figure, in the form of 𝑚3can

be calculated. This is shown for each city that has been analysed in Table 1, Column I.


Total Revenue Available

From the biodiesel production process explained briefly in Figure 1, the product after the oil extraction

would suggest the fuel would be considered in its pure form, with no blend. Pure form biodiesel is

signified as B100, meaning that it is 100% biodiesel. This type of fuel quality is recognised by the

Department of the Environment of Australia in the Fuel Quality Standards Act, 2000.

On March the 9th 2016, B100 biodiesel can be purchased by an end user within Australia, for example,

Ecotech Biodiesel [4] at $1.07 per litre or $1070.00 Australian Dollars per 𝑚3 inclusive of GST. It should

be noted, that the current market conditions for all types of oil are at the lowest level seen since 2003.

Table 1 Indicates the total available resource located for the given city if 100% of the dispersed resource

was collected and could be utilised to produce energy. Table 1, Column J, also gives the reader an

approximation of how much revenue can be made in Australian dollars when sold to the final end user.

This figure indicates maximum potential revenue with consideration to the price of the biofuel.

Reality would indicate that collection of 100% of the dispersed coffee grind resource would not be

feasible; so moving forward, a more precise model will be calculated with a revised collection percentage.


TABLE 1 – GLOBAL COFFEE CONSUMPTION, WCR COLLECTION AND POTENTIAL MAXIMUM REVENUE

Analysis of Resource Collection from Australian Cafés

It is currently known from statistics reports from IBISWorld that Australian Cafes & Coffee Shops [5]

generate 5.3 billion AUD of revenue every year with 7,264 business enterprises and a total of 9,634

establishments.

A further break down from the statistics indicates that of those 5.3 billion Australian dollars, 51.5% of

the revenue is generated directly from coffee sales. This would mean that approximately 2.7295 billion

AUD in revenue is made from coffee sales in café & coffee shops around Australia alone.

Food standards Australia and New Zealand indicate that on average the medium/regular sized coffee sold

at cafés and coffee shops is approximately 350mL [6]. As per the Specialty Coffee Association of America


[7] , it is noted that 0.055g of roasted coffee bean per 1mL of water is required per standard cupping ratio,

or equivalently 1.5 shots of coffee at 9 grams per shot per standard cupping ratio.

This indicates that the medium sized coffee would contain approximately 13.5 grams of roasted coffee

beans. It has been noted from research that the price of an average medium sized coffee throughout

Australia can differ in price with respect to location, to give an overall indication to the stakeholders; the

national average of $3.63 has been used for approximation [8].

With this given information, a mass value of roasted coffee beans used by coffee shops around Australia

can be calculated. This equation is shown below:

$2.7295𝑏𝑖𝑙𝑙𝑖𝑜𝑛

$3.63= 751.9284𝑚𝑖𝑙𝑙𝑖𝑜𝑛 𝑐𝑢𝑝𝑠 𝑜𝑓 𝑐𝑜𝑓𝑓𝑒𝑒 𝑠𝑜𝑙𝑑 𝑓𝑟𝑜𝑚 𝐶𝑎𝑓𝑒 & 𝐶𝑜𝑓𝑓𝑒𝑒 𝑆ℎ𝑜𝑝𝑠 𝑖𝑛 𝐴𝑢𝑠𝑡𝑟𝑎𝑙𝑖𝑎

751.9284 𝑚𝑖𝑙𝑙𝑖𝑜𝑛 𝑐𝑢𝑝𝑠 ∗ 0.0135𝑘𝑔

= 10151033 𝑘𝑔𝑠 𝑜𝑓 𝑟𝑜𝑎𝑠𝑡𝑒𝑑 𝑏𝑒𝑎𝑛 𝑓𝑟𝑜𝑚 𝐶𝑎𝑓𝑒 & 𝐶𝑜𝑓𝑓𝑒𝑒 𝑆ℎ𝑜𝑝𝑠 𝑖𝑛 𝐴𝑢𝑠𝑡𝑟𝑎𝑙𝑖𝑎

Knowing that café and coffee shops in Australia consume 10.151 million kilograms of roasted beans; and

that there is currently in Australia 9,634 establishments in this market, an average roasted coffee bean

consumption per establishment can be generated as well as an approximate average model for available

Waste Coffee Residue (WCR).

10151033𝑘𝑔

9,634𝑠𝑡𝑎𝑏𝑙𝑖𝑠ℎ𝑚𝑒𝑛𝑡𝑠= 1053.668𝑘𝑔 𝑜𝑓 𝑟𝑜𝑎𝑠𝑡𝑒𝑑 𝑏𝑒𝑎𝑛𝑠 𝑝𝑒𝑟 𝑒𝑠𝑡𝑎𝑏𝑙𝑖𝑠ℎ𝑚𝑒𝑛𝑡

With the figure of average roasted beans per café & coffee shop establishment within Australia now

calculated, the factors for the resource grind conversion of 2.17:1 of roasted beans and the 57.2% total

mass of moisture can be now included to the equation to get an approximate figure for grind resource

available per establishment.

1053.668𝑘𝑔 ∗ 2.17 ∗ (1 − 0.572) = 978.6𝑘𝑔 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑝𝑒𝑟 𝑒𝑠𝑡𝑎𝑏𝑙𝑖𝑠ℎ𝑚𝑒𝑛𝑡


Coffee & Café Establishments within Australia

The breakdown of the 9,634 establishments within Australia is shown Figure 3. [5]

FIGURE 3 – COFFEE SHOP ESTABLISHMENTS IN AUSTRALIA (%)

Utilising this information from IBISWorld [5], values can be calculated to show the total available grind

that could be collected from coffee shops and cafés within each State of Australia as per Table 2.

TABLE 2 – TOTAL WCR RESOURCE AVAILABLE FROM CAFES AND COFFEE SHOPS IN AUSTRALIA

With knowledge of how much grind resource is available in each café and coffee establishment across

Australia as per Table 2, further calculations can be completed to derive the total volume of biofuel

Victoria, 28.8

N.T, 0.5

Queensland, 17.5

West Australia, 8.6

South Australia, 5.6

ACT, 2

NSW, 35.1

Tasmania, 1.9

Coffee Shop Establishments in Australia (%)


resource available and potential revenue of sale of the biofuel from all café and coffee shops within

Australia.

The value of fuel is as per mentioned earlier from in this section of the report $1070 per 𝑚3.

TABLE 3 – POTENTIAL REVENUE OF BIOFUEL FOR CAFÉ AND COFFEE SHOPS WITHIN AUSTRALIA

If all the dispersed coffee grind resource was collected from every single café and coffee shop within

Australia the total volume of biodiesel available would be 1678𝑚3 with the maximum revenue

approximately $1,794,971 as per Table 3.

Market Segmentation of Grind within Australia

The market for importing green coffee beans within Australia has grown continuously the past few years.

Most of this growth within Australia is due to the strong domestic demand for local roasted coffee beans

[9]. Although most of the recent growth in terms of domestic demand is for specialty roasted coffee beans

and pods, instant coffee still remains the most consumed coffee product.

As per the data collected by the Australian Bureau of Statistics in 2011-2012, approximately 17 million

cups of coffee were consumed on a given average day in Australia [10]. Of these 17 million cups of

coffee consumed, a breakdown of 2/3 of the total amount consumed was instant coffee, with the

remaining 1/3 consumed being either roasted coffee beans or fresh coffee grounds [10].


Instant coffee is consumed at twice the rate of roasted coffee beans and freshly ground coffee in Australia;

however the total green bean equivalent (GBE) to produce the consumed Instant coffee is less than what

is required to produce the alternative.

A smaller amount of GBE is required as the consumer utilises only a small portion of soluble product to

produce the instant coffee. The ICO, also known as the International Coffee Organization has agreed on

the following conversion factor of “soluble coffee to green bean by multiplying the net weight of

soluble coffee by 2.6” [11].

The average serving standard of soluble coffee is approximately 2.5 grams of roast bean of product per

cup which converts to 6.5 grams of GBE. As previously documented, the average quantity of roast

coffee bean per cup of coffee is approximately 13.5 grams which when applying the adaptation factor

for GBE, equates to 16.065 grams. When a comparison of both the values is undertaken as per Table 4,

it is evident that there is nearly three times as much GBE coffee per cup and a total of 17,433kg

consumption difference of GBE per day in terms of weight between soluble and roasted coffee.

TABLE 4 – MARKET SEGMENTATION OF COFFEE WITHIN AUSTRALIA

It is evident that a figure of 2.73kg of green bean per capita in Australia is used to produce coffee.

However, Australia imports over 70 million kg of green bean coffee per year as per Figure 4, which

equates to a value closer to 3kg of green bean per capita. The additional 0.3kg per capita or 7,200,000 kg


per year of green bean indicates that potentially a high amount of coffee has gone rancid or unused or

even that the green beans are being utilised for alternative applications, such as herbal medicine.

[12]

FIGURE 4 – TYPES OF COFFEE IMPORTED INTO AUSTRALIA FROM 2007 TO 2011

From an economic perspective to achieve maximum profit, all alternatives into the quantity and collection

of the resource available must be considered. The information presented in Table 4 is significant due to

the fact that the initial intended business model was to collect predominantly the waste from coffee shops

and cafés in Australia.

Using the figures in Table 4, and the intended business model discussed above, it is apparent that if total

grind waste collection from only coffee shops and cafés was to occur, only 12% of the available resource

in Australia could be collected. With this knowledge now available, the business model has now had to

be reconsidered and a concept which will improve the overall percentage collection of the waste grind

resource will be implemented.

Model Derived Resource Calculation

Although calculating the total resource available in a given area is useful, it does not give enough

information on whether or not the project will be feasible from an economic perspective.


Calculating an accurate model which will endeavour to predict the amount of resource that is likely to be

collected for a given area will provide enough information to produce economic and commercial

constraints for the project.

Initially, the project collection is separated into three models that will sum up to the total expected

resource available for collection in a given city. These have initially been selected based on the research

already undertaken, these are:

1. The collection of grind resource from soluble coffee production manufacturers.

2. The collection of grind resource from café and coffee shops in a given area.

3. The collection of grind resource from homes/workplace in a given area

The idea behind the collection methodology is to try and develop an approach where the collection of the

grind resource is maximised from the above models whilst keeping the capital and operational expenditure

down. Explanation with respect to supply chain and logistics will be further developed in the next stage

of this paper; however, for now a central collection point for a designated area which can include the

collection from each model mentioned above will be implemented.

The collection philosophy will utilise already existing supermarkets based in a given area where

population density and coffee shop establishment density is high. These figures are seen to be the most

influential figures when calculating the consumption and availability of the grind resource.

Collection Model 1 - Soluble Coffee Production Collection Throughout the research component of this project, it became evident that obtaining the collection grind

from soluble coffee production manufacturers (Model 1) would be very unlikely. The reason being for

this is that these companies already have a solid foundation in place that enables them to harness and

utilise the energy from the grind resource in a sustainable way. The other main reason is that there are

very limited soluble coffee manufacturing companies around the world, especially within Australia.


The biggest Australian manufacturer of soluble coffee is Nestle brand which account for 74% of the

market [13]. Currently, the biggest soluble coffee production factory in Australia is Nestle owned and is

located in Gympie, 200km north of Brisbane which produces over 10,000 tonnes of instant plus roast and

ground coffee per year.

The Gympie Factory utilises the discarded coffee grounds as a clean, renewable fuel source. In this

system, the spent coffee grounds leave the coffee production process at 2 tonne per hour with moisture

content of 75%. From the process the grounds enter a dewatering screw press which reduces the moisture

content to approximately 55% where the grinds are air conveyed to a fluidised bed boiler [14], the grind

is then burnt as fuel to produce steam for the coffee production process. The spent coffee grinds account

for directly 60% of the steam required, with the remainder produced from hardwood sawdust for the co-

generation system.

The system utilises a Babcock Wilcox Towerpak boiler and has a steam output of 24 tonnes per hour at

22 bar [15]. The co-generation scheme incorporates a cold-start butane system and is projected to save

4,000 tonnes per annum of greenhouse gas emissions with the fluidised bed boiler having a thermal

efficiency of 75% with particulate emissions lower that 10 parts per million [16].

The total energy conversion between the biofuel extraction system discussed within this report and the

scheme currently implemented at the Nestle factory in Gympie can be calculated and the efficiency of

each compared. The approximate comparison can be done comparing the heating value (calorific value)

of the fuel substances. The calorific value can be measured in units of energy per unit of the substance in

mass.

WCR, depending on type of coffee bean consumed and measured (variation in the oil yield changes with

respect to variety of coffee, solvent type and cultivation climate), has a high calorific power value of

approximately 5,000 kcal/kg (20.92 𝑀𝐽/𝑘𝑔) [17]. In comparison with that of dry hardwood, ~17 𝑀𝑗/𝑘𝑔

[18], WCR has a superior calorific value and is often the preferred choice in industrial boilers.


Utilising the production figures available a Flow Diagram as per Figure 5 can be arranged to find the total

input to the boiler in terms of mass flow rate of WCR. From this information and applying the approximate

calorific value already known, the approximate output energy of the system can be calculated.

FIGURE 5 – FLOW DIAGRAM OF SPENT COFFEE GROUND WASTE TO FLUIDISED BED BOILER

Of the 1,111 kg per hour of WCR that is sent to the fluidised bed boiler, 55% of this is moisture. The

‘boiler’ process removes the moisture and for each kg of remaining residue left, the calorific value is

multiplied. Not included in the back of the envelope calculation for the system is the 75% thermal

efficiency of the fluidised bed boiler.

The equation below, represents the total available energy content of the WCR when burnt neglecting

inefficiencies and factors such as boiler blowdown.

