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Analysis of shipbuilding fabrication process with enterprise ontology Ji-Hyun Park a , Kyung-Hoon Kim b , Jae-Hak J. Bae a,a School of Computer Engineering and Information Technology, University of Ulsan, Mugeo 2-Dong, Nam-Gu, Ulsan 680-749, Republic of Korea b Hyundai Heavy Industries Co., Ltd., Jeonha-Dong, Dong-Gu, Ulsan 682-792, Republic of Korea article info Article history: Available online 8 January 2011 Keywords: Enterprise ontology Business process analysis Shipbuilding fabrication process Protégé abstract This paper describes the analysis and evaluation of shipbuilding process based on an enterprise ontology. Shipbuilding process is composed of steel fabrication, assembly, erection, launching, sea trials, naming, and delivery. Among them, the fabrication process has been analyzed and evaluated in this study. An enterprise ontology is a cognitive model containing knowledge unique to the enterprise, and enables the representation and sharing of the enterprise’s process knowledge. We have built an enterprise ontol- ogy, and represented the shipbuilding process using plug-ins of Protégé. In addition, we have analyzed the current state of the process and dependency among the workflow elements using a Prolog inference engine, and evaluated the shipbuilding process. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction These days the activities of enterprises are continuously global- ized and the business environment is changing rapidly and compli- catedly. In response to the changing business environment, new business models and business processes are being developed. Busi- ness enterprises should maintain the competitiveness by accom- modating changes in business environment quickly and flexibly. For this, they need continuous and automated management and improvement of the business process. For successful business pro- cess management, it is required to analyze and evaluate the cur- rent process accurately. When an enterprise tries to manage its business processes, it needs enterprise-specific knowledge. Ontol- ogy can be utilized as a base technology for representing and man- aging an enterprise’s process knowledge. The overall goal of enterprise modeling is to take an enterprise- wide view of an organization. In order to achieve, use, and main- tain such an enterprise-wide view, strong facilities for integration, communication, flexibility and support are required. To achieve both effective integration and effective business planning, it is important that all parties involves involved have a shared under- standing of the relevant aspects of a business enterprise. In partic- ular, when terms are used in a certain context, it must be clear what concept is being referred to. The idea is to provide one set of terms and definitions which adequately and accurately covers the relevant concepts in the enterprise modeling domain (Uschold, King, Moralee, & Zorgios, 1998). The construction works of the set of such terms and definitions were TOVE’s Organisation Ontology (Gruninger & Fox, 1996), AIAI’s Enterprise Ontology (Uschold et al., 1998), Cycorp’s Cyc Knowledge Base (Stephen & Lenat, 2002), W.H. Inmon’s Data Model Resource Book (Silverston, Inmon, & Graziano, 1997), BORO program of the CEO project (Chris & Mile- na, 2003). Compared to other industries, the shipbuilding industry has processes more complex and difficult to manage as it manages numerous kinds of components and large-size structures. Ontology enables not only to manage the current processes but also to man- age the process intelligently by deriving the implied knowledge from the ontology through search and inference. Accordingly, it is necessary to introduce intelligent process management technol- ogy based on ontology. There has been the study of ontology-based business process such as BMO (Business Management Ontology) (Dieter, 2003). There have been only a few studies on the application of ontol- ogy to the shipbuilding industry including the ontology related to ship sales (Ha & Jung, 2007), the ontology for ship design knowl- edge representation (Feng, 2008), and the ontology related to mar- ine environment (Bermudez, Graybeal, & Arko, 2006). However, there has been no case of ontology building for the process management. In this study, we describe the analysis and evaluation of ship- building process based on an enterprise ontology. And we check that it is possible to manage intelligently business processes with the enterprise ontology, In order to prove this, we built the enter- prise ontology and analyzed and evaluated the shipbuilding pro- cess using the ontology. This paper is structured as follows. Following this chapter, we review related works in Chapter 2. In Chapter 3, we describe the representation, analysis and evaluation of shipbuilding process 0747-5632/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2010.10.021 Corresponding author. Tel.: +82 052 259 2221; fax: +82 052 259 1687. E-mail address: [email protected] (Jae-Hak J. Bae). Computers in Human Behavior 27 (2011) 1519–1526 Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Analysis of shipbuilding fabrication process with enterprise ontology

