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.
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
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
Sustainability 2018, 10, 3778
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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].
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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
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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.
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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
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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 2018,
e-commerce
delivery
demand.
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REVIEW
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3.2.
Case Study:
Study: CJ
CJ Logistics
Logistics
3.2. Case
This
the case
case of
of CJ
CJ Logistics,
Logistics, Korea’s
Korea’s largest
largest logistics
logistics company.
company. It
examines the
the sorting
sorting
This study
study uses
uses the
It examines
process,
docks and
and hub
hub terminals,
terminals, which
process, especially
especially regarding
regarding decisions
decisionsabout
aboutloading/unloading
loading/unloading docks
which are
are
at
the
core
of
courier
services,
to
examine
the
effective
use
of
big
data/BDA
through
BI.
at the core of courier services, to examine the effective use of big data/BDA through BI.
CJ
as the
CJ Logistics
Logistics was
was selected
selected as
the research
research subject
subject as
as it
it is
is the
the largest
largest logistics
logistics service
service provider
provider in
in
Korea
with
the
highest
market
share
and
sales
revenue
of
KRW
7110.3
billion
in
2017
[66].
In
addition,
Korea with the highest market share and sales revenue of KRW 7110.3 billion in 2017 [66]. In addition,
as
shown in
inFigure
Figure1 1(big
(big
data
case
of Logistics,
CJ Logistics,
March
2018),
the company
is an innovation
as shown
data
case
of CJ
March
2018),
the company
is an innovation
leader
leader
the industry.
is traditionally
considered
a 3D
business
thatuses
usesBIBIbased
based on
on high-tech
in the in
industry.
It is It
traditionally
considered
a 3D
business
that
high-tech
automation-oriented
technology, engineering,
and system
system and
and solution
plus consulting
(TES +
C),
automation-oriented technology,
engineering, and
solution plus
consulting (TES
+ C),
while
actively
and
rapidly
adopting
big
data/BDA
at
the
same
time.
while actively and rapidly adopting big data/BDA at the same time.
Figure 1. Technology,
engineering, system
system and
and solution
solution plus
plus consulting
consulting (TES
(TES ++ C)
C) of
of CJ
CJ Logistics.
Logistics.
Technology, engineering,
CJ Logistics
Logisticsis aismarket
a market
leader equipped
with cutting-edge
logistics including
technologies,
leader equipped
with cutting-edge
logistics technologies,
realincluding
real-time
tracking
of freight, courier
an integrated
courier
and freight
tracking
systemusers
that enables
time tracking
of freight,
an integrated
and freight
tracking
system
that enables
to view
users
to view
customer
information
and satellite
requirements,
tracking, and
temperature
customer
information
and
requirements,
vehiclesatellite
tracking,vehicle
and temperature
control
systems
control
systems
[67]. In 2017,
CJ Logistics
invested
than KRW
120 billion
to automate
itsthrough
sorting
[67]. In 2017,
CJ Logistics
invested
more than
KRW more
120 billion
to automate
its sorting
process
process
through
sub-terminals
to
aid
sustainable
growth.
CJ
Logistics’
infrastructure
is
more
than
sub-terminals to aid sustainable growth. CJ Logistics’ infrastructure is more than three times bigger
three
times
than
that of itsinclosest
competitor
the courier
service
industry.
Withmore
five than
hub
than that
of bigger
its closest
competitor
the courier
serviceinindustry.
With
five hub
terminals,
terminals,
more than
270more
sub-terminals,
andvehicles,
more than
vehicles,
CJ Logistics
processes
more
270 sub-terminals,
and
than 16,000
CJ 16,000
Logistics
processes
more than
5.3 million
than
5.3 million
packages
perhub
day.terminal
Its megain
hub
terminalGyeonggi-do
in Gwangju, Gyeonggi-do
Province—which
packages
per day.
Its mega
Gwangju,
Province—which
was due for
was
due for in
completion
in August
with an investment
more
than400
KRW
400 billion—will
completion
August 2018
with 2018
an investment
of more of
than
KRW
billion—will
utilize
utilize
convergence
technologies
such
as big
data,
robots,
IoT its
to services
expand for
its the
services
for the
convergence
technologies
such as big
data,
robots,
and
IoT toand
expand
convenience
convenience
of its
customers
Korea.
