1. SID: 450595389
STUDENT NAME: LEONARD KAM ONG
SID: 450595389
ITLS5200 – QUANTITATIVE LOGISTICS AND TRANSPORT
DATE: 09/10/2015
TIME SERIES ANALYSIS
The Institute of Transport and Logistics Studies
The University of Sydney

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ii
EXECUTIVE SUMMARY
This report discusses about the importance of applying Time Series Analysis within the Maritime Logistics and
Supply Chain industry. Firstly, a brief introduction to Time Series Analysis is mentioned then followed by an
overall explanation of the topic by discussing the theory, methodologies and measurements used. The report lists
out various scenarios and case studies from the literature such as the capacity constraints at the Sydney Ports
and the Port of Busan. The literature also mentions how time series analysis is frequently applied throughout the
Maritime Logistics and Supply Chain industry from companies including freight forwarders, stevedores, maritime
transportation and port authorities.
The report also assesses, analyses and discusses the data set from the Bureau of Infrastructure, Transport and
Regional Economics (BITRE) based on the Total Container Throughput within the Sydney Ports Terminals. The
container throughput is a common indicator used to assess the efficiency of a container terminal’s productivity
and to be aware of the total volume of containers that have been processed through the container terminals.
Microsoft Excel 2013 has been used to create the forecasts for one time period ahead based on the BITRE data
set. Graphical representations such as charts and further explanations including the results of the forecast are
also provided.
Ethical issues have been identified with the application of Time Series Analysis in regards to the redevelopment
within the Port of Busan. The Busan Port Authority along with the Korean government demonstrated how they
have dealt with the ethical issues they were facing against the opposing parties from the city of Busan. Lastly, the
report concludes with the findings and research in regards to Time Series Analysis.

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Table of Contents
EXECUTIVE SUMMARY ii
1 INTRODUCTION .......................................................................................................................... 1
2 TIME SERIES ANALYSIS ............................................................................................................. 1
3 TIME SERIES ANALYSIS WITHIN MARITIME LOGISTICS AND SUPPLY CHAINS ......................... 2
4 TIME SERIES ANALYSIS FOR SYDNEY PORTS .......................................................................... 3
4.1 SYDNEY PORTS DATA SET................................................................................................... 3
4.2 DEMAND FORECASTING METHOD ....................................................................................... 4
4.3 THE SELECTED MODELLING METHOD................................................................................. 4
5 ETHICAL ISSUES WITHIN THE PORT OF BUSAN ........................................................................ 5
6 CONCLUSION ............................................................................................................................. 6
APPENDIX A .................................................................................................................................... 7
REFERENCES.................................................................................................................................10