1,111𝑘𝑔

ℎ𝑟∗ 20.92

𝑀𝐽

𝑘𝑔∗ (1 − 0.55) = 10,458.95

𝑀𝐽

ℎ𝑟

As previously mentioned, the weight of the 1,111𝑘𝑔

ℎ𝑟 WCR fed to the fluidised bed boiler is of moisture

content 55% equating to 611𝑘𝑔

ℎ𝑟 . Energy is required to evaporate the water value of 611

𝑘𝑔

ℎ𝑟. The energy

consumption of this phase change for this process can be calculated utilising the heat of vaporization for

water which is 2.257𝑀𝐽

𝑘𝑔 .

611𝑘𝑔

ℎ𝑟∗ 2.257

𝑀𝐽

𝑘𝑔= 1379.027

𝑀𝐽

ℎ𝑟


Therefore the total energy available from the WCR which is burnt as fuel for the coffee production process

at Gympie factory is as per equation below.

10,458.95 𝑀𝐽

ℎ𝑟− 1,379.027

𝑀𝐽

ℎ𝑟= 9,079.923

𝑀𝐽

ℎ𝑟

As Nestle is a production and manufacturing operation, it is assumed they operate 24 hours a day. If a

factor of 20% for shutdowns and maintenance was assumed the total operating time of the plant over the

year would be (8760*0.8) approximately 7000 hours.

7,000 ℎ𝑟 ∗ 15,133.21𝑀𝐽

ℎ𝑟 = 105,932,470

𝑀𝐽

ℎ𝑟

Table 5 displays the current energy model requirements incorporating resource quantities for Nestle

Gympie factory for the coffee production system inclusive of assumptions.

TABLE 5 – NESTLE GYMPIE ASSUMED CURRENT ENERGY REQUIREMENTS

Assumed Current Energy Model Energy (MJ/hr) Resource

(kg/hr) Energy (GJ/Yr.)

Resource (kg/Yr.)

Waste Coffee Residue (20.92MJ/kg) 10459.0 500.0 73296.3 3500000.0

Sawdust (17MJ/kg) 6053.3 356.1 42421.4 2492527.1

Water Boil off (2.257MJ/kg) -1379.0 611.0 -9664.2 4277000.0

Total Energy Required for Gympie 15133.2 MJ/hr 106053.51 GJ/Yr.

If the WCR was converted into biofuel as discussed in this paper with the calorific value 37.88 MJ/kg [1],

the energy content and the quantity of the biofuel available would be as follows:

1,111𝑘𝑔

ℎ𝑟∗ (1 − 0.55) ∗ 0.1973 ∗ 0.804 = 79.31

𝑘𝑔

ℎ𝑟𝑜𝑓 𝑏𝑖𝑜𝑓𝑢𝑒𝑙 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒

79.31𝑘𝑔

ℎ𝑟∗ 37.88

𝑀𝐽

𝑘𝑔= 3,004.137

𝑀𝐽

ℎ𝑟


The energy content value of the biofuel 3,004.137𝑀𝐽

ℎ𝑟 can be subtracted from the initial energy content

to determine the remaining energy available in the WCR to be burnt to produce steam in the cogeneration

facility.

10,458.95𝑀𝐽

ℎ𝑟− 3004.137

𝑀𝐽

ℎ𝑟= 7454.813

𝑀𝐽

ℎ𝑟

The coffee grinds produce 60% of the steam required to provide energy for the coffee production process.

The 60% value equates to 9,079.923𝑀𝐽

ℎ𝑟 which indicates the coffee production at Gympie Factory requires

an energy total of approximately 15,133.21𝑀𝐽

ℎ𝑟. Hardwood Sawdust is used to provide the remaining 40%

(6,053.282𝑀𝐽

ℎ𝑟) of steam for the cogeneration system.

For example, if the biofuel was extracted and harnessed, and the WCR only were utilised for steam

conversion, an increase of sawdust would be required to provide the surplus energy. However, before this

can be accurately calculated, consideration of power consumption of the DME process needs to be taken

into account.

After correspondence with the stakeholder of the DME process; Dr. Karne DeBoer, it was ascertained

that the power consumption and electrical requirements of the system would be 0.2kWh/kg of oil

produced.

The total energy required from the sawdust can be calculated as per the following equation below.

15,133.21 𝑀𝐽

ℎ𝑟− 7,454.813

𝑀𝐽

ℎ𝑟− 57.1

𝑀𝐽

ℎ𝑟= 7,735.5

𝑀𝐽

ℎ𝑟

This results in an increase of 11% from the original 40% of total input energy required for the co-

generation system from the hardwood saw dust.

7,735.5 / 15,133.21 = 51.12%


TABLE 6 – NESTLE GYMPIE PROPOSED ENERGY MODEL

Proposed Energy Model Energy (MJ/hr)

Resource (kg/hr)

Energy (GJ/Yr.) Resource (kg/Yr.)

Waste Coffee Resiude (20.92MJ/kg) 10459.0 500.0

Oil Extracted for Biodiesel (37.88MJ/kg) -3004.1 79.3 -21053.0 555146.8

Electrical Power Requirements of DME Process (0.2kWh/kg) -57.1 -400.2

Remaining WCR Energy Content (17.72MJ/kg) 7454.8 420.7 52243.3 2944853.2

Sawdust (17MJ/kg) 7735.5 455.0 54210.4 3185204.6

Total Energy Required for Gympie 15133.2 MJ/hr 106053.51 GJ/Yr.

Table 6 above indicates the requirements if the proposed model of the DME extraction system for biofuel

was implemented at Nestlé’s Gympie production factory. The results in both Table 5 and Table 6 clearly

show the 11% increase of Sawdust required to provide the additional energy lost to the biofuel production

would equate to an additional 98.9 kg/hr (455-356.1). Over the year Nestle Gympie production factory

would require an additional 692,677 kilograms of sawdust to maintain the same level of power generation.

However, the available biofuel that can be extracted and harnessed for sale would be 79.3 kg/hr or

555,146.8 kg/Yr.

For the purpose of this report, as Nestle brand account for 74% of the market for soluble coffee production

within Australia [13], and with the majority of that percentage produced at the Gympie factory, all other

soluble coffee producing factories will be neglected when modelling the collection amount of the

dispersed resource for a given area.

Further analysis for Model 1 “The collection of grind resource from soluble coffee production

manufacturers” will include Levelized Cost of Energy (LCOE), maximum profit and revenue calculations

inclusive of capital and operational expenditure.

Perth, Western Australia Quantity

As the study for this scope of work was undertaken in Perth, Western Australia, a thorough analysis for

this city has been completed.


A supermarket chain, Independent Grocers Alliance (IGA) has been identified as the supermarket of

choice for the central collection point within Western Australia. The thought behind this is not only does

the supermarket chain have a market share of 24% of grocery sales within the state [19]; it has

approximately 140 store supermarket locations [20], which span across the state and will be able to

provide the most adequate collection model for the dispersed resource.

As the economy changes and an increasing number of competitors enter the supermarket industry across

the state over time, the idea of solely utilising IGA may change. The idea to only utilise one chain store

initially was that it would provide the supermarket chain with marketing incentive over its competitors to

attract and retain customers, it would also make business agreements easier to manage; and a reward

program could be implemented to entice and encourage people or businesses to drop off their grind

resource.

Attached in Appendix 1 of this paper is the data collated through research with respect to the location of

all IGA stores within Western Australia. This includes the population of people who reside in the same

suburb of each given IGA (data collected via Wikipedia – Suburb Profile), and includes the number of

cafés, coffee shops and restaurants in this suburb (data collected via truelocal.com).

The philosophy of the model and likely the largest risk to the concept is that it relies solely on the delivery

of the grind waste resource. To account for this in a more accurate way, a factor to account for the

likelihood of receiving the grind from café and coffee shops is implemented into the calculation.

It is understood that if café and coffee shop owners had easy access to a waste deposit system, which

would be located in the same suburb and within a certain distance of the actual business entity, then this

would encourage most owners to drop their grind waste off to the centrally located IGA every few days,

or even every week.

Furthermore, implementing a reward for the delivery of the grind waste, as well as the motivation of

supporting a renewable energy initiative will also increase the probability of the resource collection. The

reward or incentives for the delivery of the grind waste in this paper has not been explored in depth. This


will need to be negotiated and written into an agreement with the local participating supermarkets and

cafés. A consideration will be to implement an initiative that can be used in a similar way to current

petroleum credits given by supermarkets. Upon the delivery of “x” amount of grind resource, “x” amount

of cost can be saved whilst shopping at the supermarket itself. Not only does this benefit the stakeholders

of the DME product as no direct cost is required, it will also work as a marketing strategy for participating

supermarkets by encouraging more shoppers to their stores.

The alternative use of the grind resource for other purposes such as compost needs to be taken into

consideration when applying a likelihood of collection factor from these business entities.

With some initial research and with respect to the factors cited above, an assumption of 70-80% of café

and coffee shop owners would willingly provide the grind waste resource to the local collection area. To

allow for this in the model, a factor of 0.75 has been used as a multiplication factor when determining

likelihood of grind waste collectable from café and coffee shops. This figure will be scrutinised in the

sensitivity analysis of this paper to show just how significant a change of this assumption can be for

project feasibility.

Having already calculated the average amount of annual grind waste resource available at each café and

coffee shop in Perth as part of the “Analysis of Grind Resource Collection from Australian Cafés” section

in this report, 978.6kg can be implemented into the model.

Collection Model 2 – Approx. collection of grind resource from café and coffee shops in Perth (CM2)

CAVG = Average of grind waste resource available from cafés and coffee shops within

Australia (kg)

CLOC = Café and coffee shop located in same suburb as IGA supermarket (Appendix 1)

LCOL = Likelihood of collection of resource factor (Assumption to be 0.75)

CM2= Collection Model 2 (kg)

𝐂M2 = ∗ 𝐂AVG ∗ 𝐂LOC ∗ 𝐋COL


TABLE 7 – MODEL 2 CALCULATIONS FOR PERTH

This indicates a probable collection quantity from Model 2 café and coffee shops in Perth, Western

Australia will collect a total of 487,086 kg of grind waste annually.

Collection Model 3 - The collection of grind resource from homes in a given area (CM3)

SPOP = Population within the same suburb as the IGA supermarkets (Appendix 1)

RBPC = Roast Bean per Capita (kg)

CCAF = Factor how much coffee is consumed at café and coffee shops from total

consumption

Café & Coffee Shop Consumption / (RBPC * Population)

o

10151033

2.52∗24000000𝑘𝑔

𝑘𝑔

𝑦𝑒𝑎𝑟

= 0.167

MSHS = Market share of IGA in comparison with other supermarkets in Perth, Western

Australia

RCALC = Resource Multiplication Factor (inclusive of Moisture content and lost coffee

within Production)

LCOL3 = Likelihood collection of resource factor (Assumption to be 0.7)

CM3 = Collection Model 3 (kg)

𝐂M3 = 𝐒POP ∗ 𝐑BPC ∗ (1 − 𝐂CAF ) ∗ 𝐌SHS ∗ 𝐑CALC ∗ 𝐋COL3


TABLE 8 – MODEL 3 CALCULATIONS FOR PERTH

This indicates a probable collection quantity from homes and workplace in Perth, Western Australia will

accumulate a total of 360,773 kg of grind waste annually.

The total WCR expected from Perth utilising Model 2 and 3 over a year period would be 847,859 kg as

per Table 9.

TABLE 9 – RESOURCES AVAILABLE FROM WCR COLLECTION OVER A YEAR PERIOD IN AUSTRALIA

Adaption of Collection Models to Other Australian Cities

Applying the calculations for Model 2 and 3 throughout other major cities within Australia has been

accomplished by developing and manipulating information with respect to the Perth model.

The number of establishments in each major city has already been identified in Table 2. In comparison to

Perth, the market share for IGA is dissimilar for every major city within Australia. For this reason, for

cities outside of Perth, the IGA model will be substituted with a non-specific supermarket that represents

25% of the market share for that city.

By dividing the population of Perth residents that reside in the same suburb as the IGA supermarket

against the total population of Perth, a ratio can be obtained. This ratio can be applied as an estimated


multiplication factor when calculating the population that reside within the same suburb as the non-

specific supermarket for other cities within Australia.

1,101,478

2,021,203 = 0.545

The number of cafes located within the same suburb as the supermarket has been considered in a similar

way. By dividing the establishments located within the same suburb as the supermarkets in Perth against

the total café and coffee shops within the same city and greater metro area the multiplication ratio can be

obtained and applied within the report for other Australian cities.

663.65

829= 0.8005

These ratio values for Australian cities have been used to calculate the expected resource available as

per Table 10 and Table 11.

TABLE 10 – DERIVED WCR COLLECTION MODEL CALCULATIONS OVER A YEAR PERIOD IN AUSTRALIA

City and Greater

Metro Area

Total Establishments

Total Population

~80% Total Establishments

~60% Total Population

CM2 (kg) CM3 (kg) CM2 + CM3 =

Total Collection

Sydney 3382 4,840,628 2707 2638142 1987026 864085 2851111

Melbourne 2775 4,440,328 2221 2419979 1630395 792629 2423024

Brisbane 1686 2,274,560 1350 1239635 990575 406024 1396600

Adelaide 829 1,304,631 664 711024 487062 232886 719948

Canberra 540 386,000 432 210370 317266 68904 386170

Hobart 193 219,243 154 119487 113393 39136 152530

Darwin 183 140,386 146 76510 107518 25060 132578


TABLE 11 – RESOURCES AVAILABLE FROM WCR COLLECTION OVER A YEAR PERIOD IN AUSTRALIA

City and Greater Metro Area

WCR Available (kg)

Total Available Biofuel (kg)

Total Available Biofuel (m3)

Total Available Bio-Mass Pellets

(kg)

Sydney 2851111 452269 507 976220

Melbourne 2423024 384362 431 829643

Brisbane 1396600 221541 249 478196

Adelaide 719948 114205 128 246510

Canberra 386170 61258 69 132225

Hobart 152530 24196 27 52226

Darwin 132578 21031 24 45395

Supply Chain and Logistics

The collection quantity of the resource is now identified and further analysis of the most efficient method

of obtaining the WCR or biodiesel can be considered.