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Computers in Human Behavior 27 (2011) 1519–1526

Contents lists available at ScienceDirect

Computers in Human Behavior

journal homepage: www.elsevier .com/locate /comphumbeh

Analysis of shipbuilding fabrication process with enterprise ontology

Ji-Hyun Park a, Kyung-Hoon Kim b, Jae-Hak J. Bae a,⇑a School of Computer Engineering and Information Technology, University of Ulsan, Mugeo 2-Dong, Nam-Gu, Ulsan 680-749, Republic of Koreab Hyundai Heavy Industries Co., Ltd., Jeonha-Dong, Dong-Gu, Ulsan 682-792, Republic of Korea

a r t i c l e i n f o a b s t r a c t

Article history:Available online 8 January 2011

Keywords:Enterprise ontologyBusiness process analysisShipbuilding fabrication processProtégé

0747-5632/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.chb.2010.10.021

⇑ Corresponding author. Tel.: +82 052 259 2221; faE-mail address: [email protected] (Jae-Hak J. Bae)

This paper describes the analysis and evaluation of shipbuilding process based on an enterprise ontology.Shipbuilding process is composed of steel fabrication, assembly, erection, launching, sea trials, naming,and delivery. Among them, the fabrication process has been analyzed and evaluated in this study. Anenterprise ontology is a cognitive model containing knowledge unique to the enterprise, and enablesthe representation and sharing of the enterprise’s process knowledge. We have built an enterprise ontol-ogy, and represented the shipbuilding process using plug-ins of Protégé. In addition, we have analyzedthe current state of the process and dependency among the workflow elements using a Prolog inferenceengine, and evaluated the shipbuilding process.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

These days the activities of enterprises are continuously global-ized and the business environment is changing rapidly and compli-catedly. In response to the changing business environment, newbusiness models and business processes are being developed. Busi-ness enterprises should maintain the competitiveness by accom-modating changes in business environment quickly and flexibly.For this, they need continuous and automated management andimprovement of the business process. For successful business pro-cess management, it is required to analyze and evaluate the cur-rent process accurately. When an enterprise tries to manage itsbusiness processes, it needs enterprise-specific knowledge. Ontol-ogy can be utilized as a base technology for representing and man-aging an enterprise’s process knowledge.

The overall goal of enterprise modeling is to take an enterprise-wide view of an organization. In order to achieve, use, and main-tain such an enterprise-wide view, strong facilities for integration,communication, flexibility and support are required. To achieveboth effective integration and effective business planning, it isimportant that all parties involves involved have a shared under-standing of the relevant aspects of a business enterprise. In partic-ular, when terms are used in a certain context, it must be clearwhat concept is being referred to. The idea is to provide one setof terms and definitions which adequately and accurately coversthe relevant concepts in the enterprise modeling domain (Uschold,King, Moralee, & Zorgios, 1998). The construction works of the setof such terms and definitions were TOVE’s Organisation Ontology

ll rights reserved.

x: +82 052 259 1687..

(Gruninger & Fox, 1996), AIAI’s Enterprise Ontology (Uscholdet al., 1998), Cycorp’s Cyc Knowledge Base (Stephen & Lenat,2002), W.H. Inmon’s Data Model Resource Book (Silverston, Inmon,& Graziano, 1997), BORO program of the CEO project (Chris & Mile-na, 2003).

Compared to other industries, the shipbuilding industry hasprocesses more complex and difficult to manage as it managesnumerous kinds of components and large-size structures. Ontologyenables not only to manage the current processes but also to man-age the process intelligently by deriving the implied knowledgefrom the ontology through search and inference. Accordingly, itis necessary to introduce intelligent process management technol-ogy based on ontology. There has been the study of ontology-basedbusiness process such as BMO (Business Management Ontology)(Dieter, 2003).

