Tài liệu Knowledge Management in OSS—an Enterprise Information System for the Telecommunications Industry doc

Systems Research and Behavioral Science
Syst. Res. 23,177^190 (2006)
Published onlineinWiley InterScience (www.interscience.wiley.com)
DOI:10.1002/sres.752
&
Research Paper
Knowledge Management in OSS—an
Enterprise Information System for the
Telecommunications Industry
Jiayin Qi
1
*, Li Da Xu
2
, Huaying Shu
1
and Huaizu Li
3
1
School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
2
Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk,
Virginia, USA
3
School of Management, Xian Jiaotong University, Xian, China
Knowledge management in Enterprise Information Systems (EIS) has become one of the
hottest research topics in the last few years. Operations Support Systems (OSS) is one kind
of EIS, which is becoming increasingly popular in the telecommunications industry.
However, the academic research on knowledge management in OSS is sparse. In this
paper, a knowledge management system for OSS is proposed in the framework of systems
theory. Knowledge, knowledge management, organization and information technology
are the four main interactive elements in the knowledge management system. The paper

proposes that each subsystem of the OSS is to be equipped with knowledge management
capacity, and the knowledge management of the OSS is to be realized through its
subsystems. Copyright # 2006 John Wiley & Sons, Ltd.
Keywords enterprise information systems; ERP; operations support systems; knowledge
management; management information systems
INTRODUCTION
In recent years, the topic of knowledge economy
has attracted much research interest. As a result,
a substantial number of researches have been
conducted on knowledge management from
both theoretical and empirical perspectives.
Studies show that effective knowledge manage-
ment has a positive effect on enterprise perfor-
mance and competitive advantage (Ahn and
Chang, 2004; Chuang, 2004; Joshi and Sharma,
2004; Tzokas and Saren, 2004; Badii and Sharif,
2003; Cavusgil et al., 2003; Choi and Lee, 2002).
For this reason, more and more enterprises have
emphasized the importance of knowledge man-
agement. Most of them have acquired enterprise
information systems (EIS) such as ERP as an
integrated platform with intended applications
in knowledge management.
Operations Support Systems (OSS) is a main-
stream technology which supports large-scale
network operation, maintenance and management.
Copyright # 2006 John Wiley & Sons, Ltd.
* Correspondence to: Jiayin Qi, School of Economics and Management,
Beijing University of Posts and Telecommunications, Beijing 100876,
China. E-mail:

It was put forward by TeleManagement Forum
(TMF), an international organization that has
been contributing to the information and com-
munications services industry for over 15 years.
So far OSS has been increasingly adopted by
telecom industry with NGOSS (New Generation
Operations and Software Systems) as its next
generation product. If ERP systems are the EIS
mainly help manufacturing industry achieve
competitive edge in the global market, OSS plays
a similar role in the telecom industry.
Telecommunications industry is a very specific
high-tech service industry. The main feature of
the telecommunications industry is its tight
integration of business process and IT applica-
tions; it is very important to use IT to promote its
competitiveness. OSS is generally considered as
a basic EIS which can also support knowledge
management. OSS market and applications are
growing. Taking the Asia Pacific market as an
example, it generated $8.8 billion of revenues in
2002. Revenues show an increasing trend and
the market for OSS is expected to grow at a
steady pace. The compound annual growth rate
(CAGR) of the revenues for the period 2001–2007
is forecasted to be 6.27 per cent. Industry reven-
ues are forecasted to rise to $11.87 billion by the
year 2007.
Although OSS has been acquired by many
telecom companies, the shortage of scholastic

research on OSS is obvious (Li et al., 2003a).
IEEE Xplore provides full text access to IEEE
transactions, journals, magazines and conference
proceedings since 1998, plus select contents back
to 1950, and all the current IEEE standards. Most
of the academic publications in telecommuni-
cations are included in IEEE Xplore. Using
operations support systems as key word, our
search matched 189 of 1043417 documents. In
these 189 documents, there is only one paper
related to the word knowledge. Searching other
academic journals, such as Decision Support
systems, Expert Systems with Application, Knowl-
edge-Based Systems, Computers in Industry, Expert
Systems, Data & Knowledge Engineering, Advanced
Engineering Informatics, Log istic Information Man-
agement, Information & Management, Telecommu-
nications Policy from 2003 to 2005, no papers on
OSS are found. There are some whitepapers
about OSS at www.tmforum.org, but they are not
typical research papers.
Knowledge may not show its significant value
until it is embedded in software products or
business processes. Only then can its value be
fully utilized. OSS is the basic software platform
to support value chain management for the
telecom industry. OSS should be the enabling
tools to fulfil effective knowledge management.
How could this objective be achieved? The
purpose of this paper is to explore a possible

answer to the question.
The paper is organized as follows. ‘Knowledge
Management in Systems Perspectives’ section
presents the implication of knowledge manage-
ment in systems perspectives. The relationship
among data, information and knowledge, as well
as the relationship between knowledge manage-
ment and EIS is discussed. In ‘Overview of OSS
and Knowledge Management in OSS’ sections,
an overview of OSS and the knowledge manage-
ment in OSS is discussed. ‘Discussion and
Conclusion’ section provides a summary of the
paper and future research.
KNOWLEDGE MANAGEMENT IN
SYSTEMS PERSPECTIVES
Asystemismadeupofasetofinteracting
elements sharing a particular purpose within a
boundary. The interaction among elements forms
the structure of a system. Depending on its
boundary, a system can be an economic entity,
an inventory system, or a business organization.
Knowledge management is an element of the
organizational management system (Warfield,
1989). From the point of view of the concept of
whole, a knowledge management system pro-
motes the effective use of knowledge assets of an
enterprise as a whole over time, and is an impetus
to the performance of the enterprise.
Data, Information and Knowledge
Prior to discussing knowledge management, the

