122
International Journal of Electronic Business Management, Vol. 2, No. 2, pp. 122-130 (2004)
SEMANTIC WEB AND MULTIPLE-AGENTS IN SCM
Wei-Shuo Lo1*, Tzung-Pei Hong2, Shyue-Liang Wang3 and Yu-Hui Tao4
1
Department of Information Engineering
I-Shou University
2
Department of Electrical Engineering
National University of Kaohsiung
Kaohsiung (811), Taiwan
3
Department of Computer Science
New York Institute of Technology
New York, USA
4
Department of Information Management
National Pingtung University of Science and Technology
Pingtung (912), Taiwan
ABSTRACT
In recent years, many researches about how to efficiently handling information on logistics
in supply chain have been proposed. Some focus on applying artificial intelligence to
solving the problem. Most of the above approaches, however, usually consider multiple
agents and simulation systems in single companies. Besides, they seldom consider using
semantics to allow flexible information query with different linguistic terms. In this paper,
we have proposed a framework for an e-SCM multiple-agents decision support system
(called e-SCM multi-agents system), which combines ontology, to efficiently integrate data
and information in supply chains. There are five layers in the system, including access
layer, communication layer, application layer, ontology layer and database layer. These
layers are linked together to integrate different access tools, data formats, management
information systems, semantic web and databases. Different agents execute different tasks
in each layer to achieve the purpose of integration and communication in a supply chain
with less human intervention. Our approach emphasizes on the transparent connection
manner among businesses. The proposed system can assist in business data and information
sharing in a complex supply chain management.
Keywords: Semantics Web, Multiple Agents, E-Business, Supply Chain Management
1. INTRODUCTION
*
Information and logistics are two main
problems in supply chain management. Information
flows from customers to distributors, manufacturers
and suppliers. It flows from downstream to upstream.
On the contrary, logistics flows from suppliers to
manufacturers, distributors and customers.
It flows from upstream to downstream.
Efficiently managing information and logistics in a
supply chain is very important to running a company.
Management
information
systems
and
simulations tools have been commonly used to
control information on logistics in recent years. They
can help reduce paper works and human errors in
every-day tasks, and can assist managers in handling
important information, predicting, and setting
*
Corresponding author: [email protected]
different parameters to simulate different situations,
helping make good decisions in business. They
provide an efficient and effective electronic business
environment.
Agent techniques have recently become very
popular since they possess the characteristics of
ongoing execution, environmental awareness, agent
awareness, autonomy, adaptiveness, intelligence,
mobility, anthropomorphism, and reproduce [4].
Agents are widely applied in computer science,
e-commerce, supply-chain management, decision
support systems, and among others.
Most of the previous approaches, however,
usually consider multiple agents and simulation
systems in single companies. Besides, they did not
consider using semantics to allow flexible
information query with different linguistic terms. In
this paper, we propose the e-SCM multi-agents
system, which combines ontology, to solve the above
problems. We provide a framework with five layers
W. S. Lo et al.: Semantic Web and Multiple-Agents in SCM
to integrate different access tools, data formats,
management information systems, semantic web and
databases. Different agents execute different tasks in
each layer to achieve the purpose of integration and
communication. The proposed framework can
effectively handle information and data in supply
chains.
The remaining parts of this paper are organized
as follows. The problems in supply chains are
described in Section 2; Problems in supply chains is
stated in Section 3; Information integration in supply
chains is stated in Section 4; A framework for the
proposed e-SCM multi-agents system is shown in
Section 5; The functions of the five layers in the
framework are described in Section 6; An example is
given in Section 7; Conclusions is given in Section 8.
2. REVIEW OF RELATED
WORKS
In recent years, many researches about how to
efficiently handling information on logistics in supply
chain have been proposed. Some focus on applying
artificial intelligence to solving the problem. Table 1
introduces some related works in multiple agents and
supply chain management.