This
will include
same-day
delivery,
same-day
return,
of its customers
across
Korea. across
This will
include
same-day
delivery,
same-day
return,
and scheduled
and
scheduled
delivery
services. is
The
company is simultaneously
moving
withinternational
its planned
delivery
services.
The company
simultaneously
moving forward
with forward
its planned
international
growth.
At
the
end
of
2017,
CJ
Logistics
had
a
global
network
of
238
centers
in
137
cities
growth. At the end of 2017, CJ Logistics had a global network of 238 centers in 137 cities
and
32
and
32 countries.
It opened
the Shenyang
Flagship
a mammoth
center
in Shenyang,
countries.
It opened
the Shenyang
Flagship
Center, Center,
a mammoth
logisticslogistics
center in
Shenyang,
China,
China,
on 15
June
2018.
The purpose
of this investment
was to accelerate
the company’s
in
on 15 June
2018.
The
purpose
of this investment
was to accelerate
the company’s
business business
in northern
northern
Asia,
including
three
provinces
of
northeastern
China—Liaoning,
Jilin,
and
Heilongjiang.
Asia, including three provinces of northeastern China—Liaoning, Jilin, and Heilongjiang. The
The
company
implemented
huge
capital
expenditure
broadenitsitsbusiness
businessefficiently,
efficiently, laying
laying the
company
has has
implemented
huge
capital
expenditure
to to
broaden
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
Sustainability 2018, 10, 3778
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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
Sustainability 2018, 10, x FOR PEER REVIEW
7 of 15
centers dealing with massive cargo volumes in its courier service; it also uses a point-to-point
operational
directly
connecting
origins and
destinations.
The point-to-point
system
delivers
to
and from system
terminals,
saving
time on package
arrivals
while alleviating
capacity issues
during
the
to
and
from
terminals,
saving
time
on
package
arrivals
while
alleviating
capacity
issues
during
peak season. However, growing volumes may increase costs, as they require more investmentthe
in
peak
season.
However,
growing
volumes
may can
increase
as theyadditional
require more
in
terminals;
a volume
imbalance
among
terminals
cause costs,
unnecessary
costs.investment
On the other
terminals;
a volume
imbalance
among
terminals
cause unnecessary
costs. On
the being
other
hand,
in the
hub-and-spoke
system,
packages
arecan
gathered
and sorted inadditional
a large terminal
before
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
delivered to
a destination
terminal.
advantage
of this
is that it reduces
to
terminals,
easing
the imbalance
in The
volume.
However,
thesystem
disadvantages
are thatarrival
it maytime
delay
the terminals,
easingorthe
imbalance
in volume.
However,
are that hub
it may
delay
deliveries
to distant
rural
areas during
the peak
season the
anddisadvantages
requires a large-scale
terminal
deliveries
to
distant
or
rural
areas
during
the
peak
season
and
requires
a
large-scale
hub
terminal
[67].
[67].
Since
CJ
Logistics
mostly
uses
the
hub-and-spoke
system,
whose
core
is
the
logistics
process
Since CJ Logistics mostly uses the hub-and-spoke system, whose core is the logistics process at
at
the
hub
terminal,
thisstudy
studyfocuses
focuseson
ondecisions
decisions concerning
concerning the
the loading/unloading
docks in
in the
the
the hub
terminal,
this
loading/unloading docks
process.
This
focus
point
was
selected
for
the
following
reasons.
First,
few
previous
studies
have
process. This focus point was selected for the following reasons. First, few previous studies have
focused on
has
greater
room
forfor
improvement
regarding
productivity
and
focused
on this
thissegment,
segment,even
eventhough
thoughit it
has
greater
room
improvement
regarding
productivity
efficiency
than
other
segments.
Second,
the
importance
of
this
segment
may
have
been
overlooked,
and efficiency than other segments. Second, the importance of this segment may have been
since standardizing
the process is
challenging
owing to differences
the environment,
such as the
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since standardizing
the
process is challenging
owing to in
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theshape
space.ofThird,
thereThird,
are many
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the space.
thereother
are many
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including
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management,
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to address, including outsourcing, warehouse management, freight payment, inventory
customs clearance,
andcustoms
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courier
service
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terminal
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clearance,
andMany
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Many courier
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such as
docks
and number
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based
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CJ Logistics
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dramatically
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and
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CJ
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dramatically
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productivity
and
efficiency
“seeing
the unseen”
through
data/BDA
and
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through
BI.
the use of big data/BDA and promoting faster and better decision making through BI.