terms such as data, information and knowledge
must be defined. The following is a summary of
RE S E ARCH PA P ER Syst. Res.
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
178 Jiayin Qi et al.
the distinction between data, information and
knowledge:
Data are known facts that can be recorded and
that have implicit meaning (Elmasri and
Navathe, 2004). Information is data placed in a
meaningful and useful context after that has been
processed (O’Brien, 2005). Information is user-
aimed, providing values and existing in the eyes
of the beholder (Spiegler, 2003). Knowledge is
information synthesized and contextualized to
provide further value for human activities
(Pearlson and Saunders, 2004).
The relationship among data, information and
knowledge can be depicted as shown in Figure 1.
Data is the abstract description of objects and is
the raw material that is used to generate useful
information and knowledge. Information is a
flow of processed data after being processed.
Knowledge involves the capacity of gathering
and using information. Knowledge becomes
information when it is articulated or commu-
nicated to others in the form of text, computer
outputs, speech or written words (Alavi and
Leindner, 2001; Spiegler, 2003).
Data warehouse is a large-scale storage facility

for data. Knowledge warehousing is an exten-
sion of data warehousing to facilitate the captur-
ing and coding of knowledge and to enhance the
retrieval and sharing of knowledge across the
organization (Nemati et al., 2002). Online Analy-
tical Processing (OLAP) is a software application
used to explore the data in ways that are decision
oriented (Shi et al., 2005). Data mining (DM) tools
allow for the creation of well-defined transfer-
able information (Li and Xu, 2001; Li et al.,
2003b). Knowledge discovery (KD) process
agglomerates information found by such techni-
ques as DM in generating domain knowledge
(Bendoly, 2003).
Implication of Knowledge Management
in Systems Perspective
The implication of knowledge management has
been studied by many authors (Warfield, 1989).
Table 1 summarized the selected findings.
In this paper, knowledge management is
studied in terms of systems theory and the
perspectives listed in Table 1 will be synthe-
sized. It is emphasized in this paper that
knowledge management can be used to effec-
tively manage corporate knowledge assets
especially those knowledge in business pro-
cesses. Therefore, the objective of knowledge
management is considered to promote an
enterprise’s core competency. Such an objective
can be achieved with a systematic process of

creating, maintaining, employing, sharing and
renewing knowledge.
Knowledge Management Framework
in Systems Point of View
Viewing knowledge management as a man-
made system, the boundary of the system and
Data
Information
Knowledge
Data Processing: Organizing, storing,
calculating, Retrieving, Reporting
Information Processing: Reforming,
Quantification, Qualification, Clustering,
learning, Disseminating
To be communicated
to others in the form
of text, computer
output, speech and
writing words etc.
Figure 1. Data, information and knowledge
Syst. Res. RESE ARCH PAPER
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
Know ledge Mana gement in OSS 179
the elements of the system needs to be deter-
mined. Obviously, the boundary of the knowl-
edge management system is the corporate
business environment, while the elements in
the system include knowledge architecture,
knowledge management process architecture,
organization architecture and IT architecture

(Kim et al., 2003). The other questions of interest
include the interaction among these elements,
the structure of the system, and the function of
the system.
Main Factors Influence Knowledge Management
Knowledge management system is a system to
effectively manage knowledge within an enter-
prise. Two main factors are considered influencing
the needs of practicing knowledge management.
The first factor is competition. If there is a tough
competitioninacertainindustrysector,managing
knowledge is generally in high demand. The other
factor is the volume of data. If there is a huge
volume of data that exist within an enterprise, the
data resource is available which can help convert
data into information as well as knowledge.
Elements of Knowledge Management System
Knowledge architecture, knowledge manage-
ment process architecture, organization architec-
ture and IT architecture are the four elements of
knowledge management system.
The so-called knowledge architecture is the
result of classifying organizational knowledge by
one or more dimensions. Fernandez et al. distin-
guished knowledge into human knowledge,
organizational knowledge, technological knowl-
edge and relational knowledge (Fernandez et al.,
2000). Human knowledge refers to the knowl-
edge acquired by a person that can increase
productivity and the contribution to the organi-

zation. It also includes other individual qualities
such as experience, judgement and intelligence.
A firm’s organizational knowledge includes
its norms and business guidelines, corporate cul-
ture, organizational procedures, as well as strate-
gic alliance. Technological knowledge includes
knowledge related to the access, use and innova-
tion of production techniques and technology
(Xu et al., 2005a,b). The relational knowledge
consists of the potential derived from the
intangible resources related to marketplace,
such as brands, customer loyalty, long-term
customer relationship, distribution channels, etc
(Kanjanasanpetch and Igel, 2003).
The knowledge management process architec-
ture defines a variety of processes involved in the
life cycle of knowledge, from its creation to
termination. Knowledge creation process, know-
ledge maintenance process, knowledge distribu-
tion process and knowledge review and revision
process are the four steps in the entire knowledge
management process (Bhatt et al., 2005). Creativ-
ity refers to the ability to originate novel
and useful ideas and solution (Marakas, 2003).
An organization creates knowledge through
its employees who are equipped with knowledge
and generate new ideas by breaking down
business thinking that is no longer viable
(Argyris and Schon, 1996; Lynn et al., 1996).
Knowledge maintenance refers to making use of

