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Ontology-based knowledge management for joint venture projects
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Expert Systems with Applications Expert Systems with Applications 35 (2008) 187–197 www.elsevier.com/locate/eswa
Ontology-based knowledge management for joint venture projects Kuo-Cheng Ku a
a,* ,
Anthony Wensley b, Hsing-Pei Kao
c
Department of Industrial Engineering and Management, Ming Hsin University of Science and Technology, Hsin Chu 304, Taiwan, ROC b J.L. Rotman School of Management, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6 c Institute of Industrial Management, National Central University, Chung Li 320, Taiwan, ROC
Abstract When enterprises engage in strategic joint venture projects, communication, knowledge sharing and management issues are inevitable and complex problems. Problems relating to communication, knowledge sharing and management issues generally occur in more than one phase of joint venture projects and involve various different domains. This study reviews issues related to knowledge management in the context of joint ventures (JV) from both macro and micro perspectives by using an ontology-based approach to analyze complex knowledge management (KM) issues in the context of the Integrated Circuit (IC) industry. This study presents a framework for an ontology-based model to analyze the knowledge management processing of joint ventures in the IC industry, and uses the IC foundry industry as an example to explore implementation issues. This framework uses a conceptual enterprise ontology (EO) model and domains of EO to analyze the process of EO. Moreover, data from the IC foundry industry is used as a case study. This study aimed to assist managers in increasing the possibility of success in dealing with complicated JV projects. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Enterprise ontology; Knowledge management; Joint ventures; IC foundry industry
1. Introduction Communication between project teams comprising members from within extended enterprises and different organizations is often hampered by, among other things, confusion in terms and vocabulary (Lin, Harding, and Shahbaz, 2004). Extended enterprise collaboration, such as joint venture (JV), will raise the difficulty and complexity of communication between the cooperating companies. One of the main technical issues is the knowledge management problem when processing shared implicit and explicit knowledge with different systems. Enterprise collaboration faces knowledge confliction during knowledge sharing and development. Poor knowledge management for involved enterprises can mislead the business processes and cause serious wastage of resources, and even the failure of the JV. *
Corresponding author. Tel.: +886 3 559 3142x3233; fax: +886 3 559 3142x3212. E-mail address: [email protected] (K.-C. Ku). 0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.06.010
Guarino (1997) defined ontology-based knowledge management (KM) as follows: ‘‘For KM systems in enterprises, ontology can be regarded as the classification of knowledge’’. Moreover, O’Leary (1998) described ontology-based KM as follows: ‘‘In enterprise KM systems, . . . Ontologies define the shared vocabulary used in the KM system to facilitate communication, search, storage, and representation’’. The issue of enterprise ontology (EO) derives from enterprises having their own professional terminology for use in different tasks. This study proposes a novel solution for KM problems and thus enhances collaboration during JV projects. A framework of ontology-based KM is developed to help managers and participants find solutions to various KM problems. Ambiguous knowledge acquisition from either of the JV organizations can cause serious problems in collaboration. By using an ontology-based analysis of knowledge sharing and development, enterprises involved in JVs can also reduce intellectual property leakage and increase the efficiency and effectiveness of their
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communication. The ontology-based approach can also help enhance collaborative relationships among knowledge workers from different domains. Finally, the approach advocate in this study is likely to increase the likelihood of success for the JV project. The remainder of this paper is organized as follows: Section 2 discusses the development of a particular EO in relation to the high technology industry. Additionally, this section reviews the recent literature on KM and JV. Section 3 presents the overall EO framework that forms the central focus of this paper. Issues concerning the processing of the EO, its verification, and the respective domains of joint alliance EO are also described. Moreover, Section 4 discusses an IC industry case in detail. This case is used to demonstrate the generic types of problem that are likely to occur with respect to KM in JVs. Finally, Section 5 provides a conclusion and discussion of future research directions. 2. Literature review 2.1. Literature review of related issues for JV projects JVs are considered an important strategy for industries. JVs are typically defined as an alliance between two or more parties in researching, developing, producing, selling, or distributing a product or service for profit (Kukalis and Jungemann, 1995). JVs involving international competitors have attracted growing interest among both researchers and participants (Richter and Vettel, 1995). Many scholars and researchers are working on the issues related to enterprises engaging in JVs. The literature on this subject can be sorted into five categories: strategic issues, relationship management issues, enterprises modeling issues, performance measurement issues, and KM issues (Table 1). 2.2. EO and KM issues for JVs project Gruber (1993) defined an ontology as a specification of a representational vocabulary for a shared domain of dis-
