Scientia Iranica E (2011) 18 (6), 1545–1552

Sharif University of Technology

Scientia IranicaTransactions E: Industrial Engineering

www.sciencedirect.com

Invited paper

An agent-based web service approach for supply chain collaborationO. Kwon a,1, G.P. Im b,2, K.C. Lee c,∗

aMIS, College of Management, Kyung Hee University, 1 Hoegi Dong, Dongdaemoon Gu, Seoul 130-701, Republic of Koreab Computer Information Systems, College of Business, University of Louisville, Louisville, KY 40292, United Statesc SKK Business School and Department of Interaction Science, Sungkyunkwan University, Seoul 110-745, Republic of Korea

Received 1 November 2010; revised 28 August 2011; accepted 13 September 2011

KEYWORDSAgent-based web services;Supply chain uncertainties;Flexibility;Scalability;Coordination;Case-based reasoning.

Abstract Despite the potential benefits the Internet and other related technologies offer, current supplychain management techniques do not take full advantage of these benefits. E-business environmentsprovide a facilitating infrastructure for solving the issues concerning the traditional supply chain, such asthe scalability and flexibility for the efficient collaboration between partners. Both internal and externaluncertainties to the supply chain can hinder collaboration between partners, and thus also hinder theirability to achieve their best possible performance. This research proposes using agent-based web servicesto better support collaboration within a supply chain. An advantage of agent-based web services is thatthey combine the strengths of both web services and multi-agents. Two different collaboration situationsare used in order to demonstrate the flexibility of the system. Scalability is demonstratedwhen the supplychain faces changes in the partnership candidates. Simulation methods are used in order to validate thefeasibility of this approach, and statistical tests reveal the robustness of the experimental results acrossdiverse uncertainties.

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1. Introduction

In this Internet-dominated business world, supply chaincollaboration has become an essential means for partnersin a chain to accomplish common goals that exceed awritten contract agreement. Successful collaboration betweenpartners helps them to achieve both strategic and operationaladvantages. At the operational level, collaboration bringsvisibility between both upstream and downstream partners,and also reduces the costs of inventory, premium services,and production scheduling. At the strategic level, collaboration

∗ Corresponding author. Tel.: +82 2 7600505; fax: +82 2 760 0440.E-mail addresses: obkwon@khu.ac.kr (O. Kwon),

ghiyoung.im@louisville.edu (G.P. Im), kunchanglee@gmail.com (K.C. Lee).1 Tel.: +82 2 961 2148; fax: +82 2 961 0515.2 Tel.: +1 502 852 4794; fax: +1 502 852 4799.

1026-3098© 2012 Sharif University of Technology. Production and hosting byElsevier B.V. All rights reserved. Peer review under responsibility of SharifUniversity of Technology.

doi:10.1016/j.scient.2011.11.009

enhances customer satisfaction through focused responses andalso allowspartners tomanage their resourcesmore flexibly [1].

Supply chain relationships depend on the intensity of thecollaboration among the partners [1]. Most firms aspire toachieve bilateral relationships with key partners, and it isimportant to note that technology can play an importantrole in maximizing the effectiveness of such relationshipsregardless of the intensity of their collaboration. The differenttypes of technical support discussed in the literature whichhas been used for collaboration of the supply chain havebeen classified as optimization-based, simulation-based, or asmulti-agent-based techniques. Optimization-based techniqueshelp managers identify the best possible solutions such assupply chain network design and facility location for a givensituation [2–4]. Simulation-based approaches have been usedto model the dynamic behaviors of the partners, and to resolvediverse contingencies caused by uncertainties in both supplyand demand [5,6]. Multi-agent-based approaches provideconvenient mechanisms for modeling dynamic behaviorsamong business partners [7–9].

Despite the available techniques that can be used for sup-ply chain collaboration, it is evident in this era of proliferatingweb usages in business that leveraging the Internet through e-enabled supply chain collaboration seems to be of importanceand worthy of investigation [10]. In this regard, we seek to ex-tend the multi-agent-based approaches used for collaborationby including them with technologies used for e-business. First,

1546 O. Kwon et al. / Scientia Iranica, Transactions E: Industrial Engineering 18 (2011) 1545–1552

we suggest a framework based on agent-based web services inorder to facilitate collaboration in the presence of high demandand uncertainties in the supplies available. Second, case-basedreasoning is used in order to enable the agents to learn the rulesneeded to derive an optimal solution and also to recommenda proper price and production quantity level. Target problemsare limited to the domain of production and inventory man-agement and in addressing the problems with traditional sup-ply chain collaborations in regards to high demand and supplyuncertainties. We assume that fluctuating customer demandscause high demand uncertainties, and the supply uncertaintiesresult from uncertainties in lead-time and production capacity.Next, we address the problems with the supply chain collab-oration in regard to uncertainty in the partner base. This un-certainty refers to the changing bases of available partners towork with. This situation occurs when the increase or decreaseof partner candidates outside the supply chain makes the firmsthat are inside the supply chain rehash their partner bases in or-der to achieve the best possible performance. In summary, weaddress the supply chain collaboration problemswhen the sup-ply chain faces both internal and external uncertainties withintheir collaboration network.