existing ‘discovered’ knowledge (Bhatt et al.,
2005). Knowledge distribution means the sharing
of knowledge across the organization. Knowledge
Table 1. Existing research on the implication of knowledge management
Author Perspective Implication
Siemieniuch and Sinclair (2004) Process Systematic process of applying expertise
Kwan and Balasubramanian (2003)
Wang and Ariguzo (2004)
Mesaric (2004)
Fowler and Pryke (2003) Capability Building core competencies through know-how
Badii and Sharif (2003)
Tzokas and Saren (2004)
Nemati et al. (2002) Relationship Converting information to knowledge
RE S E ARCH PAPE R Syst. Res.
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
180 Jiayin Qi et al.
review and revision is the modification and
version management of knowledge.
The organization architecture designs organi-
zational structure. Organizational structure defi-
nes the role of each knowledge management team
that is responsible for performing or supporting
knowledge management process.
The IT architecture is a technical infrastructure
for knowledge management. It defines compo-
nents of knowledge management system and
their relationships.
Interactions Am ong the Elements in
Knowledge Management System
The four elements in knowledge management

system are interrelated to each other. Knowledge
management system can not attain its purpose
without any one of the elements.
The knowledge architecture is the base of the
knowledge management process. The knowl-
edge management process consists of the main
activities in knowledge management. The orga-
nization architecture is responsible for perform-
ing or supporting knowledge management
process. IT architecture is a facilitator for enhan-
cing dynamic capabilities through knowledge
management (Sher and Lee, 2004).
Structure of Knowledge Management System
According to the interaction among the elements
in knowledge management system, the structure
of knowledge management system is shown in
Figure 2.
Both theoretical and empirical researches
have shown that knowledge management can
play a key role in creating sustainable competi-
tive advantages for corporations. In which, the
organization architecture is the guarantee of
knowledge architecture, knowledge manage-
ment process architecture and IT architecture.
Right organization architecture has positive
effects on the other three elements. On the other
hand, knowledge architecture, knowledge man-
agement process, and IT architecture all have
impacts on organization architecture. Organiza-
tion architecture has to be adapted to meet the

needs from the three elements too. Knowledge
architecture is the base of knowledge manage-
ment process. The fundamental function of the
knowledge management system is to improve
the business process and to achieve superior
business performance through effective knowl-
edge management process.
Enterprise Information Systems and
Knowledge Management
Enterprise information system (EIS) is an inte-
grated information system seeking to integrate
every single business process and function in
the enterprise to present a holistic view of the
business with a single IT architecture. It is a
powerful and integrated enterprise-level IT archi-
tecture that is also designed to facilitate knowl-
edge management within an enterprise. The
Knowledge Management system
Organization Architecture
Knowledge
Architecture
Knowledge
Management
Process
Corporation’s
business
operation
Corporation
with superior
performance

Input
Output
IT Architecture
Figure 2. The framework of knowledge management system
Syst. Res. RESE ARCH PAPER
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
Know ledge Mana gement in OSS 1 81
characteristics of EIS include (Ross and Vital,
2000):
An EIS is composed of a suite of different
modules. Typical modules include accounting,
human resource, manufacturing, logistics, custo-
mer relationship management, etc. An enterprise
can get its EIS solution through integrating a
number of modules.
Each module is business process-specific. The
use of EIS is associated with business process re-
engineering to optimize business processes.
An EIS creates an enterprise-wide transaction
structure by integrating modules, data storing/
retrieving processes, and management and ana-
lysis functionality.
An EIS is not just a software system; it repre-
sents a new kind of managerial thinking. A
successful implementation of ERP is not only
related to software selection, but also enterprise
strategy, enterprise culture, business process
reengineering (BPR), top management support,
training and others.
Considering the relationship between knowl-

edge architecture, knowledge process architec-
ture, organization architecture, IT architecture,
and enterprise operations, an EIS supports
knowledge management that encompasses all
types of knowledge in business operations. The
support provided by an EIS to an enterprise’
knowledge management is embodied in each
module for specific knowledge management.
Each module associates with a specific type of
business process, which corresponds to a specific
knowledge management. The knowledge man-
agement of the entire enterprise is realized
through the integration of individual knowledge
management module.
OVERVIEW OF OSS
Evolution of OSS
In the 1980s, the basic standard of OSS was
determined. The main usage is to manage net-
works. In the beginning of 1990s, OSS standard
has placed emphasis on both network systems
and network management. A substantial amount
of work has been completed by the International
Telecommunications Union (ITU) and the Inter-
national Organization for Standardization (ISO).
The representative standards of OSS are Tele-
communications Management Network (TMN)
and Simple Network Management Protocol
(SNMP). In recent years, the next generation
network (NGN) is coming ever closer. NGN is a
high speed multi-service packet data network