The hub
hub terminal
terminal process
process was
was selected
selected from
from the
the three
three general
general stages
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courierservices,
services,namely,
namely,
The
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delivery (Figure
(Figure 2).
2). This
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process was
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selected because
because it
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central
pick-up, transport/sorting,
transport/sorting, and
process
connecting
pick-ups
from
different
locations
with
delivery
to
different
destinations
[68,69].
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.
Sustainability 2018, 10, 3778
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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.
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Figure 3. Optimization of dock allocation process.
Objective function
Selection of
of vehicles
Objective
function (1):
(1): Selection
vehicles to
to unload
unload first
first
Selection of vehicles to unload first = ∑( W ∗ N )
=
∗
W: weighting factor for unloading priority, N: number of packages.
(1)
(1)
W: weighting factor for unloading priority, N: number of packages.
Table 1. Selection of vehicle unloading priorities according to weighting factor and time.
TableOrder
1. Selection of vehicle
unloading priorities
to weighting
factor and
Category
W according
(Before 0:00)
W (After
0:00)time.
1
Order
2
31
42
53
6
Special
sale customer
Category
Route for loading first
Special
salevolume
customer
Console
Produce
Route for
loading first
Premium
customer
Console volume
First-in, first-out (FIFO)
50 0:00) W (After 0:00)
3
W (Before
30
50
50
3 15
8
7
30
50 10
83
15 20
2
2
4
Produce
7
10
Note: W: weighting factor for unloading priority.
5
Premium customer
3
20
6
First-in,
first-out
(FIFO) unloading
2 chute allocation.
2 This was calculated
Objective function
(2)
pertains
to optimum
using volume by loading chute
each vehicle,
time between
Note: for
W: weighting
factortravel
for unloading
priority. unloading/loading chutes,
content information, and reflected travel time under the constraints. The function includes
Objective of
function
(2) pertains
to optimum
unloading
chute allocation.
This was
using
minimization
congestion
through
equal allocation
of vehicles,
minimization
ofcalculated
travel between
volume
byand
loading
chuteoffor
each with
vehicle,
travel
between
unloading/loading
chutes,
content
buildings,
allocation
vehicles
more
than time
30% console
content
to a special console
unloading
information,
andtwo
reflected
time under
the constraints.
The function
includes
minimization
of
zone, based on
types travel
of reference
information.
The reference
information
includes
(1) travel
congestion
through
equal allocation
of vehicles,
minimization
of travel
between
buildings,
and
time between
loading/unloading
chutes
and (2) unloading
service
time for
maximum,
minimum,
allocation
of volume.
vehicles with more than 30% console content to a special console unloading zone, based
and average
on two types of reference information. The reference information includes (1) travel time between
Sustainability 2018, 10, 3778
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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.
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Figure 4. (a) Before optimization of dock allocation; DaeJeon Hub Terminal of CJ Logistics; (b) After
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.
optimization of dock allocation using BDA; DaeJeon Hub Terminal of CJ Logistics.
4. Discussion and Conclusions
4. Discussion and Conclusions
Business activities that are believed to be sufficiently empirical and productive to ensure efficiency
Business
activities
that
are believed
be sufficiently
empirical
andand
productive
ensure
can benefit from
different
perspectives
andtobreakthroughs
upon
acquiring
analyzingtobig
data,
efficiency
can
benefit
from
different
perspectives
and
breakthroughs
upon
acquiring
and
analyzing
and can be realized through BI. The value of big data depends on the types of data extracted and
big
be realized
through
BI. The
value ofisbig
depends
on the raw
types
of data
howdata,
theyand
arecan
utilized.
The crucial
factor,
however,
thedata
method
of turning
data
intoextracted
valuable
and
how
they
are
utilized.
The
crucial
factor,
however,
is
the
method
of
turning
raw
data
information, and not the quality or quantity of the data. Therefore, it is vital to identify the type into
and
valuable
information,
and notaccording
the quality
quantity
of and
the data.
is vitaluse
to identify
the
scope of data
to be collected
to or
their
purpose
focusTherefore,
area. The it
efficient
of big data
type and scope of data to be collected according to their purpose and focus area. The efficient use of
Sustainability 2018, 10, 3778
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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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COSO Framework for Warehouse Management
Internal Control Evaluation: Enabling Smart
Warehouse Systems
Ratna Sari
Raymond Kosala
Benny Ranti
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
rkosala@binus.edu
Faculty of Computer Science,
Universitas Indonesia,
Depok 16424, Indonesia
ranti@ui.ac.id
Computer Science Department, BINUS
Graduate Program – Doctor of
Computer Science, Bina Nusantara
University, Jakarta, Indonesia 11480
rasari@binus.edu
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
Suhono Harso Supangkat
Sekolah Teknik Elektro dan
Informatika,
Institut Teknologi Bandung,
Bandung, Indonesia
suhono@lpik.itb.ac.id
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.