Hence, the purpose of this paper is to propose an agent-oriented web service framework and a methodology for supplychain collaboration in the presence of both internal and externaluncertainties. The main advantage of our proposed frameworkis that it can handle two different levels of collaborativesituation, namely Independence and Collaboration, as wellas different types of uncertainty. At the Independence level,the partners in the supply chain have an ‘‘arm’s length’’relationship with one another and collaborate minimally.The Collaboration level assumes a strategic relationship andintensive collaboration among the partners. The ability ofour framework to flexibly handle two different collaborativesituations can help companies effectively achieve the bilateralcollaborative relationships that are desired by most firms [1].

This study is organized as follows. In Section 2, we providea literature review concerning web services, multi-agents, andagent-based web services. Section 3 provides a broad overviewof the components of our proposed system. In Section 4, wedescribe the design and implementation of the agent-basedweb services, and the core collaboration processes. Section 5contains the results from the experiments performed andgeneral conclusions are drawn in Section 6.

2. Background

In this section, we provide an overview of web services,multi-agents, and multi-agent web services.

2.1. Web services

Web services are defined as self-contained, self-describing,and Internet-basedmodular applications [11].Web services area new standardwhich enablesmachine-to-machine interactionvia the Internet using both simple protocols and interfaces. AnExtensibleMarkup Language (XML)-based service description iscentral for the description of a web service. Such a descriptioncovers all the necessary details needed in order to interact withthe services, including message formats, transport protocols,and methods. Web services share Universal Description,Discovery and Integration (UDDI), which is a centralized servicedirectory, for its service discovery [12].

Web services are delivered and invoked as streams ofservices and they allow pervasive access from any typeof platform (interoperability). This interoperability betweensystems is the basis for dynamic formation of businesspartnerships. Having a universal specification for web servicesis ideal for business services that need to be completelydecentralized and distributed over the Internet. Thus, webservices become an ideal technical platform for facilitatingcollaboration among partners or business units within a supplychain [13].

2.2. Multi-agents

Multi-agents are software agents with capabilities ofautonomy, social ability, reactivity, and the ability to beproactive [14]. In a multi-agent system, the problem-solvingtasks of each functional unit (e.g., the firm) are populated bya number of heterogeneous intelligent agents with diversegoals and capabilities [15]. Each agent, then, is designed torepresent a specific functional unit. The requirements for theaction strategies and policies to be used may be enteredinto the agent beforehand. Different levels of collaborativerequirement can be easily incorporated into the agent asdifferent goals are made based on different types of scenario.Multi-agents are very effective in addressing both coordinationand conflicts among the firms. When a conflict occurs amongthe functional units, a single authority or committee wouldfind it especially hard to reconcile the problem to fully satisfyall units concerned. The use of a multi-agent system canlead to a more coherent mechanism for solving conflictsbetween functional units. Conflict resolution can be achievedby incorporating a central coordination agent to help supportthe multi-agent collaboration [16]. Central coordination agentsmay have meta-rules and higher-order priorities for managingthe behavior of the agents [17]. Herein, we show how acentral coordination agent, Multi-Agent Coordination Enhancerfor Supply Chain Management (MACE-SCM), can be used tofacilitate both communication and collaboration among theagents.

2.3. Agent-based web services

Conventional web services are simple and passive; theyseldom have the capability of intelligently collaborating withone another. Hence, in our research, we combine web serviceswith multi-agents to overcome such limitations. Agent-basedweb services take advantage of the features of the intelligentagent for a number of reasons [18]. First, web services arepassively invoked. Since they must receive messages fromexternal users, they themselves cannot be used for more activetasks or communicative behavior, which leads to dynamiccollaboration problems in the supply chain. Agents can onlyovercome this passiveness when they are combined with webservices. Second, web services are not autonomous, but agentsmust be autonomous in order to pursue their own goals whilesimultaneously cooperating with other agents to accomplishthe overall goals of the supply chain. Web services are alsonot flexible in emerging situations, such as the introductionof a new supplier. Agent-based web services, by contrast, areboth sufficiently autonomous and flexible to address dynamicvendor selection.

In addition, agent-based web services support scalabil-ity, which is essential for optimal supply chain collaboration.Scalability is the basis for the success of a wide-scale imple-mentation of the supply chain solution [19]. As information

O. Kwon et al. / Scientia Iranica, Transactions E: Industrial Engineering 18 (2011) 1545–1552 1547

technology evolves, technology-enabled partners can adaptto initiate new ways of conducting business. Moreover, asthe number of partnership candidates increases in a sup-ply chain, the combination of partnership making greatlyincreases as well as what happens to the dynamic require-ments of the customers [20]. The changing of both technologyand market environments makes supply chain collaborationchallenging, and thus scalability issues becomemuch more im-portant. Scalability via agent-based web services is achievedbecause these agents use robust service discovery in order to se-lect partnerswithwhich to collaborate [1,21]. Intelligent agentsare expected to adapt their behavior in response to the dynamiccharacteristics of web services in a user context, and also tocarry out the complex interactions with multiple services [22].Suchweb service discovery has been considered crucial for suc-cessful implementation of agent-based web services [23].

3. System overview

In this section, we present an overview of the system.First, the problem context that provides the basis for theprototype system is introduced. Second, the overall frameworkis displayed and the distinct components are explained indetail. Last, we explain the MACE-SCM that is responsible forcollaboration between the agents.