capable of supporting the traditional functions
of voice networks, data networks/internets
and even mobility by providing quality-assured
transmission, switching and services over IP and
ATM cores. The competitiveness is in managing
service, not managing network resources. Thus
the OSS has shifted from network-oriented to
service-oriented. During the process of develop-
ing OSS standards, support has been provided
by service providers (SP), network operation
providers, equipment manufacturing enter-
prises, and software suppliers.
Definition of OSS
OSS stands for Operations Support Systems. OSS
is a common term for the collection of all the
support systems required to run a telecom
operator’s business. OSS is consisted of four
subsystems: Operation Support System (OSS),
Business Support System (BSS), Resource Sup-
port System (RSS), and System Support System
(SSS). The functions of OSS consist of activation,
inventory management, fault management, and
workforce management, etc. BSS includes custo-
mer care, multi-service provisioning, service
assurance, and billing, etc. RSS handles network
resource management, operation information
management, customer basic information man-
agement and customer service information, etc.
SSS deals with log file, system parameters, etc.
Figure 3 provides a framework of OSS in which

OSS and BSS are the main functions.
The main functions of OSS include,
* Customer care: provide an interface to the
customers for all issues related to customer
order, sales, billing, and problem handling.
* Multi-service provision: activate instances of
service for particular customers.
RE S E ARCH PAPE R Syst. Res.
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
182 Jiayin Qi et al.
* Service assurance: monitor and uphold the
quality of the delivered services.
* Billing: charge for the service.
* Planning and administration: plan, design and
administer the services and infrastructures.
* EAI (Enterprise Application Integration):
automate the exchange of data between inter-
nal applications.
* Activation: execute a service in an optimal and
well-defined order
Customer/Market
OSS
BSS
Customer Care
Multiservice
Provision
Service Assurance
Billing
& Planning
Administration

Activation
Inventory
Management
Fault Management
Workforce
Management
RSS
Network resource
management
informationOperation
management
basic Customer
information management
serviceCustomer
information management
SSS
User Management
System monitoring system parameters
Versioning
Backuping Log file
EAI
Figure 3. OSS structure
Syst. Res. RESE ARCH PAPER
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
Know ledge Mana gement in OSS 1 83
* Inventory management: keep track of the
equipment such as where it is, how it is confi-
gured, and its status.
* Fault management: handle alarms.
* Workforce management: manage and sche-

dule teams of technicians, installers and engi-
neers.
In this paper, a network operator is defined as
a telecommunication service provider with a
network infrastructure and provides multiple
services. It could be a network, a fixed-line access
network of any kind, or a mobile 2/2.5/3G
mobile network. This type of network operator is
named as telecom operator throughout the
paper. Of course, the research is related to Ser-
vice Provider (SP) and Content Provider (CP)
with no infrastructure of their own although
their tasks are simpler since they only manage
services and IT infrastructure.
TOM and OSS
OSS is intended to cover TOM (Telecom Opera-
tions Map) provided by the organization
TMForum. TOM model focuses on the opera-
tional processes within the telecommunication
industry. It was designed as a blueprint for pro-
cess direction and a starting point for developing
and integrating OSS. The relationship between
TOM and OSS is shown in Figure 4.
FAB (fulfilment, assurance and billing) is the
core area of operations for telecom operators. FAB
defines the process for fulfilling an order, assuring
the defined level of performance and facilitating
billing for the services provided. FAB is carried
out through the following vertical processes:
Customer interface management process: It is

responsible for the dialogue with customer.
Customer care process: It deals with the custo-
mer needs, ways to identify the needs and how to
achieve it.
Service/product development and operation
process: It handles how the service is offered and
how to achieve it.
Network and systems management process: It
handles resources required for achieving the
service offered to the customer.
Features of OSS
OSS is a kind of EIS, which is applied to tele-
communications industry. Corresponding to the
characteristics of EIS, OSS’ characteristics can be
described as,
The key idea of OSS is the modularization of
telecommunications operation management. Tel-
ecom operators face a lot of uncertainty. The
appearance of new services is very quick. The
modular design of OSS is considered a necessity
(Wade, 2000).
OSS realizes the end-to-end customer business
operation processes. TOM is an important refer-
ence function model for OSS planning. The TOM
model contains a detailed description of the most
important processes involved in running a telecom
operator’s operation. Service fulfilment, service
assurance, and billing are the three basic customer
business operation processes. OSS implementation
will inevitably consider business process reengi-

neering (Wade, 2000; Huang et al., 2003).
OSS is a highly integrated software architec-
ture. Integrating multi-sections’ businesses in a
single software platform efficiently for improv-
ing customer service is one of the aims of OSS.
This task requires a high level of integration
among each subsystem.
OSS is not just a software system, but also
represents managerial thinking. Using TOM as
an important reference model, OSS encourages
telecom operators pay more attention to the
customers rather than just do billing as in the
past (Walsh, 1998).
Generally speaking, OSS can work not only for
telecom operators, but also for those other enter-
prises with characteristics resemble to that of
telecom operators with special network resources,
special service flow, and value chain based on
these network resources and service flows; for
example, large power plants (Feng et al.,2001),
traffic management (Takahashi, 1998), and others
(Miyamoto et al., 1997; Sherif and Ho, 2000).
Objectives of OSS
As for the motivation for OSS’ implementation,
there are six main reasons (Schroter, 1998):
RE S E ARCH PAPE R Syst. Res.
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
18 4 Jiayin Qi et al.
(1) rapid development and deployment of new
services (Everitt and Virgin, 1996); (2) cost reduc-