Most of the above approaches, however,
usually consider multiple agents and simulation
systems in single companies. Besides, they did not
consider using semantics to allow flexible
information query with different linguistic terms. In
2001, Lee proposed architecture for semantic web
[7], which is shown in Figure 1. The contents in
documents are stored in XML and RDF formats,
which can easily represent the document structures.
There are modules for rules, logic and other inference
methods to support the semantic induction. The
ontology vocabulary provides a flexible linguistic
matching when different terms are used.
In this paper, we propose an e-SCM
multi-agents system, which combines ontology, to
solve the above problems.
Rule
User
Interface
Proof
Logic
Data
Ontology vocabulary
SelfResource Description
\describing
document Framework + RDF Schema
Internet
XML + Name + XML Schema
Unicode
Table 1: Related works in multiple agents and supply
chain management
Researchers
Issues for related works
They
proposed
a
Swaminathan,
simulation-based
framework
Smith and
for developing customized
Sadeh-Koniecpoll
supply chain models from a
(1998)
library of software components.
They highlighted some issues
Walsh and
for
making
progress
in
Wellman
modeling
supply
chain
(1999)
formation.
Chen, Peng, Finin They described a framework of
and Labrou
negotiation-based multi-agent
(1999)
for supply chain management.
They proposed a collaborative
agent
system
architecture
(CASA) and an infrastructure
Shen, Ulieru and
for the collaborative agent
Norrie (1999)
system (ICAS) as a general
architecture for Internet-based
multi-agent system.
They
provided
a
re-configurable,
multilevel,
Sadeh, Hildum agent-based architecture, called
and Kjenstad MASCOT (Multi-Agent Supply
(1999)
Chain cOordination Tool), for
coordinating supply chain
planning and scheduling.
They developed a framework
for agent-based electronic
Kim, Paulson and
markets
Petrie (1999)
(e-markets) with supply chain
coordination
They proposed an agent–based
software system for assisting in
Pathak,
decision-making
regarding
Nordstrom and
supply chain management and
Kurokawa
in efficient and effective use of
(1999)
electronic data interchange
(EDI).
3. PROBLEMS IN A SUPPLY
CHAIN
Trust
Digital signature
Data
123
Universal
resource indicator
Figure 1: The architecture of the semantic web
presented [7]
Logistics play an important role in a supply
chain. Logistics can be divided into two types:
inbound logistics and outbound logistics. Inbound
logistic represents a purchase process for raw
material of a company, and outbound logistic
represents a distribution process of products.
Copacino defined the problems appearing in logistics
as an integrated pipeline [2], which is shown in
Figure 2.
International Journal of Electronic Business Management, Vol. 2, No. 2 (2004)
124
In Figure 2, there are three main activities supply, operation and distribution. They are described
as follows.
Supply: It concerns about the tasks of sourcing,
purchasing, inbound-transportation, raw material
and pasts inventory.
Operation: It mainly conducts the business
processes themselves. The tasks include
production planning, production scheduling, and
work in process inventory.
Distribution: It focuses on satisfying the
requirements of customers. The tasks include
forecasting, customer service, finished goods
inventory,
warehousing,
and
outbound
transportation.
Logistics
Information
SUPPLY
-Sourcing -Purchasing -Inbound
Transportation
OPERATION
-Production Planning -Production Scheduling
-Work in Process Inventory
automatically conduct both the electronic processes
and information with smart agents. Most of these
agents can move quickly and conduct information
correctly. The details will be described in Section 5.
4. INFORMATION
INTEGRATION IN A SUPPLY
CHAIN
Information integration is very important in a
supply chain. Along with the recent progress in
information
technologies,
current
system
development is very different from the previous one.
