The COSO framework provides five components for effective internal control: control environment, risk assessment, control activities, information and communication, and monitoring activities. These components work together to help businesses achieve objectives, ensure reliable reporting, and comply with laws and regulations. Risk assessment involves identifying risks that could impact objectives, while control activities are policies and procedures used to mitigate risks. Information and communication allows for effective communication of responsibilities, and monitoring activities provide feedback to improve the system. The framework is useful for auditing and helps ensure accountability, reliability and regulatory compliance within an organization.
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Running head: COSO FRAMEWORK COMPONENTS 1
COSO FRAMEWORK COMPONENTS 8
COSO Framework Components
BhanuKrishna Mokka
ITS-831 - Infotech Importance in Strategic Planning University
of the Cumberlands
Dr. Eric Hollis
March 28, 2020
Abstract
The utilization of the COSO framework in an internal audit of
firms is imperative in the identification of accomplished
objectives. Additionally, the framework provides
analyses of risks in the external and internal environment,
which can impact business operations. Risks identification has
an impact on the financial performance of
4. the business. Control activities ensure procedures and policies
are followed, whereas a control environment enables integrity
and ethics in the managerial activities of
the employees. This ensures operations, procedures, and
organizational activities are done as per the set standards for
compliance and accountability. Information
and communication effectiveness ensure message conveyance
and feedback. Monitoring and review activities provide
feedback for improvement activities in the
1
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Source Matches (15)
evaluation of internal control system effectiveness.
Achievement of objectives and optimal operations assist the
business to operate smoothly within accountability,
compliance, and reliability elements in internal control. The
elements are imperative in financial auditing practices in
5. financial reporting. The integrated internal
control framework provides security in the achievement of
objectives.
Introduction The Committee of Sponsoring Organizations of the
Treadway Commission (COSO) framework components is
imperative in business processes for
efficacy and effectiveness. The framework provides security in
the achievement of objectives with respect to business
operations. The activities within the components
are significant in auditing, as well as internal control during
reporting. The management of the business entity has to ensure
the framework is undertaken to curb
risks in financial performance besides improving management
effectiveness. Accountability, reliability, and compliance with
regulation are upheld through the
framework components. The components can also be used in
evaluating internal operational activities. Framework
Components The components consist of
interrelated concepts that are utilized in the management of the
business. Understanding the scope and dimension of the
components will enable the identification of
its impact on the business objectives. The components are as
discussed below.
Control Environment The control environment entails
managerial activities that create an organizational environment
with internal control in ethical and governance
standards. This is in support of business employees in the
organizational culture. Discipline and structure in the
organizational framework ensure an acceptable and
significant in managing people (Sari et al., 2018). Integrity in
administration also to be upheld in order to ensure
accountability and compliance. The control
environment can impact business objectives in that adopting
6. managerial practices in employee management has to consider
the ethical standards required for
compliance. Additionally, the component also impacts
management style affecting delegation and control of people as
per the standards of conduct.
Risk Assessment The identification and analyses of risks in the
internal and external environment have to be carried out in the
achievement of organization objectives
(Sari, R., Kosala, R., Ranti, B. & Supangkat, S, 2018). This is
to identify ways to manage the risks and how they can impact
business operations. The process results in
the adoption of internal controls to mitigate the risks as per the
assigned objectives. Further, the assessment leads to factor
analysis, which is essential in the
management of risks (Rae et al., 2017). The component impacts
business objectives as their development and achievement have
to consider risks in the environment.
This can affect profits as well as the performance of the
organization, which also influences organizational reputation.
Control Activities The component includes procedures and
policies carried out in operating processes through management
directives. Directives given are
significant in mitigating risks identified in objectives
achievement (Sari et al., 2018). The controls are in all functions
and levels. Approvals, verifications, performance
reviews, and authorizations have to be observed in the processes
for the control activities (Anderson & Eubanks, 2015). The
control activities can impact the reporting
of audits as approval, as well as authorization, has to be
followed in the reporting structures, which might affect
oversight responsibilities of the auditor. Information
and Communication Communication and information systems
enhance effectiveness in the communication processes in
7. operation and control of business
operations. Policies and procedures, as communicated, influence
accountability and compliance (Sari et al., 2018). Further,
communication with external stakeholders
supports the chain supply process.
4
5
6
This is essential in logistics technology in communication with
external parties and data analytics (Jin & Kim, 2018). Data
collected from the processes are utilized in an
analytical application in objectives achievement and enhanced
decision-making. The component can impact objective
achievement in that information obtained has to
be relevant and applicable in control and risk management.
Monitoring and Review Evaluation of the components should be
done in a timely and effective manner in the provision of
feedback. The feedback extends to
compliance reporting besides been essential in financial
reporting (Sari et al., 2018). Deficiencies in the framework
components are reported for corrective action. The
component is a continuous process; hence a continuous
improvement of the operational systems is observed (Anderson
& Eubanks, 2015). Separate and ongoing
evaluations are carried for management and control measures
reporting. The changes from the monitoring and review
processes can impact internal audit assurance
and control due to communication deficiencies. This can
influence the accomplishment of objectives in a systematic
approach.
8. Integrating COSO Framework into Company Integrating the
framework compliance in the warehouse systems organization
can be imperative in internal audit services
offered to clients. This will ensure that all auditors are informed
on the framework components and how changes can impact their
procedures in effectiveness
assurance. Additionally, the framework integration can be
combined with technology, especially on business intelligence
for efficiency. Data obtained from the
business intelligence processes can be utilized in all
components reducing errors and baseline monitoring activities
that utilize data analytics. This will ensure that
real-time information is provided to the warehouse firm, which
is accurate and reliable.
Conclusion The framework components are essential in internal
control processes as well as compliance with regulations. Risks
assessment and management results
in control activities and environment for operational
effectiveness. Further, information and communication systems
enhance reporting of activities through the
identified procedures reducing communication deficiencies.
Monitoring and review as a continuous process ensure
continuous improvement through the evaluation
of defects in the existing components. The framework can be
enhanced through the integration of data analytics and business
intelligence minimizing errors and
promoting data reliability.
References Anderson, D. & Eubanks, G. (2015). Governance
and internal control: Leveraging COSO across the three lines of
defense. COSO. Retrieved from
https://aechile.cl/wp-content/uploads/2015/07/COSO-2015-
3LOD-PDF-1.pdf
9. Jin, D. & Kim, H. (2018). Integrated understanding of big data,
big data analysis, and business intelligence: a case study of
logistics. Sustainability case report.
Retrieved from https://www.mdpi.com/journal/sustainability
Rae, K., Sands, J. & Subramaniam, N. (2017). Associations
among the five components within COSO internal control-
integrated framework as the
underpinning of quality corporate governance. Special issue on
corporate governance 3(11). Retrieved from
https://ro.uow.edu.au/cgi/viewcontent.cgi?
article=1752&context=aabfj
Sari, R., Kosala, R., Ranti, B. & Supangkat, S. (2018). COSO
framework for warehouse management internal control
evaluation: enabling smart warehouse
systems.
7 1
8
9
10 8
7
8 8
11. 1
Student paper
COSO FRAMEWORK COMPONENTS 1
Original source
COMPONENTS OF COSO FRAMEWORK 1
2
Student paper
COSO FRAMEWORK COMPONENTS 8
COSO Framework Components
Original source
Components of COSO Framework
Components of COSO Framework
3
Student paper
March 28, 2020
Original source
Saturday, March 28, 2020
4
Student paper
12. The integrated internal control
framework provides security in the
achievement of objectives.
Original source
Internal Control — Integrated Framework
5
Student paper
The components are as discussed below.
Original source
Seven are discussed below
6
Student paper
Control Activities The component
includes procedures and policies carried
out in operating processes through
management directives.
Original source
Control activities are the policies and
procedures that help ensure that
management directives are carried out
7
Student paper
13. References Anderson, D.
Original source
D., & Anderson, A
1
Student paper
Leveraging COSO across the three lines
of defense.
Original source
Leveraging COSO across the three lines
of defense
8
Student paper
Integrated understanding of big data, big
data analysis, and business intelligence: a
case study of logistics.
Original source
Integrated Understanding of Big Data,
Big Data Analysis, and Business
Intelligence A Case Study of Logistics
9
Student paper
14. Retrieved from
https://www.mdpi.com/journal/sustainab
ility
Original source
Retrieved from
https://www.mdpi.com/journal/sustainab
ility
10
Student paper
Rae, K., Sands, J.
Original source
Rae, K., J
8
Student paper
Associations among the five components
within COSO internal control-integrated
framework as the underpinning of
quality corporate governance.