tion through operation automation; (3) business
process integration (Xia and Rao, 1997);
(4) uniform software platform (Furley, 1996;
Appel and Polosky, 1988); (5) customer service
level improvement (Giannelli et al., 1990);
(6) efficient network resource and customer
resource management (Appel and Polosky,
1988; Kittel et al., 2000).
The objective of OSS is to achieve superior
performance, which is embodied in higher
TOM
Customer
Customer Interface Management process
Customer Care Process

Service/Product Development and Operations Process
Physical Network and Information
Technology
selaS
r
edrO

gnild
naH
m
e
lborP

gni
l

dn
a
H
rem
o
tsu
C
so
Q
tn
em
egan
a
M
g
ni
ci
ov
n
I
noitc
e
l
lo
C
gninna
lP ec
ivre
S
tnempo

leve
D

ecivreS
noitar
u
g
if
n
oC

melb
o
r
P
e
c
i
v
r
eS

n
oi
t
ul
ose
R

ytila

u
Q

e
civ
reS

tn
e
me
g
anaM

d
n
a
g
n
it
aR

g
n
i
t
n
u
o
csi
D

Network and Systems Management Process

gni
n
n
a
lP
k
row
teN

tn
e
m
po
l
e
veD
k
r
o
w
teN

g
n
i
noi
s

i
vorP

y
r
o
tn
e
v
nI
kro
w
t
e
N

t
n
em
e
ga
naM
kro
w
t
e
N

e
c

n
a
n
etn
i
a
M

n
oit
a
ro
t
seR
&

a
t
a
D

k
r
o
w
teN
t
n
e
m

e
g
a
naM
S
S
O
Figure 4. TOM and OSS
Syst. Res. RESE ARCH PAPER
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
Know ledge Mana gement in OSS 1 85
average revenue per user (ARPU), better ser-
vices, higher customer satisfaction, and improv-
ed asset utilization, etc.
KNOWLEDGE MANAGEMENT IN OSS
Business Environment in
Telecommunications Industry
The telecommunications environment can be
characterized by its inherent distributive, contin-
uous expansion in the size of network, and
the particular importance of fault-tolerance
requirement. These characteristics are reflected
in the design of software systems. Software sys-
tems in telecommunications have to cope with the
universe of telecommunications protocols, numer-
ous hardware platforms, and network architec-
tures (Csele
´
nyi et al., 1998). The characteristics of
telecommunications software systems include

high software cost, concurrency, distributivity,
reliability, diversity and complexity (Patel, 2002).
Except the above-mentioned industry charac-
teristics, telecom operators are facing more and
more challenges nowadays. Factors such as
globalization and technology innovation repre-
sent radical challenges to telecom operators. They
must be more and more competitive to survive.
Today’s telecommunication market introduces
more competitions; meanwhile offers more choi-
ces for customers, lower price and the pressure to
improve service quality for operators. As the
previous monopoly situation is no longer exist,
new entrants come into the market. In emerging
economy, state-owned operators are fully or
partially privatized in order to survive better
(Stienstra et al., 2004).
Globalization promotes the domestic competi-
tion. Global telecommunication market gives
opportunities to some operators because of the
economies of scale in telecommunication net-
work, such as BT and Vodafone. It also brings
radical domestic competition since more new
entrants enter to the market.
Internet technology causes an extraordinary
growth of the Internet and IP services and
applications. Customers are increasingly free
to choose different service components from
different vendors and assemble their own solu-
tion (Li and Whalley 2004).

Industry deregulation, globalization, and IP
make the telecommunication industry full of
intensified competition. The telecommunication
market involves a shift from a stable market to an
increasingly user-driven market place. The suc-
cess of a telecom operator will entirely depend
on the operator’s ability to create services and
applications that are embraced by the users.
Same as the success brought by knowledge
management to the manufacturing sector, know-
ledge management is increasingly helping the
telecomm sector to keep sustainable competi-
tiveness and competency.
Knowledge Management in BSS
BSS focuses on developing the core business by
defining marketing and offering strategies, new
products implementation and managing existing
products. Customer interface management pro-
cess and customer care process are the two major
aspects involved in BSS. Dialogue carrying, ser-
vice ordering, service activation, trouble admin-
istration, and billing account review make up all
the activities in BSS.
Staff knowledge, organizational knowledge,
and relational knowledge form the know-
ledge architecture of BSS. There is a plenty of
staff knowledge involved such as sales staff’s
experience. There are also rich organizational
knowledge existing in the customer interface
management process and customer care process.