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1 INTRODUCTION
Time series analysis applies methods and techniques for forecasting actual demand; which are often used
throughout the Maritime Logistics and Supply Chain (MLASC) industry. These methods can also be applied to
model demand in the MLASC industry such as the demand for global transportation or the volume of incoming
and outgoing container cargo from a container terminal. The report highlights the benefits, challenges and issues
of applying time series analysis for companies within the MLASC industry; while also presenting an example data
set on how to interpret and model actual demand. A combination of basic to more advanced methods are used to
model the example data set provided.
This report is divided into six sections. A brief introduction to the time series analysis techniques is given in
section 2, followed by a discussion on how the time series analysis is used in the MLASC industry by considering
examples from literature in section 3. Section 4 is further divided into three sub-sections, where section 4.1
describes the data set under study, section 4.2 explains the time series analysis techniques used for demand
forecasting along with the results and section 4.3 explains the best forecast and the methods used for choosing
that forecast. In section 5, an example of the ethical issues involved in the time series analysis is discussed,
which is followed by the concluding remarks in the last section.
2 TIME SERIES ANALYSIS
Time series analysis utilises majority of the basic techniques in statistics and regression modelling as a
combination to initiate and model the best possible forecast. The data sets are generally observed over some
specific time periods (on a yearly, quarterly, monthly or weekly basis) for any number of variables (Clifton 2015a).
These data sets are collected over time to help understand the behaviour of different variables in terms of how
they change through different time periods. These patterns of time series data may consist of cyclical patterns,
trends, seasonality and other patterns within the data set (Clifton 2015a, Piennar and Vogt 2012). It is feasible for
the industries to utilise these patterns in the time series data to make forecasts for future purposes. Thus,
industries can rely on using time series modelling techniques for their own awareness and to better organise for
the preparation of any unforeseen circumstances in the future (Piennar and Vogt 2012).
There are several methods to execute or model the time series data for the purpose of demand forecasting. One
of the basic techniques is called the Simple Moving Average (SMA). SMA allows the user to select their own
range of time periods to prepare a forecast. The method is based on calculating the averages of the observed
time periods in any given time series data (Clifton 2015a, Piennar and Vogt 2012). Clifton (2015a) states the most
common range of time periods for calculating the averages can be in between three to seven time periods.
Another technique used for demand forecasting is the Weighted Moving Average (WMA). WMA determines how
important a certain time period is by assigning a percentage level of relative importance to it (Clifton 2015a). The
higher the weight, the more important that specific time period is. Another alternative is Exponential Smoothing
(ES) method for demand forecasting which updates the forecast for the current time period on the basis of
previous time periods (Clifton 2015b). These methods have a limitation as they do not incorporate the trends or
seasonality in the forecast. There are several other advanced techniques which can be applied in order to
incorporate trends or seasonal patterns such as Multiplicative Seasonal Method (MSM), Holt’s Method (HM) and
Holt-Winter’s method (HWM). Selecting the most suitable technique depends upon the type of time-series data
under study (Clifton 2015b, Piennar and Vogt 2012).
In order to compare the forecasts obtained from the above mentioned methods, certain measurements of error
are used. The measures of error compare and determine the accuracy of forecasts. These include the Mean
Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percent Error (MAPE). MAD provides a
basic measure on far the forecast errors of the data set deviates from the mean of the absolute values. MSE
provides a basic measure of error on how large or small the size of the square for each of the forecast errors to
the mean of the overall data set. MAPE provides an estimation of accuracy in percentages based on the average
of errors in the data set (Piennar and Vogt 2012). A lower MSE, MAD and MAPE is normally the goal to achieve
for the best estimation to model the forecast (Clifton 2015a, Piennar and Vogt 2012).
In short, time series analysis utilises data sets that are collected over some specific time periods for any number
of variables. Basic techniques for time series analysis include SMA, WMA and ES while the advanced techniques
are MSM, HM and HWM. To model the best forecasts, choosing the most suitable technique for the time series
data is a requirement.