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
978-1-5386-6589-3/18/$31.00 ©2018 IEEE
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]
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
Table 1. Component of Internal Control in COSO [1]
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.
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:
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.
Table 2. Coso Matrix Performance in PT. XYZ
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].
The business process changes purposed as follow:
DATABASE
Robot Machine
Systems
Warehouse
Management
Systems
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.
Figure 3. Three-Tier Architecture [5]
Figure 5 – The Process of Inventory Movement
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%20ArticleFinal%20COSO%20Version%20Proof_5-31-13.pdf
[4] Jokipii, A. (2009). Determinants and consequences of
internal control in firms: a contingency theory based
analysis. Springer Science-Business Media, 115-144
[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.
[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.
Managing and Using Information Systems:
A Strategic Approach – Sixth Edition
Keri Pearlson, Carol Saunders,
and Dennis Galletta
© Copyright 2016
John Wiley & Sons, Inc.
Chapter 10
Information Systems Sourcing
© 2016 John Wiley & Sons, Inc.
2
Kellwood Opening Case
• Why did Kellwood outsource?
• Why did Kellwood decide to backsource after 13
years?
• What was the result?
© 2016 John Wiley & Sons, Inc.
3
Sourcing Decision Framework
© 2016 John Wiley & Sons, Inc.
4
Sourcing Options
Domestic
Insourcing
Outsourcing
Domestic in-house
production
Domestic outsourcing
Company produces its
products domestically without
any outside contracts
Offshore
Offshore in-house
sourcing
Company uses services supplied
by its own foreign-based affiliate
(subsidiary)
Company uses services supplied
by another domestic-based
company
Offshore outsourcing
Company uses services supplied
by an unaffiliated foreign-based
company
Figure 10.3. Different Forms of Sourcing.
(Source: http://www.dbresearch.com/ servlet/reweb2.ReWEB?rwsite=DBR_INTERNET_EN-PROD)
© 2016 John Wiley & Sons, Inc.
5
INSOURCING
A firm provides IS services or develops IS in its own inhouse IS organization
© 2016 John Wiley & Sons, Inc.
6
IT Outsourcing
• With IT, there is equipment and personnel involved
• Equipment and facilities are sold to outside
vendors
• Personnel might be hired by outside vendors
• Services are hired from the vendors
• Common length of agreement: 10 years
© 2016 John Wiley & Sons, Inc.
7
Insourcing drivers and challenges
Insourcing Drivers
Insourcing Challenges
Core competencies related to
systems
Inadequate support from top
management to acquire needed
resources
Confidentiality or sensitive system
components or services
Temptation from finding a reliable,
competent outsourcing provider
Time available in-house to develop
software
Expertise for software development
in-house
© 2016 John Wiley & Sons, Inc.
8
Economics of Outsourcing
• Benefits:
• Sell equipment, buildings (large cash inflow)
• Downsized payroll – outsourcer hires employees
• Costs:
• Services provided for a fee
• Fixed costs usually over 10-year term
© 2016 John Wiley & Sons, Inc.
9
Drivers and disadvantages of outsourcing
Drivers
Disadvantages
• Offer cost savings
• Offer service quality
• Ease transition to new
technologies
• Offer better strategic focus
• Provide better mgmt of IS staff
• Handle peaks
• Consolidate data centers
• Infusion of cash
•
•
•
•
•
•
Abdication of control
High switching costs
Lack of technological innovation
Loss of strategic advantage
Reliance on outsourcer
Problems with
security/confidentiality
• Evaporation of cost savings
© 2016 John Wiley & Sons, Inc.
10
Decisions about How to Outsource
Successfully
• Decisions about whether or not to outsource need care and
deliberation.
• Requires numerous other decisions about mitigating
outsourcing risks.
• Three major decision areas: selection, contracting, and
scope.