Deeper customer knowledge can give rise carriers
an edge in developing pricing models (Limbach,
2004). In addition, relational knowledge exists in
BSS such as reputation, brands, customer loyalty
and distribution channel knowledge. Those are
the important factors influencing CRM.
Successful sales experiences can be acquired
and shared among the employees in the sales
and marketing department. Replication, imitation,
elicitation and innovation will be the main acti-
vities for knowledge creation. Some knowledge on
routine problems, success experience, standard
business process, can be considered as existing
‘discovered’ knowledge to be maintained and
reused. Sharing of existing knowledge distributes
RE S E ARCH PAPE R Syst. Res.
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
18 6 Jiayin Qi et al.
knowledge at the organizational level. Due to the
fact that the telecommunications industry changes
rapidly, new services, new regulation policies,
new market environments, all require continual
revision of existing knowledge.
BSS is at the front-end in serving customers
for telecom operators. Due to the competition
in telecommunications industry, organizational
structures have increasingly been adjusted to
customer-oriented. All of these request organi-
zational knowledge process.
Telephone call centre, interactive voice

response (IVR), computer telephone integration
(CTI), predictive dialers, wireless agents, e-mail,
web self service, text chat and web collaboration
make up the technology to complete customer
communication. IP based call centre, operational
CRM and interactive CRM, billing system, and
performance management are sets of software to
support the business operation process. The
integration of these technologies and sets of
software forms the IT architecture of BSS.
Knowledge Management in OSS
OSS focuses on planning, developing and
delivering services and products in operation
domain. Service/product development and
operation process are the operational processes.
OSS deals with service generation and network
resource planning.
Human knowledge, organizational knowledge,
technological knowledge and relational knowl-
edge are all involved in OSS. Those previous
service cases, as well as proven cross-selling rules
are human knowledge. How to organize service/
product development, operation process, and
network, is considered as organizational knowl-
edge. In addition, culture, regulations, and
partnerships are considered as organizational
knowledge as well. There are many innovative
techniques and skills involved with these which
are considered as technological knowledge. Inter-
estingly, the greater the scope of services offered,

and the greater the range of quality and price
options, the more efficient (and cost efficient) the
use of the network resources. Service innovation
is a key factor for revenue growth of a telecom
company. For designing a successful marketing
strategy, some intangible resources will inevitably
be used. And a successful strategy will also create
new intangible resources. These intangible
resources are relational knowledge.
Knowledge can be created from studying
previous successful service offering. The enligh-
tening effect can create new types of human,
organizational, technological and relational
knowledge. All of the knowledge can be acquired
and reused. Sharing such knowledge can further
diffuse knowledge across the enterprise.
OSS is operated at the back end which
provides decision support for BSS. Knowledge
sharing and creating are essential to such deci-
sion support function. For reducing ‘noise’ and
eliminating barriers across sectors, smooth com-
munication is required. Organizational structure,
based on traditional command and control, must
shift to an open and collaborative structure.
The analytical CRM is an outstanding compo-
nent to support service/product development
process. Decision support system (DSS) and
expert system (ES) are both common tools.
Knowledge Management in RSS
RSS focuses on planning, developing and deli-

vering resources needed to support services and
products in the operations domain. Network and
systems management process is the operational
process in RSS.
Human knowledge, organizational knowledge
and technological knowledge are the main types
of knowledge. Those previous network resource
planning cases and the accumulated network
resource management strategy form the major
human knowledge. Database, data marts and data
warehouses about services and products represent
the major organizational knowledge. Some inno-
vation techniques are technological knowledge.
Organizational structure has influence on RSS,
but the degree of influence is much weaker than
that to OSS and BSS. Database, data mart and data
warehouse are the three data storages in RSS.
Knowledge Management in SSS
SSS is of significance to OSS as an EIS. A variety of
technological knowledge is involved with this
Syst. Res. RESE ARCH PAPER
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
Know ledge Mana gement in OSS 187
system including operating systems methods and
techniques. In general, organizational structure
has relatively minor influence on it.
Summary
BSS, OSS, RSS and SSS are integrated into a single
OSS through system integrator (SI) software.
Knowledge management varies among different

components in OSS. BSS and OSS involve with
types of knowledge throughout the entire knowl-
edge management process, requiring organiza-
tional learning, open organization structure
and certain IT architecture. RSS involves human
knowledge, organizational knowledge and tech-
nological knowledge. Organizational structure
has a less significant influence on it. Data mani-
pulation tools are needed. Technological knowl-
edge is the main type of knowledge involved in
SSS for which organizational structure has minor
effect on it. The summary of knowledge manage-
ment in OSS is described in Table 2.
Table 2. Summary of knowledge management in OSS
OSS\ Knowledge Knowledge Organization IT Function
KM Architecture Management Architecture Architecture
Process
BSS Human knowledge, Create, maintain, Team management, Call centre To provide
organizational distribute and revise project manager, CTI, operational customer
knowledge, knowledge to support communicate with CRM, interactive service
relational customer interface
knowledge management process software vender CRM, billing effectively
and customer care system, etc
process
OSS Human knowledge, Create, maintain, Team management, Analytical CRM, To support the
organizational distribute and revise project manager, DSS, etc. customer service
knowledge, knowledge to support communicate with provision
technological service/product software vender effectively
knowledge, development and
relational operation process