More and more businesses develop their information
systems from intranet to extranet, and now to
Internet. Therefore, the design of current intelligent
systems with agents is also different from before. The
difference between previous systems and our
proposed one is shown in Figure 3.
logistics flow
Information flow
Previous systems: multiple agents in one company
supplier
manufacturer
customer
distributor
Our system: multiple agents through many companies
supplier
manufacturer
distributor
customer
Logistics / Information flow center
DISTRIBUTION
-Forecasting-Customer Service
-Finished Goods Inventory
-Outbound Transportation
Figure 2: The problems appearing in logistics as an
integrated pipeline
Information flows in the direction of distribution,
operation and supply; logistics flows in the direction
of supply, operation and distribution. The above three
activities form an integrated architecture for a supply
chain, and should be considered together.
Agent techniques can used here to help design
intelligent information management systems. An
electronic process is a key factor to a business.
Business processes include much information, which
should be efficiently and effectively delivered to
managers. Managers can then use the information to
help make decisions. If the information can not be
quickly delivered, the managers may not make a
decision in time and may cause a great loss in
business. Therefore, conducting an electronic process
quickly, correctly and smartly is very important to
building an intelligent system. Our proposed e-SCM
multi-agents system considers the above issues to
SCM multiple agents / semantic web
Web Se lling / Rea l T ime Order Ma nage ment / Dema nd P lan / Supply Pla n / Procurem ent
Production Scheduling / D istr ibution L ogistics / Custom er S ervice
Figure 3: A comparison of previous and our proposed
systems
The previous systems usually use multiple
agents in a single company. Each company has its
own agent system to process its related information.
It does not consider the requirement of the systems in
the other companies. Therefore, the information
exchange between two companies may not be
transparent to other companies. However, the related
companies in a supply chain should have a
partnership, and the related information should be
shared among them. The previous systems usually
provide only one-way direction of information flow.
Along with the progress of the Internet
technology, companies can directly use a web-based
platform to access data. Information and data in a
supply chain can be put in an information center to be
shared by the companies in the chain. The companies
can load different access agents to get the desired
information in the supply chain from notebooks,
PDAs, cell phones, or radio equipments. The
information centers also have their own agents to
W. S. Lo et al.: Semantic Web and Multiple-Agents in SCM
transform formats, capture data, and output to users.
Besides, the companies may their own vocabulary;
the techniques for ontology are used to provide a
flexible semantic information access. Each company
can easily access the information in the supply chain
as long as it has enough access right. Below, we will
introduce how to implement such a multi-agent
system.
A framework for the e-SCM multi-agent system
is proposed in this section. The proposed framework
consists of five layers, which are access layer,
communication layer, application layer, ontology
layer, and database layer. The relations among the
five layers are shown in Figure 4. The major
functions of these layers are listed as follows.
1. Access Layer: It allows users to link to the
system through different utilities.
2. Communication Layer: It transforms different
information formats from access layer into a
standard format or transforms the standard
5. A FRAMEWORK FOR THE
E-SCM MULTI-AGENT SYSTEM
Layer 1
Internet
Layer 2
125
Cell Phone
Desk
Notebook
PD
Communication Platform (Protocols)
Web / Radio / Mobile Agents
Supply Plan
Demand
Plan
Order
Management
Web Sale
Procurement
Production
Scheduling
Distribution
Logistic
Customer
Service
Layer 3
SCM Systems
ERP Systems
CRM Systems
e-Business Process Integration System
Ontology Platform (Semantic Web)
Layer 4
Layer 5
Sale
Database
Order
Management
Database
Demand
Plan
Database
Procurement
/Supply Plan
Database
Figure 4: A framework for the e-SCM multi-agents system
Logistics /
Customer
Database
126
3.
4.
5.
International Journal of Electronic Business Management, Vol. 2, No. 2 (2004)
mobile phones or PDA with Mobile-IP and WAP
browser functions that allow users to log on
Internet. The diagram is shown in Figure 6. The
wireless station receives and delivers information
for WAP browsers. The interface is a platform
for showing web pages; the mobile gateway
possesses the WAP proxy gateway function; the
engine provides information retrieval and online
transaction functions to the underlying layers;
the agents can automatically move, receive and
deliver communication data by WAP, PAP, HTTP
and HTML formats; the appliance includes
cameras, printers and databases.
format in the system into the formats of the
utilities.