1. Selection: find compatible providers
2. Contracting:
1. Try for flexible management terms
2. Try for shorter (3-5 year) contracts
3. Try for SLAs (service level agreements on performance)
3. Scope – Determine if full or partial outsourcing
© 2016 John Wiley & Sons, Inc.
11
Offshoring
• Short for outsourcing offshore
• Definition:
• When the MIS organization uses contractor services in
a distant land. (Insourcing offshore would be your own
dept offshore)
• Substantial potential cost savings through reduced
labor costs.
• Some countries offer a very well educated labor
force.
• Implementation of quality standards:
• Six Sigma
• ISO 9001
© 2016 John Wiley & Sons, Inc.
12
Selecting an Offshoring Destination
• About 100 countries are now exporting software
services and products.
• What makes countries attractive for offshoring?
•
•
•
•
•
•
•
•
High English language proficiency.
Countries that are peaceful/politically stable.
Countries with lower crime rates.
Countries with friendly relationships.
Security and/or trade restrictions.
Protects intellectual property
Level of technical infrastructure available.
Good, efficient labor force
• Once a country is selected, the particular city in that
country needs to be assessed as well.
© 2016 John Wiley & Sons, Inc.
13
Selecting an Offshoring Destination
• Countries like India make an entire industry of
offshoring.
• Software Engineering Institute’s Capability Maturity
Model (CMM).
• Level 1: the software development processes are
immature, bordering on chaotic.
• Level 5: processes are quite mature, sophisticated,
systematic, reliable
• Indian firms are well known for their CMM Level 5
software development processes, making them
desirable
© 2016 John Wiley & Sons, Inc.
14
Offshore DestinationDevelopment Tiers
Carmel and Tjia suggest that there are three tiers of
software exporting nations:
•
Tier 1: Mature.
•
•
Tier 2: Emerging.
•
•
•
Brazil, Costa Rica, South Korea, and many Eastern European countries.
Tier 3: Infant.
•
•
United Kingdom, United States, Japan, Germany, France, Canada, the
Netherlands, Sweden, Finland, India, Ireland, Israel, China, and Russia.
Cuba, Vietnam, Jordan, and 15 to 25 others.
Tiers: based on industrial maturity, the extent of
clustering of some critical mass of software enterprises,
and export revenues.
The higher tiered countries have higher levels of skills and
higher costs.
© 2016 John Wiley & Sons, Inc.
15
Farshoring
• Definition: sourcing service work to a foreign, lowerwage country that is relatively far away in distance or
time zone.
• Client company hopes to benefit from one or more
ways:
• Big cost savings due to exchange rates, labor costs,
government subsidies, etc.
• For the US and UK, India and China are popular
• Oddly, India and China also offshore to other
locations
© 2016 John Wiley & Sons, Inc.
16
Nearshoring
• Definition: sourcing service work to a foreign, lower-wage
country that is relatively close in distance or time zone.
• Client company hopes to benefit from one or more ways
of being close:
• geographically, temporally, culturally, linguistically, economically, politically or
from historical linkages.
• Distance and language matter.
• There are three major global nearshore clusters:
• 20 nations around the U.S., and Canada
• 27 countries around Western Europe
• smaller cluster of three countries in East Asia
© 2016 John Wiley & Sons, Inc.
17
Captive Centers
• An overseas subsidiary that is set up to serve the
parent company.
• Alternative to offshoring or nearshoring.
• Four major stategies that are being employed:
• Hybrid Captive – performs core business processes for parent company
but outsources noncore work to offshore provider
• Shared Captive – performs work for both parent company and external
customers.
• Divested captive – have a large enough scale and scope that it could be
sold for a profit by the parent company.
• Terminated Captive – has been shut down, usually because its inferior
service was hurting the parent company’s reputation.
© 2016 John Wiley & Sons, Inc.
18
Backsourcing
• When a company takes back in-house, previously
outsourced, IS assets, activities, and skills.
• Partial or complete reversal
• Many companies have backsourced such as
Continental Airlines, Cable and Wireless, and Halifax
Bank of Scotland.
• 70% of outsourcing clients have had negative
experiences and 25% have backsourced.
• 4% of 70 North American companies would not
consider backsourcing.
© 2016 John Wiley & Sons, Inc.
19
Backsourcing Reasons
• Mirror reason for outsourcing (to reduce costs,
increase quality of service, etc.)