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3 TIME SERIES ANALYSIS WITHIN MARITIME LOGISTICS AND SUPPLY CHAINS
The modelling tools and techniques from time series analysis have been frequently applied within the MLASC
industry. The four ranges for selecting a suitable time period to forecast within the MLASC industry are from a
momentary, short-term, medium-term and long-term period (Stopford 2009). Lee and Meng (2014b) highlight that
basic quantitative and descriptive methods can also be applied for any forecasting analysis for future demand
within the MLASC industry.
The predictions that are forecasted may include various scenarios such as the global demand for container
transportation, assessing cargo-handling productivity, performance measurements, assessing the effects on
transportation costs and modelling unexpected situations with an alternative scenario (Lee and Meng 2014b,
Wang, Cullinane and Song 2005). Stopford (2009) mentions several types of companies within the MLASC
industry which rely on time series analysis to prepare and plan for better decision making outcomes. These
companies include freight forwarders, shipbuilders, governments, cargo holders and port authorities (Stopford
2009). Companies such as port authorities are required to predict the future of port development requirements for
traffic control or congestion within the ports, volume of incoming and outgoing container cargo, capacity levels at
the container terminals and whether to construct new facilities within the port (Song and Panayides 2012,
Stopford 2009). Stopford (2009) mentions that one of the key challenges and difficulties in preparing a forecast
within the MLASC industry is that the future behaviours are not always predictable.
Song and Panayides (2012) states the increase in container growth and trade for port authorities such as Sydney
Ports have demonstrated capacity constraints. The container park within Sydney Ports has either reached or
exceeded its capacity to store empty containers in the past. Song and Panayides (2012) have also stated that in
between 2009-2010 financial year, Sydney Ports had exceeded their container throughput by up to nineteen
million TEU (Twenty-Foot Equivalent). Due to the capacity constraints, it is vital for Sydney Ports to plan new
developments at a strategic level with the assistance of modelling techniques used in time series analysis (Song
and Panayides 2012).
There are also other countries such as Korea who are also dealing with capacity constraints at one of their ports.
The Port of Busan in Korea have reported dealing with container congestion and traffic issues within their
container terminals (Lee and Meng 2014a, Song, Cullinane and Roe 2011). Song and Cullinane (2007) have
highlighted that the total amount of container throughput handled within the Korean ports as of 2001 was currently
at ten million TEU of container cargo. Song and Cullinane (2007) have also stated that a forecast of the container
traffic will rise at an estimate of twenty million TEU of container cargo by 2006 and to continue rising to thirty
million TEU by 2011. The new information based on the forecast provides awareness of the struggling port which
has alerted the Korean government to prepare and plan the redevelopment and expansion at the Port of Busan
(Song and Cullinane 2007, Song, Cullinane and Roe 2011).
The above two examples exhibits the demonstration of the forecasting techniques based on historical data to
analysis a time series data. This provides visibility on how to rectify issues such as container traffic within the Port
of Busan to make decisive plans and action for port redevelopment.
Time series analysis can also be applied to forecast any freight rates within the MLASC industry. Freight rates are
often used to allow organisations to make future predictions on how to select a suitable shipping company,
transportation methods or fleet requirement for better value and cost savings (Stopford (2009).
The demand for global container transport can also be modelled and forecasted with advanced techniques based
on data from region to region or country to country in a global scale (Lee and Meng 2014b). An example of
forecasting the transportation of containers in a global scale includes how the container flows or travels from
region to region or country to country (Lee and Meng 2014b). Lee and Meng (2014b) state that predictions can
also be forecasted based on environmental impacts, future transportation demands, return on investment within
the networking of fleets and infrastructure, trading routes and the maritime marketplace itself. Modelling a time
series analysis for global container transport is always a challenge due to the uncertainty of how containers flow
and travel in a global scale. However, it is still highly used to provide a feasible outcome within the decision
making process (Lee and Meng 2014b).

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To further optimise the modelling technique for a time series analysis, Stopford (2009) suggests to identify the
nature of variables for the appropriate technique to use. Estimating the future cost of freight rates is often difficult
to predict as it may include other behavioural variables or traits such as the global population growth, current
global economy including any business crises and cycles which may affect the result of the forecast (Lee and
Meng 2014b, Stopford 2009). The behavioural variables require extra care when applying them to any forecast.
Thus, it is also still possible to prepare and forecast ahead regardless (Stopford 2009). Behavioural variables that
are highlighted are often in relation to demand for the growth of ships, the balance of supply and demand and
predicting the merchant fleet which includes the volumes of deliveries (Stopford 2009).
Forecasting the unforeseen future demand allows organisations to utilise time series analysis. (Lee and Meng
2014b). Sydney Ports and Port of Busan have both demonstrated capacity constraints and container congestion
issues. Time series analysis has assisted both Sydney Ports and Port of Busan in preparing a forecast to plan
and arrange the redevelopment of new facilities as a countermeasure (Song and Cullinane 2007, Song and
Panayides 2012). Time series analysis can also be applied in other areas of the MLASC industry such as freight
rates and demand for global container transportation. However, uncertainty always poses as a greater challenge
when preparing a forecast (Lee and Meng 2014b, Stopford 2009).
4 TIME SERIES ANALYSIS FOR SYDNEY PORTS
4.1 SYDNEY PORTS DATASET
The data set is based on the total containers throughput within the Sydney Ports terminals from the (2015) BITRE
(Bureau of Infrastructure, Transport and Regional Economics). The total containers variable is defined as the total
number of containers (The count is not standardised to account for the different container sizes) that have been
lifted on and off the vessels or processed at the port (BITRE 2015). The total containers throughput for the
Sydney Ports terminals assesses the time period from June 2006 to Jun 2014 on a quarterly basis. The graphical
representation of the actual time series data is displayed in Figure 1 (See below in Section 4.1). The data set
demonstrates a higher peak within the December period while the lowest peak occurs during the March period.
Therefore, seasonality is present within the data set. Over the years, the total containers throughput for the
Sydney Ports terminals are increasing over time (BITRE 2015).
Figure 1: Total Containers Throughput for Sydney Ports Terminals (BITRE 2015)
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TotalContainers
Total Containers Throughput for Sydney Ports Terminals