The key principle for collection of the WCR or biofuel is to collect the resource from a central location,

which has been identified as a particular supermarket, to maximise Model 2 and 3 as discussed earlier in

this paper in the section Model Derived Resource Calculation.

The performance criterion for the selection of the final supply chain and logistics model will focus on

optimising the cost of the complex and dynamic network by analysing the capital and operational

expenditure required.

Initially four operating scenarios will be investigated, these are:

1. Processing model implemented in-store at supermarket incorporating a storage facility for WCR

drop off. Biofuel product and Bio-mass pellets to be collected from store.

2. Collection of WCR at supermarket incorporating resource transport to:

a. A central processing facility within the city where the production of the fuel can be stored

and sold.


b. Multiple processing facilities within the city neglecting ‘zones’ where resource is

considered unviable.

3. Collection of WCR at supermarket working with waste management partners to collect the

resource offering commercial and environmental incentives.

1. In-store Supermarket Processing The continuous solvent extraction process, employing dimethyl ether (DME) as the solvent has the

capability to be designed for both small and large production systems. The ability to design a robust

process system at small scale level encourages in store processing of the WCR.

By executing the in-store processing model, the transportation of the WCR is no longer required. The

disadvantage of this model is that at some point the biofuel and bio-mass pellets need to still be collected

and sold. With this in mind, once the biofuel or bio-mass pellets have been produced there is no urgency

for collection, as the longevity concerns due to rancidity of the WCR is not an issue. Biodiesel fuel if

stored in optimum conditions utilising oxidative stabilizers then the fuel could potentially last years before

it degrades.

After the production of biodiesel using the in-store DME process, a clean, air tight vessel for the storage

of the biofuel needs to be in place. A level transmitter can be fitted to monitor the height of the biodiesel

in the vessel. The level value parameter can be dynamically fed to the stakeholder/transportation company

via a mobile phone application to maximise efficiency of collection and transportation of the biofuel.

For the purpose of optimising the supply chain and logistics for in-store processing, the collection period

of the fuel from the supermarket needs to be calculated. The total available biofuel from the supermarkets

over a year period in Perth is 150𝑚3 as per Table 9. If this was divided by 140 which is the approximate

number of IGA supermarkets collecting the WCR as per Appendix 1, the average yearly production of

biodiesel for each supermarket would be 1𝑚3 per year.


A collection period of one month is considered to be a feasible collection time period for the biofuel and

bio-mass pellets. This would indicate that approximately 0.090𝑚3of fuel would be collected every

month per store. The storage system to be implemented must be capable of withholding approximately

0.1𝑚3 (100Litres) of biodiesel. With the information gathered, if monthly figures indicate that the store

would exceed this capacity then a larger storage system can be installed.

Over the month time period, a total of 182kg of available dry coffee weight is available for bio-mass

pellet conversion from each store.

FIGURE 6 – PERTH AND GREATER METRO AREA IGA LOCATIONS

Figure 6 details the dispersed location of the IGA supermarkets located within Perth and the greater

metropolitan area.

To optimise the logistics and supply chain collection for the in-store processing model, time and money

can be saved by not investing in a central location to stockpile the biofuel and bio-mass pellets. This

would require prior knowledge of market interest in the product to ensure when the fuel is collected by a

transport contractor; it can be directly delivered to the end user of the fuel. The motive for this is to save

having to pay outgoings, such as rent and utilities to temporarily stock the fuel before it is then sold


onwards to the same end user. For this model to be at all feasible, cost savings like this need to be taken

into account.

A quick comparison of collection methodologies has been undertaken to optimise the logistics of the

process to reduce operational expenditure by reducing transport time and cost.

1. 30km radius from Perth CBD – daisy chain collection (Figure 7)

2. 30km radius from Perth CBD – star collection (Figure 10)

By neglecting all stores outside the 30km radius from Perth CBD eliminates a total of 15 collection points

but reduces the potential travelling time significantly.

1.1 Daisy Chain Collection – 30km Radius from Perth CBD The daisy chain collection model works on the foundation of collecting the fuel source from store to store

in the same cycle and depositing directly to the consumer as per Figure 7.

FIGURE 7 – BLOCK DIAGRAM DAISY CHAIN LOGISTICS MODEL

If the daisy chain logistic model was considered, the likelihood of being able to collect all the fuel products

in one gathering exercise would be unlikely. The method deliberated has been broken down into three

regions to evenly distribute the IGA supermarkets. Within each region of collection there is

approximately 35-40 IGA supermarkets as per Figure 8. (Note: Not included in Figure 8 are suburbs with

multiple IGA supermarkets).

Utilising Zeemaps [21] interactive mapping solutions, each IGA is approximately 2.6 kilometres (direct

route – point to point) from one another.


FIGURE 8 – IGA SUPERMARKETS LOCATED WITHIN 30KM RADIUS OF PERTH CBD

As only a direct route has been calculated from Zeemaps it would be considered impracticable to use this

value. Pythagoras theorem has been implemented to calculate the likely distance between each IGA

utilising 2.6 kilometres as the hypotenuse.

𝑎2 + 𝑏2 = 𝑐2 𝑤ℎ𝑒𝑟𝑒 𝑐 = 2.6 𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑟𝑒𝑠

2.62 = 𝑎2 + 𝑏2 𝑓𝑜𝑟 𝑎𝑝𝑝𝑟𝑜𝑥𝑖𝑚𝑎𝑡𝑒 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑙𝑒𝑡 𝑎 = 𝑏

6.76 = 1.842 + 1.842

𝑎 + 𝑏 = 1.84 + 1.84 = 3.68𝑘𝑚

With this information, assuming 37 IGA stores per region as per Figure 8, and a distance of 3.68km

required travel distance for each IGA then a total of ~ 140𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑟𝑒𝑠 of total travel is required to obtain

all of the biofuel and bio-mass pellets per region.

Distance and time are considered the two key expenses with respect to logistics for the collection of the

biofuel and bio-mass pellets. By allowing a practicable 15-20 minutes per collection of the resource and


15 minutes travel time between each IGA (3.68km) utilising the daisy chain model, a total of 30-35

minutes can be calculated for each store.

For the 37 stores in each region as per Figure 8, the total time for collection/transport (one vehicle) would

be approximately 20 hours. If the collection period of once per month is implemented, then a total of

3.33𝑚3(3330𝐿) of biofuel and 6.734 tonnes of bio-mass pellets would be available for each region.

For the collection from all three regions applying the daisy chain collection model; a total of 60 hours

would be required, a distance of 420 kilometres would need to be travelled for a total of ~10𝑚3 of biofuel

and ~20 tonnes of bio-mass pellets every month.

TABLE 12 – AVAILABLE RESOURCES AND EXPENSE FACTORS (IN-STORE PROCESSING MODEL)

City

Total Monthly

Collection of Biofuel

(m3)

Total Monthly Collection of Bio-Mass Pellets (kg)

Travel Distance per month (3.68km distance between each

supermarket) (km)

Travel Time per Month for Collection (~32.5 minutes per

supermarket) (hours)

Perth 10 20,000 420 60.125

1.2 Daisy Chain Collection – 30km Radius from Other Australian Cities By applying the ratios calculated from the Perth model, an inexact model for other Australian cities

transportation and logistics can be calculated. Of the 140 IGA Supermarkets within Perth greater metro

area, 85% were located within a 30km radius of the central business district. This same percentage ratio

has been applied to other Australian cities. The 30km radius reference point for Sydney is Parramatta as

per Figure 9.


FIGURE 9 – 30KM RADIUS FROM PARRAMATTA, SYDNEY (HIGHLIGHTED PINK REGION SYDNEY BOUNDARY)

Yellow Pages online was used to find the total supermarkets for the greater area of Sydney, Melbourne,

Brisbane, Adelaide, Canberra, Hobart and Darwin as per Table 13

TABLE 13 – RESOURCE COLLECTION VIA DAISY CHAIN COLLECTION MODEL (IN-STORE PROCESSING MODEL)

City and Greater Metro

Total Supermarket (Data from

Yellow Pages. Search

"Supermarket, State") * 0.85

Assume Collection from

Supermarket with 25%

Market Share

Travel Distance per month

(3.68km distance

between each supermarket)

(km)

Travel Time per Month for

Collection (~32.5 minutes per

supermarket) (hours)

Total Monthly

Collection of Biofuel

Available (m3)

Total Monthly

Collection of Bio-Mass

Pellets Available (kg)

Sydney, NSW 2000 1406 351 1293 190 35.9 69149

Melbourne, VIC 3000 1063 266 978 144 30.5 58766

Brisbane, QLD 4000 805 201 741 109 17.6 33872

Adelaide, SA 5000 326 82 300 44 9.1 17461

Canberra, ACT 2601 144 36 132 19 4.9 9366

Hobart, TAS 7000 80 20 74 11 1.9 3699

Darwin, NT 0800 70 17 64 9 1.7 3215

1.3 Star Collection Model - 30km Radius from Perth CBD The star collection model (Figure 10) collects the fuel source from the IGA supermarket and deposits it

to the end consumer, before repeating the operation.


The benefit of applying this model is that whenever a given IGA store has got sufficient biofuel and bio-

mass pellets readily available for collection, then they can be collected without the constraint of requiring

all other collection depots to have an adequate supply.

FIGURE 10 – BLOCK DIAGRAM STAR COLLECTION MODEL

By focusing on a quantity based scheme as opposed to a fixed once a month collection, the logistics and

collection model can be revaluated. To increase the chance of feasibility, multiple ‘collection quantities’

need to be considered.

Ideally, the fewer amount of times the collector of the biofuel and bio-mass pellets is required to travel to

IGA, the greater chance of model feasibility. The quantity available for collection from IGA supermarkets

to be considered for this model will be values of 0.2𝑚3, 0.4𝑚3 𝑎𝑛𝑑 0.6𝑚3 𝑜𝑓 𝑏𝑖𝑜𝑓𝑢𝑒𝑙.

As mentioned previously, the average biofuel available at each IGA supermarket is approximately

1𝑚3per year, however it is now apparent (Figure 11) that the majority of the IGA stores will have an upper

bin range value of 0.6𝑚3 of biofuel available per year.


FIGURE 11 – BIOFUEL AVAILABLE FROM IGA SUPERMARKETS IN PERTH OVER A YEAR

With the information assembled from Appendix 1, the total available biofuel for a given IGA is known.

If this figure is divided by the collection quantity of the biofuel, then the amount of trips the collector of

the resource has to make to the IGA stores can be shown via a histogram.

The following histograms should be read with the understanding that the frequency indicates individual

IGA stores, and Bin indicates the range of how often the collection quantity is available at these stores.

For example, Figure 12 specifies that 27 different IGA stores within each year will have 200L of biofuel

readily available for collection 3 times.

FIGURE 12 – FREQUENCY OF COLLECTION WHEN 200L AVAILABLE AT IGA SUPERMARKET (IN-STORE PROCESSING MODEL)

0

5

10

15

20

25

30

35Fr

eq

ue

ncy

Bin - Collection of biofuel (m3) from IGA

Biofuel Available from IGA Supermarkets in Perth over a Year

Frequency

0

5

10

15

20

25

30

Fre

qu

en

cy

Bin

Frequency of Collection when 200L available at IGA Supermarket

Frequency




As per Figure 13 and Figure 14, it is not practicable to collect the biofuel 0.5 times from the IGA

supermarket. This has been considered and any further calculations have been rounded up to the nearest

whole number, 1.

Table 14 represents the collection of the biofuel from IGA supermarkets for varying biofuel quantities

utilising the star collection model within the constraints of 30km radius of Perth CBD.

0

5

10

15

20

25

30

35

40

45Fr

eq

ue

ncy

Bin


Frequency

0

5

10

15

20

25

30

35

40

Fre

qu

en

cy

Bin


Frequency


TABLE 14 – TOTAL COLLECTION PERIODS FOR BIOFUEL IN PERTH WITH VARYING QUANTITIES (IN-STORE PROCESSING MODEL)

Collection Quantities

Total Store Collections over a Year

200 Litres 798

400 Litres 423

600 Litres 292

Using Zeemaps [21], assuming the consumer of the final product biofuel and bio-mass pellets was located

in Perth CBD, the median travel distance one way to each IGA is approximately 12km, with a round trip

24km.

With the median distance identified the travel logistics for the star collection model can be represented as

per Table 15.

TABLE 15 – TOTAL DISTANCE FOR RESOURCE COLLECTION IN PERTH WITH VARYING QUANTITIES (IN-STORE PROCESSING MODEL)

Collection Quantities Total Store Collections

over a Year Total Travel Distance

Required (km) per year

200 Litres of Biofuel and

384 kg of Bio-mass pellets 798 19152





It is apparent that the process can be optimised by increasing the collection quantity. However, analysis

has been undertaken to include the lesser quantities for cost comparison for the following three reasons:

Consumer requirement for biofuel (agreement to deliver X amount of biofuel and bio-mass

pellets every month)

Storage capability at IGA (600 Litres of biodiesel and equivalent bio-mass pellets may be

considered impracticable)

Transport restrictions


If an estimate of ~20 minutes each way is allowed for travel time for the median distance (24km round

trip), inclusive of approximately 30 minutes of in-store resource collection, the total approximate time

would be 70 minutes per trip.

TABLE 16 – TIME REQUIRED FOR BIOFUEL COLLECTION FOR VARYING QUANTITIES IN PERTH (IN-STORE PROCESSING MODEL)

Collection Quantities Time taken to collect total resource (hours) for a year period


384 kg of Bio-mass pellets 931 hours





1.4 Star Collection Model - 30km Radius from Other Australian Cities A comparison of kilometres between the daisy chain model and star collection model implemented in

Perth was undertaken.