Original source
Associations among the five components
within COSO internal control-integrated
framework as the underpinning of
quality corporate governance
16. COSO Framework for Warehouse
Management Internal Control Evaluation
Enabling Smart Warehouse Systems
NPV - IRRFIN 615NPV and IRR calculationsCost of
Capital10.00%Time/yr012345Cash flow Input hereDiscounted
CF000000NPV0=cf1/((1+n)^1)=cf2/((1+n)^2)=cf3/((1+n)^3)=cf
4/((1+n)^4)=cf5/((1+n)^5)PV
factor0.90909090910.8264462810.75131480090.68301345540.6
209213231IRR0.00%
Sheet3
Running head: INSERT FIRST 50 CHARACTERS OF TITLE 1
SAMPLE PAPER
Identifying the Best Practices in Strategic Management
Gertrude Steinbeck
ORG500 – Foundations of Effective Management
Colorado State University – Global Campus
Dr. Stephanie Allong
August 6, 2015
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 2
Identifying the Best Practices in Strategic Management
Strategic management and corporate sustainability are two
important dynamics of
modern-day organizations. It is important for organizational
leaders to have an understanding of
the theoretical applications of strategic management as a means
of addressing corporate
sustainability. The purpose of this paper is to provide
definitions and an understanding of
strategic management and corporate sustainability. An overview
of the Walgreen Company, the
organization of study, is also provided in order to understand
how the company has utilized
19. strategic management to implement sustainability initiatives for
long-term financial performance.
Strategic Management
The function of management is to plan, organize, lead, and
control the operations of an
organization (Robbins & Coulter, 2007) and includes strategic
management. Strategic
management is an approach in which organizations create a
competitive advantage, enhance
productivity, and establish long-term financial performance.
Chandler (as cited in Whittington,
2008) defines strategy as “the determination of the basic long-
term goals and objectives of an
enterprise, and the adoption of courses of action and the
allocation of resources necessary for
carrying out these goals” (p. 268). Similarly, Wheelen and
Hunger (2008) define strategic
management as the managerial decisions and actions of an
organization that achieve long-run
performance of the business, with benefits such as:
organization
20. The Strategic Management Model (SMM) provides the
framework for integrating strategic
planning into an organization so that the aforementioned
benefits are realized.
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 3
Strategic Management Model
Research indicates as the concept of strategic management
evolved, many
theoretical models were proposed. Ginter, Ruck, and Duncan
(1985) identify eight
elements of the normative strategic model: vision and mission;
objective setting; external
environmental scanning; internal environmental scanning;
strategic alternatives; strategy
selection; implementation; and control. Long (as cited in Ginter
et al., 1985) stated that
normative strategic management models are an “explicit,
23. intentional, planned and rational
approach” (p. 581) to management. Similar to Ginter et al.,
Wheelen and Hunger (2008)
established the SMM (see Figure 1) which includes four main
elements: environmental
scanning, strategy formulation, strategy implementation, and
evaluation and control.
Environmental scanning is the monitoring, evaluating, and
extracting of information from
the external and internal environments in order for management
to establish plans and
make decisions. Strategy formulation includes creating long-
term plans for the
organization, including the mission, objectives, strategies and
policies. Strategy
implementation is the process of executing policies and
strategies in order to achieve the
mission and objectives. Evaluation and control require
monitoring the performance of the
organization and adjusting the process as necessary in order to
achieve desired results
(Wheelen & Hunger, 2008).
The SMM assumes the organizational learning theory, which
states that an
24. organization adapts to the changing environment and uses
gathered knowledge to
improve the fit between itself and the environment. The SMM
also assumes the
organization be a learning organization in which the gathered
knowledge can be used to
change behavior and reflect new knowledge (Wheelen &
Hunger, 2008).
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 4
Environmental
Scanning
Strategy
Formulation
Strategy
Implementation
28. organizational leaders have the responsibility of increasing
shareholder value through
corporate sustainability (Epstein, 2008). Corporate
sustainability is defined in a variety of
ways. Hollingworth (2009) described a sustainable organization
as “one that strives for
and achieves 360-organizational sustainability” (p. 1). The
author claimed an
organization is sustainable when it can endure, or maintain,
over a long-term without
permanently damaging or depleting resources including: the
organization itself; its human
resources (internal and external); the community/society/ethno-
sphere; and the planet’s
environment. He then claimed that if one of the four resources
is not sustainable, issues
with the remaining resources will eventually develop
(Hollingworth, 2009). Brundtland
(as cited in Epstein, 2008) described sustainability as the
economic development that
addresses the needs of the present generation without depleting
resources needed by
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30. IDENTIFYING THE BEST PRACTICES IN STRATEGIC 5
future generations Epstein (2008) adds to the definition from a
business perspective by
including corporate social responsibility. Epstein also states
that organizations have a
responsibility to stakeholders to improve management practices
in order to add value by
addressing corporate social, environmental and economic
impacts (Epstein, 2008).
Organizational leaders are the strategic decision makers of a
company and have a
responsibility to stakeholders (Wheelen & Hunger 2008).
Therefore, it is important to
have an understanding of why corporate sustainability is
important, and how the nine
principles of sustainability performance guide strategic
management.
Importance of Corporate Sustainability
In addition to making a profit, organizations have a
responsibility to society,
which includes addressing its economic, social, and
environmental impacts, otherwise
31. known as social responsibility. Friedman and Carroll had two
opposing views of
corporate social responsibility. Friedman argued that the sole
responsibility of business
was to use resources and activities that enhanced profits
(Wheelen & Hunger, 2008).
Carroll (1979) argued that social responsibility included much
more that making a profit;
he proposed businesses must include the economic, legal,
ethical and discretionary
categories of business performance.
services to meet the
needs/wants of society in order to make a profit;
company is expected to
abide by;
statements, but also
include the norms and beliefs held by society;
This is another example of
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number would also be included.
Notice that the period is at the end
of the parentheses.
IDENTIFYING THE BEST PRACTICES IN STRATEGIC 6
on by the
organization including voluntary activities and philanthropic
contributions
(Carroll, 1979).
33. The importance of corporate sustainability, therefore, is that an
organization is
responsible for financial performance, but it also has additional
responsibilities to
stakeholders and society in general.
The Nine Principles of Sustainability Performance
The nine principles, as presented by Epstein and Roy (2003)
(see Table 1), further
define sustainability, are measureable, and can easily be
incorporated into strategic
management (Epstein, 2008). These principles include ethics,
governance, transparency,
business relationships, financial return, community
involvement, value of products and
services, employment practices and protection of the
environment.
A table or figure should fit all on one
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your paper. It is easier for the reader
to view the table or figure when
34. presented as a whole instead of split
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 7
Table 1
The Nine Principles of Sustainability Performance
1. Ethics The company establishes, promotes, monitors and
maintains ethical
standards and practices in dealing with all of the company
stakeholders.
2. Governance The company manages all of its resources
conscientiously and effectively,
recognizing the fiduciary duty of corporate boards and managers
to focus
on the interests of all company stakeholders.
3. Transparency The company provides timely disclosure of
information about its
products, services and activities, thus permitting stakeholders to
make
informed decisions.
4. Business
relationships
The company engages in fair-trading practices with suppliers,
distributors
35. and partners.
5. Financial return The company compensates providers of
capital with a competitive return
on investment and the protection of company assets.
6. Community
involvement/
economic
development
The company fosters a mutually beneficial relationship between
the
corporation and community in which it is sensitive to the
culture, context
and needs of the community.
7. Value of
product and
services
The company respects the needs, desires and rights of its
customers and
strives to provide the highest levels of product and service
values.
8. Employment
practices
36. The company engages in human-resource management practices
that
promote personal and professional employee development,
diversity and
empowerment.
9. Protection of the
environment
The company strives to protect and restore the environment and
promote
sustainable development with products, processes, services and
other
activities.
Note. There should be a general note about the table here.
Adapted from “Improving
sustainability performance: Specifying, implementing and
measuring key principles” by M.
Epstein, & M. Roy, 2003, Journal of General Management,
29(1), pp.15-31.
Walgreens Company
Walgreens Company is a retail drugstore that is in the primary
business of prescription
and non-prescription drugs, and general merchandise including
beauty care, personal care,
37. household items, photofinishing, greeting cards, and seasonal
items (Reuters, 2010). More
recently, the organization diversified its offerings through
worksite healthcare facilities, home
care facilities, specialty pharmacies, and mail service
pharmacies (Walgreens Company, 2010).
When using a Table in your paper, make
sure you use the word “Table” with the
Table number. Then insert the title of the
Table in italics, with all major words
capitalized. Underneath the Table you must
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was adapted from or general information
about the Table. Follow this example.
Notice it does not follow the Reference
citation format.
IDENTIFYING THE BEST PRACTICES IN STRATEGIC 8
38. Walgreen Company established a strong organizational culture
focusing on consumer and
employee satisfaction. The mission of Walgreens is:
We will provide the most convenient access to consumer goods
and services . . .
and pharmacy, health and wellness services . . . in America. We
will earn the trust
of our customers and build shareholder value. We will treat
each other with
respect and dignity and do the same for all we serve. We will
offer employees of
all backgrounds a place to build a career. (Walgreens, 2010a,
para. 1)
Walgreens was established in 1901 by pharmacist Charles R.
