An Examination into Emerging Technologies Employed to Manage Freight

This report provides an in-depth analysis of emerging technologies in freight transportation, with a focus on data analytics. The study explores the current trends and the impact of technological advancements on the freight industry. The report begins with an abstract, table of contents, and background information on the evolution of the transport sector. It includes a comprehensive literature review, discussing the advantages of data analytics, such as transparency, performance management, accuracy, and fraud reduction, while also acknowledging the disadvantages like privacy breaches and costs. The theoretical framework incorporates Lewin’s and Kotter's models of change to understand the integration of these technologies. A case study of CJ Logistics in South Korea showcases the successful application of data analytics and big data in courier services. The report concludes with a summary of findings and references for further study.

Running Head: TECHNOLOGIES FOR TRANSPORTATION MGMT COMPLEXITIES 1
AN EXAMINATION INTO EMERGING TECHNOLOGIES EMPLOYED TO MANAGE
TRANSPORTATION MANAGEMENT COMPLEXITIES
Student’s Name
Institutional Affiliation

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 2
Abstract
This paper sought to assess and understand the employment of emerging trends in the
freight industry. The freight industry like any other industry is rapidly growing and changing.
With technology changing the phases and faces of companies, the only best way to remain
relevant in the market is embracing change. While it seems hard, to change, embracing and
integrating technological advances with the former ways of conducting business is not wrong.
This paper did not conduct actual research buts based its arguments on research done prior on
the same topic. With one case study of successful embracing of Big Data analysis, this study
was limited to time and more case studies regarding the same.
This paper focused on Data analytics as an emerging technological trend that could be used to
improve efficiency in the freight industry.

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 3
Table of Contents
Abstract......................................................................................................................................2
Background Of The Study.........................................................................................................4
Literature Review.......................................................................................................................5
ADVANTAGES OF USING DATA ANALYTICS..............................................................................5
Theoretical Framework..............................................................................................................8
Case Study. Drawn From Sustainability –A Case Study.........................................................10
Conclusions..............................................................................................................................11
References................................................................................................................................13

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 4
Background of the study
The transport sector has evolved over the years with more and more advancements
being done every single day. Of all these advancements most of them are technological
advances which are aimed at making the industry better.it should be noticed and understood
that technological advances have advantages and disadvantages (Jin, & Kim, 2018). Some of
these demerits are as a result of trails gone wrong. One of the key technological
advancements employed in the freight industry is the use of data analytics.
The freight industry has over the years grown to be a more expansive and expensive
industry. This has called out for more advancements since humanity is evolving and
discovering more in terms of technology. Technological advancements have been created to
ease the burden initially placed on humans. This applies to all industries including the freight
industry. The transportation sector, though odd has been also at the center of these advances.
The freight industry is as delicate as the medical industry (Ismail, 2018). Managing an
industry that is rapidly growing can be challenging which therefore needs stringent measures
to be put in place to make sure that management smooth and flawless.
Technology has its effects in every sector and the transport sector is not left out.
Embracing technology to run businesses and companies is the way to go nowadays. With
every addition in the industry it's interesting to note that these emerging trends have their
effects on whichever industry they are applied or embraced (Hussain, Lei, Akram, Haider,
Hussain, & Ali, 2018). This paper seeks to understand the effects of data analytics as a
technological tool used in the freight industry.
In a bid to understand this tool, the study was carried out based on prior research done
into the same but in the previous years. The findings indicate that a majority of people, in the
freight industry wouldn’t mind embracing more technological advances to improve the

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 5
sector. This study also sought to establish the link between existing modes of communication
to any new and additional ways employed to make operations run smoother.
Literature review
Data analytics is a process in which data is scientifically analysed to enhance
productivity. It includes extracting data from various sources and grouping and cleaning them
to examine behavioural changes.
Data analytics includes the use of soft wares such as Python, R programming,
Apache-Spark, Tableau, SAS, Excel sheets among others. Data analytics involves four steps.
The first step is to sort the data to ease on cleaning. Data has to be categorised in terms of
gender, income levels, age or even demographics. Data has values which may have grouped
according to levels.
The second step is the data collection. Data collection can be done using many
different forms. These sources include online sources, magazines, computers or even people
(Toobaei, & Schubert, 2014). After the data has been collected, it is sorted and analysed for
processing.at this stage statistical soft wares are used as they only can read statistical data.
Before data is analysed it is first cleaned up. Data cleaning involves the removal of
corrupt or inaccurate data. This step is crucial as it helps correct any errors before an analyst
can go through it.
Advantages of using Data Analytics
Transparency
The use of such soft wares ensures that companies do not lose data or currency due to
transparency issues. Transparency issues, poor processing of orders are among the many
issues facing the freight industry.to solve this many of the companies are resorting to using
technology to boost their services and enhance productivity (Nick 2018). Talking of

