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 pages in length, not including the required cover page and reference page.

Managing and Using Information Systems:
A Strategic Approach – Sixth Edition

Keri Pearlson, Carol Saunders,
and Dennis Galletta

© Copyright 2016
John Wiley & Sons, Inc.

Chapter 12
Knowledge Management, Business Intelligence, and Analytics

2

Opening Case: Netflix
What gave Netflix assurance that House of Cards would be a success?
What gives Netflix a competitive advantage?
© 2016 John Wiley & Sons, Inc.
3

Data from 33 million people provided strong evidence that the British version was very popular and Kevin Spacey was also a popular actor
Again, data from millions of users, showing what they watch, when they pause, when they rewind, etc. Patterns emerge that speak volumes about preferences. Their analytics algorithms provide better data than focus groups (and in real time).
3

More Real World Examples
Caesar’s and Capital One both collect and analyze customer data.
Result: They can determine who are the most profitable customers and then follow up with them.
Caesar’s: frequent gamblers
Capital One: charge a lot and pay off slowly
They provide products that would appeal to the profitable customers.
© 2016 John Wiley & Sons, Inc.
4

4

A Real World Example from Sports
Oakland As and Boston Red Sox baseball teams
Crunched the numbers on the potential players, such as on-base percentage
Others who did not do the analysis failed to recognize the talent
© 2016 John Wiley & Sons, Inc.
5

5

Five Ways Data Analytics can Help an Organization (McKinsey and Co.)
Making data more transparent and usable more quickly
Exposing variability and boosting performance
Tailoring products and services
Improving decision-making
Improving products
© 2016 John Wiley & Sons, Inc.
6

Terminology
Knowledge management: The processes needed to generate, capture, codify and transfer knowledge across the organization to achieve competitive advantage
Business intelligence: The set of technologies and processes that use data to understand and analyze business performance
Business analytics: The use of quantitative and predictive models, algorithms, and evidence-based management to drive decisions
© 2016 John Wiley & Sons, Inc.
7

Data, Information, and Knowledge (reprise)
© 2016 John Wiley & Sons, Inc.
8

8

The Value of Managing Knowledge
Value Sources of Value
Sharing best practices Avoid reinventing the wheel
Build on valuable work and expertise
Sustainable competitive advantage Shorten innovation life cycle
Promote long term results and returns
Managing overload Filter data to find relevant knowledge
Organize and store for easy retrieval
Rapid change Build on/customize previous work for agility
Streamline and build dynamic processes
Quick response to changes
Embedded knowledge from products Smart products can gather information
Blur distinction between manufacturing/service
Add value to products
Globalization Decrease cycle times by sharing knowledge globally
Manage global competitive pressures
Adapt to local conditions
Insurance for downsizing Protect against loss of knowledge when departures occur
Provide portability for workers who change roles
Reduce time to acquire knowledge

© 2016 John Wiley & Sons, Inc.
9

Dimensions of Knowledge

Explicit
Teachable
Articulable
Observable in use
Scripted
Simple
Documented
Tacit
Not teachable
Not articulable
Not observable
Rich
Complex
Undocumented
Examples:
Estimating work
Deciding best action
Examples:
Explicit steps
Procedure manuals
© 2016 John Wiley & Sons, Inc.
10

10

Four Modes of Knowledge Conversion
(and examples)

Transferring by mentoring, apprenticeship
Transferring by models, metaphors
Learning by doing; studying manuals
Obtaining and following manuals
© 2016 John Wiley & Sons, Inc.
11

Knowledge Management – Four Processes
Generate – discover “new” knowledge
Capture – scan, organize, and package it
Codify – represent it for easy access and transfer (even as simple as using hash tags to create a folksonomy)
Transfer – transmit it from one person to another to absorb it
© 2016 John Wiley & Sons, Inc.
12

12

Measures of KM Project Success
Example of specific benefits of a KM project:
Enhanced effectiveness
Revenue generated from extant knowledge assets
Increased value of extant products and services
Increased organizational adaptability
More efficient re-use of knowledge assets
Reduced costs
Reduced cycle time
© 2016 John Wiley & Sons, Inc.
13

13

Components of Business Analytics
Component Definition Example
Data Sources Data streams and repositories Data warehouses; weather data
Software Tools Applications and processes for statistical analysis, forecasting, predictive modeling, and optimization Data mining process; forecasting software package
Data-Driven Environment Organizational environment that creates and sustains the use of analytics tools Reward system that encourages the use of the analytics tools; willingness to test or experiment
Skilled Workforce Workforce that has the training, experience, and capability to use the analytics tools Data scientists, chief data officers, chief analytics officers, analysts, etc. Netflix, Caesars and Capital One have these skills

© 2016 John Wiley & Sons, Inc.
14

14

Data Sources for Analytics
Structured (customers, weather patterns) or unstructured (Tweets, YouTube videos)
Internal or external
Data warehouses full of a variety of information
Real-time information such as stock market prices
© 2016 John Wiley & Sons, Inc.
15