It was evident that the total distance for travelled kilometres over the year for daisy chain model would

be (420*12) 5,040 kilometres whereas the absolute minimum kilometres travelled for the biofuel

collection in the star chain would be 7,008 kilometres. As travel and time is considered the most important

of the logistics model, the star collection model for the “other Australian cities” has been neglected.

2. WCR Collection from IGA supermarket

The collection of the WCR must be over an adequate time period so enough resource can be collected to

make the transportation (operational) expense of the resource economically viable. Along with the

collection period, preventing the resource from deteriorating and becoming rancid is as of key importance

for the WCR collection model.


“Rancidity is produced by aerial oxidation of unsaturated fat present in foods and other products” [22].

When the coffee grinds are exposed to air, its unsaturated components are “converted into hydro

peroxides, which break down into volatile aldehydes, esters, alcohols, ketones, and hydrocarbons, some

of which have disagreeable odours” [22].

To prolong the resource, the WCR storage container will be installed in a refrigerated room. At lower

temperatures, the oxidation process that would spoil the resource will slow down preserving the WCR.

Antioxidants is a substance that inhibits oxidation to counteract the deterioration of food products.

Antioxidants can also be used to prevent fat oxidation to also prolong the collection period.

Further analysis and research would be required prior to final investment into the project to find the ideal

duration for which coffee grinds can be stored with respect to moisture and temperature over time.

To find a suitable logistics model, four collection periods for WCR collection will be analysed. These

periods will be one week, two week and monthly collection cycles. Of these cycles, the logistic approach

for collecting the WCR will be incorporated:

a. To a central processing facility within the city where the production of the fuel can be

stored and sold.

b. To one of many multiple processing facilities within the city where the product of the fuel

can be stored and sold.

2.1 Logistics and Collection for Central Process Facility in Perth

For the collection of the WCR, a daisy chain collection model will be implemented as per Figure 7. Star

collection has been neglected for this model due to the constraint on collection period and the necessity

of gathering the resource before it starts to deteriorate.

A feasible location for the central processing facility in Perth has been chosen to be in the industrial

section of Balcatta, 14 kilometres north of Perth. The primary intention for selecting Balcatta as the


central location was rental expenditure in comparison with other locations as well as access to key

highways and freeways.

Although the reference point of the central processing facility is located in Balcatta, the 30km radius from

Perth, CBD still will set the constraints of the collection area. Therefore, the collection distance calculated

incorporating the three sections as per Figure 8 will still be 420 (120*3) kilometres.

TABLE 17 – DISTANCE TRAVELLED AND WCR COLLECTION FOR DAISY CHAIN MODEL IN PERTH FOR THREE COURIER ZONES

Distance Travelled per period (km)

Collection Quantity of WCR (kg) per

Cycle from one zone

Collection Quantity of WCR (kg) per Cycle from all three zones

Distance Travelled over a year per (km)

1 Week 420 5418.8 16256.5 21840.0

2 Week 420 10837.7 32513.0 10920.0

3 week 420 16256.5 48769.5 7280.0

1 month 420 23842.9 71528.6 5040.0

With the collection information gathered, it is evident that anything over the 1 week collection period

from one courier zone could potentially be an issue with respect to transport mass. As per Main Roads

Western Australia and the National Heavy Vehicle Regulator (NHVR), general mass limit compliance is

required [23]. The maximum mass limit per single axle, single tyre is 6 tonne with the single axle, dual

tyre capable of 10 tonne [24]. As 10 tonne is still less than the collection quantity per cycle for collection

periods greater than a week another solution is required.

As part of the Heavy Vehicle National Law (HVNL), an accreditation or exemption may be available. If

this is deemed unacceptable then a superior transport truck is required. Specific tri axle and tandem axle

dual and single tyre configurations conform to a transport mass weight of ~15-20 tonne [24].

For project logistics, it has been assumed that an exemption has been granted and the mass values per

courier transport is as per Table 17.

If the 32.5 minutes per supermarket collection and travel time was included, the amount of transport hours

required over the year for the different collection methodologies would be as per Table 18.


TABLE 18 – TOTAL TIME AND DISTANCE TRAVELLED FOR DIFFERENT COLLECTION PERIODS IN PERTH

Collection Period Total Distance Travelled

for a year period Time taken to collect total

resource (hours) for a year period

1 Week 21,840 km 3,943 hours

2 Week 10,920 km 1,972 hours

3 Week 7,280 km 1,314 hours

1 Month 5,040 km 910 hours

2.2 Logistics and Collection for Central Process Facility Australian Cities By combining the information available from Table 11 and Table 13, the other Australian cities can be

further analysed for collection of the WCR from IGA. Transport mass limits has been neglected for the

following as this can be overcome by having multiple collection zones like the three zones implemented

in the Perth model.

TABLE 19 – WCR QUANTITY FOR DIFFERENT COLLECTION PERIODS IN AUSTRALIA

City and Greater Metro

Distance Travelled per period (km)

1 Week Collection of

WCR (kg)


WCR (kg)


WCR (kg)

1 Month Collection of

WCR (kg)

Sydney, NSW 2000 1293 54829 109658 164487 237593

Melbourne, VIC 3000 978 46597 93193 139790 201919

Brisbane, QLD 4000 741 26858 53715 80573 116383

Adelaide, SA 5000 300 13845 27690 41535 59996

Canberra, ACT 2601 132 2933 5867 8800 12711

Hobart, TAS 7000 74 7426 14853 22279 32181

Darwin, NT 0800 64 2550 5099 7649 11048


TABLE 20 – TOTAL TIME AND DISTANCE TRAVELLED FOR DIFFERENT COLLECTION PERIODS IN AUSTRALIA

Australian Cities

1 Week Collection distance travelled

(km/year)

1 Week Collection

Time Required (hr/year)


(km/year)

2 Week Collection



(km/year)

3 Week Collection


1 Month Collection distance travelled

(km/year)

1 Month Collection


Sydney, NSW 2000 67236.0 9886.5 33618.0 4943.3 22412.0 3295.5 15516.0 2281.5

Melbourne, VIC 3000 50856.0 7492.3 25428.0 3746.2 16952.0 2497.4 11736.0 1729.0

Brisbane, QLD 4000 38532.0 5661.5 19266.0 2830.8 12844.0 1887.2 8892.0 1306.5

Perth, WA 6000 21840.0 3943.3 10920.0 1971.7 7280.0 1314.4 5040.0 910.0

Adelaide, SA 5000 15600.0 2309.7 7800.0 1154.8 5200.0 769.9 3600.0 533.0

Canberra, ACT 2601 6864.0 1014.0 3432.0 507.0 2288.0 338.0 1584.0 234.0

Hobart, TAS 7000 3848.0 563.3 1924.0 281.7 1282.7 187.8 888.0 130.0

Darwin, NT 0800 3328.0 478.8 1664.0 239.4 1109.3 159.6 768.0 110.5

2.3 Logistics and Collection for Multiple Process Facilities in Perth Collecting the WCR resource utilising multiple process facilities within Perth has been considered.

Knowing that Perth has a total of approximately 150𝑚3of biofuel as per Table 9, installing multiple

processing facilities seems initially unviable. However, due to a full assessment of feasibility and logistic

models, the calculation has been undertaken for research purposes.

With the data attached in Appendix 1, each IGA has been separated into 10 collection zones throughout

Perth CBD as per Column C. A pivot table, Table 21, has been implemented to accurately analyse the

data.


TABLE 21 – PIVOT TABLE OF COLLECTION ZONES IN PERTH (MULTIPLE PROCESS MODEL)

Zones Sum of Quantity of

IGA in Zone Sum of Population of

People in Zone Café/Coffee Shops in IGA

Location

1 21 122339 56.55

2 12 57254 28.6

3 10 107587 28.6

4 18 142684 255.45

5 8 37788 42.25

6 24 138025 64.35

7 27 289904 118.95

8 10 58315 38.35

9 3 11798 1.3

10 11 135784 29.25

Grand Total 144 1101478 664

From Table 21, the data has been extracted and the collection model 1 and model 2 have been

implemented resulting in the following outcomes as per Table 22.

TABLE 22 – MULTIPLE PROCESS FACILITIES SEPARATED IN ZONES

Zones WCR

(kg/day)

WCR (millions of

kg/year)

Oil extraction @ 19.73%

(millions of kg)

Esterification Oil in to biodiesel is 80.4%

(millions of kg)

Conversion to Volume in (m3)

1 223.4 0.081538376 0.016087522 0.012934367 14.51

2 108.8 0.039725045 0.007837751 0.006301552 7.07

3 154.0 0.056210887 0.011090408 0.008916688 10.00

4 641.2 0.234055103 0.046179072 0.037127974 41.65

5 118.8 0.04335876 0.008554683 0.006877965 7.72

6 253.1 0.092395823 0.018229696 0.014656676 16.44

7 499.1 0.182179665 0.035944048 0.028899015 32.42

8 129.4 0.047222218 0.009316944 0.007490823 8.40

9 13.2 0.004817551 0.000950503 0.000764204 0.86

10 180.6 0.065923048 0.013006617 0.01045732 11.73

Total 2321.7 0.847 0.167 0.134 150.8

As per the results in Table 22, several zones can be eliminated solely because of the quantity of the biofuel

resource available in that zone. The zones that have been neglected going forward will be values that are

significantly less than15𝑚3

𝑦𝑒𝑎𝑟 of biofuel.


It would be expected that if this model was implemented, a total of four processing models in Perth would

be required which would result in a total biofuel resource of 105𝑚3

𝑦𝑒𝑎𝑟 . The zones that would remain are

Zones 1, 4, 6 and Zone 7. A summary of location of these zones can be observed by analysing the

Appendix 1 data.

TABLE 23 – WCR SUMMARY OF PERTH MULTIPLE PROCESS ZONES

Zones IGA Stores in Zone Total WCR

Available (kg) Biofuel Available

(m3) Bio-Mass Pellets

Available (kg)

1 21 81538.4 14.51 27918.7

4 18 234055.1 41.65 80140.5

6 24 92395.8 16.44 31636.3

7 27 182179.7 32.42 62378.3

As per Table 23, it is evident that a total of 90 (21+18+24+27) IGA supermarkets are within these four

zones. Using the calculated approximate distance between IGA of 3.68km the total distance travelled per

collection can be obtained.

Analysing weekly, fortnightly, 3 weekly and monthly collection periods the results in Table 24 and Table

25.

TABLE 24 – WCR QUANTITY FOR ZONE COLLECTION IN PERTH

Zones Distance Travelled

per period (km) 1 Week Collection

of WCR (kg) 2 Week Collection

of WCR (kg) 3 Week Collection

of WCR (kg) 1 Month Collection

of WCR (kg)

1 77.3 1568.0 3136.1 4704.1 6794.9

4 66.2 4501.1 9002.1 13503.2 19504.6

6 88.3 1776.8 3553.7 5330.5 7699.7

7 99.4 3503.5 7006.9 10510.4 15181.6

TABLE 25 – TOTAL DISTANCE FOR ZONE COLLECTION FOR DIFFERENT COLLECTION PERIODS (MULTIPLE PROCESS MODEL)

Zones Distance Travelled

per period (km)


(km/year)


(km/year)


(km/year)

1 Month Collection distance travelled

(km/year)

1 77.3 4019.6 2009.8 1339.9 927.6

4 66.2 3442.4 1721.2 1147.5 794.4

6 88.3 4020.6 2295.8 1530.5 1059.6

7 99.4 5168.8 2584.4 1722.9 1192.8


The idea behind this method would be utilising waste collection yards in a given zone so rent would not

be required. However, in the cost analysis section of this report a rent free scenario as well as an expense

for rent has been calculated for comparison.

2.4 Logistics and Collection for Multiple Process Facilities in Australia

Unlike the Perth scheme which is accurately modelled for different zones, only the ratio with respect to

Perth’s data can the rest of Australian cities be calculated.

The estimated ratio has been obtained with respect to population difference and population density in

comparison to Perth. Previously in this report, the population ratio was solely used as a multiplication

factor when comparing against Perth, however when calculating accurate ‘zone’ information population

density needs to be considered in the computation.

Initially, a comparison solely with population density to calculate the ratio was undertaken. However, the

figures are somewhat misguiding.

The reason being for this, Sydney has 114 sq. km with a density over 5,000 persons per sq. km. In contrast,

Melbourne only has 34 sq. km and Brisbane has a mere 3 sq. km and no other capital cities have any.

When comparing Sydney’s population density with respect to Canberra however, the population density

for Canberra is 450 in contrast to 347 𝑝𝑒𝑟𝑠𝑜𝑛/𝑠𝑞. 𝑘𝑚 in Sydney. This indicates that although Sydney has

a more dense population in most areas, it also has a larger land area in which is not as dense.

So to get the most accurate ‘approximate’ ratio for zone collection, the population has been multiplied by

the population density to give a value as per Table 26.


TABLE 26 – FEASIBLE ZONE COLLECTION CALCULATION FOR AUSTRALIAN CITIES

Australian Cities Population Population

Density (persons/sq.km)

Multiplication Value

Ratio W.R.T Perth

Multiplication Value

Approx. Feasible Zones > than 15m^3/year

Sydney, NSW 2000 4,840,628 347 1679697916 2.64 10.6

Melbourne, VIC 3000 4,440,328 440 1953744320 3.07 12.3

Brisbane, QLD 4000 2,274,560 140 318438400 0.50 2.0

Perth, WA 6000 2,021,203 315 636678945 1.00 4.0

Adelaide, SA 5000 1,304,631 390 508806090 0.80 3.2

Canberra, ACT 2601 386,000 450 173700000 0.27 1.1

Hobart, TAS 7000 219,243 130 28501590 0.04 0.2

Darwin, NT 0800 140,386 43 6036598 0.01 0.0

With the derived information from the ratio analysis, implementing a process facility in Hobart and

Darwin can be eliminated due to no feasible economic collection zones.