• Costs were higher than expected
• Poor service
• Change in management
• Change in the way IS is perceived within the
company
• New situations (mergers, acquisitions, etc.)
© 2016 John Wiley & Sons, Inc.
20
Crowdsourcing
• Definition:
• Taking a task traditionally performed by an employee or
contractor, and
• Outsourcing it to an undefined, generally large group of
people,
• In the form of an open call.
• Used by companies to increase productivity, lower
production costs, and fill skill gaps.
• Can be used for a variety of tasks.
• Companies do not have control over the people doing
the work.
© 2016 John Wiley & Sons, Inc.
21
Partnering Arrangements
• Strategic networks: arrangements made with
other organizations to offer synergistic or
complementary services
• Example: The Mitsui Keiretsu contains over 30 firms
spanning many industries. The members use each
others’ services and don’t compete: Toshiba,
Fujifilm, Sony are members
• Business ecosystems (see chapter 9): Informal,
emerging relationships
© 2016 John Wiley & Sons, Inc.
22
Deciding Where Onshore, Offshore, or in the Cloud?
• New option: cloud computing
• See chapter 6 for basic definitions; advantages;
disadvantages.
• Works when outsourcing or insourcing
© 2016 John Wiley & Sons, Inc.
23
Cloud Computing Options
• On-premise
• Private clouds
• Data—managed by the company or offsite by a third party.
• Community clouds.
• Cloud infrastructure is shared by several organizations
• Supports the shared concerns of a specific community.
• Public clouds.
• Data is stored outside of the corporate data centers
• In the cloud provider’s environment
• Hybrid clouds
• Combination of two or more other clouds.
© 2016 John Wiley & Sons, Inc.
24
Public Clouds – Versions
• Infrastructure as a Service (IaaS).
• Infrastructure through grids or clusters of virtualized servers,
networks, storage, and systems software.
• Designed to augment or replace the functions of an entire data
center.
• The customer may have full control of the actual server
configuration.
• More risk management control over the data and environment.
• Platform as a Service (PaaS).
• Virtualized servers
• Clients can run existing applications or develop new ones
• Provider manages the hardware, operating system, and
capacity
• Limits the enterprise risk management capabilities.
© 2016 John Wiley & Sons, Inc.
25
Public Clouds – Versions
Software as a Service (SaaS) or Application Service Provider (ASP).
• Software application functionality through a web browser.
• The platform and infrastructure are fully managed by the cloud
provider.
• If the operating system or underlying service isn’t configured
correctly, the data at the higher application layer may be at risk.
• The most widely known and used form of cloud computing.
Some managers shy away from cloud computing because they are
concerned about:
• security—specifically about external threats from remote
hackers and security breaches as the data travels to and from
the cloud.
• data privacy.
© 2016 John Wiley & Sons, Inc.
26
Managing and Using Information Systems:
A Strategic Approach – Sixth Edition
Keri Pearlson, Carol Saunders,
and Dennis Galletta
© Copyright 2016
John Wiley & Sons, Inc.
Managing and Using Information Systems:
A Strategic Approach – Sixth Edition
Keri Pearlson, Carol Saunders,
and Dennis Galletta
© Copyright 2016
John Wiley & Sons, Inc.
Chapter 9
Governance of the Information
Systems Organization
Learning Objectives
• Understand how governance structures define
how decisions are made
• Describe governance based on organization
structure, decision rights, and control
• Discuss examples and strategies for
implementation.
© 2016 John Wiley & Sons, Inc.
3
Intel’s Transformation
• Huge performance improvements between 2013
and 2014
• Was it due to a spending increase?
• Intel’s evolution
• 1992: Centralized IT
• 2003: Protect Era – lockdown (SOX & virus)
• 2009: Protect to Enable Era (BYOD pressure)
© 2016 John Wiley & Sons, Inc.
4
Intel Reached Level 3:
1. Developing programs and delivering services
2. Contributing business value
3. Transforming the firm
Previously: categorized problems as “business” or “IT”
Now: Integrated solutions are the only way
© 2016 John Wiley & Sons, Inc.
5
IT Governance
• Governance (in business) is all about making
decisions that
• Define expectations,
• Grant authority, or
• Ensure performance.
• Empowerment and monitoring will help align
behavior with business goals.
• Empowerment: granting the right to make decisions.
• Monitoring: evaluating performance.