course – including definitions of classes, relations, functions, and other objects. The use of the term has a long history in philosophy, where it refers to the nature of existents and existence. In a broad sense ontologies form the basis of what is relevant, what can be referred to and discussed, and what can be modeled in a particular domain. Research into ontologies has become increasingly important to knowledge-based systems and KM. In the context of modern organizations it has been argued that any ontology must take account of activities, agents, roles, positions, goals, communication, authority, and commitment (Fox, Barbuceanu, and Gruninger, 1996). In this context, EO is a collection of terms and definitions relevant to enterprises (Uschold, King, Moralee, and Zorgios, 1998). Much of the previous research has focused on developing ontologies for particular organizations. Recent studies on EO and KM for JV projects are shown as follows: Kidd in 2000 proposed a novel development in knowledge brokering based on a ‘trusted agent’ located off-shore. In this agent-based model, the data warehouse concept is adopted as the basis of a search for prospects and an attempt to gain benefits from a JV. This study first raised issues concerning KM in the context of collaboration among JV. Madni, Lin, and Madni (2001) presented the IDEONTM extensible EO for enterprise design, management and control processes. This work first provides a reference model for the interoperability between new and legacy business applications to integrate and adapt business strategies and ongoing operations to external and internal environmental changes. Jolly (2002) conducted an investigation to identify decision and knowledge sharing problems in JVs. This study showed the importance of KM and trust for inter-firm collaboration. Moreover, Wong, Maher, and Luk (2002) outlined the development and attraction of the ‘‘JV’’ approach to foreign investment in China. Attempting to discover what strategic management knowledge was transferred from the Western partner. This study examines the issue of international KM for JV projects.
Table 1 Literature review of JVs research Strategy
Relationship management
Enterprises modeling
Performance measurement
Knowledge management
Kukalis and Jungemann (1995), Mills and Chen (1996), Naylor and Lewis (1997), Vanhonacker (1997), Maccoby (1997), Chen and Chen (2002), Rigby and Zook (2002), Yasuda (2005)
Shaughnessy (1995), Littler and Leverick (1995), Martinsons and Tseng (1995), Beamish and Inkpen (1995), Gifford (1998), Norwood and Mansfield (1999), Fey and Beamish (2000), Hobbs and Andersen (2001), Steier (2001), Meyerson (2001), Ghosn (2002), Buckley et al. (2002), Walker and Johnnes (2003), Bayona et al. (2006)
Mesak and Mayyasi (1995), Richter and Vettel (1995), Nakamura et al. (1996), Williams et al. (1998), Wang et al. (2004), Storey (2005)
Luo (1996), Park and Kim (1997), Pearce and Hatfield (2002), Beamish and Berdrow (2003), Swierczek and Dhakal (2004), Mohr and Puck (2005)
Abecker et al. (1998), Kidd (2000), Jolly (2002), Wong et al. (2002), Tsang (2002), Walker and Johannes (2003), Li et al. (2003), Gerwin and Ferris (2004), Chadam and Pastuszak (2005), Revilla et al. (2005), Wong (2005)
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Other research regarding the research JV topic, such as that of Li, Hsieh, and Sun (2003), has provided an ontology-based KM system for the metal industry to study knowledge sharing and development from the macro view in traditional industry. Li, Wu, and Yang (2004) presented an ontology-oriented approach that uses logical description to formally represent concepts and roles (relations) relating to the partner view of domain knowledge. This work extends this approach to the high technology industry to further study the application of KM. This study summarizes previous research and reviews KM from both the macro and micro perspectives by using the ontology-based approach to analyze complex knowledge sharing and development in the high technology industry.
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Design Time Enterprise A
Enterprise B
Domain Knowledge
Build Time
Infer
Domain Knowledge
Infer
Own
Domain Experts
Own
Execute
Enterprises Ontology
Execute
Domain Experts
Produce
Link Time
3. Framework of ontology-based KM
Joint Ventures Dictionary
Use
3.1. Key concept of the framework Ontologies are increasingly considered a key technology for enabling semantics-driven knowledge processing (Maedche, Motik, Stojanovic, Studer, and Volz, 2003). McKeown (1992) described Semantics-driven approaches as ‘‘using knowledge about the case frames of verbs to drive interpretation’’. Enterprise KM entails formally managing knowledge resources to facilitate access and reuse of knowledge, typically through the use of advanced information technology (O’Leary, 1998). The development of EO for JVs will typically require activities that involve knowledge sharing, knowledge negotiation, knowledge creation and the resolution of knowledge conflicts. This study develops an EO for a JV, a process for EO development and a dictionary for JV EO components. The development of an EO for a JV can be divided into four phases: Design, Building, Linking, and Running. During the design phase the two enterprises that are entering into a JV represent two, essentially independent, knowledge repositories. The two jointing enterprises have independent domain experts owns its domain knowledge. After triggering the JV project, both sides began to prepare and design the knowledge sharing and developing processes. Knowledge sharing and development start from the communication and collaboration processes during the Build phase. Two independent domain expert groups begin to create the new knowledge base via sharing and inferring their own domain knowledge. In the Link Time phase, the EO plays a key role in integrating the joint enterprises. Domain experts from different enterprises have to work out the EO together to produce the JVs dictionary for both enterprises in the following collaborative JV project. Finally, in the run time phase, the knowledge workers use the JVs dictionary to complete collaborative missions. The overall processes continues until the termination of the JV relationship. Fig. 1 shows the key conceptual framework use in this study.