If the average collection per zone for Perth was used as a basis for the remaining Australian cities, then it

could be implied on average approximation that 50,518kg of WCR is available per year from each zone,

and an average of 22.5 IGA’s are located within each zone.

Table 27 and Table 28 indicate the total time, transport required and collection available if multiple

process facilities were implemented within each city using different collection period from 1 week to a

month.

TABLE 27 – TOTAL DISTANCE TRAVELLED FOR ZONE COLLECTION IN AUSTRALIA

Australian Cities Zones

Total Collection of WCR

(kg/year) using average zone of

50,518kg

Distance required for transport for

all Zone Collection

(km)

Sydney, NSW 2000 11 533114.76 874

Melbourne, VIC 3000 12 620093.60 1016

Brisbane, QLD 4000 2 101068.30 166

Perth, WA 6000 4 202073.80 331

Adelaide, SA 5000 3 161488.58 265

Canberra, ACT 2601 1 55130.17 90


TABLE 28 – TOTAL TIME AND DISTANCE FOR DIFFERENT COLLECTION PERIODS IN AUSTRALIA (MULTIPLE PROCESS MODEL)

Australian Cities

Zones

1 Week Collection distance travelled (km/year)

1 Week Collection



2 Week Collection



3 Week Collection


1 Month Collection distance travelled (km/year)

1 Month Collection


Sydney, NSW 2000 11 45448.0 6687.9 22724.0 3343.9 15149.3 2229.3 10488.0 1748.0

Melbourne, VIC 3000 12 52832.0 7779.0 26416.0 3889.5 17610.7 2593.0 12192.0 2032.0

Brisbane, QLD 4000 2 8632.0 1267.9 4316.0 633.9 2877.3 422.6 1992.0 332.0

Perth, WA 6000 4 17212.0 2535.0 8606.0 1267.5 5737.3 845.0 3972.0 662.0

Adelaide, SA 5000 3 13780.0 2025.9 6890.0 1012.9 4593.3 675.3 3180.0 530.0

Canberra, ACT 2601 1 4680.0 691.6 2340.0 345.8 1560.0 230.5 1080.0 180.0

3. WCR Collection from IGA Supermarket by Waste Management Partners The philosophy of this supply chain and logistics model would be to utilise waste management partners

whom already collet waste from supermarkets within the given city. Usually the end destination of this

waste would end up at either the recycling yard or local tip.

If a commercial agreement could be arranged between the stakeholders of the DME process which would

allow the stakeholders to set up a processing facility at the local recycling yard or tip, this could be a very

efficient system both commercially and environmentally.

Without going into too much detail with respect to potential agreements for the waste management

partners, a simple arrangement to provide a percentage of the biofuel credits as an incentive. Not only

would this be better and ‘greener’ for the environment as it would offset the combustion of fossil fuels

from the waste management partners, but it would also be potentially commercially viable if the

percentages were mutually acceptable.

The biofuel credit percentage chosen for the cost analysis section of this paper is 30% of the gross profit

in the equivalent value of biofuel produced via the DME process.


With this in mind, the total resource available from this model will be as per Table 9 for Perth, and Table

11 for the remaining Australian cities. The cost analysis of this paper will assume one process facility

within each city.

Cost Analysis The cost analysis of this report assess the economics of biofuel production from the grind waste of coffee

beans with emphasis on logistics and supply chain through an analysis of multiple scenarios.

These scenarios are based on assumptions to derive mathematical models that would give an accurate

collection figure in order to find the break-even cost of the process. This break-even value is considered

important as it will indicate whether or not the project could potentially be commercially viable with

respect to profitability, operational and capital expenditure.

Nestle, Gympie Cost Analysis of Proposed Model An important part of the Nestle, Gympie model is the availability and price of sawdust for the fluidised

bed boiler. Without knowledge of the agreement Nestle has with sawmills in Queensland, a range of

values will be explored from $3 to $20 per tonne.

The energy requirements of the current model and proposed DME model have been calculated and the

additional energy requirements of the sawdust are known as per Table 6. With this data, the cost analysis

of the proposed model can be determined.

TABLE 29 – ASSUMED CURRENT VS PROPOSED SAWDUST REQUIREMENTS FOR NESTLE ENERGY MODEL

Assumed Current Energy Model - Requirements 356.1kg/hr of Sawdust

*Assumption - Plant Production @ 7,000 hours per year

Sawdust $3 tonne $5 tonne $8 tonne $12 tonne $15 tonne $20 tonne

Daily $25.64 $42.73 $68.37 $102.56 $128.20 $170.93

Yearly $7,477.58 $12,462.64 $19,940.22 $29,910.33 $37,387.91 $49,850.54

Proposed Energy Model - Requirements 455kg/hr of Sawdust

*Assumption - Plant Production @ 7,000 hours per year

Sawdust $3 tonne $5 tonne $8 tonne $12 tonne $15 tonne $20 tonne

Daily $32.76 $54.60 $87.36 $131.04 $163.80 $218.40

Yearly $9,555.61 $15,926.02 $25,481.64 $38,222.46 $47,778.07 $63,704.09


With the quantity of sawdust that is being purchased, it would be very unlikely that Nestle would be

paying greater than $8 per tonne in Australia, so this value has been used as a reference point. An

additional $5541.42 per year of sawdust is required to ensure the plant can meet the power requirements

of the proposed model. Table 30 indicates the cost of energy for each system.

TABLE 30 – NESTLE ENERGY MODEL COMPARISSON

Cost per tonne of Sawdust ($/tonne) $8.00

Sawdust Energy Content (GJ/kg) 0.017

Current Model Proposed Model

Total Energy Required from Sawdust (GJ/hr) 6.0533 7.7355

Total Energy Provided from WCR (GJ/hr) 9.08 7.4548

Total Energy Provided by System (GJ/hr) 15.1333 15.1903

Required Sawdust (tonne/hr) 0.356 0.455

Cost of Energy for Operation ($/hr) 2.8488 3.64

Cost of Energy ($/GJ) 0.188247111 0.239626604

Calculating the additional cost of the plants energy requirements has now been confirmed, the profit of

the production of the oil can be analysed. As per stakeholder advice, the oil before the esterification

process can be sold depending on market demand. An advised value from the stakeholder to calculate for

the feasibility analysis is a varying value which ranges from $600.00 per tonne and $1000.00 per tonne.

As the capital investment of the plant has not been confirmed, an expense assumption per year (Table 31)

has been calculated using market research in terms of biofuel process systems.

TABLE 31 – OPERATIONAL EXPENDITURE IF DME PROCESS MODEL IMPLEMENTED ON SITE

Operational Expenditure Yearly

Maintenance Contractor 40 Hours per Week $85,000

Maintenance Contractor on Call Allowance of 4 Hours per Week $6,538.40

Additional Sawdust Required for Plant Energy $5,541.42

DME - Equipment Replacement (Approx.) $10,000

Unknown Cost Allowance $10,000

Insurance $5,000

Total Expenditure $122,080

With the expected production of 555.15 tonnes of coffee oil per year as calculated in Table 6 at Nestle,

Gympie, the profit can be analysed with the varying sale price of the oil. Table 31 displays the difference


in terms of profitability of the varying sale price of the oil. In an ideal world, selling the oil for biofuel

production at $1000.00 dollars per tonne would provide a profit of nearly $450,000.00 per year.

FIGURE 15 – GROSS PROFIT PER YEAR (1ST YEAR) COMPARING SALE PRICE OF COFFEE OIL

For an accurate feasibility study, the low range, high range and median value of sale price of the oil has

been further analysed over a 16 year period. The 16 year period has been chosen as this has been identified

as the expected life of the process facility.

Currently, the capital investment required for the plant has been neglected, however the assumption is

that it would be an equity investment and therefore interest repayments can be ignored.

It has been allowed for in the model that every year, the expense and maintenance costs would increase

by 8% from the previous year. This is to allow for additional wear of equipment which will require more

attention, and also tolerates for increasing prices from material and maintenance contractor services.

For the size of the plant, these maintenance costs are quite high and will more than likely not increase by

8% every year. However for a feasibility analysis, it is always best to over cost expense rather than under

cost. With this 8% increase every year in mind, it has been calculated that in the 15th year, the maintenance

will cost a total of $387,258.

y = 555.15x - 122080

$0.00

$50,000.00

$100,000.00

$150,000.00

$200,000.00

$250,000.00

$300,000.00

$350,000.00

$400,000.00

$450,000.00

$500,000.00

$500.00 $600.00 $700.00 $800.00 $900.00 $1,000.00 $1,100.00

Pro

fit

(Rev

enu

e -

Exp

en

se)

Sale Price of Coffee oil ($/tonne)

Gross Profit per Year With Respect to Sale Price ($/tonne)

Profit per Year

Linear (Profit per Year)


Like any process, over time the production output of the plant will be optimised and streamlined. It is also

likely that Nestle will increase their production of coffee resulting in growth of coffee grinds which will

directly affect the profit of the project. An allowance assumption of 3% growth every year from the initial

555.15 tonne a year has been added to the scheme. This would mean in the 15th year, the likely oil output

of the facility will be 890 tonnes.

The price of the oil over time has not changed, with the assumption it would stay approximately the same

in value over the 16 year period.

The following gross profit plot Figure 16, has taken into account the increase in maintenance and

production over time.

FIGURE 16 – GROSS PROFIT OVER LIFESPAN OF PROJECT COMPARING SALE PRICE OF COFFEE OIL

Over the 16 year time period, the gross profit for each analysed case neglecting capital investment is as

per Table 32. Initially, these results indicate that by implementing the DME process system at Nestle

would result in a positive investment; however a comparison with respect to inflation needs to be

considered.

y = -442.7x2 + 5677.6x + 319685

y = -504.42x2 + 2481.6x + 208556

y = -357.34x2 + 8585.2x + 431311

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

0 2 4 6 8 10 12 14 16

Pro

fit

($)

Lifespan of the Project

Gross Profit Over time

$800 per tonne

$600 per tonne

$1,000 per tonne

Poly. ($800 per tonne)

Poly. ($600 per tonne)

Poly. ($1,000 per tonne)


TABLE 32 – TOTAL GROSS PROFIT VALUE NEGLECTING CAPITAL EXPENDITURE FOR DME PROCESS IMPLEMENTED AT NESTLE

Case Gross Profit

$600/Tonne $3,128,346

$800/Tonne $5,544,535

$1000/Tonne $7,960,724

If the assumption is made that inflation throughout the 16 year period would be 3% per year, then the

compounding return of 3% per year from the initial capital can be considered the break-even value of the

project.

However, 3% Internal Rate of Return (IRR) does not make the project economically viable as there is

some risk. As the actual model itself is considered medium to low risk, then a respectable investment

would be considered to be anything greater than three times inflation, in this case 9% IRR pre-tax per

year.

Therefore, anything greater than 9% compounding return per year over the life span of the project is

considered a feasible business model.

With these values now identified as key inputs to the feasibility analysis, the maximum capital investment

to achieve these targets can be calculated.

This is displayed in Figure 17, where the breakeven value for 3% IRR compounding yearly return over

the lifespan of the project can be achieved with a maximum capital expenditure value of $1,949,500. The

economic project feasibility value of 9% compounding yearly return over the lifespan of the project is

also identified in Figure 17 and has been determined to be a maximum capital investment of $788,000.


FIGURE 17 – INTEREST RETURN PER YEAR PRE-TAX AGAINST VARYING CAPITAL EXPENDITURE FOR NESTLE MODEL

As the capital investment of the project drops below the $788,000 value as presented in Figure 17, an

exponential like increase in terms of interest return per year over the life span of the project is seen,

resulting in a great potential investment opportunity for stockholders.

In-store Supermarket Processing

Australian Cities

With respect to the data collected in Table 12 and Table 13 of this report, a cost analysis inclusive of

project feasibility of this daisy chain collection model of the biofuel and bio-mass pellets in Australia can

be considered.

Research has been undertaken exhausting the Department of Transport of Western Australia Freight

Guideline rates calculator of 2013 for metropolitan areas, based on diesel fuel costs of $1.607 per litre

[25]. This provisional rates based on Western Australian prices has been adapted to other Australian cities

due to the expected deviation of costs not exceeding +- 5%.

-5.000%

0.000%

5.000%

10.000%

15.000%

20.000%

25.000%

30.000%

35.000%

0 1,000,000 2,000,000 3,000,000 4,000,000

Inte

rest

Rat

e R

etu

rn (

IRR

) p

er y

ear

pre

-tax

Capital Expenditure

Interest Return per Year pre-tax against Varying Capital Expenditure

$600/Tonnne

$800/Tonnne

$1000/Tonnne

Feasibility (9%)

Break-Even (3%)

$1,949,500$788,000


As per the Department of Transport of WA [25] a 22.5 tonne (Gross Vehicle Mass) GVM rigid truck, 2

axles has been selected as the optimal vehicle, so the prices have been based on this selection as per Table

33.

TABLE 33 – DEPARTMENT OF TRANSPORT OF W.A FREIGHT GUIDELINE RATES

Provisional Rates Oct, 2013 Heavy Vehicle Types

Metropolitan Based on Diesel Fuel cost of $1.607 per litre based on one driver

Hourly Rate (exc. GST) Rate per km (exc. GST)

22.5 tonne GVM, Rigid Truck (2 axles)

$63.82 $2.92

With these values now identified in Table 33, the expense of this model can be calculated, initially

neglecting capital expenditure.

The assumption has been made that each process itself has a similar lifespan of approximately 10 years

with the expense growing at approximately 5% per year with the resource available for sale to grow at

3% per year.