There have been only a few studies on the application of ontol-ogy to the shipbuilding industry including the ontology related toship sales (Ha & Jung, 2007), the ontology for ship design knowl-edge representation (Feng, 2008), and the ontology related to mar-ine environment (Bermudez, Graybeal, & Arko, 2006). However,there has been no case of ontology building for the processmanagement.

In this study, we describe the analysis and evaluation of ship-building process based on an enterprise ontology. And we checkthat it is possible to manage intelligently business processes withthe enterprise ontology, In order to prove this, we built the enter-prise ontology and analyzed and evaluated the shipbuilding pro-cess using the ontology.

This paper is structured as follows. Following this chapter, wereview related works in Chapter 2. In Chapter 3, we describe therepresentation, analysis and evaluation of shipbuilding process

1520 J.-H. Park et al. / Computers in Human Behavior 27 (2011) 1519–1526

based on an enterprise ontology. In the last Chapter, we drawconclusions.

2. Related works

This chapter reviews recent process management technologiesand the enterprise ontology as a base technology for representingenterprises’ process knowledge.

2.1. Process management

Process management technology was started with BPR/PI in thelate 1980s and has developed rapidly. Large-scale package soft-ware such as ERP, SCM and CRM has been developed, and Work-flow Management Systems pursuing process automation beganto emerge at the end of 1990. In addition, process managementhas made further progress through the emergence of Process Map-ping Tools for graphical process management and Business ProcessManagement System (BPMS) (Smith & Fingar, 2001).

In most production enterprises, it needs to manage not only or-dinary business processes but also production process because theproduction is one of major business activities. Currently, many pro-duction enterprises are utilizing various production control toolssuch as G2. However, these production control tools also need abase technology for managing terms used in enterprises, conceptsand relations necessary to define processes. Therefore, we purposeto utilize an enterprise ontology as the base technology.

2.2. Enterprise ontology

Enterprise ontology (EO) (Uschold & Gruninger, 1996; Uscholdet al., 1998) is a collection of terms and definitions relevant to busi-ness enterprise, and was developed through Enterprise Project exe-cuted by Edinburgh University. The enterprise ontology isclassified into five categories, and defines terms and relations for

Fig. 1. Basic concepts and som

each part: (1) meta-ontology and time; (2) activity, plan, capabilityand resource; (3) organization; (4) strategy; and (5) marketing. Theenterprise ontology performs the role of a communication mediumamong different enterprises or systems. Also, the enterprise ontol-ogy plays important roles in acquiring, manipulating, and repre-senting the enterprise knowledge and structuring and organizinglibraries of knowledge.

The theory that underlies notion of enterprise ontology is theP-theory. An enterprise will be defined as a heterogeneous systemin the category of social systems. The P-theory consists of four axi-oms and one theorem: the operation axiom, the transaction axiom,the composition axiom, the distinction axiom and the organizationtheorem. The operation axiom describes that we abstract from thesubjects in order to concentrate on the different actor roles theyfulfill. The transaction axiom states that production and coordina-tion acts occur in generic socionomic patterns, called transactions.The composition axiom states that every transaction is either en-closed in some other transaction or it is a customer transactionor it is a self-activating transaction. The distinction axiom is aboutthe integrating role that human beings play in constituting anenterprise. The organization theorem is presented that builds onthe four axioms. It states that an enterprise is a layered nestingof three homogeneous aspect systems (Dietz, 2006).

3. Building and expansion of enterprise ontology

In this chapter, we describe the building and expansion theenterprise ontology.

3.1. Building the base of enterprise ontology

As a general rule, ontology can be constructed through the fol-lowing stages: establishment the goal and the scope, organizingand definition of the essential concepts, formalization and build-

e subordinate concepts.

Fig. 2. Enterprise ontology constructed with Protégé (concept of ‘‘Activity’’).

J.-H. Park et al. / Computers in Human Behavior 27 (2011) 1519–1526 1521

ing, integrating with the existing another ontologies, evaluation,and maintenance and improvement.