Walgreen Sr. (Walgreens, 2010b).
Prior to establishing the company, Mr. Walgreen struggled with
the direction the pharmacy
industry was headed; the lack of quality customer service and
care for people concerned him.
Today, Walgreens is the largest drugstore chain in the United
States employing over 238,000
people. Sales in 2009 exceeded $63 billion, in which 65% of
sales were from prescriptions
drugs. The organization has expanded into all 50 states, as well
39. as the District of Colombia and
Puerto Rico, for a total of 7,496 stores and 350 Take Care
clinics (Walgreens Company, 2010,
para. 3).
Conclusion
Strategic management and corporate sustainability are two
important practices in today’s
competitive global environment. In order to effectively
implement strategic management in light
of corporate sustainability, leaders must have an understanding
of such concepts. This paper has
provided a background and understanding of strategic
management and corporate sustainability.
An overview and history of Walgreen Company was also
presented in order to identify best
practices in strategic management that enhance corporate
sustainability.
If you are using information from
multiple web pages from one
website, you need to distinguish
which citation came from which
40. web page. You can distinguish each
page, by putting the letters, “a,”
“b”, etc. with the year.
If a quotation is longer than 40 words, it
must be in a block format. The block
format is indented ½ inch (or 5 spaces
from the left) from the left margin. Do not
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 9
References
Carroll, A. B. (1979). A three-dimensional conceptual model of
corporate performance. The
Academy of Management Review, 4(4), 497.
Collins, J. (2001). Good to great. New York, NY: HarperCollins
Publishers Inc.
Epstein, M. J. (2008). Making sustainability work. San
Francisco, CA: Greenleaf
Publishing Limited.
Epstein, M., & Roy, M. (2003). Improving sustainability
41. performance: Specifying, implementing
and measuring key principles. Journal of General Management,
29(1), 15-31.
French, S. (2009). Critiquing the language of strategic
management. The Journal of Management
Development, 28(1), 6-17. doi: 10.1108/02621710910923836
Ginter, P., Ruck, A., & Duncan, W. (1985). Planners’
perceptions of the strategic management
process. Journal of Management Studies, 22(6), 581-596.
Hollingworth, M. (2009, November/December). Building 360
organizational sustainability. Ivey
Business Journal, 73(6), 2.
Walgreens. (2010a). Mission statement. Retrieved from
http://news.walgreens.com/article_display.cfm?article_id=1042
Walgreens. (2010b). Our past. Retrieved from
http://www.walgreens.com/marketing/about/history/default.html
Reuters. (2010). Walgreen Co. Retrieved from
http://www.reuters.com/finance/stocks/companyProfile?symbol
=WAG.N
Robbins, S. P., & Coulter, M. (2007). Management (9th ed.).
42. Upper Saddle River, NJ: Pearson
Prentice Hall.
Walgreens Company. (2010). 2009 Annual report. Retrieved
from
List sources in
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When a citation
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43. website). If they are live,
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 10
http://investor.walgreens.com/annual.cfm
Wheelen, T. L., & Hunger, J. D. (2008). Strategic management
and business policy (11th ed.).
Upper Saddle River, NJ: Pearson Prentice Hall.
44. Whittington, R. (2008). Alfred Chandler, founder of strategy:
Lost tradition and renewed
inspiration. Business History Review, 82(2), 267-277.
Note: Level Headers 3, 4, and 5 are also used but much less
frequently. Click here for
more information on their format and use.
For more information on CSU-
Global APA requirements for
formatting in APA, and examples of
in-text and reference citations, see
the CSU-Global Guide to Writing
and APA Requirements.
https://owl.english.purdue.edu/owl/resource/560/16/
IDENTIFYING THE BEST PRACTICES IN STRATEGIC 11
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Carroll, A. B. (1979). A three-dimensional conceptual model of
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Publishers Inc. [This is a book
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performance: Specifying, implementing
and measuring key principles. Journal of General Management,
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management. The Journal of Management
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 12
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48. University, Jakarta, Indonesia 11480
[email protected]
Raymond Kosala
Computer Science Department, BINUS
Graduate Program – Doctor of
Computer Science, Bina Nusantara
University, Jakarta, Indonesia 11480
[email protected]
Benny Ranti
Faculty of Computer Science,
Universitas Indonesia,
Depok 16424, Indonesia
[email protected]
Suhono Harso Supangkat
Sekolah Teknik Elektro dan
Informatika,
Institut Teknologi Bandung,
Bandung, Indonesia
[email protected]
49. Abstract— There are many ways for the company to
improve its performance, one of them is optimizing the
internal control of the company's activities. Internal
control is intended to evaluate company activities and
operations. This study took a case study at PT. XYZ
related to the evaluation of internal controls in
warehouse management using the COSO framework
approach. From 5 elements and 17 Principle, study
found, there are 2 principles that have not been applied
in PT. XYZ; enforced accountability and control over
technology. The recommendation given is system
improvement as intended the inventory system to be
more accurate and reliable to enable smart warehouse
systems inside organizations.
Keywords: internal control, COSO framework, warehouse
management, evaluation
I. INTRODUCTION
50. There are many ways for the company to improve its
performance, one of them is optimizing the internal control
of the company's activities and also implementation of the
new system to increase efficiency and effectiveness in all
business process activities [4]. Internal control is a process
undertaken by company management to assist the
achievement of operations, reporting and in accordance with
the compliance [9]. The internal optimization is needed
because it describes the overall rules and procedures used by
management to improve management effectiveness in the
business and identify lack of internal control in the business
processes that it can make the organization vulnerable and
possible risks occurs, eventually all these risks can have an
impact on a company's financial performance [2].
In warehouse management, internal controls devoted to
optimizing the functions, including the process of finished
goods inventory, and it useful to organize the distribution
process to the market. According to Rita Makumbi (2013)
51. [6] the function of the warehouse management is one of a
service that can help the company's operational functions
run smoothly as a store of raw material, unfinished goods,
until stock the finished goods or inventory. One of the
problem in warehouse management is high production of
manufacture, company must pay attention to the process
from the beginning of production, to the process of goods
delivery, and inventory calculations.
One of famous approach for warehouse management
control is using COSO framework. COSO framework is one
of tools to maintain the effectiveness and efficiency of
inventory process in organizations [12]. COSO framework
also known as integrated framework that can help company
to:(1) warehouse operation process more effective and
efficient; (2) accountable and reliable of inventory stock
calculation; (3) compliances with government law and
regulations [8].
This research took case study from PT. XYZ as one of
52. company who implemented the warehouse management.
Based on observing in PT. XYZ, we found that company
still difficulty to balance the production and inventory
storage in warehouse which impact to lack of inventory
control.
II. LITERATURE REVIEW
Early definition of internal control is the plan of
organization to coordinate methods and measure all the
element in process business safe, accurate, reliable,
encourage the prescribed managerial policies [10]. Another
definition of internal control is philosophy of risk alignment,
risk management, ethics, policies, resources, tasks and
responsibilities according to organizational capacity to
manage risk [12].
In warehousing planning and control, company produces
various product, company needs good control over its
inventory which two main objectives such as (1) warehouse
inventory planning and control; (2) reliable inventory report
53. to support financial statements [11]
Related to COSO framework, basic concepts of internal
control are:(a) internal control is an integrated process and a
tool that can be used to achieve organization goals; (b)
Internal control is not only limited to policies and
procedures but should include all levels within the
organization; (c) Internal control can only provide a
reasonable guarantee, not an absolute guarantee, because
there are limitations that can obstruct the absoluteness of the
internal control itself; (d) Internal Control will ultimately
result in achievement of goals in categories of financial
statements, compliance, operational activities [13].
Using COSO framework for evaluating the internal
control helps company to calculate the probability of risk
which can occur adversely [2]. However COSO can
maintain and support the company to maintain risk which
known can give positive feedback nor negative [12].
54. COSO framework is consist of five: (1) Control
environment; (2) Risk assessment; (3) Control activities; (4)
Information & Communication; (5) Monitoring activities
[7].
Figure 1. The COSO Cube [3]
Table 1. Component of Internal Control in COSO [1]
III. METHODOLOGY
With COSO framework approach this research starting
with process business analysis as preliminary measurement
and basic analysis in PT. XYZ then continue with internal
control evaluation as follow:
Figure 2. The Research Flow for Warehouse Management
Evaluation in PT. XYZ
For detail performed as follows:
55. 1) Meeting related to explaining flow of evaluation
process.
2) Conducting interviews with stakeholders such as IS
team leader operations, IS analyst, supervisor factory
logistics, team leader factory logistics, warehouse staff,
forklift drivers, internal control, and IPG (Information
Protection & Governance) to observe and also learn
detail about how the business process run, systems
used and also the company's internal control
procedures.
3) Documents checking related to the process of the
finished goods inventory.
4) Doing directly observations in order to learn and
understand more clearly about the working procedures
associated with the process of finished goods
inventory.