3.1. Problem context

This research assumes a supply chain that consists ofretailers, manufacturers and suppliers. A customer purchases aproduct if it is in stock from the retailer. If the retailer cannotmeet the customer’s demand, then the product is ordered froma manufacturer and backlogged. The manufacturer producesproducts by assembling the components from a supplier. Themanufacturer receives product orders from the retailer andplaces an order for the needed components with its supplier.The supplier produces the components and supplies themto the manufacturer. The supply chain incurs linear holdingcosts and linear backorder costs at each stage. The goal of theretailer is to maximize profits by minimizing stockouts andinventory costs. Themanufacturer pursues profit maximizationthrough the minimization of inventory costs and efficientmanagement of manufacturing and procurement processes.The supplier is interested in maximizing profits by maintaininglow turnaround time and low inventory.

3.2. Overall framework

Figure 1 presents the overall system framework withits three distinct components: agent-based web services,coordination and service ontologies, and the web servicedirectory (UDDI). We assume that only retailers, manufacturersand suppliers exist in the supply chain. The retailer agent(R-Agent), the manufacturer agent (M-Agent), and the supplieragent (S-Agent) model the retailer, the manufacturer, andthe supplier, respectively. In addition, a coordinator agent(MACE-SCM) is introduced in order to produce more strategiccollaboration among the agents. These agents are enrolled asweb services. The introduction of the MACE-SCM is consistentwith previous studies in how they describe the advantage ofhaving a separate collaboration engine in addition to operatingan optimization engine locally [16,24].

In our system, a buyer initiates a transaction by visiting anR-Agent. To find the appropriate R-Agent, the buyer’s programmay want to visit the service repository (UDDI). This repository

Figure 1: System overview.

is used to search for the uniform resource identifiers (URI)address, which points to an agent’s location, and also to acquirethe address of the service ontology, which is a URI address.Then the buyer’s program imports the R-Agent ’s detailed data,such as the products, contact information, advertisements, andpricing information. The data provided are used in order toselect the best retailer. The buyer’s program is the clientmodulethat can invoke any retailer’s web services in the supply chain.The selected R-Agent then seeks a coordinator web service,such as the MACE-SCM, by visiting the collaboration ontology.The collaboration ontology holds the information required forcollaboration during the process of collaboration between theweb services.

3.3. Coordination and service ontologies

The coordination and service ontologies consist of generallanguages (OWL, OWL-S, etc.) or sophisticated supply chainontology models (Enterprise ontology, TOVE, IDEON, etc.)agreed upon by the supply chain. These ontologies define theterminologies that are used by all participants in the supplychain. Within a supply chain, the service providers describetheir services using the terms of the supply chain’s ontology,while service requesters use the terms of the ontology toformulate queries over the registry of the supply chain.

3.4. Agent-based web services components

The detailed roles of the agent-based web services aredescribed as follows:

R-Agent: Receives a customer’s order, and sells the requestedproduct if it is in stock. If the retailer cannot fulfill the order,then the product is ordered from the manufacturer (M-Agent)and backlogged. The R-Agent pursues the retailer’s goal andmakes a decision on how much to order from the M-Agent.

M-Agent: Manufactures the requested products by assem-bling components. It receives product orders from the retailer(R-Agent), and places needed component orders to the supplier(S-Agent). The M-Agent pursues the manufacturer’s goal, andmakes decisions on how much to produce and how much toorder from the supplier (S-Agent). This agent may expand itssupplier base in the presence of high supply uncertainties.

S-Agent: Produces components, receives rawmaterials fromthe outside, and supplies the components to the manufacturer

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(M-Agent). The S-Agent pursues the supplier’s goal and makesa decision on how much to produce.

MACE-SCM: Acts as a central coordinator by enabling anadditional level of collaboration between the agents. The mainpriority of this agent is to maximize the overall supply chainprofits by assisting the other three agents and providing themwith superior information acquired from its case base. MACE-SCM may or may not rely on the case base. The case baseprovides additional information that facilitates better decision-making. The case base contains information on an offering setof competitors and environmental data such as total demand,market share, total revenue, and total cost. The MACE-SCMallows the other three agents to access the case base. Our casebase reasoning algorithm aims to select a case which is themost similar to the current SCMdistribution problem. Tversky’sWeighted Euclidean distance method is used for similaritycomputation for case selection (see Eq. (1)):

di =

∀j

(xj − yij)p 1

p

(1)

where p is the multiplier (p = 0, 2, 4, . . .), while xj and yijindicates the j-th item value of the current situation and i-thcase. As p increases, the discrepancy between xj and yij becomesmore exaggerated, and hence outliers are more discouragedfrombeing selected as themost similar case. In this experiment,we set p as 2 for simplicity.

Our agent-based web services rely on the well-knownHypertext Transfer Protocol (HTTP) and XML message format.Such standards become a natural source of seamless transactionwhen existing supply chain partners search and select newpartners for collaboration. Seamlessness is defined as theability to conduct supply chain activities smoothly withoutexperiencing awkward transitions or indications of disparity.Seamlessness becomes importantwhen the partners in a supplychain experience a lack of information concerning who theyare, what their businesses are, and how to interact betweenthem [25]. This feature allows the supply chain to improveinter-organizational communication, to minimize the barrierspreventing information flow, and hence to improve the chain’soverall performance [26].