© 2016 John Wiley & Sons, Inc.
6
IT Governance
• IT governance focuses on how decision rights can
be distributed differently to facilitate three
possible modes of decision making:
• centralized,
• decentralized, or
• hybrid
• Organizational structure plays a major role.
© 2016 John Wiley & Sons, Inc.
7
Four Perspectives
•
•
•
•
Traditional – Centralized vs decentralized
Accountability and allocation of decision rights
Ecosystem
Control structures from legislation
© 2016 John Wiley & Sons, Inc.
8
Centralized vs. Decentralized
Organizational Structures
• Centralized – bring together all staff, hardware,
software, data, and processing into a single location.
• Decentralized – the components in the centralized
structure are scattered in different locations to
address local business needs.
• Federalism – a hybrid of centralized and
decentralized structures.
© 2016 John Wiley & Sons, Inc.
9
Organizational continuum
Federalism
• Most companies would like to achieve the
advantages of both centralization and
decentralization.
• Leads to federalism
• Distributes, power, hardware, software, data and
personnel
• Between a central IS group and IS in business units
• A hybrid approach
• Some decisions centralized; some decentralized
© 2016 John Wiley & Sons, Inc.
11
Federal IT
© 2016 John Wiley & Sons, Inc.
12
Recent Global Survey
Percent of firms reporting that they are:
• Centralized: 70.6%
• Decentralized: 13.5%
• Federated: 12.7%
© 2016 John Wiley & Sons, Inc.
13
Figure 9.4 IT Accountability and Decision Rights Mismatches
Low
Accountability
High
Strategic Norm (Level 3
Decision High Technocentric Gap
• Danger of overspending on IT balance)
Rights
creating an oversupply
•
•
Low
IT assets may not be utilized
to meet business demand
Business group frustration
with IT group
Support Norm (Level 1
balance)
•
•
Works for organizations
where IT is viewed as a
support function
Focus is on business
efficiency
© 2016 John Wiley & Sons, Inc.
•
•
IT is viewed as competent
IT is viewed as strategic to
business
Business Gap
•
•
•
Cost considerations
dominate IT decision
IT assets may not utilize
internal competencies to
meet business demand
IT group frustration with
business group
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Figure 9.5 Five major categories of IT decisions.
Category
Description
Examples of Affected IS
Activities
IT Principles
How to determine IT assets that are needed Participating in setting
strategic direction
IT Architecture
How to structure IT assets
IT Infrastructure How to build IT assets
Strategies
Business
Application
Needs
IT Investment
and
Prioritization
Establishing architecture
and standards
How to acquire, implement and maintain IT
(insource or outsource)
Managing Internet and
network services; data;
human resources; mobile
computing
Developing and maintaining
information systems
How much to invest and where to invest in
IT assets
Anticipating new
technologies
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Political Archetypes (Weill & Ross)
• Archetypes label the combinations of people who
either provide information or have key IT decision
rights
• Business monarchy, IT monarchy, feudal, federal, IT
duopoly, and anarchy.
• Decisions can be made at several levels in the
organization (Figure 9.6).
• Enterprise-wide, business unit, and region/group
within a business unit.
© 2016 John Wiley & Sons, Inc.
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Political Archetypes
• Organizations vary widely in their archetypes
selected
• The duopoly is used by the largest portion (36%) of
organizations for IT principles decisions.
• IT monarchy is the most popular for IT architecture
(73%) and infrastructure decisions (59%).
© 2016 John Wiley & Sons, Inc.
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Figure 9.6 IT governance archetypes
© 2016 John Wiley & Sons, Inc.
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Emergent Governance:
Digital Ecosystems
• Challenge a “top down” approach
• Self-interested, self-organizing, autonomous sets
of technologies from different sources
• Firms find opportunities to exploit new
technologies that were not anticipated
• Good examples:
• Google Maps
• YouTube
© 2016 John Wiley & Sons, Inc.
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Another Interesting Example
• Electronic Health Record
• Can connect to perhaps planned sources:
• Pharmacy
• Lab
• Insurance Company
• And can connect to unplanned sources:
• Banks – for payment
• Tax authority – for matching deductions
• Smartphone apps – for many purposes
© 2016 John Wiley & Sons, Inc.
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How to Govern in this case?
• Might be difficult to impossible!
• The systems might simply emerge and evolve over
time
• No one entity can plan these systems in their
entirety
© 2016 John Wiley & Sons, Inc.