Use
Run Time
Knowledge Workers
Collaborate
Knowledge Workers
Fig. 1. Key concept of framework.
3.2. Ontology-based KM This study provides a model of ontology-based KM for JVs. The ontology-based KM process model consists of seven important steps for reference by managers when implementing JV projects, including: Knowledge acquisition, knowledge identification, ontology analysis, ontology implementation, ontology verification, knowledge reposition, and knowledge sharing/development. Fig. 2 shows the model of ontology-based KM for JVs project. Each of the seven steps is described in detail below. Knowledge acquisition: Domain experts provide the domain knowledge for the JV which can involve the overall business processes and specific technological transfer. The knowledge acquisition process contains the joint domain knowledge from individual enterprises increase the complexity of KM. Poor knowledge acquisition procedure can lead to incomplete EO result. Knowledge identification: Domain experts identify what knowledge needs to be shared and what knowledge does not need to be shared in this step. This step prevent the leakage of knowledge and avoids arguments regarding knowledge sharing. Ontology analysis: This step analyzes the collected data and information. Based on the analysis, the ontology can be divided into information ontology and domain ontology (Abecker, Bernardi, Hinkelmann, Ku¨hn, and Sintek, 1998).
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1996). The verification of ontology for JVs projects involves more complicated processes and is further discussed in Section 3.3. Knowledge reposition: The new created ontologies from joint enterprises are stored in the public database for reposition. The public database provides a channel for accessing the new ontologies and supports subsequent knowledge sharing and development. Knowledge sharing/development: The joint venture dictionary is generated in this step. The joint venture dictionary consists of the new definitions and axioms for the JVs project. The enterprises entering into the JV share and develop the knowledge required for the JV through the joint ventures dictionary, which acts as the basis for improved collaboration.
Knowledge Acquisition
Knowledge Identification
Ontology Analysis
3.3. Verification of EO
Ontology Implementation
Ontology Verification
Knowledge Reposition
Knowledge Sharing/ Development
Fig. 2. Ontology-based KM process for JVs project.
After processing the EO for enterprises entering into a JV, ontology verification can ensure the correctness of the joint venture dictionary and thus enhance collaboration during JV evolution. Fig. 3 shows the EO verification process. First, the definitions and axioms of the domain knowledge are collected from the knowledge bases. The other definitions from the jointing ontologies can be imported to the collecting knowledge bases. The verification screens the two parts of definitions and axioms. The clear part is sent to the JV dictionary for knowledge workers. Meanwhile, the other parts are sent to the inference engine to distinguish the inferable axioms using the definitions and axioms in the knowledge bases. Some axioms may not be able to be inferred owing to incomplete content preventing the inference engine from distinguishing them. These axioms are stored in the other knowledge base for later manual analysis. 3.4. Domains of the enterprises entering into the JV EO
The information ontology is a meta model that describes knowledge objects and contains generic concepts and attributes of all information about knowledge objects. The domain ontology consists of the concepts, attributes, and instances of the enterprises involved in the JV. Domain ontology is designed to achieve semantic matching when searching for knowledge objects (Li et al., 2003). The ontology analysis can also include verifying that the party possessing some technology agrees to that technology being transferred to another party. Ontology implementation: The ontology implementation step executes the ontology process and ensures the completion of information and domain ontology after the ontology analysis. Ontology verification: Ontology verification includes verification of: (1) Each individual definition and axiom. (2) Collection of definitions and axioms that are stated explicitly in the definitions of the ontology. (3) Definitions that are imported from other ontologies. (4) Axioms that can be inferred using other definitions (Go´mez-Pe´rez,
The domains of EO in a JV may involve major business processes and related information. This study categorizes Axioms can be inferred using other Definitions and Axioms
Collecting Definitions & Axioms
Yes
Inference
Not Clear
Verification Check
No
Clear
Store
Joint Ventures Dictionary
Fig. 3. EO verification process.