Our system can better tackle emerging problems thatoccur either within or outside the supply chain when the part-ners involved are less concerned about themechanisms of com-munication and interaction. Seamlessness can be considered attwo layers. At a higher level, seamlessness is accomplished insmooth transitions or at least with no indications of disparity.As one partner searches, orders, and gets products from anotherpartner, it does not recognize or does not evenneed to recognizehow the supply chain is managed and how the partners collab-orate with one another. At a lower level, seamlessness meansthe integration of more than two computer programs or intelli-gent agents working together to simulate the appearance of be-ing one programwith a single user interface. This seamlessnessfeature may increase the scalability of our system by decreas-ing the amount of time required for achieving the same level ofcommunication.

3.5. Web service directory

For scalability, the basic mechanism that we suggest is theservice directory known as UDDI. Figure 2 demonstrates ascenariowhenboth transaction and collaboration are facilitatedby UDDI. The UDDI Business Registrymakes the partnerswithin

Figure 2: Web service directory-based collaboration.

a supply chain much more visible to one another and moreaccessible to the other potential partners. The firms can registerand find informationwithin theUDDI registry node. All partnersin the supply chain must report on what their businesses andservices are, and how their customers will be served into theservice repository a priori. Partners, including customers, canthen perform transactions by simply querying a set of firmssuitable for their service needs. This same mechanism is usedto handle both a change in and an increase in the load ofpartnership search and selection problems.

4. Implementation

To examine the feasibility of the model, the prototype sys-tem was implemented using Java applications under JDK1.4.1and Apache SOAP 2.0. The case base and other data tables weremade in Jet Engine and linked using ODBC/JDBC connections.

4.1. Collaboration situations

The present research concentrates on two collaboration sce-narios between partners, namely Independence and Collabo-ration Levels. The two scenarios represent Level 2 and Level3 collaborations, respectively. At these two levels, the agentsuse case-based reasoning in order to learn the rules needed togenerate optimal solutions. This is possible because case-basedlearning is performed simply by adding a new case wheneverone occurs and then finding similar case subjects already en-tered into the database in order to input the features they havein common at a later time.

Independence level: Partners have arm’s length relationshipsand a low level of collaboration. The agents collaborate mini-mally by exchanging order information and pursuing individ-ual goals. The resulting production and inventory policies maynot be optimal for the entire supply chain. The case base, whichrepresents additional information, is not provided at this level.

Collaboration level: Partners have strategic relationshipsand collaborate intensely with one another. The agents shareextensive information beyond the transactional level andpursue global goals for the entire supply chain. Such solutionsmay not be optimal for individual agents, but are expected tobe optimal for the entire supply chain. This level overcomes thelimitations of the prior systems which permitted only a limitedlevel of collaboration [7,27].

O. Kwon et al. / Scientia Iranica, Transactions E: Industrial Engineering 18 (2011) 1545–1552 1549

Web service directory

MACE-SCM MACE-SCM

(a) Web service directory based search

(b) MACE-led search

Figure 3: Partnership search methods.

At this level, we established two different situations forthe use of a case base. In the first situation, we assume thatthe agents leverage make use of the case base, meaning thatthey can make better decisions with the additional marketinformation. The specific information that plays a key role inthe context is the price and the corresponding market shareinformation. In the second situation, the agents do not rely onthe case base. The agents collaborate intensively to generatesolutions for the entire supply chain and this is the reason whythey may or may not rely on a case base.

4.2. Internal and external uncertainties

As discussed earlier, internal uncertainties refer to theuncertainties within the supply chain that result from fluctu-ating customer demand, stochastic lead time, limited produc-tion capacity and unstable yield. In our research, we considerthe internal uncertainties of both demand and supply. Demanduncertainties regard the volatility of customer demand. Regard-ing the assumptions of customer demand, we fixed the meanand changed the standard deviation after each simulation run(i); namely, sd (i) = (i − 1)/N where i = 1 · N . The customerdemand distribution follows the normal distribution. We lim-ited the supply uncertainties to the only account for lead-timeand production capacity uncertainties. Supplier’s uncertainty oflead-time and production capacity is implemented similar todemand uncertainty. That is, we fixed the means of the lead-time and production capacity, and changed only the standarddeviations after each simulation run.

External uncertainties focus on the uncertainties that existoutside the supply chain. In particular, a varying number ofpartnership candidates were considered in this paper. A supplychain can experience a change in the number of partnershipcandidates. This change can occur at any stage of the supplychain; i.e., from retailers to suppliers. A partner is selectedwhenthey can provide the entire order quantity at the lowest price.

We illustrate two different partnership search methods(Figure 3). First, in the presence of the web service directory,firms use the directory to conduct a partner search. Thepartnership candidates subscribe their service profiles to theweb service directory.MACE-SCMcan therefore simultaneouslyacquire the services information from the directory, excludeinferior services from the acquired set of services, andindividually examine the remaining candidates. Second, in theabsence of the web service directory, the firms rely on the

10000900080007000600050004000300020001000

0

Num

ber

of c

andi

date

s

1 7 13 19 25 31 37 43 49Simulation run

55 61 73 7967 85 91 97

Situation 1 Situation 2

Figure 4: Different rates of candidate increases.