Data Mining
Combing through massive amounts of customer data, usually focused on:
Buying patterns/habits (for cross-selling)
Preferences (to help identify new products/ features/enhancements to products)
Unusual purchases (spotting theft)
It also identifies previously unknown relationships among data.
Complex statistics can uncover clusters on many dimensions not known previously
(e.g., People who like movie x also like movie y)
© 2016 John Wiley & Sons, Inc.
16

Four Categories of Data Mining Tools
Statistical analysis: Answers questions such as “Why is this happening?”
Forecasting/Extrapolation: Answers questions such as “What if these trends continue?”
Predictive modeling: Answers questions such as “What will happen next?”
Optimization: Answers questions such as “What is the best that can happen?”
© 2016 John Wiley & Sons, Inc.
17

How to be Successful
Achieve a data driven culture
Develop skills for data mining
Use a Chief Analytics Officer (CAO) or Chief Data Officer (CDO)
Shoot for high maturity level (see next slide)
© 2016 John Wiley & Sons, Inc.
18

Level Description Source of Business Value
1 – Reporting What happened? Reduce costs of summarizing, printing
2 – Analyzing Why did it happen? Understanding root causes
3 – Describing What is happening now Real-time understanding & corrective action
4 – Predicting What will happen? Can take best action
5 – Prescribing How should we respond? Dynamic correction

Five Maturity Levels of Analytical Capabilities
© 2016 John Wiley & Sons, Inc.
19

BI and Competitive Advantage
There is a very large amount of data in databases.
Big data: techniques and technologies that make it economical to deal with very large datasets at the extreme end of the scale: e.g., 1021 data items
Large datasets can uncover potential trends and causal issues
Specialized computers and tools are needed to mine the data.
Big data emerged because of the rich, unstructured data streams that are created by social IT.
© 2016 John Wiley & Sons, Inc.
20

Practical Example
Asthma outbreaks can be predicted by U. of Arizona researchers with 70% accuracy
They examine tweets and Google searches for words and phrases like
“wheezing” “sneezing” “inhaler” “can’t breathe”
Relatively rare words (1% of tweets) but 15,000/day
They examine the context of the words:
“It was so romantic I couldn’t catch my breath” vs
“After a run I couldn’t catch my breath”
Helps hospitals make work scheduling decisions
© 2016 John Wiley & Sons, Inc.
21

Sentiment Analysis
Can analyze tweets and Facebook likes for
Real-time customer reactions to products
Spotting trends in reactions
Useful for politicians, advertisers, software versions, sales opportunities

© 2016 John Wiley & Sons, Inc.
22

Google Analytics and Salesforce.com
Listening to the community: Identifying and monitoring all conversations in the social Web on a particular topic or brand.
Learning who is in the community: Identifying demographics such as age, gender, location, and other trends to foster closer relationships.
Engaging people in the community: Communicating directly with customers on social platforms such as Facebook, YouTube, LinkedIn, and Twitter using a single app.
Tracking what is being said: Measuring and tracking demographics, conversations, sentiment, status, and customer voice using a dashboard and other reporting tools.
Building an audience: Using algorithms to analyze data from internal and external sources to understand customer attributes, behaviors, and profiles, then to find new similar customers
© 2016 John Wiley & Sons, Inc.
23

Google Analytics
Web site testing and optimizing: Understanding traffic to Web sites and optimizing a site’s content and design for increasing traffic.
Search optimization: Understanding how Google sees an organization’s Web site, how other sites link to it, and how specific search queries drive traffic to it.
Search term interest and insights: Understanding interests in particular search terms globally, as well as regionally, top searches for similar terms, and popularity over time. 
Advertising support and management: Identifying the best ways to spend advertising resources for online media.
© 2016 John Wiley & Sons, Inc.
24

Internet of Things (IoT)
Much big data comes from IoT
Sensor data in products can allow the products to:
Call for service (elevators, heart monitors)
Parallel park, identify location/speed (cars)
Alert you to the age of food (refrigerator)
Waters the lawn when soil is dry (sprinklers)
Self-driving cars find best route (Google)
© 2016 John Wiley & Sons, Inc.
25

Intellectual Capital vs Intellectual Property
Intellectual Capital: the process for managing knowledge
Intellectual Property: the outputs; the desired product for the process
Intellectual Property rights differ remarkably by country
© 2016 John Wiley & Sons, Inc.
26

Closing Caveats
These are emerging concepts and disciplines
Sometimes knowledge should remain hidden (tacit) for protection
We should remain focused on future events, not just look over the past
A supportive culture is needed in a firm to enable effective KM and BI
© 2016 John Wiley & Sons, Inc.
27

Managing and Using Information Systems:
A Strategic Approach – Sixth Edition
Keri Pearlson, Carol Saunders,
and Dennis Galletta

© Copyright 2016
John Wiley & Sons, Inc.

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

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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 = Â( 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 = Â( 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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