The COSO framework of internal controls is practiced within companies around the world. The objectives of the COSO framework are closely related to its five components. For this week’s activity, please discuss these five components of the COSO framework. Be sure to include each components’ impact on each of the COSO framework objectives. What do you feel an auditor would most be concerned with during an IT audit? Lastly, discuss suggestions for integrating COSO framework compliance into a company in which you are familiar. 

Your paper should meet the following requirements:

• Be approximately 2-4 pages in length, not including the required cover page and reference page.

• Follow APA6 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.

• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.

• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

Note: 

  

Abstract not intended,

The abstract should have  paragraph 150 min and 250 max,

Abstract no citation and page by itself. 

Body: paragraph should be in 3 to 5 sentences.

References: 

sustainability

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; yutajin002@gmail.com
* Correspondence: hjkim@assist.ac.kr; 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
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 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 www.mdpi.com/journal/sustainability

http://www.mdpi.com/journal/sustainability

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https://orcid.org/0000-0003-3698-4665

http://www.mdpi.com/2071-1050/10/10/3778?type=check_update&version=1

http://dx.doi.org/10.3390/su10103778

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,
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
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 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
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
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 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 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 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 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 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
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].

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 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 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 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 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, 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

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
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
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
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
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.

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.

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

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 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 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
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 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 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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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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• Introduction
• Literature Review

Toward an Integrated Understanding of Big Data, BDA, and BI
In-Depth Research through Case Studies

• Practical Business Application

Courier Service Overview
Case Study: CJ Logistics
Data and Methodology
Simulation and Adoption Result

Discussion and Conclusions
References

978-1-5386-6589-3/18/$31.00©2018 IEEE

COSO Framework for Warehouse

Management

Internal Control Evaluation: Enabling Smart

Warehouse Systems

Ratna Sari
Information Systems Department,

School of Information Systems,

Bina Nusantara University,

Jakarta 11480, Indonesia

Computer Science Department, BINUS

Graduate Program – Doctor of

Computer Science, Bina Nusantara

University, Jakarta, Indonesia 11480

rasari@binus.edu

Raymond Kosala
Computer Science Department, BINUS

Graduate Program – Doctor of
Computer Science, Bina Nusantara
University, Jakarta, Indonesia 11480

rkosala@binus.edu

Benny Ranti
Faculty of Computer Science,

Universitas Indonesia,

Depok 16424, Indonesia

ranti@ui.ac.id

Suhono Harso Supangkat
Sekolah Teknik Elektro dan

Informatika,

Institut Teknologi Bandung,

Bandung, Indonesia

suhono@lpik.itb.ac.id

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

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)

[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

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

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].

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:

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:
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.

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

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)

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

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

[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

https://www.coso.org/documents/COSO%20McNallyT

ransition%20Article-

Final%20COSO%20Version%20Proof_5-31-13

[4] Jokipii, A. (2009). Determinants and consequences of

internal control in firms: a contingency theory based

analysis. Springer Science-Business Media, 115-144

[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.

[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.

Runninghead: 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

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:

 Clearer sense of vision for the organization

 Sharper focus on what is strategically important

 Improved understanding of a changing environment

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, 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

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

Evaluation

and

Control

External:

Opportunities

Threats

Mission

Objectives

Strategies

Policies

Programs

Budgets

Procedures

Performance

Societal

Environmental

Task Environmental

Internal:

Strengths

Weaknesses

Structure

Culture

Resources
Figure 1. The strategic management model was adapted from Strategic management and business policy

(11th ed.) by T. L. Wheelen, & J. D. Hunger, 2008, Upper Saddle River, NJ: Pearson Prentice Hall.

Corporate Sustainability

In addition to enhancing financial performance through strategic management,

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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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

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.

 Economic responsibilities include producing goods and services to meet the

needs/wants of society in order to make a profit;

 Legal responsibilities are the laws and regulations the company is expected to

abide by;

 Ethical responsibilities are included in the previous two statements, but also

include the norms and beliefs held by society;

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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 6

 Discretionary responsibilities are other responsibilities taken on by the

organization including voluntary activities and philanthropic contributions

(Carroll, 1979).

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.

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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

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

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,

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).

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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 8

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 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.

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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 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.). Upper Saddle River, NJ: Pearson

Prentice Hall.

Walgreens Company. (2010). 2009 Annual report. Retrieved from

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to be able to distinguish

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each web page. To do this,

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each citation.

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.

Whittington, R. (2008). Alfred Chandler, founder of strategy: Lost tradition and renewed

inspiration. Business History Review, 82(2), 267-277.

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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 11

References

Carroll, A. B. (1979). A three-dimensional conceptual model of corporate performance. The

Academy of Management Review, 4(4), 497. [This is a journal article citation. Articles

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citation.]

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French, S. (2009). Critiquing the language of strategic management. The Journal of Management

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process. Journal of Management Studies, 22(6), 581-596.

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Business Journal Online. Retrieved from

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is published online, so you would include the URL.]

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http://www.reuters.com/finance/stocks/companyProfile?symbol=WAG.N

IDENTIFYING THE BEST PRACTICES IN STRATEGIC 12

Walgreens. (2010a). Mission statement. Retrieved from

http://news.walgreens.com/article_display.cfm?article_id=1042 [This is a website citation

with a corporate author. If you retrieve information from various pages of this particular

website, you need to cite each web page. However, because the author and the year will

be exactly the same, the lowercase letters, “a,” “b,” etc. need to be added to the year. The

in-text citation would be: (Walgreens, 2010a).]

Walgreens. (2010b). Our past. Retrieved from

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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
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

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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 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 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 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.

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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.

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

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.

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corporatecomplianceinsights 83%

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protiviti 62%

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COSO FRAMEWORK COMPONENTS 1

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COMPONENTS OF COSO FRAMEWORK 1

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COSO FRAMEWORK COMPONENTS 8
COSO Framework Components

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Components of COSO Framework
Components of COSO Framework

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March 28, 2020

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Saturday, March 28, 2020

4
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The integrated internal control
framework provides security in the
achievement of objectives.

Original source

Internal Control — Integrated Framework

5
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The components are as discussed below.

Original source

Seven are discussed below

6
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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

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

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

7
Student paper

Retrieved from
https://ro.uow.edu.au/cgi/viewcontent.cg
i?article=1752&context=aabfj

Original source

Retrieved from
https://ro.uow.edu.au/cgi/viewcontent.cg
i

8
Student paper

Sari, R., Kosala, R., Ranti, B.

Original source

Sari, R., Kosala, R., Ranti, B., & Supangkat,
S

8
Student paper

COSO framework for warehouse
management internal control evaluation:
enabling smart warehouse systems.

Original source

COSO Framework for Warehouse
Management Internal Control Evaluation
Enabling Smart Warehouse Systems