MACE-SCM and use it to perform a partner search. In thisscenario, MACE-SCM sequentially searches for each partner. Inan experiment described later in the paper, we only consideredthe first method of candidate selection. The second method isgiven only for illustrative purposes.

5. Experiment

We conducted a series of experiments to test the feasibilityof our model. The experiments are concerned with Eq. (1)scalability (Experiment 1), and (2) the relative performancegiven different collaboration situations (Experiments 2–4).Experiment 1 evaluates the performance of the web servicefeature within the system and experiment 2 examines theperformance of the agent-based collaboration.

5.1. Scalability

We assume a growing market and thus an increasingnumber of partnership candidateswith time (x) as xa+b, wherea, b ≥ 0. To test scalability, for example, we construct twodifferent rates of candidate increases (Figure 4). In situation 1,the number of candidates is increasing at a rate of x2 + 10,where x is the number of simulation runs (a proxy for time)and 10 is the initial number of partners. In situation 2, thenumber of candidates increases more dramatically at a rateof x3 + 10. We tested whether the system is scalable withthe two different increasing rates. The number of retailers,manufacturers and suppliers are statistically equivalent andadded together because we have little reason to differentiatethe rate of the increases in candidates at this phase in theresearch study. The effects of the variation on the scalability ofthe systemwill be addressed in a future publication. Scalabilityis measured as the degree of the performance outcome. Weconsider the stabilized performance outcomes as an indicatorof good scalability. Hence, we examined if the agent-basedweb service will be scalable with the increasing number ofpartnership candidates by comparing the performance betweenthe case of situation 1 and that of situation 2. This leads to thefollowing hypothesis:

H1: The performance of situation 1 and situation 2 will not bestatistically different.

We used the relative profit as a measure of performanceoutcome. The profit derived from the candidate pool indicatesthe maximum possible profit that is obtainable with the entirepool of candidates. The number of total candidates is initializedat a fixed level and do not change throughout the differentexperiments performed. Each candidate is provided with anarbitrary production capacity, inventory, backlog, setup costs,

1550 O. Kwon et al. / Scientia Iranica, Transactions E: Industrial Engineering 18 (2011) 1545–1552

pe

rfor

man

ce

simulation run

Situation 1

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Situation 2

Figure 5: Performance results.

and price level. The profit is calculated after conducting anexhaustive search for all candidates in the pool, and representsthe best possible result for the supply chain as a whole.

Relative Profit = Profit derived from the web service/Profitderived from the candidate pool.

The profit derived from the web service indicates theobtainable profit that can be achieved from all candidatesregistered to web service directory. The number of candidatesat this level varies according to the functions described earlier.The candidates are randomly selected from the pool and areregistered to the directory. The profit is calculated once anexhaustive search for all candidates registered to the directoryhas been conducted. Case-based reasoning is used to facilitatethe derivation of optimal solutions.

Candidate increases can be applied to any context, but inthis study were specifically implemented at the CollaborationLevel with a case base. Thus, the objective function of theMACE-SCM (c) and the Profit(c, t) was used to calculate profitsfor the entire supply chain. The performance outcome of theexperiment is shown in Figure 5. The range of the relative profitshould be between 0 and 1, where 1 occurs when the profitderived from the web service is equal to the profit derivedfrom the candidate pool. Higher levels of the relative profitindicate a better scalability of the agent-basedweb servicewithan increase in the number of partnership candidates. Figure 5shows that the performance is poor at early stages of thesimulation but rapidly stabilizes at later stages.

Table 1 explains the hypothesis 1 (H1) statistically. Thenull hypothesis states that the performance results of thetwo different situations will yield equal results. If the p-valueis greater than 0.1, 0.05 or 0.01, then the null hypothesiscannot be statistically rejected. Based on such a principle, wecan conclude that the statistical test results for hypothesis 1indicate that the null hypothesis is statistically supported witha significance level ofmore than 10%. Interestingly,we observedthe performance convergence in our scalability test. Despitethe increasing number of candidates, we have repeatedly foundthat there is only a marginal improvement in the terms ofrelative profit when the number becomes sufficiently large.That is, the number of candidates is not a determinant of thesystem profit levels. These observations imply that scalabilityin terms of the number of candidates available is satisfiedin our agent-oriented web service structure. We thereforeconclude that the web service is scalable with the increaseof partnership candidates. This experiment is based on therealistic assumption that the supply chain faces a change in thenumber of partnership candidates, and often timeswith limitedinformation about them. Consequently, our agent-orientedweb

Table 1: Experimental design for scalability.

Hypotheses Performancemeasures

Difference p-value

Hypothesis 1 Average profit 0.072% 0.7130(N = 100)∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

Table 2: Experimental design for collaboration.

Experiment Internal uncertainity Externaluncertainity

Supply sideDemand

sideLead time Production

capacityPartnerbase

1 No No No Yes2 Yes No No No3 No Yes No No4 No No Yes No

service structure shows scalability in terms of the number ofcandidates available in that it achieves the equivalent amountof profit regardless of the registered number of candidates. Ingeneral, this result implies that our system can bring stabilityto the supply chain in turbulent market environments.