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4.2 DEMAND FORECASTING METHOD
The BITRE (2015) data set has been modelled by using the nine methods including the basic methods SMA,
WMA and ES. Since our data set has signs of trends and seasonality, the data set has also been modelled by
more advanced techniques such as Regression Modelling (RM), MSM, HM and HWM (Clifton 2015b, Piennar and
Vogt 2012). As mentioned earlier, these advanced techniques help us forecast the data for future time periods by
incorporating the trends and seasonality in the existing data under observation. Table 1 lists the nine methods
used to model the time series data along with a brief description of how each method is executed. It also shows
the forecasts for the quarter of September 2014 results.
Table 1: Results for the time series techniques used for the Total Containers Throughput for Sydney Ports
Terminals from BITRE (2015)
(Clifton 2015a, Clifton 2015b, Piennar and Vogt 2012)
4.3 THE SELECTED MODELLING TECHNIQUE
Table 2 represents the findings of forecast for the quarter of September 2014 using the nine methods mentioned
previously. It also gives the calculations for MAD, MSE and MAPE for each of the nine forecasts (Clifton 2015a,
Clifton 2015b, Piennar and Vogt 2012).
The modelling technique that demonstrated poorly out of all the nine methods in the BITRE (2015) data set is the
SMA (3) [MAD = 21951.689, MSE = 763119100.593 and MAPE = 0.066]. The demand forecast for the total
container throughput is also graphically demonstrated in Figure 1 (See Appendix A).
The two methods of ES (0.5) and ES (Solver) also did not perform as well in relation to the other methods based
on the values of MAD, MSE and MAPE (See Table 2). Figure 2 (See Appendix A) illustrates the demand forecast
for ES (0.5) and ES (Solver) graphically and it can be seen that the forecasts does not fit the actual demand.
From Table 2, based on the values of MAD, MSE and MAPE; WMA (3) and RM (Unadjusted Forecast) both
achieved a slightly improved result compared to SMA (3), ES (0.5) and ES (Solver) methods. The demand
forecasts by using WMA (3) is visually represented in Figure 1 (Appendix A) and it was evident that it does not
match the actual demand. RM (Unadjusted Forecast) visually indicates in Figure 3 (Appendix A) a basic trend line
that increased positively over time. Therefore, in regards to the measurements of error as well as the graphical
displays for SMA (3), WMA (3), ES (0.5), ES (Solver) and RM (Unadjusted Forecast) do not provide appropriate
forecasts for the quarter of September 2014 for the BITRE (2015) data set.
Since our data has trends and seasonality, more advanced methods such as HM, HWM (Additive), HWM
(Multiplicative) and MSM (Adjusted Forecast) were used (Clifton 2015b, Piennar and Vogt 2012). HM achieved
slightly similar results for MAD, MSE and MAPE as those for ES (0.5) and ES (Solver) (See Table 2). HM is
graphically depicted in Figure 4 (Appendix A) and does not fit the actual demand. However, HWM (Additive) and
HWM (Multiplicative) achieved a lower value of MAD, MSE and MAPE as compared to SMA (3), WMA (3), RM
(Unadjusted Forecast), ES (0.5), ES (Solver) and HM. Both HWM (Additive) and HWM (Multiplicative) are
graphically depicted in Figure 5 (Appendix A). It can be seen from Figure 5 that both HWM (Additive) and HWM
(Multiplicative) are exaggerating the demand forecasts after the quarter of December 2008 compared to the
original data’s peak points.
Time Series Techniques Description September 2014 Quarter (Forecast)
SMA Calculated for three time periods SMA (3) 364945.333
WMA Calculated for three time periods WMA (3) 373337.325
ES Calculated for α = 0.50 358523.534
ES (Solver) Calculated from Microsoft Excel 2013 Solver. α = 0.389 358745.275
Regression y = 2743.828x - 280126.097 280126.097
MSM Adjusted forecast for seasonal index 286954.661
Holt's Method Calculated for α = 0.200, β = 0.296 364212.158
Holt-Winter (Additive) Calculated for α = 0.550, β = 0.097, γ = 0.527 382179.484
Holt-Winter (Multiplicative) Calculated for α = 0.598, β = 0.105, γ = 0.635 383944.484