knowledge
RSS Human knowledge, Create, maintain, Team management Database, data To support the
organizational distribute knowledge mart, warehouse, above activities
knowledge, to support network etc. effectively
technological and systems
knowledge management process
SSS Technological Revise knowledge to Team management OS, such as Unix To support the
knowledge support OSS’ regular operation etc. above activities
effectively
OSS Human knowledge, Create, maintain, Team management, Enterprise To gain superior
In knowledge, distribute and revise project manager, Information advantage
general knowledge, knowledge to support communicate with Systems (EIS) through
organizational the horizontal business software effectively
knowledge, process of fulfilment vendor providing
technological assurance and billing end-to-end
knowledge, (FAB) customer service
relational
knowledge
RE S E ARCH PAPE R Syst. Res.
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
188 Jiayin Qi et al.
DISCUSSION AND CONCLUSION
An integrated OSS is a combination of applica-
tions that interact with each other to enable sup-
port, administration and management of services
for telecom industry. It includes systems that
manage the networking infrastructure, planning
tools, billing systems, customer care, trouble
management tools and the like. It is the funda-
mental integrated software platform for telecom

operators. It is an EIS used in the telecommuni-
cations industry.
Although there are some researches on the
knowledge management in EIS, especially in ERP,
there are only limited researches on knowledge
management in OSS. In this paper, an overview of
knowledge management in OSS and its frame-
work is provided. Future research will be focusing
on knowledge management in OSS implementa-
tion, key knowledge management techniques in
OSS (Liao, 2003; Sher and Lee, 2004), knowledge
version management in OSS, etc.
In this paper, a knowledge management model
in OSS with systems point of view is proposed.
A knowledge management framework for OSS in
systems perspectives is also developed.
REFERENCES
Ahn J, Chang S. 2004. Assessing the contribution
of knowledge to business performance: the KP
3
methodology. Decision Support Systems 36(4): 403–
416.
Alavi M, Leidner D. 2001. Knowledge management
and knowledge management systems: conceptual
foundations and research issues. MIS Quarterly
25(1).
Appel J, Polosky M. 1988. Pacific Bell’s network and
systems concept of the 90’s. IEEE Journal on Selected
Areas in Communications 6(4): 627–632.
Argyris C, Schon D. 1996. Organizational Learning.

Addison Wesley: Reading, MA.
Badii A, Sharif A. 2003. Information management and
knowledge integration for enterprise innovation.
Logistics Information Management 16(2): 145–155.
Bendoly E. 2003. Theory and support for process
frameworks of knowledge discovery and data
mining from ERP systems. Information & Manage-
ment 40(7): 639–647.
Bhatt G, Gupta J, Kitchens F. 2005. An exploratory
study of groupware use in the knowledge manage-
ment process. Journal of Enterprise Information Man-
agement 18(1): 28–46.
Cavusgil S, Calantone R, Zhao Y. 2003. Tacit Knowl-
edge transfer and firm innovation capability. Journal
of Business & Industrial Marketing 18(1): 6–21.
Choi B, Lee H. 2002. Knowledge management strategy
and its link to knowledge creation process. Expert
Systems with Application 23(3): 173–187.
Chuang S. 2004. A resource-based perspective on
knowledge management capability and competitive
advantage: an empirical investigation. Expert Sys-
tems with Applications 27(3): 459–465.
Csele
´
nyi I, Szabo
’’
R, Szabo
’’
I, Latour-Henner A,
Bjorkman N. 1998. Experimental platform for tele-

communications resource management. Computer
communications 21(17): 1624–1640.
Elmasri R, Navathe S. 2003. Fundamentals of Database
Systems. Addison Wesley: Boston.
Everitt H, Virgin M. 1996. Full services network
operations and management. Proceedings of IEEE
Network Operations and Management Symposium,
vol. 4, pp. 15–19.
Feng Q, Bai X, Cao Y, Jin Y. 2001. Models of electric
market operation support system based on UML.
Proceedings of IEEE 2001 International Conference on
Computer Networks and Mobile Computing, pp. 13–18.
Ferna
´
ndez E, Montes J, Va
´
zquez C. 2000. Typology
and strategic analysis of intangible resources: a
resource-based approach. Technovation 20(2): 81–92.
Fowler A, Pryke J. 2003. Knowledge management in
public service provision: the child support agency.
International Journal of Service Industry Management
14(3): 254–283.
Furley N. 1996. OSS architecture framework. Proceed-
ings of IEEE Network Operations and Management
Symposium, vol. 3, pp. 15–19.
Giannelli M, Pileri S, Saracco R. 1990. Operations
support systems in Italy: experiences and perspec-
tives of integration. Proceedings of IEEE International
Telecommunication Symposium, pp. 572–576.

Huang T, Lee C, Chen Y, et al. 2003. A new architecture
to operation support systems. Proceedings of IEEE
10th International Conference on Telecommunications,
pp. 1236–1242.
Joshi A, Sharma S. 2004. Customer knowledge devel-
opment: antecedents and impact on new product
performance. Journal of Marketing 68(4): 47–59.
Kanjanasanpetch P, Igel B. 2003. Managing knowledge
in enterprise resource planning (ERP) implementa-
tion. Proceedings of IEEE Engineering Management
Conference, pp. 30–35.
Kim Y, Yu S, Lee J. 2003. Knowledge strategy planning:
methodology and case. Expert Systems with Appli-
cations 24(3): 295–307.
Kittel A, Gudur J, Gundlapudi S, Lederman A. 2000.
GNI-building integrated OSS infrastructure from
the ground up. Proceedings of IEEE Network Opera-
tions and Management Symposium, pp. 945–946.
Syst. Res. RESE ARCH PAPER
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
Know ledge Mana gement in OSS 1 89
Kwan M, Balasubramanian P. 2003. Knowledge scope:
managing knowledge in context. Decision Support
Systems 35(4): 467–486.
Li F, Whalley J. 2004. Deconstruction of the telecom-
munications industry: from value chains to value
networks. Telecommunications Policy 26(9–10): 451–
472.
Li H, Xu L. 2001. Feature space theory-a mathematical
foundation for data mining. Knowledge-Based Systems