We built manually the base of enterprise ontology for businessenterprise’s specific terms and definitions (Park, 2008; Park, Yang,& Bae, 2008). It is based on the Enterprise Ontology developed byEdinburgh University. We considered five categories includingmeta-ontology as the basic concepts and defined the subordinateterms of categories under them. Then we defined the terms thatpresent the distinctive features as properties and the terms thatpresent the relationship among terms as relations. Fig. 1 representsseven basic concepts and some subordinate concepts are defined inconstructed ontology. We built the ontology out of the manuallydefined concepts using the ontology editor Protégé (Knublauch,2003).

Fig. 3. Shipbuilding fabrication process (

3.2. Expansion of enterprise ontology

We checked whether there are any existing ontologies that canreuse and integrated the ontology which is in Knowledge SystemsLaboratory (KSL) (Gruber, 1991) at Stanford University. Also, we in-clude a business process models to represent business processesexplicitly and extended the ontology by adding concepts of Enter-prise Architecture into it for design the Strategic Information Sys-tem. Moreover, we built a Prolog inference engine, which isavailable inference within ontology.

On the other hand, we built the ontology for shipbuilding pro-cess in order to manage the knowledge of shipbuilding processand then merged it with the enterprise ontology (Park, Kim, Yang,& Bae, 2008; Park, Yang, & Bae, 2009). Fig. 2 is a picture the enter-

representation by OntoViz Plug-ins).

Table 1Prolog query and query results for analysis of the current fabrication process.

Prolog query Query results

(1) Can the workflow components of shipbuildingfabrication process be confirmed?

Information on activities, actors, and resources in the fabrication process can be derived

(2) Can information on a specific activity amongworkflow components be obtained?

For a specific activity (for example, the work ‘steel surface-preparation of B111 LOT’), information such asactor, activity owner, work allocation organization, input resources, used machines, and outputs can bederived (see Fig. 4)

(3) Can information on a specific actor amongworkflow components be confirmed?

For a specific actor, information such as their works and the outputs of their works can be derived

(4) How many unit organizations related to theprocess are there?

It is shown that there are three unit organizations related to the fabrication process, which are Steal Class,Equipment Class, and Surface-Preparation Class

(5) How much does it cost to operate fabricationprocess?

It finds out costs of input resources required in operating fabrication process

Fig. 4. Identification of specific instance information.

routingDep(PreActivity, PreProcess, Rout ing, PostActivity, PostProcess) :- allRoutings(Routing, PreActivity, PreProcess, PostActivity, PostProcess). allRoutings(Routing, PreActivity, PreProcess, PostActivity, PostProcess) :- instanceof(Routing, frame('Routing')), preActivity(Routing, PreActivityL), postActivity(Routing, PostActivityL), member(PreActivity, PreActivityL), member(PostActivity, PostActivityL), existInProcess(PreActivity, PreProcessL), member(PreProcess, PreProcessL), existInProcess(PostActivity, PostProcessL), member(PostProcess, PostProcessL).

Fig. 5. Definition of routing dependency.

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prise ontology is built with Protégé and shows features of concept‘‘Activity’’ that were represented informally in Edinburgh Enter-prise Ontology.

4. Shipbuilding fabrication analysis based on EO

In this chapter, we describe the representation, analysis andevaluation of shipbuilding process using the enterprise ontology,Protégé plug-ins, and Prolog inference engine.

4.1. Representation of shipbuilding process

For successful process management, first, we need to representand analyze the current process precisely. Using OntoViz plug-in ofProtégé, we represented the fabrication process graphically. Fig. 3shows the entire fabrication process, which is composed of steelwarehousing, steel piling, steel delivery work at the steel stockyard, pre-processing work, and cutting work. Through ontologysearch, it is shown information on the entire process as well asworkers, responsible persons, department to be allocated to work,and used resources and outputs in each work. If the process is rep-resented using the enterprise ontology as in Fig. 3, all workflowelements defined in the ontology can be represented and, if re-quired, only necessary elements can be viewed.

4.2. Analysis of shipbuilding process

For continuous management of processes, we need to analyzethe current processes accurately, identify problems in the pro-cesses, and select processes to be improved. In this section, we ana-lyze the current state of the fabrication process and dependencyrelationship among the workflow components of the processthrough ontology inference.