An allowance of 12 hours per year (1 hour per month) is required for additional maintenance of each

process system as well as an allowance of $300 per year of maintenance equipment per system.

TABLE 34 – TOTAL EXPENSE PER YEAR FOR DAISY CHAIN COLLECTION MODEL

Daisy Chain Biofuel Collection Model

Cost of Travel per Year

Cost of Maintenance Per Year Total Expense per

Year Cost of km

Cost of Labour

Cost of Labour

Maintenance Cost

Sydney, NSW 2000 $45,307 $145,510 $336,960 $105,300 $633,076

Melbourne, VIC 3000 $34,269 $110,281 $255,360 $79,800 $479,710

Brisbane, QLD 4000 $25,965 $83,477 $192,960 $60,300 $362,701

Perth, WA 6000 $14,717 $46,046 $134,400 $42,000 $237,163

Adelaide, SA 5000 $10,512 $33,697 $78,720 $24,600 $147,529

Canberra, ACT 2601 $4,625 $14,551 $34,560 $10,800 $64,536

Hobart, TAS 7000 $2,593 $8,424 $19,200 $6,000 $36,217

Darwin, NT 0800 $2,243 $6,893 $16,320 $5,100 $30,555

$1,991,488


Unlike the Nestle Gympie Model, esterification of the oil is assumed for the In-store processing which

will result in a final product of biofuel. Expected sale price of biofuel would be $1070 per 𝑚3 [4] and the

assumed feasible sale price of the bio-mass pellets is $150 per tonne.

As per the collection quantity from Table 12 and Table 13, the total revenue that this logistics model can

obtain is displayed in Table 35.

TABLE 35 – TOTAL REVENUE PER YEAR FOR DAISY CHAIN COLLECTION MODEL

Daisy Chain Biofuel Collection Model

Revenue per Year

Biofuel Biomass Total Revenue (Inc. GST)

Sydney, NSW 2000 $460,956 $124,468 $585,424

Melbourne, VIC 3000 $391,620 $105,779 $497,399

Brisbane, QLD 4000 $225,984 $60,970 $286,954

Perth, WA 6000 $128,400 $36,000 $164,400

Adelaide, SA 5000 $116,844 $31,430 $148,274

Canberra, ACT 2601 $62,916 $16,859 $79,775

Hobart, TAS 7000 $24,396 $6,658 $31,054

Darwin, NT 0800 $21,828 $5,787 $27,615

It is evident that even without taking into consideration the capital expenditure of the individual process

systems, the operational expenditure exceeds the total revenue available resulting in a non-feasible

business model. Over the 10 year lifespan of the project, the losses per year are further quantifiable as

displayed in Figure 18.


FIGURE 18 – GROSS LOSS PER YEAR FOR INSTORE PROCESSING MODEL IN AUSTRALIA

Consideration with respect to likely cost of capital expenditure has been conducted even though the

business model advises that operational expenditure alone would be greater than potential revenue. This

consideration has been undertaken to provide a full analysis of the cost of the model.

As the individual process systems required for in-store processing do not require a large quantity of coffee

grind waste, it is expected the initial capital investment required for each system will not be more than

$10,000.

As the capital expenditure at this moment in time is unknown, a varying initial expenditure value not

including interest over the project lifespan will be explored from $1,000 to $10,000 per process system.

By multiplying the amount of process systems required per city (Table 13) by the varying capital

expenditure will result in Table 36.

-$800,000

-$700,000

-$600,000

-$500,000

-$400,000

-$300,000

-$200,000

-$100,000

$0

0 2 4 6 8 10

Pro

fit

or

Loss

per

Yea

r

Lifespan of Project (Years)

Gross Loss per Year of the Project for Each City over the Lifespan of the Expected Process System

Sydney, NSW 2000

Melbourne, VIC 3000

Brisbane, QLD 4000

Perth, WA 6000

Adelaide, SA 5000

Canberra, ACT 2601

Hobart, TAS 7000

Darwin, NT 0800


TABLE 36 – VARYING CAPITAL EXPENDITURE MULTIPLIED BY QUANTITY OF PROCESSES FOR EACH CITY

Australian City $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000 $9,000 $10,000

Sydney, NSW 2000 $351,000 $702,000 $1,053,000 $1,404,000 $1,755,000 $2,106,000 $2,457,000 $2,808,000 $3,159,000 $3,510,000

Melbourne, VIC 3000 $266,000 $532,000 $798,000 $1,064,000 $1,330,000 $1,596,000 $1,862,000 $2,128,000 $2,394,000 $2,660,000

Brisbane, QLD 4000 $201,000 $402,000 $603,000 $804,000 $1,005,000 $1,206,000 $1,407,000 $1,608,000 $1,809,000 $2,010,000

Perth, WA 6000 $140,000 $280,000 $420,000 $560,000 $700,000 $840,000 $980,000 $1,120,000 $1,260,000 $1,400,000

Adelaide, SA 5000 $82,000 $164,000 $246,000 $328,000 $410,000 $492,000 $574,000 $656,000 $738,000 $820,000

Canberra, ACT 2601 $36,000 $72,000 $108,000 $144,000 $180,000 $216,000 $252,000 $288,000 $324,000 $360,000

Hobart, TAS 7000 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 $160,000 $180,000 $200,000

Darwin, NT 0800 $17,000 $34,000 $51,000 $68,000 $85,000 $102,000 $119,000 $136,000 $153,000 $170,000

If it was assumed that the actual cost of the process was $5,000 per system, then the business model if

implemented in Perth, over a 10 year life span of the business will lose ($700,000 + $2100315)

approximately 2.8 million Australian dollars.

Without carrying out specific operational costs for the star collection model, the maximum total revenue

will not exceed the capital expenditure even if it was assumed that operational expenditure would cost

zero dollars.

This confirms that neither the daisy chain nor star collection model for in-store processing of the coffee

grind resource to produce biofuel and bio-mass pellets will be a feasible business model. This is

significantly due to the overwhelming cost of the initial capital expenditure and the operational

expenditure with respect to potential revenue.


WCR Collection from IGA supermarket

Central Processing System

When calculating the collection of WCR from local IGA Supermarkets for a central processing facility,

the operational expense of collecting the grind needs to be considered as well as the expense of the

production facility itself.

Analysing the production facility expenses is similar to what was calculated for the Nestle, Gympie

model. However, an inclusion of an additional cost can be seen in Table 37, to allow for renting an

industrial commercial property is required. Research has proven that to rent a suitable property in Balcatta,

Western Australia will cost approximately $25,000 per year. It is assumed that a similar cost for rent is to

be expected across other Australian cities.

TABLE 37 – OPERATIONAL EXPENDITURE OF PRODUCTION PROCESS FOR CENTRAL PROCESS SYSTEM

Operational Expenditure Yearly

Maintenance Contractor 40 Hours per Week $80,000

DME - Equipment Replacement (Approx.) $10,000

Unknown Cost Allowance (electricity, water) $20,000

Commercial/Industrial Rental Property $25,000

Insurance $5,000

Total Expenditure of Production Process $140,000

The transport costs associated for this collection model are similar to that of Table 33 for cost per

kilometre of travel and hourly cost of labour for transport. By applying these figures with respect to the

data already collected in Table 20 the following operational expense for collection can be considered as

per Table 38.


TABLE 38 – OPERATIONAL EXPENDITURE FOR COLLECTING WCR

Australian Cities Total Yearly Operational Expense for Collection of WCR

1 Week Model 2 Week Model 3 Week Model 1 Month Model

Sydney, NSW 2000 $827,286 $413,646 $275,762 $190,912

Melbourne, VIC 3000 $626,658 $313,332 $208,884 $144,614

Brisbane, QLD 4000 $473,830 $236,918 $157,946 $109,345

Perth, WA 6000 $315,434 $157,720 $105,143 $72,793

Adelaide, SA 5000 $192,957 $96,475 $64,319 $44,528

Canberra, ACT 2601 $84,756 $42,378 $28,252 $19,559

Hobart, TAS 7000 $47,186 $23,596 $15,731 $10,890

Darwin, NT 0800 $40,275 $20,137 $13,425 $9,295

Whether or not the collection of the WCR from supermarkets is either on a weekly model or monthly

model the total revenue from the sale of the biofuel and bio-mass pellets will be the same due to the

quantity of the available resource. The total maximum revenue for each city utilising the data from Table

17 and Table 19 results in the following maximum revenue as per Table 39.

TABLE 39 – TOTAL AVAILABLE REVENUE FOR CENTRAL PROCESSING SYSTEMS

Australian City Total Revenue

Sydney, NSW 2000 $689,257

Melbourne, VIC 3000 $585,766

Brisbane, QLD 4000 $337,627

Perth, WA 6000 $207,505

Adelaide, SA 5000 $174,046

Canberra, ACT 2601 $36,877

Hobart, TAS 7000 $93,359

Darwin, NT 0800 $32,050

If the operational expenditure from both the production process and the different collection period models

were combined and plotted against the total revenue for a given city, the gross profit for each collection

period can be analysed to find which periods of collection are not feasible as per Figure 19.


FIGURE 19 – GROSS PROFIT OVER A YEAR PERIOD COMPARING DIFFERENT COLLECTION PERIODS

It is evident as per Figure 19 that the operational cost will have a major influence typically for cities which

have a low quantity of WCR available.

It can be seen that the only viable options for the project with respect to the assumptions of the operational

expenditure would be Sydney, Melbourne and Brisbane. Also, for these cities to have a viable business

model, the collection period must be greater than every 2 weeks; it is approximately at this point when

the project starts to become feasible.

Ideally, the business model would suggest that collection of the resource should be carried out once per

month to maximise the gross profit of the business.

As per the Nestle model, the break-even value for the project is considered to be 3% compounding

because of inflation. However project feasibility for an investment decision of this model will increase to

an IRR value of 15% to allow for a much larger risk with respect to the reliance on expecting people and

café shops owners to deliver the WCR. A sensitivity analysis will further explore the effect on the reliance

of the collection of the resource.

-300000

-200000

-100000

0

100000

200000

300000

400000

0 1 2 3 4

Gro

ss P

roft

Ove

r a

Year

Collection Period Comparisson of (1 Week to 1 Month)

Gross Profit over a year period comparing different collection periods

Sydney, NSW 2000

Melbourne, VIC 3000

Brisbane, QLD 4000

Perth, WA 6000

Adelaide, SA 5000

Canberra, ACT 2601

Hobart, TAS 7000

Darwin, NT 0800


Figure 20 displays the likely return of the project with respect to capital expenditure for monthly

collection period. For the project to be considered feasible for Brisbane the capital expenditure must

remain below $193,262. Implementing the project in Melbourne will be feasible so long as the capital

investment required for the project is less than $854,993 with Sydney being more flexible allowing for a

maximum capital expenditure of $1,020,049.

FIGURE 20 – INTERNAL RATE OF RETURN VARYING CAPITAL EXPENDITURE FOR CENTRAL PROCESSING MODEL

This suggests that implementing this project model in Sydney, Melbourne and Brisbane will provide a

profit and a good return for potential investors. However, the project is still a risk because of the reliance

of the WCR collection and this will be further identified within the sensitivity analysis.

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

0 1,000,000 2,000,000 3,000,000 4,000,000

Inte

rnal

Rat

e o

f R

etu

rn (

IRR

) p

re-t

ax p

er y

ear

Capital Expenditure

Internal Rate of Return per Year pre-tax with Varying Capital Expenditure

Sydney

Melbourne

Brisbane

Breakeven

Feasibility


Multiple Processing System

To determine the cost and feasibility of the multiple process systems located in a given city, it should be

again noted that the philosophy is to utilise existing council recycling and waste collection yards to store

the process facility free of rent.

The operational expenditure in Table 40 has been calculated based on viable collection zones as per

information from Table 27. At this point in time the collection expenditure has not been included, and

will only be included if there is a significant difference between the maximum revenue and operational

expenditure of production process.

TABLE 40 – OPERATIONAL EXPENDITURE OF PRODUCTION PROCESS FOR MULTIPLE PROCESSING SYSTEM MODEL

Operational Expenditure of Production Process

Sydney Melbourne Brisbane Perth Adelaide Canberra

Maintenance Contractor (2 hr/zone per fortnight @ 80 p/h)

$45,760 $49,920 $8,320 $16,640 $12,480 $4,160

Equipment Replacement (Approx. $1500 per year per zone)

$16,500 $18,000 $3,000 $6,000 $4,500 $1,500

Unknown Cost Allowance ($2000 per year per zone)

$22,000 $24,000 $4,000 $8,000 $6,000 $2,000

Insurance ($2000 per year per zone) $22,000 $24,000 $4,000 $8,000 $6,000 $2,000

Total Expenditure of Production Process

$106,260 $115,920 $19,320 $38,640 $28,980 $9,660

The total feasible revenue for this model has been calculated utilising the zone data from Table 28.

TABLE 41 – RESOURCES AVAILABLE AND REVENUE FOR MULTIPLE PROCESSING SYSTEM IN AUSTRALIA

Revenue Biofuel (m3) Bio-Mass (tonne) Total Yearly Revenue

Sydney, NSW 2000 95 183 $128,881

Melbourne, VIC 3000 110 212 $149,908

Brisbane, QLD 4000 18 35 $24,433

Perth, WA 6000 36 69 $48,851

Adelaide, SA 5000 29 55 $39,040

Canberra, ACT 2601 10 19 $13,328

It is obvious at this early stage of the analysis that the multiple process systems will not be a feasible

business model in any city of Australia.


This is because even though the zone collection area has been optimised, the difference from just the

operational expenditure of the production process with respect to the maximum available revenue

currently neglecting the expense of the transportation and delivery of the WCR suggests that this would

not be a good investment even if the capital expenditure was 0 dollars. No further analysis of this model

has been undertaken.