Application Layer: It provides three application
systems to process the integrated information in
the supply chain. They are SCM, ERP, and CRM
systems. This layer is also called the e-Business
system layer.
Ontology Layer: It uses semantics-web
technology to improve the flexibility of access in
different terms. Different companies may have
their own terms. Different terms from two
companies may, however, represent the same or
similar meanings. This layer helps provide
semantic retrieval on database or data
warehouse.
Database Layer: It stores related data used in a
supply chain. The database may be distributed in
different sites or be centralized in only one site.
Data
about
sale,
order,
production,
manufacturing, purchase, distribution, and
customer service are stored in the database.
I
N
T
E
R
F
A
C
E
6. THE FUNCTIONS OF THE
FIVE LAYERS
6.1 Access Layer
Layer 1 is the access layer, which is used as the
user interface. Users can use a variety of utilities to
access the e-SCM multi-agents system. For example,
users can use desk computers, cell phones,
notebooks, PDA, and among others. The
characteristic of the access layer is wide accessibility.
Users can link to the e-SCM multi-agents system
from anywhere.
6.2 Communication Layer
Layer 2 is the communication layer, which
converts the formats of inputs to the system and the
format of outputs to the different interfaces. Three
converting agents are designed for performing this
function, respectively for web, radio, and mobile
inputs. Each agent has four main modules, receiving,
assessment, transformation, and answer, as shown in
Figure 5. The receiving module gets the information
from the interface, which was then evaluated by the
assessment module to decide whether to provide the
service. If the assessment is positive, then the
transformation module transforms the format of the
user’s information and sends it into the e-SCM
multi-agents system. If the system gets some outputs,
then the transformation module transforms the
internal format to the output format and the answer
module sends it out to the user interface.
Different converting agents take of different
format conversion. The functions of the three agents
are described below.
1. Mobile Agent: It is mainly used to support the
Webagent
Receiving
Assessment
Answer
Transforma
tion
Radioagent
Mobileagent
e-SCMsystem
Figure 5: The modules in each converting agent on
the communication layer
Web Engine
Cell
Phone
Wireless
station
PDA
I
N
T
E
R
F
A
C
E
•CGI Script
•Java Service
•Home page
Mobile
Gateway
Agent
Appliance
•Camera
•Printer
•Database
Agent Server
Figure 6. The diagram for the mobile agent
2.
3.
Radio Agent: It is mainly used to support the
radio utilities with Radio-IP and Radio browser
functions that allow users to log on Internet. The
diagram is shown in Figure 7. The radio station
receives and delivers information for Radio
browsers. The interface is a platform for showing
web pages; the radio gateway possesses the WAP
proxy gateway function.
Web Agent: It is mainly used to support the
notebooks and desk computers with Internet-IP
and Internet browser functions that allow users to
log on Internet. The diagram is shown in Figure
W. S. Lo et al.: Semantic Web and Multiple-Agents in SCM
8. The interface is a platform for showing web
pages; the Internet gateway possesses the
Internet proxy gateway function.
Web Engine
Radio
Radio station
I
N
T
E
R
F
A
C
E
•CGI Script
•Java Service
•Home page
Radio
Gateway
Agent
Appliance
•Camera
•Printer
•Database
satellite
Agent Server
Figure 7: The diagram for the radio agent
127
3.
scheduling and distribution.
CRM system: It is responsible for solving
problems from customers in a supply chain. It
however needs to handle upstream and
downstream data and information in a supply
chain, which make the integration tasks become
more difficult than in the SCM and CRM
systems. It uses several agents to perform the
tasks about order management, distribution, web
sale and customer service.
6.4 Ontology Layer
Layer 4 is the ontology layer, which manages
the concepts and terms used in the e-SCM
multi-agents system. It includes a set of vocabulary,
semantic relationships of terms, and some simple
inference rules and logic for supply chains. If
different terms are used, the ontology layer will first
transform the terms into the ones used in the
databases. This layer provides a platform for the
system to show semantic behaviors.