IV. ANALYSIS AND RESULT
A. FINDINGS
Based on the results of research and interviews as
part of internal control evaluation, here are the results:
56. Based on the result above, total of 17 principles from
COSO framework known as 2 principles is in red area for
medium and high risk area, 6 principles is in yellow area
which “not fully adapted” for medium and high risk area
and green area for total 9 principles from low and high
risk area.
For the red area, we conducted deeply investigation
as high level evaluation for give the best
recommendation. We found incorrect procedure during
the process of inventory cycle in warehouse, due to goods
receipt in warehouse is not loaded to the shelf directly
and it put to wrong shelf. The impact, a lot of expired
inventory due to incorrect process in goods issue. The
inventory are stored in a multilevel shelf. During the
good issue and shipment for delivery, it was taken
randomly.
Another issued for the red area is control activities for
control over technology. PT. XYZ not only use
warehouse management but also already used one of the
systems like robot machine systems for put the inventory
during the goods receipt. The process starts when
shipping case sent by the conveyor and the systems will
create into one pallet by robot machine then the next step
is data will be stored in the robot database, but once in
while systems went down, there is no back up so the
process will be stopped or create manually. The effect for
this case is lack of control for goods receipt.
57. B. RECOMMENDATION
After we found the fact findings about internal control
evaluation for warehouse management in PT. XYZ, the
recommendation is as follow:
• Conducting customization through warehouse
management system at PT. XYZ.
• Change business processes related to system
requirements.
The recommendation above expected, will support and
improved the process in PT. XYZ such as:(1) Eliminate the
manual process; (2) Provide reliable information about
location of inventory stored and retrieved; (3) Trackable
inventory; (4) Provide real-time information related to
inventory in the warehouse.
The recommendation of design architecture for
warehouse management customization is using Three-Tier
Architecture. While the warehouse management will
integrated with robot machine and the application will store
into one single application server. This design purpose with
benefit: (1) optimized the server for storage, data process
58. and retrieving database; (2) Reduce data duplication [5].
Figure 3. Three-Tier Architecture [5]
The business process changes purposed as follow:
Robot Machine
Systems
Warehouse
Management
Systems
DATABASE
Interface Process Integration
Mobile Scanner (Goods Issue)
Inventory Barcode Create
Automatic Inventory Stock Calculation
Recommendation for Goods Issue
Movement (First In First Out Method
Adoption)
59. Figure 4. System Design
System design from figure 4, describes about additional
interface process integration as bridging between warehouse
management systems and robot machine systems which all
data from the systems will save into single database.
Otherwise the process will improve since the inventory
movement will follow with FEFO (First Expired First Out),
like picture describe in figure 5.
Table 2. Coso Matrix Performance in PT. XYZ
In the figure 5 shown the inventory movement while
systems automatically will scan and check the criteria. If the
criteria of the product proper the next step systems will
input into inventory systems and robot systems will take the
product into the pallet specifically based on criteria and
60. create delivery notes, afterwards the inventory staff will put
into shelf storing. For the next process, PT. XYZ move the
process of inventory into FEFO System (First Expired First
Out): the systems will create the delivery note (inventory
selection based on expired date) and show which the
inventory should out and help the inventory staff find the
correct inventory.
V. CONCLUSION
COSO framework not only providing better internal
control but also measurement of compliance risk due to
reviewing the organization operational as well. COSO
framework can support the risk mitigation, which can give
recommendation and also solution to the company.
Through 5 elements and 17 principles, it will help
company reach the objective nor goal of effectiveness and
efficiency company operation. Another opinion COSO
framework is likely common audit that enables controls not
the business operations but also all personnel inside of
company.
REFERENCES
61. [1] COSO Framework. (2016). Retrieved from
http://www.bussvc.wisc.edu/intcntrls/cosoframework.h
tml
[2] Diane J. Janvrin, E. A. (2012). The Updated COSO
Internal Control— Integrated Framework:
Recommendations and Opportunities for Future
Research. JOURNAL OF INFORMATION SYSTEMS,
189-213.
[3] J. Stephen McNally, C. (2013, June 2013). The 2013
COSO Framework & SOX Compliance : ONE
APPROACH TO AN EFFECTIVE TRANSITION.
Retrieved from
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ransition%20Article-
Final%20COSO%20Version%20Proof_5-31-13.pdf
[4] Jokipii, A. (2009). Determinants and consequences of
internal control in firms: a contingency theory based
analysis. Springer Science-Business Media, 115-144
62. [5] Kambalyal, C. (2010). Three Tier Architecture.
Retrieved from
http://channukambalyal.tripod.com/NTierArchitecture.
pdf
[6] Makumbi, R. (2013). Introduction to Warehousing
Principles and Practices. Lambert Academic
Publishing.
Figure 5 – The Process of Inventory Movement
[7] Martin, K., Sanders, E., & Scalan, G. (2014). The
Potential Impact of COSO Internal Control Integrated
Framework Revision on Internal Audit Structured
SOX Work Program . Elsivier - Research in
Accounting Regulations.
[8] Mary B. Curtis, F. H. (2000). The components of a
comprehensive framework of internal control. The
CPA Journal, 64-66.
63. [9] Miles E.A. Everson, S. E. (2013). Internal Control —
Integrated Framework. NY: Committee of Sponsoring
Organizations of the Treadway Commission.
[10] Procedure, A. I. (2008). Codification of auditing
standards and procedures . University of Mississippi
Library. Accounting Collection.
[11] Ravee, J. M. (2009). Pengantar Akuntansi-Adaptasi
Indonesia . Jakarta: Salemba Empat.
[12] Thomas V. Scannell, S. C. (2013). Supply Chain Risk
Management within the Context of COSO’s Enterprise
Risk Management Framework. Journal of Business
Administration Research, 15-28, Vol. 2, No. 1.
[13] Tsay, B.-Y. (2010). Designing an Internal Control
Assessment Program Using COSO's Guidance on
Monitoring. New York: The CPA Journal.
sustainability
64. Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil,
Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018;
Published: 19 October 2018
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Abstract: Efficient decision making based on business
intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development
of information and communication
technology has made collection and analysis of big data
essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA).
However, many of these studies are
not linked to BI, as companies do not understand and utilize the
concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review
the literature on BI, big data,
and BDA to show that they are not separate methods but an
integrated decision support system.
Second, we explore how businesses use big data and BDA
practically in conjunction with BI through
a case study of sorting and logistics processing of a typical
courier enterprise. We focus on the
65. company’s cost efficiency as regards to data collection, data
analysis/simulation, and the results from
actual application. Our findings may enable companies to
achieve management efficiency by utilizing
big data through efficient BI without investing in additional
infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to
maintain or increase competitiveness.
Keywords: business application; big data; big data analysis;
business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and
continuously evolving methods of
extracting valuable information through big data and big data
analysis (BDA) for business intelligence
(BI) to make better decisions. The term “big data” refers to
large amounts of information or data at
a certain point in time and within a particular scope. However,
big data have a short lifecycle with
rapidly decreasing effective value, which makes it difficult for
academic research to keep up with their
fast pace. In addition, big data have no limits regarding their
type, form, or scale, and their scope is
too vast to narrow them down to a specific area of study.
Big data can also simply refer to a huge amount of complex
data, but their type, characteristics,
scale, quality, and depth vary depending on the capabilities and
purpose of each company.
The same holds for the reliability and usability of the results
gathered from analysis of the data.
Previous studies generally agree on three main properties that
66. define big data, namely, volume,
velocity, and variety, or the “3Vs” [1–4], which have recently
been expanded to “5Vs” with the addition
of veracity/verification and value [5–10].
There are numerous multi-dimensional methods for choosing
how much data to gather and how
to analyze and utilize the data. In brief, the methodology for
extracting valuable information and
taking full advantage of it could be more important than the
data’s quality and quantity. A substantial
amount of research has been devoted to establishing and
developing theories concerning big data,
Sustainability 2018, 10, 3778; doi:10.3390/su10103778
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1050/10/10/3778?type=check_update&version=1
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http://www.mdpi.com/journal/sustainability
Sustainability 2018, 10, 3778 2 of 15
BDA, and BI to address this need, but it is still challenging for
a company to find, understand, integrate,
and use the findings of these studies, which are often conducted
independently and cover only select
aspects of the subject.
BDA refers to the overall process of applying advanced analytic
skills, such as data mining,
67. statistical analysis, and predictive analysis, to identify patterns,
correlations, trends, and other useful
techniques [11–15]. BDA contributes to increasing the
operational efficiency and business profits,
and is becoming essential to businesses as big data spreads and
grows rapidly.
BI is a decision support system that includes the overall process
of gathering extensive data,
extracting useful data, and providing analytical applications. In
general, BI has three common
technological elements: a data warehouse integrating an online
transaction processing system;
a database addressing specific topics; online analytical
processing that is used to analyze data in
multi-dimensions in order to use those data; and data mining,
which involves a series of technological
methods for extracting useful knowledge from the gathered data
[16–20].