5.2. Collaboration situations

We conducted a series of experiments to compare theperformance of two different levels of collaboration. TheCollaboration Level follows the Collaboration Processes, andmay or may not rely on the case base. The Independence Levelneither follows the Collaboration Processes nor utilizes thecase base. As the level of collaboration intensifies from theIndependence Level to the Collaboration Levelwithout the casebase and then to the Collaboration Levelwith the case base, theperformance is expected to gradually improve. This leads to thefollowing hypotheses.

H2: The Collaboration Level with case base will outperform theIndependence Level.

H3: The Collaboration Level with case base will outperform theCollaboration Level without case base.

H4: The Collaboration Level without case base will outperformthe Independence Level.

We designed three experiments to test a variety of demandand supply uncertainties (Table 2). Experiments 2, 3, and 4 aredesigned to test demand, lead time, and production capacityuncertainty, respectively. Each decision support level wassimulated 100 times and the time span of each simulation was12 periods in duration. In each period, the customer demand,lead-time, and production capacity variables weremanipulatedby fixing the means and changing only the standard deviationsas described earlier. The performance was measured by takingthe average profit. One-way ANOVAs were performed in orderto better understand the effect of the different collaborationlevels on the performance. The results presented in Table 3provide support for Hypotheses 2 and 3 when p < 0.01. Noneof the three experiments support Hypothesis 4.

The above results imply that the Collaboration Processes ofMACE-SCM may not be effective in the presence of particulardemand and supply uncertainties. On the other hand, theperformance at the Collaboration Level with a case baseis superior to all other levels, regardless of the presence

O. Kwon et al. / Scientia Iranica, Transactions E: Industrial Engineering 18 (2011) 1545–1552 1551

Table 3: One-way ANOVAs.

Hypothesis Performance Experiment 2 Experiment 3 Experiment 4

H2(Coll. w/ case > Ind.) Average profit 17.096a 30.297 21.4420.000** 0.000** 0.000**

H3(Coll. w/ case > Coll. w/o case) Average profit 20.4015 28.876 22.4350.000** 0.000** 0.000**

H4(Coll. w/o case > Ind.) Average profit 1.652 0.483 0.2450.121 0.629 0.806

* p < 0.05.** p < 0.01.a t-values.

of demand and supply uncertainties. This demonstrates theapparent synergy effect between the MACE-SCM CollaborationProcesses and the case-based reasoning. Case-based reasoningrecommends a proper price and production quantity level foreach agent based on the estimated market share information.This additional information, in addition to existing MACE-SCMCollaboration Processes, leads to an optimal performanceregardless of uncertain situations.

In this paper, we did not show the relative excellence of theproposed idea. First of all, most of the multi-agent mechanismshave seldom shown the uncertainty because the supply chainenvironment was assumed deterministically in their work [8].Ideas of e-SCM mechanism cited in this paper [7–9] couldnot be quantitatively compared directly to ours due to thefollowing reasons. First, Kimbrough et el. modeled an electronicsupply chain managed by artificial agents [7]. They successfullyshowed that artificial agents do better than humans: Theagents are able to search very efficiently. However, sincethey suggested rule-based architecture, they failed to resolve‘‘mesa effect’’: similar but not identical past experience is notrecommended to the decision makers. Demand and supplieruncertainty contains the problem that new SCM collaborationproblemmight not be pre-asserted or unknown.When it comesto [8], the authors proposed a multi-agent simulation modelfor analyzing the dominant player’s behavior of supply chains.However, they assumed that the players (suppliers, etc.) arefixed, which are called dominant players. Therefore, scalabilityissues cannot be addressed with this study. Lastly, the authorsof [9] did mention the SCM dynamics, no matter what causesthe dynamics. A full-scale example, IBM with 2000 inventories,is used to show the necessity of the proposed method.However, we could not find further evidence that they actuallyperformed with performance measures. Hence, their idea wasnot comparable. Moreover, they did not consider the openness:eSCM must be controlled by a specific standard, which will notactually work. Thus, when compared to the others that havebeen documented qualitatively, rather than quantitatively, theproposed idea is compatible with the problems of scalabilityand collaboration with demand and supplier uncertainty.

6. Conclusions

The objective of the present research was to develop aframework based on agent-based web services to facilitate col-laboration in the presence of internal and external uncertain-ties. For example, Toyota has about 200 suppliers while Fordhas about 8000. Moreover, entering into or dropping from thesupplier list happens vividly. Recently, they have begun reduc-ing the number of suppliers to make them more manageable.Indeed, they have a decision making problem, dynamic sup-plier selection, and this problem actually supports the neces-sity of the novel algorithm which automatically manages the

uncertainty. In this regard, the suggested system was success-ful in addressing the different uncertainties and thus facilitat-ing collaboration between the partners in the supply chain. Inthe presence of external uncertainties, the system is capable ofscalability and generates an equivalent amount of profit corre-sponding to the levels obtainable assuming the availability ofperfect information. When the partners face diverse internaluncertainties, the collaboration with a case base achieves thebest performance. These findings emphasize that our system isable to model different situations of uncertainty and also pro-vides some guidelines based on the simulation results for thedecision makers to follow.