4.2.1. Analysis of the current fabrication processTable 1 is a summary of the prolog queries considered to ana-

lyze the current state of the fabrication process and the results ofthe queries. Through ontology inference, several workflow compo-nents in the fabrication process could be identified, and also thecurrent fabrication process, related organizations, and the totalcost of resources could be analyzed.

Fig. 4 is an example of ontology inference, showing the queryfor getting information on specific instances in the fabrication pro-cess and its results. In relation to the work of ‘steel surface-prepa-ration of B111 LOT,’ detailed information is presented including thefacts that actor of this work is ‘pre-processing worker’, the personis responsible for the work is ‘surface-preparation class chief’, andthis work is allocated to ‘surface-preparation class (C51511)’, andinput resources used in the work are machines such as ‘no. 1 con-veyor’ and ‘no. 1 dryer’ and ‘before surface prepared steel1’, andoutput of this work is ‘surface-prepared steel1’.

4.2.2. Dependency analysis of fabrication processA workflow process is composed of different kinds of compo-

nents or entities including activities, roles, resources, events, con-trol data, applications, etc. These entities play different roles in aworkflow process and they interact with and affect one anotherwithin workflow process (Dai & Covvey, 2005). We analyzeddependency in the shipbuilding process with respect to activityrouting and data, and identified elements affected by change inthe workflow.

4.2.2.1. Routing dependency analysis. A routing dependency (Daiet al., 2005) describes and defines the execution order of activitiesin a process. We made it possible to express routing dependencyrelationship by adding ‘preActivity’ slot and ‘postActivity’ slot,which indicate information on preceding activities and succeedingactivities, respectively, to the routing class in ontology is con-structed with Protégé. Fig. 5 below defines the routing dependencyrelationship between neighboring activities PreActivity and Post-

Fig. 6. Result of routing dependency analysis (‘steel surface-preparation of B111 LOT’).

dataDep(PostA, PostP, input (Data, PreA, PreP)) :- data(Data, PreA, PreP), requiredBy(Data, PostAL), member(PostA, PostAL), existInProcess(PostA, PostPL), member(PostP, PostPL). data(D, A, P) :- activity(A), providesResource(A, DL), member(D, DL), existInProcess(A, PL), member(P, PL).

Fig. 7. Definition of data dependency.

J.-H. Park et al. / Computers in Human Behavior 27 (2011) 1519–1526 1523

Activity. Here, PreProcess and PostProcess are processes belongingto PreActivity and PostActivity, respectively, and Routing is therouting relationship between the two activities. ‘preActivity (Rout-ing, PreActivityL)’ returns a list of preceding activities of ‘Routing’and ‘postActivity (Routing, PostActivityL)’ returns a list of succeed-ing activities. ‘existInProcess (PreActivity, PreProcessL)’ returns alist of processes belonging to PreActivity and ‘existInProcess (Post-Activity, PostProcessL)’ returns a list of processes belonging toPosActivity.

Also we defined query rules (Park et al., 2008), with whichdependency relationship among workflow elements can be in-ferred, using the definition of routing dependency and searchingof the ontology knowledge. Fig. 6 shows the results of analyzingthe routing of the activity ‘steel surface-preparation of B111 LOT’through our query rules. r is a query on preceding activities thatare in a routing dependency relationship with this activity, and s

is the results of the query and showing that the activity ‘issue ofB111 LOT’ is a preceding activity in a dependency relationship withthe activity ‘steel surface-preparation of B111 LOT’. t is a query onsucceeding activities in a routing dependency relationship with theactivity ‘steel surface-preparation of B111 LOT’, and u shows thata succeeding activity in a dependency relationship with the activ-ity ‘steel surface-preparation of B111 LOT’ is the activity ‘steel cut-ting of B111 LOT’.

4.2.2.2. Data dependency analysis. A data dependency (Dai et al.,2005) means that the input data of one activity depends on theoutput data of other activities. We defined the data dependencyrelationship using predefined attributes in ontology. Input dataused the ‘requiredBy’ slot of the Resource class, and output dataused the ‘providesResource’ slot of the Activity class. Fig. 7 belowdefines data dependency relationship that activity PostA in thePostP process is dependent on the output of activity PreA. Here,‘requiredBy (Data, PostAL)’ returns a list of activities that use the‘Data’ as input data, and ‘providesResource (A, DL)’ returns a listof output data provided as results of activity ‘A’.