Some areas of BI and BDA, such as data analysis and data
mining, overlap. This is to be expected,
as the raw data in BI have recently expanded to become big data
in volume and scope. This has
necessitated reorganization of the field and concepts of BI to
provide business insights and enable
better decision making based on BDA [21]. Although BI and
BDA are generally studied independently,
it is challenging and often unnecessary to distinguish between
the two concepts when performing
business tasks.
Given the cost of gathering and analyzing big data, it is
important to identify what data to collect,
the range of the data, and the most cost-effective purpose of the
data using BI. For this purpose, it is
68. effective to understand and apply the methodology based on
experiences of companies shared through
a case study. Therefore, the present study has the following
aims. First, we explore the meaning of BI,
big data, and BDA through a literature review and show that
they are not separate methods, but rather
an organically connected and integrated decision support
system. Second, we use a case study to
examine how big data and BDA are applied in practice through
BI for greater understanding of the
topic. The case study is conducted on a large and rapidly
growing courier service in the logistics
industry, which has a long history of research. In particular, we
examine how the company efficiently
allocates vehicles in hub terminals by collecting, analyzing, and
applying big data to make informed
decisions quickly, as well as uses BI to enhance productivity
and cost-effectiveness.
The rest of the paper proceeds as follows. Section 2 reviews the
research background and literature
related to BI, big data, and BDA. Section 3 presents the case
study for the company and industry and
discusses the case in detail. Finally, Section 4 concludes by
discussing the implications and directions
for future research.
2. Literature Review
Big data have become a subject of growing importance,
especially since Manyika et al. pointed out
that they should be regarded as a key factor to increase
corporate productivity and competitiveness [22].
Many researchers have shown interest in big data, as the rapid
development of information and
communication technology (ICT) generates a significant amount
69. of data. This has led to lively
discussions about the collection, storage, and application of
such data. In 2012, Kang et al. argued that
the value of big data lies in making forecasts by recognizing
situations, creating new value, simulating
different scenarios, and analyzing patterns through analysis of
the data on a massive scale [23]. In 2011,
only 38 studies related to big data and BDA were listed in the
Science Citation Index Expanded (SCIE),
Social Science Citation Index (SSCI), Arts & Humanities
Citation Index (AHCI), and Emerging Sources
Citation Index (ESCI), but in 2012, this number increased to 92,
and then rapidly increased to 1009 in
2015 and 3890 in 2017 [24].
Sustainability 2018, 10, 3778 3 of 15
2.1. Toward an Integrated Understanding of Big Data, BDA, and
BI
The research boom regarding big data has led to the
development of BDA, through which
valuable information is extracted from a company’s data.
Companies are well aware of the increasing
importance and investment need for BDA, as shown by Tankard
[25], who claimed that a company can
secure higher market share than its rivals and has the potential
to increase its operating profit margin
ratio by up to 60% by using big data effectively [25,26]. In the
logistics industry, big data are used
more widely than ever for supporting and optimizing
operational processes, including supply chain
management. Big data have been instrumental in developing
new products and services, planning
70. supply, managing inventory and risks, and providing customized
services [26–29].
BI has a longer history of research than that of big data. In
1865, Richard Millar Devens mentioned
the concept in the Cyclopaedia of Commercial and Business
Anecdotes [30], after which Luhn began
using it in its modern meaning in 1958 [31]. Thereafter, Vitt et
al. defined BI as an information system
and method for decision making that incorporates the four-step
cycle of analysis, insight, action,
and performance measurement [32]. Solomon suggested a
framework of BI and argued that research
in the area was necessary [20]. Then, Turban et al. [33]
expanded the scope of research to embrace
data mining, warehousing and acquisition, and business
analysis, and a growing number of studies
followed. Miškuf and Zolotová studied BI using Cognos—a BI
solution system adopted by IBM—and
the case of U.S. Steel to ascertain how to best apply
enterprise/manufacturing intelligence to manage
manufacturing data efficiently [30]. Van-Hau pointed out the
lack of a general framework in BI that
would allow businesses to integrate results and systematically
use them, as well as discussed issues
that needed to be researched further [34]. In summary, the
concept of BI has been expanding with
regard to application systems and technologies that support
enterprises in making better choices by
gathering, storing, analyzing, and accessing data more
effectively [35].
Previous research has dealt mostly with management and
decision support systems and
applications in BI, as well as technological aspects such as
algorithms and computing for big data
71. and BDA. However, the research areas are broadening, and
topics are becoming more diverse
based on different macroeconomic environments, pace of
technological progress, and division of
the research field. Therefore, many studies on BI, big data, and
BDA have been conducted separately.
More importantly, big data research has a relatively short
history, as it only started attracting significant
attention since around 2012, when rapid development of ICTs
led to discussions on how to gather
and use the unprecedented amount of data generated. On the
other hand, BI has long been a point of
interest among researchers.
The boundaries between these concepts—big data, BDA, and
BI—are often unclear and ambiguous
for companies. Generally, BI consists of an information value
chain for gathering raw data,
turning these data into useful information, management decision
making, driving business results,
and raising corporate value [36]. However, considering that
“raw data” have been expanded to “big
data” owing to the development of ICT and data storage, it is
safe to say that BI and big data/BDA are
presently not independent methods but organically coexist as an
integrated decision support system,
incorporating all processes from data gathering to management
decision making in business.
As research interest in big data began to grow since 2012, Chen
et al. grouped previous works
in the literature into BI and analytics and divided the evolution
process of the subject into stages to
examine the main characteristics and features of each stage
[37]. Subsequently, Wixom et al. proposed
the necessity of studying BI—including big data/BDA—and
72. business analytics to address changes
in the field, since there was increasing awareness about the use
and need of big data after the BI
Conference of the Communications of the Association for
Information Systems in 2009 and 2010 [38].
Fan et al. studied BI in the marketing sector in a big data
environment and concluded that big data
and BDA are disruptive technologies that reorganize the
processes of BI to gain business insights for
better decision making [21]. In addition, Bala and Balachandran
defined cloud computing and big
data as the two of the most important technologies in recent
years and explored the improvement
of decision-making processes through BI by integrating these
two key technologies for storing and
Sustainability 2018, 10, 3778 4 of 15
distributing data using cloud computing [39]. These cases
illustrate that an increasing number of
researchers are approaching BI and big data/BDA as an
integrated concept.
2.2. In-Depth Research through Case Studies
The growing interest in big data/BDA and rapid development in
this area have strengthened
BI as a decision support system, thereby promoting corporate
management and enhancing business
value by providing more valuable information to generate
innovative ideas for new products and
services. This has led to a rise in customer satisfaction,
improved inventory and risk management,
improved supply chain risk management, creation of
73. competitive information, and provision of
real-time business insights [26–29,40–42].
Considering the short lifecycle of big data and their use in
companies, there are numerous,
multi-dimensional methods for deciding how much data to
gather and how to analyze and utilize the
data speedily and effectively. As David et al. emphasized in The
Parable of Google Flu: Traps in Big Data
Analysis, the essential element is turning data into valuable
information, not the quantity of data or
new data itself [43]. It is thus important to establish a database
of integrated convergent knowledge
and continue to develop this by accumulating knowledge and
experiences through case studies based
on practical use that apply the principals of BI and big
data/BDA effectively. Below, we list examples
of successful studies on the use and application of big
data/BDA in practice.
• Zhong et al. examined a big data approach that facilitates
several innovations that can guide
end-users to implement associated decisions through radio
frequency identification (RFID) to
support logistics management with RFID-Cuboids, map tables,
and a spatiotemporal sequential
logistics trajectory [44].
• Marcos et al. studied both the environment and approaches to
conduct BDA, such as data
management, model development, visualization, user
interaction, and business models [45].
• Kim reported several successful cases of big data application.
Examples include analysis of
competing scenarios through 66,000 simulated elections
74. conducted per day to understand the
decisions of individual voters during the 2012 reelection
campaign of former US president Barack
Obama and delivery routes and time management based on
vehicle and parcel locations adopted
by UPS, a US courier service company [46].
• Wang et al. redefined big data business analytics of logistics
and supply chain management as
supply chain analytics and discussed its importance [47].
• Queiroz and Telles studied the level of awareness of BDA in
Brazilian companies through surveys
conducted via questionnaires and proposed a framework to
analyze companies’ maturity in
implementing BDA projects in logistics and supply chain
management [48].
• Hopkins analyzed the impact of BDA and Internet of things
(IoT), such as truck telematics and
geo-information in supporting large logistics companies to
improve drivers’ safety and operating
cost-efficiency [49].
The above examples of big data/BDA used by governments or
corporations, as well as entities
dealing with methods in either specific or general areas, make it
clear that there is an abundance of
studies on the need for and efficiency of big data. However, big
data and BDA have not been studied
until recently, and few studies use real corporate examples—
especially in the logistics industry—that
provide valuable business insights through detailed methods and
results.