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 6
technological advances, some of the changes effected are the adoption of data analysis and
big data logistics, logistics automation and IoT, elastic logistics among many others.
The adoption of data analysis as a technological tool in this industry is key since the
industry is broad and dynamic (Zakir, Seymour, & Berg, 2015). This means that its nature
provides a perfect setup for the use of data. Research has shown that a large number of
shippers at 93% and third party logistics companies at 98% agree to use data analytics to
make crucial decisions. 71% of them also believe in the improvement of quality and
performance by using data analytics as a tool.
Data analytics helps in managing performance
Performance managers want to ensure that all schedules are well adhered to by the
staff and that there are no inefficiencies. Data analytics tools help them monitor all
performances and ensure that they can understand any challenges that arise causing work to
slow down (Rozados, & Tjahjono, 2014). The data can help in monitoring the workforce.
It can also indicate alerts on machine breakdown early enough to give room for repair
and maintenance. Companies have already embarked on building systems that help in
monitoring and analysing performance. These systems are for monitoring and analysing
performance.
Accuracy
Data analytics through the use of data cleansing aids in detecting and correcting errors
from the datasets. This ensures quality data which is a win for both the clients and the
company workforce. Such errors, especially in companies that process large amounts of data,
can go undetected since the human eye gets tired after a certain period (Robinson, 2016) Also
going through the same dataset over and over again tends to bring laxity to the operator thus
making it hard to notice some errors.
Reduction of fraud and risk

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 7
Most of the companies in the freight industry have in one way encountered fraud in
the process of doing business. Fraud may not necessarily come from the outside environment,
sometimes fraudulent dealings arise from the workforce its main objective is to combat both
internal and external threats .data analytics help provide complete organizational security.
Customer experience
Poor or mismanagement of company operations can result in the emergence of several
costly issues. One of the issues is poor customer service. Once customers notice a change in
the dissemination of services, their loyalty to that particular company changes. If the service
is poor, the customer shift is inevitable. Data analytics help in giving the customer a fulfilling
service experience when employed in areas like branding, optimization of different business
operations and even the control of various processes, effectiveness and consistency is
achieved.
Disadvantages of data analytics
Everything that has advantages has disadvantages also. The data analytics tool has its own
shortcomings which include;
Privacy breach
This refers to access to one's information without their permission. Data analytics
does not fall short of this. Major companies can access their customer’s information
regarding online purchases, subscriptions and can as well share the information to third party
companies for benefits.
Cost
Data analytics tools are expensive to acquire and require skilled personnel. The cost
varies depending on features and applications supported. A company that is willing to adopt
data analytics as a technological advancement must be willing to meet up the cost.

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 8
This study seeks to understand the technological advances in the freight industry. The
technological advances are too huge to be ignored and thus each sector sees it worthy ton
employ the same for better performance and to also keep up with the ever-changing business
environment, embracing any change is a process that may not be smooth as it sounds since
some of these advancements are complex. This is to show that some merits and demerits that
accompany every change that needs to be effected.
Theoretical Framework
Change in any business environment is inevitable but it is fatal if the people aspect of
change is ignored or poorly managed. This is because in every business environment
including the freight industry, the workforce is made up of individuals who ensure safe
delivery of goods, servicing of machinery and the achievement of the company vision and
mission.
To understand this, two models of change were explored. They are
 The Lewin’s model of Change
 John Kotter’s 8 model of change
The Lewin’s model of change
Changes in any workplace cannot be addressed without addressing the Lewins model.
His model addresses the change process in the work process. In his model, Kurt Lewin
addressed the organizational change in just three steps. These steps are unfreezing, changing
and refreezing.
The first stage is the unfreezing stage, the need for change is recognized and
determined. Here the management addresses this need with the workforce and urges the
workforce to shed off the old behaviours and attitudes. It is also during this stage that the
workforce raises their fears and concerns.