Contributions of the research can be summarized asfollows. First, we provide a flexible solution for collaborationin the presence of internal and external uncertainties. Thisflexibility comes from incorporating multi-agents and case-based reasoning capabilities with web services. Standard webservices in the supply chain context are combined with multi-agents in this paper in order to overcome their passivity,where services do not occur unless relevant invocations areissued from the external entities. This combination allowsus to cope with the continuous change of collaborationsituations (structural flexibility). To further the capability ofmulti-agents, we provide case-based reasoning, which helpsresolve different types of uncertainty (contextual flexibility).We defined flexibility as the ability to deal with internalor external uncertainty. Since case-base reasoning estimatesthe level of user preference by returning the most similarresults based on inputted features, such as price level, theagents can then negotiate with one another using the mostup-to-date information even if a change in the productioncapacity, lead time or the number of partnership candidatesoccurs. This method makes coping with either internal orexternal uncertainty a success. In summary, the use of the case-based reasoning contributes to the flexibility of our proposedframework.

Second, we leveraged e-business technologies so that theycould provide scalable solutions. Scalability is recognized asa major success factor in the development of an e-enabledsupply chain and web services. However, the implementationof scalability has been challenging because of the difficultiesin aggregating and filtering information from multiple sourcesand also because of the heterogeneous transaction protocolsbetween the systems. The proliferation of Internet technologies,such as the agent-based web services, make it possibleto overcome these hurdles with the support of seamlesstransitions.

Lastly, we provide managers with easy-to-use solutions. Tomake a decision on how to collaborate among partners, themanager can first use the agent-based web services in order tofind the optimal candidates for partnership. Since the relativeprofits in our experiments indicate the overall robustness of

1552 O. Kwon et al. / Scientia Iranica, Transactions E: Industrial Engineering 18 (2011) 1545–1552

this technology, the managers can now rely on the proposedtechnology by using the Internet. Next, themanagers can designthe best collaboration structure for their needs based on theresults from the second experiment. To do this the managermust consider additional factors of their selected partners, suchas their transaction history, reliability, and relationship quality.

However, the framework and implementation proposedin this paper need to be extended further and requiresadditional research. The current framework can cope withonly two levels of collaboration, which were the Independenceand Collaboration levels. In reality, the determining factorsof collaboration between a focal firm’s and their suppliers,include breakdowns, yields, quality level, reliability, processstability, capacity constraint, changeover, and lead-time. Thus,the current operation of determining relationship changesneeds to incorporate the additional factors mentioned aboveand further fine-tuned in order to reflect the reality of the realbusiness world and to increase the usefulness of the model toits users. Moreover, message communications and variabilitymight affect the service performances addressed in this paper.These remain as further research issues.

Acknowledgments

This research is supported by the Ubiquitous Computing andNetwork (UCN) Project, Knowledge and Economy Frontier R&DProgram of theMinistry of Knowledge Economy (MKE) in Koreaas a result of UCN’s subproject 11C3-T2-10M.

References

[1] Sabath, R. and Fontanella, J. ‘‘The unfulfilled promise of supply chaincollaboration’’, Supply Chain Management Review, pp. 24–29 (2002).

[2] Shapiro, J.F.,Modeling the Supply Chain, Thompson Learning, Pacific Grove,CA, USA (2001).

[3] Melo, M.T., Nickel, S. and Saldanha-da-Gama, F. ‘‘Facility location andsupply chain management—a review’’, European Journal of OperationResearch, 196, pp. 401–412 (2009).

[4] Pishvaee, M.S., Rabbani, M. and Torabi, S.A. ‘‘A robust optimizationapproach to closed-loop supply chain network design under uncertainty’’,Applied Mathematical Modelling, 35, pp. 637–649 (2011).

[5] Lau, J.S.K., Huang, G.Q. and Mak, K.L. ‘‘Web-based simulation portalfor investigating impacts of sharing production information on supplychain dynamics from the perspective of inventory allocation’’, IntegratedManufacturing Systems, 13(5), pp. 345–358 (2002).

[6] Shukla, S.K., Tiwari, M.K., Wan, H. and Shankar, R. ‘‘Optimization of thesupply chain network: simulation: taguchi, and psychoclonal algorithmembedded approach’’, Computers & Industrial Engineering, 58(1), pp. 29–39(2010).

[7] Kimbrough, S.O., Wu, D.J. and Zhong, F. ‘‘Computers play the beer game:can artificial agents manage supply chains?’’ Decision Support Systems,33(3), pp. 323–333 (2002).

[8] Sheng, J., Li, Z. and Liu, H. ‘‘Multi-agent simulation for the dominantplayers’ behavior in supply chains’’, Simulation Modelling Practice andTheory, 18(6), pp. 850–859 (2010).

[9] Swaminathan, J.M., Smith, S.F. and Sadeh, N.M. ‘‘Modeling supply chaindynamics: a multiagent approach’’, Decision Sciences, 29(3), pp. 607–632(1998).

[10] Swaminathan, J.M. and Tayur, S.R. ‘‘Models for supply chains in e-business’’, Management Science, 49(10), pp. 1387–1406 (2003).

[11] Curbera, F., Duftler,M., Khalaf, R., Nagy,W.,Mukhi, N. andWeerawarana, S.‘‘Unraveling the web services web—an introduction to SOAP, WSDL, andUDDI’’, IEEE Internet Computing, 6(2), pp. 86–93 (2002).