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The most appropriate demand modelling technique out of the nine methods is found out to be MSM (Adjusted
Forecast). MSM (Adjusted Forecast) demonstrates the lowest overall value of the MAD = 11106.109, MSE =
176829409.572 and MAPE = 0.035. MSM (Adjusted Forecast) is also demonstrated graphically in Figure 3
(Appendix A). It can easily be seen that the demand forecast obtained by using MSM (Adjusted Forecast) closely
resemble the original demand in terms of trends and seasonality (Clifton 2015b). Therefore, we conclude that
MSM (Adjusted Forecast) is the most suitable method for demand modelling for the total container throughput of
Sydney Ports (BITRE 2015).
Table 2: Model testing results of Total Containers Throughput for Sydney Ports Terminals from BITRE (2015)
(Clifton 2015a, Clifton 2015b, Piennar and Vogt 2012)
5 ETHICAL ISSUES WITHIN THE PORT OF BUSAN
During the early stages of the redevelopment for the Port of Busan in Korea, container congestion and traffic
issues had been identified at the container terminals. All container terminals at the Port of Busan were struggling
to meet demand due to the increase volume, economic development and competition from nearby countries (Lee
and Meng 2014a). Thus, requiring urgent attention to address the congestions and traffic issues at the container
terminal as soon as possible, such as developing an additional number of berths (Lee and Meng 2014a). The
future demand for the port was expected to increase exponentially over time based on the findings used in time
series analysis (Song, Cullinane and Roe 2011).
Although the redevelopment project includes extending the berth capacity to meet the future demand. The
general public that were within a close proximity to the port of Busan had also been affected by the decisions
made from the Korean government and Busan Port Authority (BPA) (Lee and Meng 2014a). Conflicts occurred as
highlighted by Lee and Meng (2014a) between several social groups such as citizens, policyholders and
stakeholders which in turn increased pressure towards the Korean government and BPA to begin the
redevelopment project. Although the redevelopment project provided benefits for the port itself, the reactions from
the social groups or other parties opposed the project due to the changing nature of the city of Busan (Lee and
Meng 2014a). Bird (2009) highlights that economic developments can effect on how a business plans to develop
itself towards the society including politics and the general public around it. Thus, it is vital to ensure that any
business development should also incorporate with the ethical concepts for human rights, needs and values
(Hanson 2014).
The fundamentals of ethics can be applied to businesses, if they are willing to communicate any business
developments, such as the redevelopment for the Port of Busan, to the related parties or social groups (Bird
2009, Lee and Meng 2014a). The Korean government and BPA made a good ethical decision to address the
concerns from the opposing groups. Lee and Meng (2014a) state that the BPA held conferences and public
meetings with the citizens and city councils of the Busan city. The frequent communication and cooperation
between the two parties assisted with a smooth transition for the redevelopment project to begin.
The volume of incoming shipments was expected to rise in the near future at Busan’s container terminals. Due to
the current congestion issues as mentioned previously, it was a dire situation for the Port of Busan to be in (Lee
and Meng 2014a). In order to ensure the redevelopment for the Port of Busan had commenced, the fundamentals
of ethics had to be applied towards the opposing groups. In short, the Korean government and port authorities
demonstrated good ethical choices to address the concerns and views from the general public of the city of
Busan. These ethical choices were communicated thoroughly by the arrangements of conferences and meetings
held by the BPA (Lee and Meng 2014a).
Measures SMA (3) WMA (3) ES(0.5) ES(Solver)
Regression
(Unadjusted
Forecast)
Multiplicative
seasonal
Model
(Adjusted
Forecast) Holt's Method Holt-Winter (A) Holt-Winter (M)
Sep 2014
(FY 2014,
Q2)
Forecast 364945.333 373337.325 358523.534 358745.275 280126.097 286954.661 364212.158 382179.484 383944.484
MAD 21951.689 17584.555 21646.004 21252.967 17770.710 11106.109 20771.205 12870.302 13083.326
MSE 763119100.593 570673395.009 712660530.990 701020994.868 465968851.726 176829409.572 679791483.041 323465818.991 344560558.836
MAPE 0.066 0.053 0.065 0.064 0.055 0.035 0.064 0.040 0.040