W eb E n gin e
Notebo ok
Inte rne t
C om puter
I
N
T
E
R
F
A
C
E
•CG I Sc ript
•Ja va Se rvice
•H ome pa ge
Inter net
Gate w ay
Agent
Ap p lian ce
•Ca me ra
•P rinter
•D a ta ba se
Agent S e rve r
Figure 8: The diagram for the web agent
6.3 Application Layer
Layer 3 is the application layer, which forms
the core part in the e-SCM multi-agents system. It is
also called the e-SCM layer. There are three systems
on this layer, which are SCM, ERP and CRM. Each
system has several agents to help performing the
tasks. These systems and agents must work together
and communication well to get good performance
and validity. The functions for these three systems are
described below.
1. SCM system: It is responsible for solving
problems from suppliers and procurements in a
supply chain. It uses several agents to perform
the tasks about supply plan, procurement,
demand plan and production scheduling in
inbound logistics.
2. ERP system: It is responsible for solving
problems from enterprise itself. It however needs
to handle upstream and downstream data and
information in a supply chain, which make the
integration tasks become more difficult than in
the SCM and CRM systems. It uses several
agents to perform the tasks about real time order
management,
demand
plan,
production
6.5 Database Layer
Layer 5 is the database layer, which stores
related data in supply chains for supporting a variety
of business processes. It is very important to the
e-SCM multi-agent system. Many databases exist on
this layer. For example, there may be databases
respectively for orders, customers, production,
package,
tracking,
suppliers,
procurements,
inventory, and among others. The databases are
mainly used for query, maintenance, and data mining.
They can be effectively utilized through the ontology
platform for a semantic consideration. Each data is
processed with several phases to be put in a database.
Figure 9 shows the processes.
T a b le
P ic t u r e
R aw
d a ta
Sound
D a ta
P r e - p r o c e s s in g
F o r m a tte d
d a ta
S to re
W eb page
T r a n s a c t io n
d a ta
D a ta b a s e
M an ag em en t
S ystem
D a ta b a s e s
Q u ery
M a in te n a n c e
D a ta
M in in g
Figure 9: The store processes for databases
In Figure 9, the data sources may be
transaction data, tables, pictures, sounds and web
pages designed and used by different users. They
form the raw data, which are unformatted. They are
then processed by the data pre-processing phase and
useful and formatted data are then extracted from
them. Noises are also removed in this phase. The data
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International Journal of Electronic Business Management, Vol. 2, No. 2 (2004)
represented in a consistent formatted way greatly
improve the efficiency of store and retrieval. The
formatted data are then put into databases for
different applications.
A database management system acts as a
bridge between the system and the database [3]. For
example, when a query enters, it must request
sufficient information from the databases to work
properly. The databases will be retrieved through the
database management system. Using a database
management system to access data has the following
advantages.
1. It can easily maintain the information.
Decision-making usually requires a lot of data,
and the data may be frequently changed along
with time. A database management system can
help administrators easily add, delete and update
data, and help decision support systems
correctly retrieve the appropriate data for
analysis.
2. It can effectively protect data. Some data or
information, such as domain knowledge and
business data, is very important to enterprises. A
database management system can protect the
databases from illegal use.
3. It can usually support several programming
languages. This makes development of decision
support systems easy and flexible.
4. It can usually support a network environment.
For a decision support system implemented on
the Internet, its database management system
must have the ability to work in a network
environment.
Several
famous
database
management systems have provided web kits
that can integrate traditional databases with
world-wide-web environments, and have
well-established functions in network data
retrieval and network security control.
7. AN EXAMPLE
In this section, we have provided an example to
demonstrate that our framework can solve problems
of supply chain. Assume a user wants to understand
the current state of his order, such as whether the
products for this order are in the production process
or have been delivered to a warehouse. The
processing for this situation is shown in Figure 10,
which consists of the following steps.