Fig. 8. Result of data dependency an

We defines the query rules (Park et al., 2008) which are infer-able the data dependency relationship among workflow elementsusing the definition of data dependency and searching of the ontol-ogy knowledge. Fig. 8 is an example of data dependency analysis,which is shows information on activities in a dependency relation-ship with the input and output data of the activity ‘piling-up ofB111 LOT’. r is a query asking about the activity producing the in-put data of the activity ‘piling-up of B111 LOT’, and s is the resultof query r and showing that the activity ‘steel warehousing ofB111 LOT’ produces the input data of the activity ‘piling-up ofB111 LOT’. t is a query asking about the activity is dependenton the output data of the activity ‘piling-up of B111 LOT’, and u

is the result of query t and showing that the activity ‘issue ofB111 LOT’ is dependent on the output data of the activity ‘piling-up of B111 LOT’.

4.3. Evaluation of shipbuilding process

There are two approaches for measuring the performance ofbusiness processes: quantitative analysis based on the numeric val-ues of critical variables related to process execution such as re-sources, cost, time and success rate. And qualitative analysisassesses how the process can contribute to the goal.

Actor activity diagramming (AAD) (Schaap, 2001) is a businessprocess modeling tool based on business process analysis in termsof activities, actors and transactions, and makes it easy to evaluatemodeled business processes in terms of effectiveness and effi-ciency. And ‘Goal Reached, Energy Used (GREU) value-pair’(Schaap, 2002) was proposed for a basic measurement of businessprocesses from the viewpoint of goal reaching and resources usingin conjunction together with AAD. ‘‘Goal Reached’’ can be definedas ‘‘a product or service that is in the state meeting the specifica-tions belong to that state’’. ‘‘Energy’’ is somewhat is necessary forproduct or service to move from a state to another state and it de-fines three basic categories in the context of business processes:actors, resources or information.

This section evaluates the fabrication process quantitatively bymaking AAD of the ship fabrication process and observing GREU-values. In addition, it proved that fabrication process can be evalu-ated automatically through ontology inference.

4.3.1. Evaluation using AAD and GREU-valuesFig. 9 is AAD of fabrication process. The item on the top is ‘actor’.

The black rectangle indicates ‘activity’ and interconnected blackrectangles mean activities performed simultaneously by more thanone actor. The white rectangle means ‘product’ or ‘service’ in anystate, namely, ‘transaction’.

alysis (‘piling-up of B111 LOT’).

Fig. 9. Actor activity diagram of fabrication process.

Table 2Goal reached and energy used values of fabrication process.

Activity Statebefore

Stateafter

Goalreached (%)

Actor-time (energy used)

Warehousing State 0 State 1 100 Time (warehousing worker + steel class chief)Piling-up State 1 State 2 100 Time (crane worker + machine class chief)Issue State 2 State 3 100 Time (issue worker + steel class chief)Surface-

preparationState 3 State 4 100 Time (pre-processing worker + pre-processing class chief)

Cutting State 4 State 5 100 Time (cutting worker + cutting class chief)Total business

processState 0 State 5 100 Time (warehousing worker + steel class chief + crane worker + machine class chief + issue worker + pre-

processing worker + pre-processing class chief + cutting worker + cutting class chief)

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Table 2 below is the results observing GREU-values for AAD offabrication process. It shows that a total of nine actors are neces-sary to execute the fabrication process.

4.3.2. Evaluation through ontology inferenceEvaluation through AAD and GREU-values is performed manu-

ally. However, it can be done automatically based on ontology.Fig. 10 is the result of identifying information on actors in the fab-

Fig. 10. Identification of actors in fabrication process.

rication process through ontology inference. It finds that there arenine actors in the fabrication process as in the result of the analysisusing AAD and GREU.

Not only necessary actors but also the cost of resources for exe-cuting the fabrication process was found from the result of fabrica-tion process analysis through ontology inference. Fig. 11 below

Fig. 11. Identification of the cost for executing the fabrication process.