Import Definition fromjointing Ontologies
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the major knowledge domains as the Technology domain, Finance domain, Marketing domain, Production domain, and Human resources domain. The Technology domain serves as an example demonstrating the JV EO development processes (Fig. 4). During JVs, the Technology 1 domain represents the Technology domain ontology in company A to JV. The Technology 1 domain may consist of the Product 1 domain, Process 1 domain, Equipment 1 domain, Information Technology 1 domain, and Quality 1 domain, which represent the domains of product, processes, equipments, information technology, and quality, respectively, in company A. The Technology 2 domain represents the technology domain ontology from company B. The Technology 2 domain consists of Product 2 domain, Process 2 domain, Equipment 2 domain, Information Technology 2 domain, and Quality 2 domain, which represent the domains of product, processes, equipment, information technology, and quality, respectively, from the company B. The Product 1 domain consists of four sub-product domains from company A, namely: Sub-Product 1A, Sub-Product 1B, Sub-Product 1C, and Sub-Product 1D (Fig. 5). Meanwhile, the Product 2 domain consists of four sub-product domains from company B: Sub-Product 2A, Sub-Product 2B, Sub-Product 2C, and Sub-Product 2D from company B. During the JV, the domains of EO are combined, resulting in the creation of new ontological domains, namely technology joint venture domain, product joint venture domain, Sub-Product joint venture domain, which represent the technology domain of joint ventures,
Product 1
Product 2
Process 1
Equipment 1
Process 2
Technology 2
Technology 1
Information Technology 1
Quality 1
Equipment 2
Information Technology 2
Finance 1
Joint Ventures Project
Finance 2
Marketing 1
Marketing 2
Production 1
Production 2
Human Resource 1
Human Resource 2
Fig. 4. Domains of EO in JVs project.
Quality 2
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product domain of joint ventures, and sub-product domain of joint ventures, respectively. 4. Example 4.1. Introduction to the IC foundry industry Fig. 6 shows how IC devices are mostly produced by Integrated Device Manufacturer (IDM) and Application Specific Integrated Circuit (ASIC) in the initial phase of the IC business (Tseng, 2002). The IDM and ASIC companies include functions of system/IC design, wafer manufacturing, assembly and testing. After the emergence of IC design companies (Fabless company), IC Foundries started to play a very important role in the business. Foundries manufacture ICs for design companies or other IDM and ASIC companies. Foundries have expanded their technical support for Intellectual Property (IP) design companies by integrating design services and wafer manufacturing. Each IC foundry company usually has several wafer fabrication foundries located in different areas, and even different countries. Wafer fabrication usually requires 100–500 processes over several weeks. Besides the time-consuming operations, complex manufacturing characteristics such as reentry process, lot splitting, batch operation, and maximum lot waiting time complicate capacity planning for wafer fabrication’’ (Chen, Chen, Lin, and Rau, 2005, p. 710). Features of the IC operation life cycle, independent of the JV itself, include Design, Engineering, and Logistics Collaboration, as shown in Fig. 7 (Tseng, 2002). Capacity expansion in IC industries is determined during design collaboration through interactions among the IP design selection, wafer layout design and wafer mask making process. Engineering collaboration begins with manufacturing evaluation and continues until logistic arrangement. Meanwhile, logistics collaboration begins with capacity booking and ordering process continuing through to the logistic management process. These collaborations may happen concurrently through different organization units and functions. 4.2. Trend of IC foundry JVs To remain competitive in the evolving semiconductor industry environment, some IDM/ASIC companies, system companies, and Fabless companies have begun to enter into joint ventures with IC Foundry companies. Table 2 shows a clear increase in JVs involving IC Foundry companies in the semiconductor industry over the past five years. The strategic purposes of these JVs include new technology sharing, capacity sharing, and gaining increased support for production. The above table demonstrates that most strategic JV projects require technological collaboration and knowledge sharing/development. Specifically, IC Foundry companies must integrate complex business processes, respond to the
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K.-C. Ku et al. / Expert Systems with Applications 35 (2008) 187–197 Human Resource 2
Human Resource 1
Production 1
Production 2
Marketing 1
Marketing 2
Quality 2
Joint Ventures Project
Quality 1 Finance 1
Finance 2
Information Technology 2
Information Technology 1 Technology 1 SubProduct 1.D
Equipment 1
SubProduct 1.C
Process 1 Product JV
SubProduct 1.B
SubProduct 1.A
Technology 2
Technology JV
Process JV
Equipment JV
Information Technology JV
Product 1
Quality JV
Equipment 2
SubProduct 2.D
Process 2
SubProduct 2.C
Product 2
SubProduct 2.B
SubProduct 2.A
Sub-Product JV
Fig. 5. Domains of jointing EO.