Researchers should endeavor to provide second-hand experience
75. through specific case studies
using big data/BDA-based BI, and then accumulate and
integrate such case studies to establish a
database of integrated convergent knowledge. This could enable
corporations to adjust to changing
environments and improve the productivity and efficiency of the
organization.
Sustainability 2018, 10, 3778 5 of 15
3. Practical Business Application
The present study aims to examine the overall status of the
logistics industry (an industry with
continuously growing demand and prominence) and the courier
service industry (an industry used
by more consumers than any other logistics market segment) as
well as business applications related
to big data/BDA and BI. The final aim is to assist corporations
in reducing trial-and-error periods in
management, establishing long-term strategies, and enhancing
cost-effectiveness of the corporations.
3.1. Courier Service Overview
Given consumers’ increasing focus on personal service and
convenience in consumer products, as
well as global economic development, the manufacturing sector
is converting from mass production of
limited items to multi-item, small-scale production. This is
rapidly increasing the volume and sales of
courier services as more consumers buy online. Increased online
purchases are also a result of ICT
advances. According to the Korean Statistical Information
76. Service, Korea’s e-retail sales amounted
to KRW 79,954,478 million in 2017, an increase of 21.85%
from KRW 65,617,046 million in 2016,
and a massive 107.69% increase from 2013 [50]. The courier
service industry has become the biggest
beneficiary of this dramatic increase in the volume of goods
transported and is a suitable yardstick to
measure the growth of the logistics industry [51,52].
Traditionally, logistics was considered a support
industry for manufacturing and consumption and was mainly
perceived as a cost, but it has since
emerged as the main industry connecting producers and
consumers. Manufacturing corporations
regard supply expansion based on ICT to meet consumers’
demands as a key growth strategy, and the
courier service industry has shown remarkable growth owing to
the sharp increase in the need for
parcel transportation [53].
A courier service is generally defined as comprising the entire
process of transportation,
from receiving a parcel to packaging, transporting, and
delivering the parcel to the final destination
under the transporter’s responsibility and at the customer’s
request [54,55]. The courier service
industry usually faces oligopolistic market competition, as it is
an enormous service system
that requires huge initial investment. Courier service companies
are normally large operational
organizations that deal with large amounts of cargo, hub
terminals, general information systems, and a
wide range of transportation vehicles and consist of a
complicated network of labor and equipment [51].
Davis previously examined the usefulness of courier services by
using information technology
77. in the logistics industry [56]. DeLone and McLean showed that
a successful information system
environment is a significant factor influencing user satisfaction
as it models its influences on
individuals and organizations [57]. Kim et al. focused on the
use of transportation routes,
freight distribution centers, and brokerage points for efficient
parcel transportation via main roads [58].
Visser and Lanzendorf [59] analyzed the effects of business-to-
consumer (B2C) e-commerce for cargo
transportation, revealing that an increase in the demand for
courier services leads to changes in
freight per ton, distance, size, and fill rate of trucks. The
authors illustrated the relationship between
consolidation and transportation routes in courier companies
[59]. Jeong et al. discussed the allocation
of service centers to terminals with a given number of cargo
terminals and locations [60], while Goh
and Min examined the time of delivery by the capacity of cargo
terminals [61]. Meanwhile, Sherif et al.
presented an integrated model of the number and location of
warehouses, allocation of customers to
warehouses, and number and routes of vehicles to minimize
transportation cost, fixed cost, operational
cost, and route cost [62]. Lim et al. focused on the improvement
of service quality while considering
price reduction due to the increase of online demand, volume of
delivery, and short-term responses,
as well as the lack of mid- and long-term responses due to
increase in online transactions [63]. Park et al.
investigated methods of increasing productivity while
considering both logistics and employees by
utilizing a wireless Internet system [64], while Kim and Choi
explored the effects of a corporation’s
logistics technology on courier services based on online
shopping malls as courier service users [65].
78. In summary, most previous research concerning the courier
service industry focused on the
analysis of courier service networks and delivery efficiency in
terms of optimal logistics structures,
Sustainability 2018, 10, 3778 6 of 15
methods for improving service quality, and minimization of
costs in terms of operational requirements.
Only a few case studies gathered and analyzed big data or BI
applications in the field, considering the
increase in e-commerce delivery demand.
3.2. Case Study: CJ Logistics
This study uses the case of CJ Logistics, Korea’s largest
logistics company. It examines the sorting
process, especially regarding decisions about loading/unloading
docks and hub terminals, which are
at the core of courier services, to examine the effective use of
big data/BDA through BI.
CJ Logistics was selected as the research subject as it is the
largest logistics service provider in
Korea with the highest market share and sales revenue of KRW
7110.3 billion in 2017 [66]. In addition,
as shown in Figure 1 (big data case of CJ Logistics, March
2018), the company is an innovation
leader in the industry. It is traditionally considered a 3D
business that uses BI based on high-tech
automation-oriented technology, engineering, and system and
solution plus consulting (TES + C),
while actively and rapidly adopting big data/BDA at the same
79. time.
Sustainability 2018, 10, x FOR PEER REVIEW 6 of 15
3.2. Case Study: CJ Logistics
This study uses the case of CJ Logistics, Korea’s largest
logistics company. It examines the sorting
process, especially regarding decisions about loading/unloading
docks and hub terminals, which are
at the core of courier services, to examine the effective use of
big data/BDA through BI.
CJ Logistics was selected as the research subject as it is the
largest logistics service provider in
Korea with the highest market share and sales revenue of KRW
7110.3 billion in 2017 [66]. In addition,
as shown in Figure 1 (big data case of CJ Logistics, March
2018), the company is an innovation leader
in the industry. It is traditionally considered a 3D business that
uses BI based on high-tech
automation-oriented technology, engineering, and system and
solution plus consulting (TES + C),
while actively and rapidly adopting big data/BDA at the same
time.
Figure 1. Technology, engineering, system and solution plus
consulting (TES + C) of CJ Logistics.
CJ Logistics is a market leader equipped with cutting-edge
logistics technologies, including real-
time tracking of freight, an integrated courier and freight
tracking system that enables users to view
customer information and requirements, satellite vehicle
80. tracking, and temperature control systems
[67]. In 2017, CJ Logistics invested more than KRW 120 billion
to automate its sorting process through
sub-terminals to aid sustainable growth. CJ Logistics’
infrastructure is more than three times bigger
than that of its closest competitor in the courier service
industry. With five hub terminals, more than
270 sub-terminals, and more than 16,000 vehicles, CJ Logistics
processes more than 5.3 million
packages per day. Its mega hub terminal in Gwangju, Gyeonggi-
do Province—which was due for
completion in August 2018 with an investment of more than
KRW 400 billion—will utilize
convergence technologies such as big data, robots, and IoT to
expand its services for the convenience
of its customers across Korea. This will include same-day
delivery, same-day return, and scheduled
delivery services. The company is simultaneously moving
forward with its planned international
growth. At the end of 2017, CJ Logistics had a global network
of 238 centers in 137 cities and 32
countries. It opened the Shenyang Flagship Center, a mammoth
logistics center in Shenyang, China,
on 15 June 2018. The purpose of this investment was to
accelerate the company’s business in northern
Asia, including three provinces of northeastern China—
Liaoning, Jilin, and Heilongjiang. The
company has implemented huge capital expenditure to broaden
its business efficiently, laying the
groundwork for sustainable growth and expansion by raising the
entrance barrier for rivals (big data
case of CJ Logistics, March 2018).
CJ Logistics mainly uses a hub-and-spoke system, which
connects points via hubs or logistics
centers dealing with massive cargo volumes in its courier
81. service; it also uses a point-to-point
operational system directly connecting origins and destinations.
The point-to-point system delivers
Figure 1. Technology, engineering, system and solution plus
consulting (TES + C) of CJ Logistics.
CJ Logistics is a market leader equipped with cutting-edge
logistics technologies,
including real-time tracking of freight, an integrated courier and
freight tracking system that enables
users to view customer information and requirements, satellite
vehicle tracking, and temperature
control systems [67]. In 2017, CJ Logistics invested more than
KRW 120 billion to automate its sorting
process through sub-terminals to aid sustainable growth. CJ
Logistics’ infrastructure is more than
three times bigger than that of its closest competitor in the
courier service industry. With five hub
terminals, more than 270 sub-terminals, and more than 16,000
vehicles, CJ Logistics processes more
than 5.3 million packages per day. Its mega hub terminal in
Gwangju, Gyeonggi-do Province—which
was due for completion in August 2018 with an investment of
more than KRW 400 billion—will
utilize convergence technologies such as big data, robots, and
IoT to expand its services for the
convenience of its customers across Korea. This will include
same-day delivery, same-day return,
and scheduled delivery services. The company is simultaneously
moving forward with its planned
international growth. At the end of 2017, CJ Logistics had a
global network of 238 centers in 137 cities
and 32 countries. It opened the Shenyang Flagship Center, a
mammoth logistics center in Shenyang,
China, on 15 June 2018. The purpose of this investment was to
82. accelerate the company’s business in
northern Asia, including three provinces of northeastern
China—Liaoning, Jilin, and Heilongjiang.