[12] http://www.uddi.org. 2005.[13] Tarantilis, C.D., Kiranoudis, C.T. and Theodorakopoulos, N.D. ‘‘A web-

based ERP system for business services and supply chain management:application to real-world process scheduling’’, European Journal ofOperational Research, 187(3), pp. 1310–1326 (2008).

[14] Wooldridge, M. and Jennings, N.R. ‘‘Intelligent agents: theory andpractice’’, The Knowledge Engineering Review, 10(2), pp. 115–152 (1995).

[15] McMullen, P.R. ‘‘An ant colony optimization approach to addressing aJIT sequencing problem with multiple objectives’’, Artificial Intelligence inEngineering, 15(3), pp. 309–317 (2001).

[16] Sogomonian, A.G. and Tang, C.A. ‘‘Modeling framework for coordinatingpromotion and production decisions within a firm’’, Management Science,39, pp. 191–203 (1993).

[17] Maturana, F.P. and Norrie, D.H. ‘‘Distributed decision-making using thecontract net within amediator architecture’’, Decision Support Systems, 20,pp. 53–64 (1997).

[18] Huhns, M.N. ‘‘Agents as web services’’, IEEE Internet Computing, 6(4),pp. 93–95 (2002).

[19] Barratt, M. ‘‘Understanding the meaning of collaboration in the supplychain’’, Supply Chain Management, 9(1), pp. 30–42 (2004).

[20] James, M., Grosvenor, R. and Pickett, P. ‘‘e-Distribution: internet-basedmanagement of a merchandiser supply chain’’, Supply Chain Management,9(1), pp. 7–15 (2004).

[21] Sherman, R.J. ‘‘Collaborative planning, forecasting and replenishment(CFPR): realizing the promise of efficient consumer response throughcollaborative technology’’, Journal of Marketing Theory and Practice, 6(9),pp. 6–9 (1998).

[22] Kuno, H. and Sahai, A. ‘‘My agent wants to talk to your service: personal-izing web services through agents’’ Proceedings of First International Work-shop on Challenges in Open Agent Systems (AAMAS’02), Bologna, Italy 2002.

[23] Buhler, P.A. and Vidal, J.M. ‘‘Towards the synthesis of web services andagent behaviors’’ Proceedings of First International Workshop on Challengesin Open Agent Systems (AAMAS’02), Bologna, Italy 2002.

[24] Eliashberg, J. and Steinberg, J.R. ‘‘Marketing-production joint decisionmaking’’, In Management Science in Marketing, J. Eliashberg and G. Lilien,Eds., North-Holland, Amsterdam, UK (1993).

[25] Ireland, R. and Bruce, R. ‘‘CPFR: only the beginning of collaboration’’, SupplyChain Management Review, pp. 80–88 (2000).

[26] Love, P.E.D., Irani, Z. and Edwards, D.F. ‘‘A seamless supply chainmanagement model for construction’’, Supply Chain Management, 9(1),pp. 43–56 (2004).

[27] Lau, J.S.K., Huang, G.Q. and Mak, K.L. ‘‘Impacts of sharing productioninformation on supply chain dynamics: a multi-agent simulation study’’Proceedings of 30th International Conference of Computers & IndustrialEngineering, Tinos Island, Aegean Sea, Greece, pp. 527–532 (2002).

Ohbyung Kwon is presently a full professor at College of Management, KyungHee University, South Korea, where he initially joined in 2004. In 2002,he worked in Institute of Software Research International (ISRI) at CarnegieMellon University to perform a project on context-aware computing, webservice and semantic web. He received M.S. and Ph.D. degree at KAIST in1990 and 1995, respectively. He is now an adjunct professor at San DiegoState University (SDSU). His current research interests include context-awareservices, case-based reasoning and DSS. He has presented various papersin leading information system journals including Decision Support Systems,Simulation, International Journal of Computer Integrated Manufacturing, andBehavior and Information Technology.

Ghiyoung Im is a Visiting Assistant Professor in the Department of ComputerInformation Systems at the University of Louisville. He received his Ph.D. fromGeorgia State University and a M.S. from NYU Stern School of Business. Hisresearch interests center on IT-enabled organizational learning, knowledgemanagement, and coordination in interorganizational relationships, and theirimplications for firm strategy and supply chain management. His research hasappeared in Management Science, Journal of the Association for InformationSystems, Information Systems Journal, and The DATA BASE for Advances inInformation Systems, among others.

Kun Chang Lee is a professor in the SKK Business School and WCU (WorldClass University) professor in the Department of Interaction Science atSungkyunkwan University, Seoul, Korea. His recent research interests includecreativity science and context-modeling. Regarding the creativity science, heinvestigates the creativity revelation process at individual-team-organizationlevel in modern firms, and its influence on corporate performance. For thecontext modeling, he develops a new breed of context prediction mechanismwhere user’s behavior is predicted and a proper ubiquitous service is providedto them in ubiquitous contexts. His research methodologies vary from purepsychological theories and questionnaire survey to cutting-edge artificialintelligence methods such as multi-agent simulations, neural networks,Bayesian networks, and PSO (Particle Swarm Optimization).