System Co.
IDM/ASIC
IP System Design IC Design
Fabless Co. System Design IC Design
Design Service Fab Foundry
Assemble Test
Contract Assemble Test
Fig. 6. IC foundry business.
challenge of collaborating with respect to different technologies, and engage in complex KM. Poor KM and ineffective communications can cause JV project failure or even result in lost business in the competitive semiconductor industry.
4.3. Case study of the IC foundry industry 4.3.1. Example of processing EO for IC foundry industry Considering the case of two IC Foundry companies establishing a strategic joint venture to share advanced technology, the jointing companies first perform technology transfer and sharing during the JV project. As discussed in the previous section, the ontology-based KM for the JV project in this study consists of six steps: knowledge acquisition, ontology analysis, ontology implementation, ontology verification, knowledge reposition, and knowledge sharing/development. In this case, the advanced technological product, process, and equipment in the IC industry are used as an example to describe the ontology processes for managing knowledge sharing and development in the JVs project. Fig. 8 shows the example of processing EO for the jointing IC Foundry companies. Knowledge acquisition may occur in one of the advanced technologies of the 0.13 lm process and the product type of Logic provided by the product engineers from company A. The product engineers of company B may also provide experience in running 0.13 lm process and product type of Logic during collaborative meetings
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IC Operation Life Cycle Points of View
Manufacturing Evaluation
Capacity Booking & Ordering
Design IP Selection
Layout
Mask Making
Manufacturing
Manufacturing Quality
Logistic Management
Design Collaboration
Engineering Collaboration
Logistic Collaboration
Fig. 7. IC operation life cycle resource from.
Table 2 Major IC foundry JV events in past five years Year
Joint ventures (JVs)
Rationale
2005
Advanced Micro Devices Inc. (AMD) and United Microelectronics Corp. (UMC)
Establish a joint 300-mm wafer foundry venture in Singapore for high-volume production of PC processors and other logic products starting with 65-nm technology
2004
Fujitsu and Sumitomo Electric
Increase sales of a wide variety of compound semiconductor devices through development and manufacturing
2003
Jazz Semiconductor and Hua Hong NEC (HHNEC) Taiwan Semiconductor Mfg Co. Ltd (TSMC) and OmniVision Technology, Inc. Infineon Technologies AG and United Expitaxy Company (UEC)
Increases current Jazz capacity for digital, analog and RF CMOS processes as well as SiGe Bi-CMOS Provides integrated back-end manufacturing services for image sensors, including color filters Matches Infineon’s optoelectronic chip technology with UEC, its back-end expertise
2001
Micron Technology Inc. and Hynix Semiconductor Inc.
Micron Technology Inc. uses the Korean chip maker as a foundry for DRAMs
2000
Hitachi, Ltd. and United Microelectronics Corporation (UMC) Amkor Technology and Toshiba Corporation’s Semiconductor Company IBM China Company Limited (IBM China) and China Great Wall Shenzhen Co., Limited Chartered Semiconductor Manufacturing and Lucent Technologies’ Microelectronics Group Malaysia’s Mimos Bhd and Integrated Silicon Solution Inc (ISSI)
Achieves maximum production capacity for UMC 300 mm wafer facility
Philips, the Taiwan Semiconductor Manufacturing Company and the Economic Development Board of Singapore
Produces chips at sizes of 0.25 lm, 0.18 lm and smaller
1999
Collaborates with Iwate Toshiba’s assembly and test operations, will allow for manufacturing operations at two independent subcontract assembly houses Provides electronic manufacturing services for Nokia JV manufacturing operations Forged a $700 m R& D agreement for next-generation IC communications Establishes a fabless design and marketing organization for non-volatile memory products
and discussions. The equipment used in the process consists of the photo, thin film, etch, and diffusion for both companies. Furthermore, the related equipment engineers help to provide related equipment information. The ontology analysis process starts from analyzing the collected definition and related information of the 0.13 lm process, Logic product, which may include the design IP, the layout to produce masks, mask layers, manufacturing issues, quality requirement, and shipping procedures. The information of equipment may include the recipes of each machine, maintenance schedules, machine vendors, spare parts, machine dimensions, etc. Some information may be reserved for business certain issues and is also discussed in the process.