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Mechanisms for Making Decisions
• Policies and Standards (60% of firms)
• Review board or committee
• Steering committee (or governance council)
• Key stakeholders
• Can be at different levels:
• Higher level (focus on CIO effectiveness)
• Lower level (focus on details of various projects)
© 2016 John Wiley & Sons, Inc.
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Summary of Three Governance
Frameworks
Governance
Main Concept
Framework
CentralizationDecisions can be made by a
Decentralization central authority or by
autonomous individuals or
groups in an organization.
Possible Best
Practice
A hybrid,
Federal
approach
Decision
Archetypes
Specifying patterns based upon Tailor the
allocating decision rights and
archetype to the
accountability.
situation
Digital
Ecosystems
Members of the ecosystem
contribute their strengths,
giving the whole ecosystem a
complete set of capabilities.
© 2016 John Wiley & Sons, Inc.
Build flexibility
and adaptability
into governance.
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A Fourth – Out of a Firm’s Control:
Legislation
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Sarbanes-Oxley Act (SoX) (2002)
• To increase regulatory visibility and accountability of
public companies and their financial health
• All companies subject to the SEC are subject to SoX.
• CEOs and CFOs must personally certify and be
accountable for their firm’s financial records and
accounting.
• Firms must provide real-time disclosures of any events
that may affect a firm’s stock price or financial
performance.
• 20 year jail term is the alternative.
• IT departments play a major role in ensuring the
accuracy of financial data.
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IT Control and Sarbanes-Oxley
• In 2004 and 2005, IT departments began to
• Identify controls,
• Determine design effectiveness, and
• Test to validate operation of controls
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IT Control and Sarbanes-Oxley
Five IT control weaknesses are repeatedly uncovered by
auditors:
• Failure to segregate duties within applications, and failure
to set up new accounts and terminate old ones in a timely
manner
• Lack of proper oversight for making application changes,
including appointing a person to make a change and
another to perform quality assurance on it
• Inadequate review of audit logs to not only ensure that
systems were running smoothly but that there also was an
audit log of the audit log
• Failure to identify abnormal transactions in a timely
manner
• Lack of understanding of key system configurations
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Frameworks for Implementing SoX
• COSO – Committee of Sponsoring Organzations of the
Treadway Commission.
• Created three control objectives for management and
auditors that focused on dealing with risks to internal
control
• Operations –maintain and improve operating
effectiveness; protect the firm’s assets
• Compliance –with relevant laws and regulations.
• Financial reporting –in accordance with GAAP
© 2016 John Wiley & Sons, Inc.
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Control Components
Five essential control components were created to
make sure a company is meeting its objectives:
• Control environment (culture of the firm)
• Assessment of most critical risks to internal
controls
• Control processes that outline important
processes and guidelines
• Communication of those procedures
• Monitoring of internal controls by management
© 2016 John Wiley & Sons, Inc.
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Frameworks (continued)
• COBIT (Control Objectives for Information and Related
Technology)
• IT governance framework that is consistent with COSO
controls.
• Issued in 1996 by Information Systems Audit & Control
Association (ISACA)
• A company must
• Determine the processes/risks to be managed.
• Set up control objectives and KPIs (key performance indicators)
• Develop activities to reach the KPIs
• Advantages – well-suited to organizations focused on risk
management and mitigation, and very detailed.
• Disadvantages – costly and time consuming
© 2016 John Wiley & Sons, Inc.
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IS and the Implementation of SoX Compliance
• The IS department and CIO are involved with the
implementation of SoX.
• Section 404 deals with management’s assessment of internal
controls.
• Six tactics that CIOs can use in working with auditors, CFOs,
and CEOs (Fig. 9.9):
• Knowledge building (Build a knowledge base)
• Knowledge deployment (Disseminate knowledge to
management.)
• Innovation directive (Organize for implementing SoX)
• Mobilization (Persuade players and subsidiaries to cooperate)
• Standardization (Negotiate agreements, build rules)
• Subsidy (Fund the costs)
• A CIO’s ability to employ these various tactics depends upon
his/her power (relating to the SoX implementation).
© 2016 John Wiley & Sons, Inc.
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Managing and Using Information Systems:
A Strategic Approach – Sixth Edition
Keri Pearlson, Carol Saunders,
and Dennis Galletta
© Copyright 2016
John Wiley & Sons, Inc.