14(5–6): 253–258.
Li H, Xu L, Wang J, Mo Z. 2003a. Feature space theory
in data mining. Expert Systems 20(2): 60–71.
Li T, Yang N, Wu H. 2003b. Operation Support
Systems—Theory, Strategy and Practice. Renmin Post
& Communications Press: Beijing.
Liao S. 2003. Knowledge management technologies
and applications-literature review from 1995 to
2002. Expert Systems with Applications 25(2): 155–164.
Limbach I. 2004. Mobile OSS: fitting the bill. Total
Telecom April: 30–31.
Lynn G, Morone J, Paulson A. 1996. Marketing
and discontinuous innovation: the probe and learn
process. California Management Review 38(3): 8–37.
Marakas G. 2003. Decision Support Systems in the
Twenty-first Century. Prentice-Hall: Upper Saddle,
NJ.
Mesaric J. 2004. Knowledge management—necessity
and challenge in small and medium enterprise.
Proceedings of 26th International Conference on Infor-
mation Technology Interfaces, ITI, pp. 481–485.
Miyamoto Y, Hayshi M, Koyano K, et al. 1997.
Development of operation support systems for
refuse incineration plant. IEEE Proceedings of 26th
SICE Annual Conference, pp. 1053–1056.
Nemati H, Steiger D, Iyer L, Herschel R. 2002.
Knowledge warehousing: an architectural integra-
tion of knowledge management, decision support,
artificial intelligence and data warehousing. Decision
Support Systems 33(2): 143–161.

O’Brien J. 2005. Introduction to Information Systems.
McGraw-Hill: Boston.
Patel A. 2002. Current status and future directions of
software architecture for telecommunications. Com-
puter Communications 25(2): 121–132.
Pearlson K, Saunders C. 2004. Managing and Using
Information Systems. Wiley: New York.
Ross J, Vitale M. 2000. The ERP revolution: surviving vs
thriving. Information Systems Frontiers 2(2): 233–241.
Schroter A. 1998. Introduction to the network infra-
structure warehouse. Proceedings of IEEE Network
Operations and Management Symposium, pp. 210–219.
Sher P, Lee V. 2004. Information technology as a
facilitator for enhancing dynamic capabilities
through knowledge management. Information &
Management 41(8): 933–945.
Sherif M, Ho S. 2000. Evolution of operation support
systems in public data networks. Proceedings of
IEEE Symposium on Computers and Commuications,
pp. 72–77.
Shi Z, Huang Y, He Q, Xu L, Liu S, Qin L, Jia Z, Li J,
Huang H, Zhao L. 2005. MSMiner-a developing
platform for OLAP. Decision Support Systems.
Siemieniuch C, Sinclair M. 2004. A framework for
organizational readiness for knowledge manage-
ment. International Journal of Operation & Production
Management 24(1): 79–98.
Spiegler I. 2003. Technology and knowledge: bridging
a ‘‘generating’’ gap. Information & Management 40(6):
533–539.

Stienstra M, Baaij M, Van Den Bosch F, Volberda H.
2004. Strategic renewal of Europe’s largest telecom
operator (1992–2001): from herd behavior towards
strategic choice? European Management Journal 22(3):
273–280.
Takahashi H. 1998. A study on designing driver’s
operation support system using driving environ-
ment-driver-vehicle interaction model. Proceedings
of IEEE World Conference on Computational Intelli-
gence, pp. 177–182.
Tzokas N, Saren M. 2004. Competitive advantage,
knowledge and relationship marketing: where,
what and how? Journal of Business & Industrial
Marketing 19(2): 124–135.
Wade V, Richardson T. 2000. Workflow-a unifying
technology for operational support systems. Proceed-
ings of Network Operations and Management Sympo-
sium, pp. 231–246.
Walsh D. 1998. Operations support systems: caught in
the winds of change. Proceedings of IEEE Network
Operations and Management Symposium, p. 255.
Wang S, Ariguzo G. 2004. Knowledge management
through the development of information schema.
Information & Management 41(4): 445–456.
Warfield J. 1989. Societal Systems. Intersystems Pub-
lications: Salinas, CA.
Xia Q, Rao M. 1997. A hybrid intelligent system for
process operations support. Proceedings of IEEE
International Conference on System Man Cybernetics,
pp. 2097–2102.

Xu L, Li Z, Li S, Tang F. 2005a. A polychromatic sets
approach to the conceptual design of machine tools.
International Journal of Production Research 43(12):
2397–2422.
Xu L, Li Z, Li S, Tang F. 2005b. A decision support
system for product design in concurrent engineer-
ing. Decision Support Systems.
RE S E ARCH PAPE R Syst. Res.
Copyright ß 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177^190 (2006)
19 0 Jiayin Q i et al.