The ontology implementation process is executed after the analysis of ontology. Certain information ontology (such as process types, components of layout, mask information, manufacturing information, etc.) and domain ontology (IP number, dimension of each mask layer, wafer specification, wafer acceptance testing specifications, inspection specifications, etc.) are created for the JV project. The example of the jointing domain ontology process is detailed in Section 4.3.2. To ensure the correctness of the ontology, the ontology verification process is continuously executed after each of the ontology implementation process. To further understand the importance of the ontology verification process, the
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Product Engineer from A company
Knowledge
Knowledge
Acquisition
Identification
A COMPANY 0.13μMPROCESS 1.PATTERN SELECTION 2.EQUIPMENT REQUIREMENT 3.MASK PREPARATION
B COMPANY 0.13 μM PROCESS 1.PATTERN SELECTION 2.EQUIPMENT REQUIREMENT 3. MASK PREPARATION
Product Engineer from B company
Discussion Meeting
Ontology Verification
Comparing Definition of 0.13μM process
No
Verification
No
Ontology
Term Usable ?
No
Term Termination
Analysis
Knowledge Reposition
Knowledge Sharing/
Yes
OK
Store Information
Assign New Term
Ontology
Yes
Implementation
Documentation & Announcement
Development Joint Ventures Dictionary
Process Engineer of A Company
Process Engineer of B Company
Fig. 8. Example of processing EO for jointing IC foundry companies.
example of ontology verification will be discussed in detail in Section 4.3.3. The knowledge reposition process starts after the completion of ontology processing. The identified terms are stored in the database for further retrieval. The knowledge sharing/development process provides information and knowledge for the other related knowledge workers to access. The joint venture dictionary is created in this process. Managers and knowledge workers, such as project managers, process engineers, manufacturing supervisors and quality engineers, can use the JV dictionary to efficiently retrieve required information and common definitions for communication and collaboration with partners. 4.3.2. Example of domains of jointing EO In the domains of joined EOs (Fig. 9), the process domains of company A are 0.13 lm, 0.15 lm, 0.18 lm,
and 0.22 lm. Meanwhile, the process domains of company B are 90 nm, 0.13 lm, 0.25 lm, and 0.35 lm. Moreover, the ontological domains merged in the JV are the new ontology for the 0.13 lm process is created after processing the JV EO. Company A can also receive technological support for 90 nm from company B to increase competition in advanced technology. The new ontological domain for the 90 nm processes is fully transferred from company B during the JV. The product domains of company A consist of OR comprise logic, DRAM, and high voltage. The product domains of company B consist of logic, NVM, SiGe, and color filter. The new ontology for the logic product is created after processing the EO for the JV. The equipment domains of companies A and B consist of photo, thin film, etch, and diffusion. The new ontologies for photo, thin film, etch, and diffusion are created after processing the EO for the JV.
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Diffusion B
Diffusion A
Etch B
Etch A
Photo JV
Thin Film JV
Etch JV
Diffusion JV
Thin Film B
Equipment B
Thin Film A
Photo B
Equipment A
Color Filter B
Photo A Equipment JV
SiGe B
High Voltage A
NVM B
DRAM A
Joint Ventures Project
Product A
Logic A
Technology A
0.18 μm A
0.13 μm A
Logic B
Technology B Technology JV
0.22 μm A
0.15 μm A
Product B
Process JV
0.35 μm B
0.25 μm B
Product JV
Process A
Process B
Logic JV
0.13 μm B
0.13 μm JV 90 nm B
90nm JV
Fig. 9. Example of domains of jointing EO.
4.3.3. Example of EO verification Fig. 10 shows an example of verification of EO for 0.13 lm process. First, the definitions and axioms of 0.13 lm process are collected from company A. Then, the definitions of 0.13 lm process from company B are imported to the ontology of company A. In the product domain of 0.13 lm, in both company A and B, there may be 1.0 V, 1.2 V and 1.5 V core options, and I/Os of 2.5 V and 3.3 V. the 1.0 V, 1.2 V and 1.5 V are verified and sent
to the JV dictionary for reference. The domain ontology of 6T SRAM cell size (2.43 lm2) for various system-onchip (SOC) applications in the networking, computing and consumer market segments cannot be verified. After the inference procedure, the definition of SOC is found a new term for the JV project. Moreover, the new term is not involved in the present project. The definition of SOC is first stored in the other database for reasons of security OR owing to security concerns.