WCR Collection from IGA Supermarket by Waste Management Partners

Assuming the collection of the grind resource as per Table 9 and Table 11 for cities within Australia, the

total revenue available for this model of logistics will be as per Table 39.

The assumption as stated within the Supply Chain and Logistics model was that only one process system

is to be installed per city utilising the Waste Management Partners to provide transportation of the WCR.

With the transportation of the WCR, a 30 percent share of the gross profit (in equivalent value of

biodiesel) will be offered to the Waste Management Partners.

With this in mind, operational expenditure for transportation can be neglected. The only operational

expenditure that is relevant for this model is the production operational expenditure ($122,080 per year)

which will be as per Nestle, Gympie model Table 31.

Canberra, Hobart and Darwin can initially be considered as not feasible as the operational expenditure is

greater than the total maximum potential revenue for these cities. Further cost and feasibility analysis has

been undertaken for the remaining cities.

As this model will have a full time maintenance contractor on site, the assumption is that similar to Nestle,

Gympie that the lifespan of the production facility itself will be 16 years with an increasing expense of

8% per year and increase of revenue of 3% per year.


TABLE 42 – GROSS PROFIT AVAILABLE UTILISING WASTE MANAGEMENT PARTNERS

Australian City Gross Profit first Year 30% Share to Waste Management Partners

Remaining Profit first Year from Production

Sydney, NSW 2000 $566,843 $170,052.90 $396,790.10

Melbourne, VIC 3000 $463,536 $139,060.94 $324,475.52

Brisbane, QLD 4000 $216,079 $64,823.82 $151,255.58

Perth, WA 6000 $83,036 $24,910.82 $58,125.24

Adelaide, SA 5000 $51,857 $15,556.95 $36,299.55

With the information from Table 42, the amount of equivalent biofuel that the Waste Management

Partners would receive in the first year of business assuming biofuel value is $1070 𝑝𝑒𝑟 𝑚3 would be as

indicated in Table 43.

TABLE 43 – FUEL SHARE AVAILABLE TO WASTE MANAGEMENT PARTNERS

Australian City Fuel Share Biodiesel (m3)

Sydney, NSW 2000 $170,052.90 158.93

Melbourne, VIC 3000 $139,060.94 129.96

Brisbane, QLD 4000 $64,823.82 60.58

Perth, WA 6000 $24,910.82 23.28

Adelaide, SA 5000 $15,556.95 14.54

The biodiesel has a calorific value of 37.88 MJ/kg [1] in comparison to 42.8 MJ/kg [26] for diesel.

Therefore the amount of diesel required to produce the same energy content neglecting other variables

would be a multiplication factor of 0.885046 of the biodiesel weight in kg.

A comparison can be undertaken between the two fuel sources to find the difference with respect to

Greenhouse Gas Emissions (GHG) as well as fuel cost savings for the Waste Management Partner’s with

the assumption they were already going to the location where the WCR was deposited (supermarkets).

The GHG emission for each fuel source is 742.29 𝑘𝑔 𝐺𝐻𝐺/𝑚3 for B100 biodiesel and 3391 𝑘𝑔 𝐺𝐻𝐺/

𝑚3 for standard diesel [27]. The price of diesel fuel in Perth, Western Australia on the 16th of May, 2016

is $1.14 per litre.


TABLE 44 – GREEN HOUSE GAS (GHG) EMISSION COMPARISSON OF BIODIESEL B100 AND EQUIVALENT DIESEL

Australian City Biodiesel (B100) Equivalent Diesel (density = 832kg/m3)

Volume (m3) GHG Emission (kg) Volume (m3) GHG Emission (kg) Diesel ($AUD)

Sydney, NSW 2000 158.9 118071 150.7 511105 $171,821

Melbourne, VIC 3000 129.9 96553 123.3 417957 $140,507

Brisbane, QLD 4000 60.6 45009 57.4 194832 $65,498

Perth, WA 6000 23.3 17296 22.1 74871 $25,170

Adelaide, SA 5000 14.5 10802 13.8 46757 $15,719

Table 44 suggests that the Waste Management partners will reduce there GHG emissions by 77% by

utilising the biofuel to displace diesel, making the model practicable from an environmental perspective.

Financially the Waste Management partners will be remunerated for collecting the WCR from

supermarkets they were already attending making the decision to form a joint venture with the

stakeholders of the DME process a good business decision.

The gross profit (70% value) per year over the lifespan of the project for each city has been displayed in

Figure 21. It is apparent that over the lifespan of the project; Perth and Adelaide will not be economically

viable.

FIGURE 21 – GROSS PROFIT AVAILABLE FOR WASTE MANAGEMENT PARTNER COLLECTION MODEL

-$200,000.00

-$100,000.00

$0.00

$100,000.00

$200,000.00

$300,000.00

$400,000.00

$500,000.00

$600,000.00

0 2 4 6 8 10 12 14 16 18

70

% o

f G

ross

Pro

fit

Year

70% of Gross Profit per Year over Lifespan of the Project

Sydney

Melbourne

Brisbane

Perth

Adelaide


Figure 22 displays the likely return of the project with respect to capital expenditure. This plot takes into

consideration only 70% of the gross profit with 30% of the gross profit awarded to the Waste Management

partners.

For the project to be considered feasible for Brisbane the capital expenditure must remain below

$242,269. Implementing the project in Melbourne will be practicable from a business perspective if the

capital investment can remain below $645,100 and viable for Sydney if capital expenditure is below

$813,271.

FIGURE 22 – INTERNAL RATE OF RETURN PER YEAR PRE-TAX FOR WASTE MANAGEMENT PARTNER COLLECTION MODEL

Figure 22 suggests that implementing this project model in Sydney, Melbourne and Brisbane will provide

a profit and a good return for potential investors, stakeholders and joint venture partners so long as the

capital investment for feasibility is below the identified constraints.

However, like the central processing facility model, the project is still a risk because of the reliance of the

WCR collection and this will be further identified within the sensitivity analysis.

-10.000%

-5.000%

0.000%

5.000%

10.000%

15.000%

20.000%

25.000%

30.000%

35.000%

0 1,000,000 2,000,000 3,000,000 4,000,000

Inte

rnal

Rat

e o

f R

etu

rn p

er

year

pre

-tax

Capital Expenditure

Internal Rate of Return per Year pre-tax with Varying Capital Expenditure

Sydney

Melbourne

Brisbane

Feasibility (15%)

Break-Even (3%)


Sensitivity Analysis Throughout the report there has been an emphasis on providing an accurate economic analysis on the

feasibility of implementing different logistic models for the production of coffee oil, biodiesel and bio-

mass pellets.

However, uncertainty of independent variables such as the collection of the WCR and also the amount of

hours Nestle operate per year in particular needs to be further analysed as these have been identified as

critical factors for the affected models.

The sensitivity analysis will change the two values from the two different collection models:

1. Collection Model 1 – Hours of Operation of Production at Nestle, Gympie. Currently the value

of 7,000 hours per year of operation have been considered. The sensitivity analysis will assess the

hours of operation of 3,000 and 5,000 hours of operation per year.

2. Collection Model 2 - Likelihood of collection of resource from café and coffee shops has been

identified throughout the report to be a multiplication factor of 0.75. The sensitivity analysis will

assess this multiplication factor at 0.3 and 0.5 to predict the alternative outcomes.

Sensitivity Analysis for Collection Model 1 For the sensitivity analysis, only the gross profit will be evaluated with respect to the change in hours of

operation.

1,111𝑘𝑔

ℎ𝑟∗ (1 − 0.55) ∗ 0.1973 ∗ 0.804 = 79.31

𝑘𝑔

ℎ𝑟𝑜𝑓 𝑏𝑖𝑜𝑓𝑢𝑒𝑙 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒

𝐹𝑜𝑟 3000 ℎ𝑜𝑢𝑟𝑠 𝑜𝑓 𝑜𝑝𝑒𝑟𝑎𝑖𝑜𝑛 𝑡𝑜𝑡𝑎𝑙 𝑏𝑖𝑜𝑓𝑢𝑒𝑙 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 = 79.31 ∗ 3000

= 237,930 𝑘𝑔 𝑜𝑣𝑒𝑟 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟


𝐹𝑜𝑟 5000 ℎ𝑜𝑢𝑟𝑠 𝑜𝑓 𝑜𝑝𝑒𝑟𝑎𝑖𝑜𝑛 𝑡𝑜𝑡𝑎𝑙 𝑏𝑖𝑜𝑓𝑢𝑒𝑙 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 = 79.31 ∗ 5000

= 396,550 𝑘𝑔 𝑜𝑣𝑒𝑟 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟

TABLE 45 – REVENUE WITH RESPECT TO DIFFERING OPERATING HOURS AT NESTLE PRODUCTION FACILITY

Operating Time over a year

Revenue for Year 1

$600 / tonne $800 / tonne $1000 / tonne

3000 Hours $142,758 $190,344 $237,930

5000 Hours $237,930 $317,240 $396,550

With the operational expenditure remaining constant at $122,080 and the assumption of 8% growth in

expenditure each year for the lifespan of the project and 3% increase in revenue the following results have

been observed as per Table 46.

TABLE 46 – GROSS PROFIT WITH RESPECT TO DIFFERING OPERATING HOURS AT NESTLE PRODUCTION FACILITY

Operating Time Summation of Gross Profit over 16 Year Lifespan of Project

$600 / tonne $800 / tonne $1000 / tonne

3000 Hours -$1,013,587 $21,960 $1,057,507

5000 Hours $1,057,507 $2,783,419 $4,509,330

It is evident that just by summing the gross profit over the 16 year period that the $600 per tonne sale

price for 3000 operating hours per year will not be feasible. It is also apparent that the gross profit for the

3000 hour model per year with a sale price of $1000 per tonne is identical as $600 per tonne at operating

5000 hours per year.

Currently throughout the report, the quantitative measurement to deem feasibility of a logistics model has

been to assess the practicality of interest return from the gross profit over the lifespan of the project. For

the collection based model, because of the high risk, a feasibility limit of 15% for investment has been

allocated. For the Nestle model however, because the risk is low a return for investors of 9%.

It is apparent that for the models to be feasible, $266,354 would be the maximum capital expenditure to

ensure a 9% return with respect to gross profit for sale price $1,000 per tonne at 3000 hours of operation

a year and also $600 per tonne at 5000 hours of operation per year.


Figure 23 indicates that for $800 per tonne for 5000 hours a year, the maximum capital investment

required to ensure a minimum of 9% return from gross profit was $701,059. For 5000 hours of operation

over a year at $1000/tonne results in a maximum investment $1,135,764.

FIGURE 23 – IRR SENSITIVITY ANALYSIS COMPARING OPERATING HOURS OF NESTLE

This sensitivity analysis suggests that the only way the model could be deemed viable from an economic

perspective at 3000 hours per year in terms of the feasibility constraints is if a guarantee sale of $1000

dollars a tonne was possible. As it is deemed too large of a risk, if production was only operated at 3000

hours over the year the project would be dismissed.

However, if the production at the plant was run at 5000 hours over the year then the project would be

deemed feasible so long as the capital expenditure constraints were met.

Sensitivity Analysis for Collection Model 2

The collection of the resource is very sensitive to a change in the multiplication factor of café and coffee

shop grind collection.

-30

-20

-10

0

10

20

30

0 500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000

Inte

rnal

Rat

e o

f R

etu

rn (

IRR

) p

re-t

ax p

er Y

ear

Capital Expenditure

Internal Rate of Return pre-tax per Year with Varying Capital Expenditure

$800/tonne (3000 hr)

$1000/tonne (3000 hr)

$600/tonne (5000 hr)

$800/tonne (5000 hr)

$1000/tonne (5000 hr)

Feasibility

Break-even


An overall analysis is undertaken by manipulating the multiplication factor from Table 10 from the initial

value of 0.75 to 0.5, and then again to 0.3 to assess the effect of this factor on the overall project feasibility.

As the in-store processing model and multiple processing system scenarios have been neglected, there

has been an emphasis on solely the central processing model for Sydney, Melbourne and Brisbane.

Currently with 0.75 multiplication factor the results are as per Figure 20.

It is apparent by the sum of the total gross profit over the lifespan of the project that reducing the

multiplication factor to 0.5 and 0.3, the model would no longer be financially viable in Brisbane as per

Table 47.

TABLE 47 – GROSS PROFIT WITH RESPECT TO SENSITIVITY ANALYSIS OF MULTIPLICATION FACTOR FOR MODEL 2

Gross Profit Multiplication Factor

0.75 0.5 0.3

Sydney $4,126,677 $2,075,873 $435,229

Melbourne $3,458,941 $1,776,214 $430,033

Brisbane $781,890 -$240,480 -$1,058,376

By assessing the interest return per year from the initial capital expenditure as per Figure 24, it can be

seen that both Sydney and Melbourne (superimposed on the plot) would only meet the feasibility return

requirements so long as the initial capital investment was approximately $100,000 or less when the

multiplication factor was 0.3.

In comparison, the initial 0.75 factor allowed for an approximate initial capital investment of $1,000,000

for a return of 15% per year whereas the 0.5 factor only allowed for a maximum of approximately

$440,000.


FIGURE 24 – IRR SENSITIVITY ANALYSIS COMPARING MULTIPLICATION FACTOR FOR LIKELIHOOD COLLECTION OF WCR

Although the 0.3 and 0.5 factors cannot be ruled unfeasible for the project for Sydney or Melbourne,

consideration must be taken into account with respect to initial capital expenditure.