The company has implemented huge capital expenditure to
broaden its business efficiently, laying the
Sustainability 2018, 10, 3778 7 of 15
groundwork for sustainable growth and expansion by raising the
entrance barrier for rivals (big data
case of CJ Logistics, March 2018).
CJ Logistics mainly uses a hub-and-spoke system, which
connects points via hubs or logistics
centers dealing with massive cargo volumes in its courier
service; it also uses a point-to-point
operational system directly connecting origins and destinations.
The point-to-point system delivers
to and from terminals, saving time on package arrivals while
alleviating capacity issues during the
peak season. However, growing volumes may increase costs, as
they require more investment in
terminals; a volume imbalance among terminals can cause
unnecessary additional costs. On the other
hand, in the hub-and-spoke system, packages are gathered and
sorted in a large terminal before being
delivered to a destination terminal. The advantage of this
system is that it reduces arrival time to
the terminals, easing the imbalance in volume. However, the
disadvantages are that it may delay
deliveries to distant or rural areas during the peak season and
requires a large-scale hub terminal [67].
Since CJ Logistics mostly uses the hub-and-spoke system,
83. whose core is the logistics process
at the hub terminal, this study focuses on decisions concerning
the loading/unloading docks in the
process. This focus point was selected for the following
reasons. First, few previous studies have
focused on this segment, even though it has greater room for
improvement regarding productivity and
efficiency than other segments. Second, the importance of this
segment may have been overlooked,
since standardizing the process is challenging owing to
differences in the environment, such as the
distance between buildings or shape of the space. Third, there
are many other difficulties to address,
including outsourcing, warehouse management, freight payment,
inventory management, packing,
customs clearance, and customer claims [51]. Many courier
service providers allocate hub terminal
docks for loading/unloading simply according to terminal
conditions, such as the distance between
docks and number of packages, mostly based on past
experience. By contrast, CJ Logistics has
dramatically improved productivity and efficiency by “seeing
the unseen” through the use of big
data/BDA and promoting faster and better decision making
through BI.
The hub terminal process was selected from the three general
stages of courier services, namely,
pick-up, transport/sorting, and delivery (Figure 2). This process
was selected because it is the central
process connecting pick-ups from different locations with
delivery to different destinations [68,69].
Sustainability 2018, 10, x FOR PEER REVIEW 7 of 15
84. to and from terminals, saving time on package arrivals while
alleviating capacity issues during the
peak season. However, growing volumes may increase costs, as
they require more investment in
terminals; a volume imbalance among terminals can cause
unnecessary additional costs. On the other
hand, in the hub-and-spoke system, packages are gathered and
sorted in a large terminal before being
delivered to a destination terminal. The advantage of this
system is that it reduces arrival time to the
terminals, easing the imbalance in volume. However, the
disadvantages are that it may delay
deliveries to distant or rural areas during the peak season and
requires a large-scale hub terminal
[67].
Since CJ Logistics mostly uses the hub-and-spoke system,
whose core is the logistics process at
the hub terminal, this study focuses on decisions concerning the
loading/unloading docks in the
process. This focus point was selected for the following
reasons. First, few previous studies have
focused on this segment, even though it has greater room for
improvement regarding productivity
and efficiency than other segments. Second, the importance of
this segment may have been
overlooked, since standardizing the process is challenging
owing to differences in the environment,
such as the distance between buildings or shape of the space.
Third, there are many other difficulties
to address, including outsourcing, warehouse management,
freight payment, inventory
management, packing, customs clearance, and customer claims
[51]. Many courier service providers
allocate hub terminal docks for loading/unloading simply
according to terminal conditions, such as
85. the distance between docks and number of packages, mostly
based on past experience. By contrast,
CJ Logistics has dramatically improved productivity and
efficiency by “seeing the unseen” through
the use of big data/BDA and promoting faster and better
decision making through BI.
The hub terminal process was selected from the three general
stages of courier services, namely,
pick-up, transport/sorting, and delivery (Figure 2). This process
was selected because it is the central
process connecting pick-ups from different locations with
delivery to different destinations [68,69].
Figure 2. General courier service structure.
An incident that occurs at the hub terminal can have a serious
impact on the entire cycle—from
pick-up to delivery—and could cause a bottleneck effect at hub
terminals. This is a significant issue
that needs to be addressed to secure growth in the industry, as it
can paralyze transportation and
delivery within a company on a large scale. Resolving this issue
alongside difficulties in other areas
by using big data/BDA could improve company productivity and
efficiency as a whole.
Figure 2. General courier service structure.
Sustainability 2018, 10, 3778 8 of 15
An incident that occurs at the hub terminal can have a serious
impact on the entire cycle—from
86. pick-up to delivery—and could cause a bottleneck effect at hub
terminals. This is a significant issue
that needs to be addressed to secure growth in the industry, as it
can paralyze transportation and
delivery within a company on a large scale. Resolving this issue
alongside difficulties in other areas by
using big data/BDA could improve company productivity and
efficiency as a whole.
3.2.1. Data and Methodology
CJ Logistics witnessed a drastic rise in online and offline B2C
transactions, experiencing a
compound annual growth rate of 9.9% from 2011 to 2016. In
addition, the courier company’s market
share rose from 42% in 2015 to 46% in 2017. To accommodate
this growth, the company increased
the number and size of its vehicles, established a demand
forecasting system, and improved its
peer-to-peer (P2P) network. These measures increased the daily
delivery per person from 262 boxes
to 344 boxes between 2015 and 2017, while the sorting capacity
of hub terminals was improved from
around 4.4 million cases to 5.3 million cases during the same
period. However, since the company’s
hub terminal capacity had reached its limit, bottlenecks in the
logistics process were becoming serious.
As a result, the rate of remaining cargo increased by 3.1%, and
the overnight delivery rate dropped by
2.3% between 2015 and 2017. This situation makes it clear that
it is imperative for the company to find
a solution through methods that could enhance hub terminal
capacity.
To address this issue, CJ Logistics decided to integrate BDA
into its existing decision-making
87. processes to understand the current situation better, enabling the
company to make better-informed
choices and identify future directions. Daejeon hub was chosen
for the pilot test. First, information
was gathered on roughly 75 million inbound invoices and 240
million packages at Daejeon hub
terminal out of a total of 260 million inbound invoices and 720
million packages at hub terminals.
The information was gathered over a three-month period
between November 2016 and January 2017.
This information was used to generate extensive data on the
unloading docks at the hub terminal
as well as on routes, transition points, moving time, loading
docks, remaining cargo, and sorting
personnel for BDA. Based on the results, the shortest distance
between loading and unloading docks,
time metrics, and vehicle loading information were integrated
with application methods (as shown
later in this subsection). The simulation produced results that
would have been impossible to obtain by
conventional dock allocation methods that are based on
classification codes and number of packages.
By reflecting the results at different sites, CJ Logistics was able
to increase its hub terminal capacity, as
shown in the following paragraphs.
Packages delivered by customers are collected at sub-terminals
in each region and transported
to hub terminals by truck. Vehicles entering the hub terminal
wait for dock allocation and are then
unloaded or loaded after being allocated, as per the process
shown in Figure 3. In the entire dock
allocation process, CJ Logistics reflected at least two types of
objective functions to identify the
first-in-line vehicle to unload among those waiting, the closest
unloading chute, and the second-in-line
88. chute and vehicle in terms of waiting time while unloading
vehicles to optimize dock allocation in the
hub terminal.
Objective function (1) sets the weighting factor for unloading
priority and reflects the number of
packages using the volume information in the vehicles for
application based on four types of “reference
information”, namely, (1) loading priority of waiting vehicles
by route; (2) customer classification
according to special sale customers, premium customers, and
general customers; (3) vehicle
classification according to unloading only, unloading/loading,
and loading only; and (4) content
classification according to console, produce, and general. These
unloading priorities were set within
the “constraints” of the remaining vehicles that had not been
unloaded, and vehicles waiting for more
than three hours that should have been unloaded first. Table 1
presents vehicle unloading priorities
based on weighting factor and time.
Sustainability 2018, 10, 3778 9 of 15
Sustainability 2018, 10, x FOR PEER REVIEW 9 of 15
Figure 3. Optimization of dock allocation process.
Objective function (1): Selection of vehicles to unload first
= ∗ (1)
W: weighting factor for unloading priority, N: number of
packages.
89. Table 1. Selection of vehicle unloading priorities according to
weighting factor and time.
Order Category W (Before 0:00) W (After 0:00)
1 Special sale customer 50 3
2 Route for loading first 30 50
3 Console volume 8 15
4 Produce 7 10
5 Premium customer 3 20
6 First-in, first-out (FIFO) 2 2
Note: W: weighting factor for unloading priority.