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Axioms can be inferred using other Definitions and Axioms
Collecting Definitions & Axiomsof 0.13 μm process
Import Definition from jointing Ontologies of 0.13 μm process
Yes
Inference (SOC)
No
Store -system-on-chip (SOC)
Not Clear
Verification Check (1.0V,1.2V, 1.5 V,SOC)
Clear Joint Ventures Dictionary - 1.0 V -1.2 V -1.5 V
Fig. 10. Example of verification of EO.
In the IC foundry industry, the collaboration of two jointing companies may involve only partial technology transfer but not full support for some strategic security issues. The verification of EO for the JV project has to protect the intellectual property of the companies. On the other hand, EO verification also helps the collaboration for both companies to run the project smoothly. Therefore, poor verification process can cause serious problems and impact JV project success. 5. Conclusion This study provides a reference framework for managers during knowledge sharing and development in a JV project. This work adopts an ontological approach to analyze KM processing in JVs. The IC Foundry industry is used as an example to study the practicality of the proposed approach. The characteristics of complex business processes and high technological issues in the IC Foundry industry increase the challenge of this research. This study aimed to provide managers with assistance to increase their changes of successfully dealing with complicated JV projects. The inaccuracy and discrepancy of the original information provided by the domain experts represents an important limitation of this study. The information of specific technology domains can provide a reference for improving information on this area. This study focuses on the KM of the technology domain. Other domains, like finance, marketing, production, and human resources, would also make interesting topics for future research. Meanwhile, future studies can examine other forms of real world strategic alliances, such as mergers or acquisitions. After we identified the process of knowledge sharing and development for the collaborative companies, knowledge access and cultural differences represent another two key issues of concern. Some previous studies concern access to knowledge from some different perspectives, including: access control (Bertino, 1998; Ray, 2004), modeling knowl-
edge access process (Gaines and Shaw, 1997; Musen, Fagan, Combs, and Shortliffe, 1999), and strategy of knowledge access (Beamish and Berdrow, 2003; Richter and Vettel, 1995). This study was not concerned with cultural differences. However, national culture is a pertinent influence on KM. Child and Rodrigues (2002) delineated that national cultural differences may foster feelings of distance and conflict in a JV, and may hinder KM between the two groups of people. Consequently, it is reasonable to expect that JV partners will rely increasingly on management involvement for knowledge acquisition with increasing cultural difference between them. References Abecker, A., Bernardi, A., Hinkelmann, K., Ku¨hn, O., & Sintek, M. (1998). Toward a technology for organizational memories. IEEE Intelligent System, 13, 40–48. Bayona, C., Corredor, P., & Santamaria, R. (2006). Technological alliances and the market valuation of new economy firms. Technovation, 26(3), 369–383. Beamish, P. W., & Berdrow, I. (2003). Learning from international joint ventures – the unintended outcome. Long Range Planning, 36, 285–303. Beamish, P. W., & Inkpen, A. C. (1995). Keeping international joint venture stable and profitable. Long Range Planning, 28(3), 26–36. Bertino, E. (1998). Data security. Data & Knowledge Engineering, 25(1–2), 199–216. Buckley, P. J., Glaister, K. W., & Husan, R. (2002). International joint ventures-partnering skills and cross-cultural issues. Long Range Planning, 35, 113–134. Chadam, J., & Pastuszak, Z. (2005). Marketing aspects of knowledgebased management in groups of companies. Industrial Management & Data Systems, 105(4), 459–475. Chen, J. C., Chen, C. W., Lin, C. J., & Rau, H. (2005). Capacity planning with capability for multiple semiconductor manufacturing fabs. Computers and Industrial Engineering, 48(4), 709–732. Chen, T., & Chen, H. (2002). Asymmetric strategic alliances: A network view. Journal of Business Research, 55(3), 1007–1013. Child, J. & Rodrigues, S. B. (2002). Corporate Governance In International Joint Ventures: Toward A Theory Of Partner Preferences. 16.01.2006. Fey, C. F., & Beamish, P. W. (2000). Joint venture conflict-the case of Russian international joint ventures. International Business Review, 9, 139–162. Fox, M. S., Barbuceanu, M., & Gruninger, M. (1996). An organisation ontology for enterprise modeling: Preliminary concepts for linking structure and behaviour. Computers in Industry, 29(1–2), 123–134. Gaines, B. R., & Shaw, M. L. G. (1997). Knowledge acquisition, modelling and inference through the World Wide Web. International Journal of Human–Computer Studies, 46(6), 729–759. Gerwin, D., & Ferris, J. S. (2004). Organizing new product development projects in strategic alliances. Organization Science, 15(1), 22–37. Ghosn, C. (2002). Saving the business without losing the company. Harvard Business Review, 80(1), 37–45. Gifford, D. Jr., (1998). How much autonomy is enough? Harvard Business Review, 76(1), 11–12. Go´mez-Pe´rez, A. (1996). Towards a framework to verify knowledge sharing technology. Expert System With Applications, 11(4), 519–529. Gruber, T. R. (1993). A translation approach to portable ontologies. Knowledge Acquisition, 5(2), 199–220. Guarino, N. (1997). Understanding, building, and sing ontologies: A commentary to using explicit ontologies in KBS development’’, by van Heijst, Schreiber, and Wielinga. International Journal of Human and Computer Studies, 46, 293–310.