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6 CONCLUSION
The time series analysis techniques have been commonly applied within the MLASC industry; including freight
forwarders, shipbuilders, governments, cargo holders and port authorities. This report discusses how time series
analysis can be applied within the MLASC industry by identifying a few case studies used in the literature; as well
as a complete analysis of demand modelling and forecast for the total number of container throughput at Sydney
Ports (BITRE 2015). The demand has been modelled by using nine methods of time series data analysis
including SMA (3), WMA (3), ES (0.5), ES (Solver), RM (Unadjusted Forecast), MSM (Adjusted Forecast), HM,
HWM (Additive) and HWM (Multiplicative). Microsoft Excel 2013 has been used to create the forecasts using all
nine methods. In order to select the best demand model and forecast for the expected container throughput of
the Sydney Ports Terminals, comparisons were made by considering the measurements of error which included
MAD, MSE and MAPE. In summary, MSM (Adjusted Forecast) was found out to be the most suitable fit for
forecasting the actual demand. It is vital for Sydney Port to be aware of the upcoming container throughput, as
the container throughput is a common indicator to assess the efficiency of a terminal’s productivity. The most
appropriate modelling technique will assist with determining how efficient the container terminal is operating; such
as planning for the redevelopment of Sydney Ports to meet their demand expectations. Lastly, the report
discusses the ethical issues involved regarding time series analysis.

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APPENDIX A
Figure 1: Demand Forecasting using SMA(3) and WMA (3):
(BITRE 2015, Clifton 2015a, Piennar and Vogt 2012)
Figure 2: Demand Forecasting using ES (0.5) and ES (Solver):
(BITRE 2015, Clifton 2015a, Piennar and Vogt 2012)
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TotalContainers
Total Containers Throughput for Sydney Ports Terminals
Total Containers
SMA (3)
WMA (3)
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Total Containers Throughput for Sydney Ports Terminals
Total Containers
ES (0.5)
ES (SOLVER)

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Figure 3: Demand Forecasting using RM (Unadjusted Forecast) and MSM (Adjusted
Forecast):
(BITRE 2015, Clifton 2015b, Piennar and Vogt 2012)
Figure 4: Demand Forecasting using HM:
(BITRE 2015, Piennar and Vogt 2012)
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TotalContainers
Total Containers Throughput for Sydney Ports Terminals
Total Containers
Unadjusted (Regression)
Adjusted (MSM)
y = 2743.828x - 280126.097
R² = 0.594
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TotalContainers
Total Containers Throughput for Sydney Ports Terminals
Total Containers
Holt's Method

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Figure 5: Demand Forecasting using HWM (Additive) and HWM (Multiplicative):
(BITRE 2015, Piennar and Vogt 2012)
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TotalContainers
Total Containers Throughput for Sydney Ports Terminals
Total Containers
Holt-Winter (A)
Holts-Winter (M)

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REFERENCES
Bird, F. 2009, ‘The ethical responsibilities of businesses in developing areas’, Journal of Business Ethics, vol. 89,
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