Objective function (2) pertains to optimum unloading chute
allocation. This was calculated using
volume by loading chute for each vehicle, travel time between
unloading/loading chutes, content
information, and reflected travel time under the constraints. The
function includes minimization of
congestion through equal allocation of vehicles, minimization
of travel between buildings, and
allocation of vehicles with more than 30% console content to a
special console unloading zone, based
on two types of reference information. The reference
information includes (1) travel time between
Figure 3. Optimization of dock allocation process.
Objective function (1): Selection of vehicles to unload first
Selection of vehicles to unload first = ∑( W ∗ N ) (1)
W: weighting factor for unloading priority, N: number of
packages.
90. Table 1. Selection of vehicle unloading priorities according to
weighting factor and time.
Order Category W (Before 0:00) W (After 0:00)
1 Special sale customer 50 3
2 Route for loading first 30 50
3 Console volume 8 15
4 Produce 7 10
5 Premium customer 3 20
6 First-in, first-out (FIFO) 2 2
Note: W: weighting factor for unloading priority.
Objective function (2) pertains to optimum unloading chute
allocation. This was calculated
using volume by loading chute for each vehicle, travel time
between unloading/loading chutes,
content information, and reflected travel time under the
constraints. The function includes
minimization of congestion through equal allocation of
vehicles, minimization of travel between
buildings, and allocation of vehicles with more than 30%
console content to a special console unloading
zone, based on two types of reference information. The
reference information includes (1) travel
time between loading/unloading chutes and (2) unloading
service time for maximum, minimum,
and average volume.
Sustainability 2018, 10, 3778 10 of 15
Objective function (2): Optimum unloading chute allocation
91. Optimum unloading chute allocation = ∑( L ∗ T ) (2)
L: Volume in the vehicles by loading chutes, T: Travel time
between loading/unloading chutes.
Although vehicles are assigned to docks through optimum
chutes, by considering operational
status at the docks and the fact that unloading procedures can
change at any time, the function repeats
the optimization of the dock allocation process to decide
whether a vehicle should be placed on hold
or assigned to a second dock, or whether a second-in-line
vehicle should be sent first to increase
efficiency. Information from the BDA was used in connection
with balancing the volume among
loading docks through tracking analysis of individual products,
fast delivery by development of
new P2P routes, expansion of hub terminal capacity, and
volume analysis of products for higher
productivity and efficiency.
3.2.2. Simulation and Adoption Result
On 6 November 2016, vehicle number “98 Ba 3490” loaded with
cargo from Jungrang sub-terminal
arrived at Daejeon hub terminal, unloaded, and then should have
reloaded 249 items (52.8% of the
total load) on the B1 and 1st floors of Building A, 177 items
(37.5% of the total load) on the 1st and 2nd
floors of Building B, and 46 items (9.7% of the total load) on
the 1st floor of Building C as can be seen
in Figure 4a, and the number of items in the red box indicate the
quantity that should be loaded in the
individual dock. Therefore, the vehicle was allocated to Dock
D7 of Building A, since there were more
packages to load at Building A than at the other docks (see the
92. purple dot in Figure 4a). It took 57 min
and 34 s to complete the unloading/loading process.
However, a simulation based on big data/BDA revealed that
dock allocation according to the
number of items to load, as shown earlier in this subsection,
was very inefficient. The choice of
Dock D7, Building A was ranked 41st, as evident from the
ranking table in Figure 4b, in terms of
efficiency, and unloading at Dock F8, Building B proved most
efficient (see the blue dot in Figure 4b).
This information could not be determined before the BDA. The
simulation results showed that
unloading at Dock F8, Building B could decrease the vehicle’s
travel time to around one-fifth of
the actual time it took when using Dock D7, Building A. The
actual travel time was three times greater
than the simulated travel time. When a simulation was
conducted using the entire fleet of vehicles,
the overall efficiency of the hub terminal rose, reducing travel
time by more than 20 min, even when
unloading at Dock D7, Building A.
CJ Logistics shared the simulation results through the internal
reporting system using BI,
thus enabling management to make decisions optimizing dock
allocations and considering the flow
of cargo traffic in hub terminals. As a result, the flow of
products improved dramatically, raising the
processing rate per hour as well as the rate of overnight
deliveries, while lowering the rate of remaining
freight. In Daejeon hub terminal, the average distribution time
per vehicle was 52 min and 42 s during
the thanksgiving season in 2016. This time decreased to 44 min
and 7 s during the same period in 2017,
a remarkable improvement of 16.3%. Building on such positive
93. results, CJ Logistics subdivided the
distribution model by days of the week, seasons, and events,
and fine-tuned the metrics of optimum
paths. This system was applied to mega hubs in metropolitan
areas. By late 2017, the system had
been applied throughout the country. The remaining cargo was
reduced by 14% from the previous
year, and the overnight delivery rate increased by 2.8% in 2017.
In summary, CJ Logistics achieved a
phenomenal rise in productivity and cost-effectiveness through
the use of big data/BDA. It still used
the existing infrastructure but expanded the application of BI
based on BDA to make decisions across
business segments, for long-term strategies, and for additional
investment by management.
Sustainability 2018, 10, 3778 11 of 15
Sustainability 2018, 10, x FOR PEER REVIEW 11 of 15
Figure 4. (a) Before optimization of dock allocation; DaeJeon
Hub Terminal of CJ Logistics; (b) After
optimization of dock allocation using BDA; DaeJeon Hub
Terminal of CJ Logistics.
4. Discussion and Conclusions
Business activities that are believed to be sufficiently empirical
and productive to ensure
efficiency can benefit from different perspectives and
breakthroughs upon acquiring and analyzing
big data, and can be realized through BI. The value of big data
depends on the types of data extracted
94. and how they are utilized. The crucial factor, however, is the
method of turning raw data into
valuable information, and not the quality or quantity of the data.
Therefore, it is vital to identify the
type and scope of data to be collected according to their purpose
and focus area. The efficient use of
Figure 4. (a) Before optimization of dock allocation; DaeJeon
Hub Terminal of CJ Logistics; (b) After
optimization of dock allocation using BDA; DaeJeon Hub
Terminal of CJ Logistics.
4. Discussion and Conclusions
Business activities that are believed to be sufficiently empirical
and productive to ensure efficiency
can benefit from different perspectives and breakthroughs upon
acquiring and analyzing big data,
and can be realized through BI. The value of big data depends
on the types of data extracted and
how they are utilized. The crucial factor, however, is the
method of turning raw data into valuable
information, and not the quality or quantity of the data.
Therefore, it is vital to identify the type and
scope of data to be collected according to their purpose and
focus area. The efficient use of big data
Sustainability 2018, 10, 3778 12 of 15
may provide an opportunity to a small or medium enterprise to
become a large corporation or market
leader by taking advantage of meaningful information, and for a
large corporation to maintain its
market share and ensure sustainable growth and
95. competitiveness. Many studies have been conducted
on BI, big data, and BDA so far, but for enterprises to
implement changes, it is necessary for them
to understand intuitively that BI, big data, and BDA cannot be
separated, but should be integrated
and utilized in the management decision support system as a
whole. As the case study of CJ Logistics
shows, the process of collecting and analyzing big data and
applying it through BI is separated neither
individually nor progressively.
The limitations of this case study include the facts that the big
data have been derived from a
limited date range, there are differences in the infrastructure
and situation of each company, and the
case study represents only a portion of a company within a
specific industry. Nonetheless, we believe
that this case study can be directly applied to other logistics
companies within the same sector and,
therefore, can help these companies achieve time and cost
efficiency without much trial and error.
Our study can also have a positive long-run impact by informing
companies in the logistics industry,
as well as in other industries, of the possibility of increasing the
efficiency and productivity of their
existing infrastructure without additional investment. CJ
Logistics’ process of expanding and applying
the experience gained through the combined use of BI, big data,
and BDA to all of its business
divisions can be a valuable example for other companies and
may provide insights concerning future
business directions and reduced trial and error. Future studies
can expand on this research to provide
practical knowledge and experience by collecting and sharing
similar case studies, including those
about volumetric analysis through ITS (Intelligence Scanner) of
96. goods, volume management through
production of boxes for each customer, classification of
customers based on volume density, and etc.
which are based on practical business applications to build
integrated knowledge.
Author Contributions: Conceptualization, D.-H.J. and H.-J.K.;
methodology, D.-H.J.; software, D.-H.J.; validation,
D.-H.J. and H.-J.K.; formal analysis, D.-H.J.; investigation, D.-
H.J.; resources, D.-H.J.; data curation, D.-H.J.;
writing—original draft preparation, D.-H.J.; writing—review
and editing, D.-H.J. and H.-J.K.; visualization,
D.-H.J.; supervision, D.-H.J. and H.-J.K.; project
administration, D.-H.J. and H.-J.K.
Funding: This research received no external funding.
Conflicts of Interest: CJ Logistics provided some part of the
data for the case study to Dong Hui Jin and validated
all the data used in this study.
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