Step 1. Customers can use different utilities to get
his order situation. For example, the
customer may use a notebook with the web
browser to connect to Internet, and through
the Internet gateway to get to the
communication layer.
Step 2. The
customers’
manager
on
the
communication layer then loads related agent
programs into the SCM system at the client
end.
Step 3. The MIS manager at the client end then sets
up and starts the agent program. The
customer first types his account number and
password to enter the e-business process
integration system.
Step 4. Our system is divided into three sub-systems
including SCM, ERP and CRM. When the
customer inputs his linguistic terms into the
system’s interface to query about his order,
the CRM sub-system starts to take care of the
processing.
Step 5. The CRM system has four main functions
including customer management, order
situation, sales service, and deliver planning.
The module for processing order situations
uses the user’s linguistic input to get the
current state of the order. The database
management system will keep the up-to-date
data about all orders every day.
Step 6. The CRM system then finds the order state
from the database management system. If
different terms are used, then the ontology
layer will first transform the terms into the
ones used in the databases.
The purchase orders in a business process can
then be transparent to customers in this way.
I
N
T
E
R
F
A
C
E
Internet-IP
Communication layer
Formats transformation
Internet
Gateway
SCM
system
management
order
situation
sales
service
Database
Database
Database
customer
ERP
system
CRM
system
e-business process integration system
customer
SCM
system
ERP
system
CRM
system
deliver
planning
Database
Figure 10: The process for order queries in the
application layer
8. CONCLUSION
In this paper, we have proposed the e-SCM
multi-agents system, which combines ontology, to
efficiently integrate data and information in supply
chains. There are five layers in the system, including
access layer, communication layer, application layer,
ontology layer and database layer. These layers are
linked together to integrate different access tools,
data formats, management information systems,
semantic web and databases. Different agents execute
W. S. Lo et al.: Semantic Web and Multiple-Agents in SCM
different tasks in each layer to achieve the purpose of
integration and communication in a supply chain with
less human intervention. Our approach emphasizes
on the transparent connection manner among
businesses. Instead of previous information
exchanges from one company to another company in
a one-to-one way, our proposed approach can allow
different companies to exchange information
simultaneously. In the future, we will try to
implement a real system based on the proposed
framework.
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ABOUT THE AUTHORS
Wei-Shuo Lo is a Ph.D student at the Department of
Information Engineering in I-Shou University. He is
also an instructor at the Department of Business
Administration in Mei-ho Institute of Technology.
His research interests are data mining and supply
chain management.
Tzung-Pei Hong is currently a professor at the
Department of Electrical Engineering and the Dean
of Academic Affairs in National University of
Kaohsiung. His current research interests include
parallel processing, machine learning, data mining,
soft computing, management information systems,
and www applications. Dr. Hong is a member of the
Association for Computing Machinery, the IEEE, the
Chinese Fuzzy Systems Association, the Taiwanese
Association for Artificial Intelligence, and the
Institute of Information and Computing Machinery.
Shyue-Liang Wang is an Associate Professor in the
Department of Computer Science, New York Institute
of Technology. Dr. Wang is a member of IEEE,
Chinese Fuzzy System Association, Chinese
Computer
Association,
Chinese
Information
Management Association, Chinese Association of
Information
and
Management,
Kaohsiung
Association for Information Development. His
current
research
interests
include
fuzzy
knowledge-based systems, intelligent information
systems, and data mining.
Yu-Hui Tao is an associate professor at the
Department of Information Management, National
Pingtung University of Science and Technology,
Taiwan, R.O.C. He received his master’s and Ph. D.
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International Journal of Electronic Business Management, Vol. 2, No. 2 (2004)
degrees in industrial and systems engineering from
the Ohio State University, Columbus, Ohio, U.S.A.
His current research interests include tools and
applications web-based applications, knowledge
management, and e-business.
(Received July 2003, revised September 2003,
accepted November 2003)