-30.00%

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

0 500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000

Inte

rnal

Rat

e o

f R

etu

rn p

er Y

ear

pre

-tax

Capital Expenditure

Internal Rate of Return per Year with Varying Capital Expenditure

Sydney (0.75)

Melbourne (0.75)

Sydney (0.5)

Melbourne (0.5)

Sydney (0.3)

Melbourne (0.3)

Feasibility

Breakeven


Conclusion

By evaluating the outcomes of the paper - explicitly the cost and sensitivity analysis, it is apparent that

implementing a production process system to produce biodiesel and biomass pellets in Australian cities other

than Sydney, Melbourne and Brisbane would not be a practicable investment for stakeholders.

It seems that in-store processing of the biofuel from the WCR product can be dismissed in all cities due to the

operational expenditure and capital expenditure exceeding the potential revenue of the products by a

significant amount.

The collection of the WCR in Sydney, Melbourne and Brisbane for a central processing facility would specify

that pending on capital investment required to build the infrastructure of the process system and with a

collection period of one month, an Internal Rate of Return (IRR) of greater than 15% pre-tax per year is viable.

However, reflection from sensitivity analysis results indicate that with the reliance on the multiplication factor

of likelihood of collection of the WCR, there is potential of significant risks.

An alternative viable option for a feasible economic business model would be to partner up with Waste

Management Partners to provide the transport of the grind to a central processing location which would

mitigate some risks. This would provide not only an economically realizable model with an IRR greater than

15% to the stakeholders (pending capital expenditure), but also an environmental incentive with the reduction

of Greenhouse Gas Emissions by utilising the alternative fuel for transport.

The production and implementation of a process system at the explored Nestle model discussed in the paper

using some assumptions indicates that this model would provide adequate energy solutions and a very healthy

IRR for potential investors pending capital investment required. Due to WCR constantly available, the

potential for risk is extremely low signifying a great investment opportunity.

The business model as a whole suffers predominantly due to the operational expenditure of collecting the

dispersed resource. If there is a way that the operational expenditure for collection could be dramatically

reduced, then there is no reason why the business model cannot attain an IRR of greater than 15% in all cities

within Australia, pending capital expenditure.


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Appendix 1

Attached At the back of Thesis Paper.

A B C D E F G

Suburb of IGA Address of IGAInteractive Map Region as per

Zee Maps (10 Regions)

Interactive Map Region as

per Zee Maps (4 Regions)

Quantity of IGA in

Suburb

Population of Suburb of

IGA

Restaurants/Café inclusive of

Surrounding Suburbs of IGA

Applecross 916 Canning Highway, Applecross 6 3 2 6579 23

Ashfield 3A Colstoun Rd, Ashfield WA 6054 8 2 1 1256 6

Atwell 129 Lydon Blvd, Atwell WA 6164 7 3 1 8646 7

Balcatta 1/207 Jones St Balcatta 4 1 1 9991 8

Balga 16/108 Princess Rd, Balga WA 6061 3 1 1 10701 6

Ballajura Illawarra Ballajura 3 1 2 18952 16

Beaconsfield Lefroy Rd, Shop 11 Beaconsfield Plaza Shopping Centre, Beaconsfield WA 6162 6 3 1 4649 4

Beaumaris Connolly Shopping Centre, 6/1 Glenelg Pl, Connolly WA 6027 1 1 1 8108 0

Beckenham 190 William St, Beckenham WA 6107 7 2 1 6627 12

Bedford 174 Grand Promenade, Bedford WA 6052 4 1 1 4575 4

Beechboro 161 Amazon Dr Beechboro 3 1 1 13997 3

Belmont Cnr Wright Street & Belmont Ave, Belmont Village Shopping Centre, Perth WA 6104 7 2 3 40083 12

Bentley 9 Hill View Pl, Bentley WA 6102 7 2 1 7625 15

Bertram Hero Cres, Bertram WA 6167 10 4 1 5102 0

Bicton 378 Canning Hwy, Bicton, WA 6157 6 3 1 6018 6

Byford 867 S Western Hwy, Byford WA 6122 9 4 1 7034 1

Bullcreek Shop 8 Parry Village Parry Ave, Bull Creek WA 6149 6 3 1 7541 4

Byford 867 S Western Hwy, Byford WA 6122 9 4 1 3335 1

CanningVale Ranford Rd, Canning Vale WA 6155 7 2 2 30666 21

Carine 10/473 Beach Rd, Duncraig WA 6023 2 1 1 6479 3

Carlisle 232 Orrong Road, Carlisle WA 6101 7 2 1 5157 4

City Beach 3 Kilpa Ct, City Beach WA 6015 2 1 2 6354 3

Como 25 Preston St, Como WA 6152 7 3 2 12423 16

Coolbellup 19 Cordelia Ave, Coolbellup WA 6163 6 3 1 4917 0

Cooloongup Ennis Ave, Cooloongup WA 6168 10 4 1 6822 1

Cottesloe 1/36 Eric St, Cottesloe WA 6011 5 3 1 6641 21

Craigie Craigie Plaza Shopping Centre/15 Perilya Rd, Craigie WA 6025 1 1 1 5602 3

Crawley 33/88 Broadway, Crawley WA 6009 5 3 1 3108 3

Dalkeith 81 Waratah Ave, Dalkeith WA 6009 5 3 1 4258 2

Dianella Dianella Plaza, 66/366 Grand Promenade, Dianella WA 6059 4 1 1 22521 8

Doubleview 187 Scarborough Beach Rd, Doubleview WA 6018 2 1 1 7576 4

Duncraig Glengarry Shopping Centre, 59 Arnisdale Rd, Duncraig WA 6023 2 1 3 15026 4

East Vic Park 860 Albany Hwy, East Victoria Park WA 6101 7 3 1 7795 29

East Fremantle 143 Canning Hwy, East Fremantle, WA 6158 6 3 1 6697 5

Edgewater Edgewater Drive Shop 1 Edgewater Shopping Centre, Edgewater WA 6027 1 1 1 4531 0

Ellenbrook Woodlake Village Shopping Centre, Sunray Cir, Ellenbrook WA 6069 3 1 1 36293 9

Glendalough 1/4-8 Jon Sanders Dr, Glendalough WA 6016 2 1 1 2203 0

Gosnells 2251 Albany Hwy, Gosnells WA 6110 7 2 1 21000 9

Gwelup N Beach Rd, Gwelup WA 6018 2 1 1 3924 1

Halls Head 12/4 Old Coast Rd, Halls Head WA 6210 10 4 1 6408 1

Hamilton Hill 2 Simms Road, Hamilton Hill WA 6163 6 3 3 9855 6

Heathridge 89 Caridean Street, Heathridge WA 6027 1 1 1 6882 0

Helena Valley 1/1 Torquata Blvd, Helena Valley WA 6056 8 2 1 3016 2

High Wycombe 37 Newburn Rd, High Wycombe WA 6057 8 2 1 11781 2

Hillarys 470 Whitfords Ave, Hillarys, WA, 6025 1 1 1 10680 26

Hilton 285 South Street, Hilton WA 6163 6 3 1 5980 2

Huntingdale Pipit Cl, Huntingdale WA 6110 7 2 1 8543 1

Inglewood 96 Tenth Ave, Inglewood, WA 6052 4 1 1 5503 14

Innaloo 1/27 Morris Pl, Innaloo WA 6018 2 1 1 7648 14

Girrawheen 60 Marangaroo Dr, Girrawheen WA 6064 3 1 1 8334 5

Joondalup Lakeside Shopping Centre, 420 Joondalup Dr, Joondalup WA 6027 1 1 1 8420 28

Kalamunda 12 Canning Rd, Kalamunda WA 6076 8 2 1 6636 9

Kardiniya Cnr South St And Gilbertson Rd, Kardinya, WA 616 6 3 1 8874 2

Kelmscott 2838 Albany Hwy, Kelmscott WA 6111 7 2 2 10019 7

Kingsley 6/100 Kingsley Dr, Kingsley WA 6026 1 1 1 13218 4

Kinross Connolly Dr, Kinross WA 6028 1 1 1 7232 2

Koondoola 1 Koondoola Ave, Koondoola WA 6064 3 1 1 3897 0

Landsdale 225 Kingsway, Darch WA 6065 3 1 1 7480 1

Leederville 313 Vincent St, Leederville WA 6007 4 1 1 2741 24

Leeming Cnr Farrington and Findlay Road, Leeming WA 6149 6 3 1 12977 2

Lesmurdie 241 Lesmurdie Rd, Lesmurdie WA 6076 8 2 2 7956 1

Lynwood 6-12 Lynwood Avenue, Shop 1 Lynwood Village, Lynwood WA 6147 7 2 1 36485 1

Mandurah Mandurah Terrace, Mandurah WA 6210 10 4 2 80683 31

Marmion Marmion Village Shopping Centre, Sheppard Way, Marmion WA 6020 2 1 1 2163 3

Maylands 1/238 Guildford Rd, Maylands WA 6051 4 1 1 12363 19

Merriwa Baltimore Parade & Jenolan Way, Merriwa WA 6030 1 4 1 5571 1

Miami 619-627 Old Coast Road, Falcon WA 6210 10 4 1 4666 0

Midland 295 Great Eastern Hwy, Midland WA 6056 8 2 1 16572 34

Mirrabooka 6/73 Honeywell Blvd, Mirrabooka WA 6061 3 1 2 7933 4

Morley 11/238 Walter Rd W, Morley WA 6062 4 1 2 20301 28

Mosman Park 1/130 Wellington St, Mosman Park WA 6012 5 3 2 8598 10

Mt Hawthorn 173 Scarborough Beach Rd, Mount Hawthorn WA 6016 4 1 1 7357 12

Mt Helena McVicar Pl, Mount Helena WA 6082 8 4 1 2700 0

Mt Lawley 629 Beaufort St, Mount Lawley WA 6050 4 1 2 10703 20

Mt Pleasant 80 Cranford Ave, Mt Pleasant WA 6153 6 3 1 6423 3

Mullalloo Shop 1, Mullaloo Shopping Centre, Koorana Road, Mullaloo WA 6027 1 1 1 5869 6

Mundijong Patterson Street, Lot 20, Mundijong WA 6123 9 4 1 1429 0

Myaree 67 North Lake Road, Myaree WA 6154 6 3 1 1800 5

Nedlands Stirling Hwy, Nedlands WA 6009 5 3 2 10833 24

Nollamara 63 Nollamara Ave, Nollamara WA 6061 4 1 1 9888 0

Northbridge 150 Newcastle St Northbridge WA 6003 4 1 1 1005 48

Lynwood 6-12 Lynwood Avenue, Shop 1 Lynwood Village, Lynwood WA 6147 7 2 1 36458 1

O'connor 10/7 O'Connor Rd, Stratton WA 6056 8 2 1 318 0

Ocean Reef Constellation Dr, Ocean Reef WA 6027 1 1 1 8108 3

Osborne Park 212 Main St, Osborne Park WA 6017 4 1 2 4047 16

Padbury Shop 11 Padbury Shopng Ctr Warburton Ave, Padbury WA 6025 1 1 1 8113 2

Perth / East Perth / West Perth 81 Royal St, East Perth WA 6004 4 1 1 20762 187

Port Kennedy 49 Chelmsford Avenue, Port Kennedy WA 6172 10 4 1 12816 5

Queens Park Supa IGA on 193 Sevenoaks St, Queens Park, WA 6107 7 2 1 3529 1

Quinns Rocks Quinns Rock Shopping Centre, 7 Tapping Way & Quinss Road, Quinns Rocks WA 6030 1 1 3 8902 4

Riverton High Rd, Riverton, Western Australia 6155 7 2 1 4666 3

Rivervale 126 Kooyong Rd, Rivervale, WA126 Kooyong Rd, Rivervale WA 6103 7 2 1 8402 4

Roleystone 21 Jarrah Road, Roleystone WA 6111 7 2 2 5975 2

Rossmoyne Cnr Central Road & Third Avenue, Rossmoyne WA 6148 6 3 1 3062 3

Safety Bay Malibu Shopping Ctr Malibu Rd, Safety Bay WA 6169 10 4 3 7305 4

Shenton Park 159 Onslow Rd, Shenton Park WA 6008 5 3 1 4350 5

South Fremantle 195 Hampton Rd, South Fremantle WA 6162 6 3 1 2900 13

South Perth 19/4 Harpers Terrace, South Perth WA 6151 7 3 2 10763 28

South Lake Cnr South Lake Drive & Berrigan Rd, South Lake WA 6164 6 3 1 5659 3

Spearwood 432 Rockingham Rd, Spearwood WA 6163 6 3 1 8940 4

Swan View 309 Morrison Rd, Swan View WA 6056 8 2 1 8080 5

Thornlie 12-14/200 Spencer Rd, Thornlie WA 6108 7 2 1 22972 10

Stirling Sanderling St, Stirling WA 6021 4 1 1 5752 2

Waikki Charthouse Shopng Ctr Charthouse Rd, Waikiki WA 6169 10 4 1 11982 3

Wanneroo Cnr Conlan Drive & WannerooRd, Wanneroo Shopping Centre, Wanneroo WA 6065 1 1 2 11901 6

Wembley Downs Crestwood at the Downs, Bournemouth Cres, Wembley Downs WA 6019 2 1 1 5881 12

Westfield Ypres Rd, Camillo WA 6111 7 2 1 2070 0

Westminster 31 Canara Rd, Westminster WA 6061 4 1 1 5175 3

Willagee 70 Archibald St, Willagee WA 6156 6 3 1 4356 1

Willetton 1/61 Apsley Rd, Willetton WA 6155 6 3 1 17243 10

Winthrop 109/141 Somerville Blvd, Winthrop WA 6150 6 3 2 6430 2

Woodvale Woodvale Shopng Ctr Trappers Dr, Woodvale WA 6026 1 1 1 9202 2

Yangebup 31 Moorhen Dr, Yangebup WA 6164 6 3 1 7125 1

141 1101478 1021

663.65 65% Coffee & Café as per Yellow Pages

Appendix 1

supply chain collection model development and feasibility … · 2016. 10. 19. · supply chain...

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