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 9
The second stage is the changing stage where the changes are planned and
implemented as well as helping the employees to cope up with the same. The third stage is
the refreezing stage where the changes are fortified and maintained. The changes are then
adopted as the new way of doing things and more ways of sustaining the changes are also
developed.
Figure 1: Lewins Model of Change
To fully understand Kurt Lewin's model, John Kotter's 8 model plan was developed.
In his model, John fully explained the organizational change in 8 steps. These 8 steps include;
Creation of a sense of urgency, building a guiding coalition, formation of strategic vision &
initiatives, enlist a volunteer army, enable action by removing barriers, generation of short-
term wins, sustain acceleration and institute change.
The
refreezing
stage
The
changing
stage
The
unfreezing
stage

TECHNOLOGIES FOR TRANSPORTATION MANAGEMENT COMPLEXITIES 10
Figure 2: Kotter's 8 model plan
These are models that are used to address the reception of change in the office
environment. The process of incorporating change in the freight industry. To understand
more of the data analytics case, study was carried out on the CJ logistics company in South
Korea.
Case study. Drawn from Sustainability –a Case Study
In this study, Jin and Kim, 2018 sought to understand the use of data analytics as a
technological tool to advance the profits and how effective it was for the CJ logistics team
(Khan, & Hashim, 2014). Their case majored on the courier services whose main building
blocks are the sorting process and the effective use big Data Analytics through Bi. The study
also majored on how the company evolved and integrated the use of technology for their
services.
According to Jin and Kim, the company was the sample for the study because it had a
larger revenue than most companies. The company was equipped with latest logistics
Creation of a
sense of
urgency
Building a
guiding
coalition
Formation of
strategic
vision &
initiatives
Enlist a
volunteer
army
Enable action
by removing
barriers
Generation of
short-term
wins
Sustain
acceleration
Institute
change.

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technologies which included freight tracking system, an integrated courier, satellite vehicle
tracking, and temperature control systems among others. To aid sustainable growth, in 2017,
CJ Logistics invested more to automate its sorting process by the way of sub terminals. It’s
key to note that the company’s infrastructure is better than that of any other in the industry.
CJ logistics by 2018 had 5 hub terminals, over 270 sub-terminals and vehicles in
excess of 16,000. With this capacity the company processed packages in excess of 5.3 million
per day in 2018. By August 2018, the company was in apposition to expand its services all
over Korea because it could use technologies such as robots and IoT. Expanding its services
meant that same-day deliveries, same-day returns and scheduled delivery services were also
effective. This will include deliveries and returns within the same day as well as planned
delivery services. By the end of 2017 the company had 238 centres in 137 cities and 32
countries signifying its determination to move forward with its scheduled international
growth.
In the period between 2011 and 2016 CJ logistics recorded a rise in B2C transactions,
making the company to register an annual growth rate of 9.9%. Also, its courier’s market
share rose to 46% in 2017 from a 42% in 2015 which led to the company increasing its
vehicles. The peer to peer (P2P) network was also improved and a forecasting system
established.
The results were evident as deliveries per person rose from 262 boxes to 344 boxes
daily in the period of two years i.e. 2015 to 2017 respectively (Kotter, 2010). The hub’s
sorting potential also improved drastically from 4.4 million to 5.3 million cases respectively.
However, these drastic changes meant that the hub terminal capacity would reach its limit.
When it eventually did the remaining cargo was at a rate of 3.1% and a 2.3% drop rate on
overnight delivery.

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This outcome provided a possibility for the company to provide a lasting solution
through ways which could enhance the hub terminal capacity (Mia, 2015). The CJ logistics
team agreed to incorporate Big Data Analytics into its decision making strategies. This meant
that the situation could be understood better and that the company would be in a position to
make better decisions and even identify new paths to follow.
Gathering of information took approximately 3 months between November 2016 and
January 2017.The first data samples were collected on 75 million inbound invoices and 240
million packages of the 260 million inbound invoices and 720 million packages. All this was
conducted at the Daejeon terminal since it was the field chosen for running the pilot test.
Conclusions
From the above case study, the following conclusions were drawn. That according to
the research done on this firm, using Big Data Analytics played a key role in the increase in
terms of numbers and sells for the CJ logistics company. The study did not however address
the issue of data collection since it only used data derived from limited range. Data should be
sourced from different and unlimited ranges to address diversity and also bring out the
contrasting features.
By the end of the study CJ logistics had already embraced Big Data Analytics for its
processes. It not only yielded well and better results, it helped improve customer relations.
Embracing technological advances for whichever company should be a priority. It however
should not supersede the workforce since it is the same team that will help implement any
changes.
More case studies on the same should be done not only for research purposes but also
for determining the effectiveness of technology advancements on companies. Most of the
companies are embracing new ways of enhancing productivity through technology. However,
this should not be used as an excuse to not integrate people as part of the workforce. If it