J.-H. Park et al. / Computers in Human Behavior 27 (2011) 1519–1526 1525

shows the cost of resources and manpower necessary for the fab-rication process, which was derived through ontology inference.By searching ontology information, we obtain information suchas the cost of input sources used in the current fabrication processand the cost of workers in the fabrication process, and therefore wecan identify the total cost. Using this information, we can evaluatethe current fabrication process quantitatively in terms of cost. Forother evaluation items as well, the fabrication process can be eval-uated quantitatively by searching and inferring ontologyinformation.

5. Evaluation and comparison

In this chapter, we evaluate our enterprise ontology-based busi-ness process management method in comparison other methodsfrom the viewpoint of the representation, analysis and evaluationof business process.

Business process is not merely a flow of activities, but rather aprocess having deep and nested structure (Barjis, 2007). The pro-posed our method can represent the business process in various as-pects of the activities, actors, data and so on, whereas othermethods normally represent business process as activities in aflowchart. Also, the whole process may not be a one way progres-sion, but a complex process of back and forth interactions. Ourmethod can represent the various interactions between objectswithin the process.

The UML Methodology offers several different sets of diagramswhich collectively provide a multi-perspective and multi-levelabstraction. However, in the UML case, models may be correct,but difficult to check whether they are constructed correctly; theymay be complete, but far too complicated for a novice analyst; theymay be detailed, but difficult to visualize, thus, creating enormouscognitive load for analysts. Most of the conventional business pro-cess models are checked and analyzed via translation to other for-mal diagrams using mapping procedures, such as translation ofUML diagrams to Petri nets (Barjis, 2007). But, our ontology-basedmethod can directly analyze the business process using the processknowledge and the rules defined on ontology for the analysis. Be-sides it can analyze that the objects within the process affects eachother.

Various process mining techniques are used for the businessprocess analysis. Process mining is defined as gathering data aboutexecuted processes by using transaction logs and using them forperforming various analysis (Van Giessel, 2004). The results of pro-cess mining can be utilized in business process reengineering. Butprocess mining can analyze only the restricted forms of processmodel using Petri nets, too (Lee et al., 2009).

The proposed our method needs not the extra methodologiesfor representation, analysis, evaluation of business process becauseit uses process knowledge and rules defined on ontology. Also, inmost of process management techniques based on object-orienteddatabase or XML, if new information occurs it has to be added todatabase. In ontology-based technique, on the contrary, the im-plied information and knowledge can be derived automaticallythrough ontology inference. In addition, the ontology may playsthe role of a standard for terms and concepts used in all systemsand provides as a basis for integrating the different businessprocesses.

6. Conclusions

These days, enterprises’ business environment is changing rap-idly and complicatedly. In order to survive increasingly intensecompetition, enterprises should keep their business competitiveby accommodating business environment changes quickly and

flexibly. For this, they are in need of continuous and automatedmanagement and improvement of their business processes.

In this paper, we describe a case of intelligent analysis of ship-building process using an enterprise ontology, which is a cognitivemodel on enterprises. First, we built an enterprise ontology for rep-resenting enterprise-specific knowledge. The fabrication processwas represented using the enterprise ontology and Protégé plug-ins, and the current state of the process and dependency amongworkflow elements within the process could be analyzed throughontology inference. Based on the results of these analyses, the fab-rication process could be evaluated quantitatively. Using the re-sults of the analyses and evaluation, we may be able to identifyproblems in the current process and to find solutions for the prob-lems. Through this, enterprise can manage business processes con-tinuously and, consequently, enhance its competitiveness.Furthermore, this suggests that enterprise ontology is useful forbusiness administration and workflow management even in indus-tries with complex processes like shipbuilding. However, it is noteasy works for one to construct the enterprise ontology, so we planto work rules to generate ontology from business documents easilyand automatically in future.

Acknowledgment

This work was supported by Basic Science Research Programthrough the National Research Foundation of Korea (NRF) fundedby the Ministry of Education, Science and Technology (MEST)(KRF-2008-313-H00009).

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