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Expert Systems with Applications Expert Systems with Applications 35 (2008) 187–197 www.elsevier.com/locate/eswa
Ontology-based knowledge management for joint venture projects Kuo-Cheng Ku a
a,* ,
Anthony Wensley b, Hsing-Pei Kao
c
Department of Industrial Engineering and Management, Ming Hsin University of Science and Technology, Hsin Chu 304, Taiwan, ROC b J.L. Rotman School of Management, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6 c Institute of Industrial Management, National Central University, Chung Li 320, Taiwan, ROC
Abstract When enterprises engage in strategic joint venture projects, communication, knowledge sharing and management issues are inevitable and complex problems. Problems relating to communication, knowledge sharing and management issues generally occur in more than one phase of joint venture projects and involve various different domains. This study reviews issues related to knowledge management in the context of joint ventures (JV) from both macro and micro perspectives by using an ontology-based approach to analyze complex knowledge management (KM) issues in the context of the Integrated Circuit (IC) industry. This study presents a framework for an ontology-based model to analyze the knowledge management processing of joint ventures in the IC industry, and uses the IC foundry industry as an example to explore implementation issues. This framework uses a conceptual enterprise ontology (EO) model and domains of EO to analyze the process of EO. Moreover, data from the IC foundry industry is used as a case study. This study aimed to assist managers in increasing the possibility of success in dealing with complicated JV projects. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Enterprise ontology; Knowledge management; Joint ventures; IC foundry industry
1. Introduction Communication between project teams comprising members from within extended enterprises and different organizations is often hampered by, among other things, confusion in terms and vocabulary (Lin, Harding, and Shahbaz, 2004). Extended enterprise collaboration, such as joint venture (JV), will raise the difficulty and complexity of communication between the cooperating companies. One of the main technical issues is the knowledge management problem when processing shared implicit and explicit knowledge with different systems. Enterprise collaboration faces knowledge confliction during knowledge sharing and development. Poor knowledge management for involved enterprises can mislead the business processes and cause serious wastage of resources, and even the failure of the JV. *
Corresponding author. Tel.: +886 3 559 3142x3233; fax: +886 3 559 3142x3212. E-mail address: [email protected] (K.-C. Ku). 0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.06.010
Guarino (1997) defined ontology-based knowledge management (KM) as follows: ‘‘For KM systems in enterprises, ontology can be regarded as the classification of knowledge’’. Moreover, O’Leary (1998) described ontology-based KM as follows: ‘‘In enterprise KM systems, . . . Ontologies define the shared vocabulary used in the KM system to facilitate communication, search, storage, and representation’’. The issue of enterprise ontology (EO) derives from enterprises having their own professional terminology for use in different tasks. This study proposes a novel solution for KM problems and thus enhances collaboration during JV projects. A framework of ontology-based KM is developed to help managers and participants find solutions to various KM problems. Ambiguous knowledge acquisition from either of the JV organizations can cause serious problems in collaboration. By using an ontology-based analysis of knowledge sharing and development, enterprises involved in JVs can also reduce intellectual property leakage and increase the efficiency and effectiveness of their
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communication. The ontology-based approach can also help enhance collaborative relationships among knowledge workers from different domains. Finally, the approach advocate in this study is likely to increase the likelihood of success for the JV project. The remainder of this paper is organized as follows: Section 2 discusses the development of a particular EO in relation to the high technology industry. Additionally, this section reviews the recent literature on KM and JV. Section 3 presents the overall EO framework that forms the central focus of this paper. Issues concerning the processing of the EO, its verification, and the respective domains of joint alliance EO are also described. Moreover, Section 4 discusses an IC industry case in detail. This case is used to demonstrate the generic types of problem that are likely to occur with respect to KM in JVs. Finally, Section 5 provides a conclusion and discussion of future research directions. 2. Literature review 2.1. Literature review of related issues for JV projects JVs are considered an important strategy for industries. JVs are typically defined as an alliance between two or more parties in researching, developing, producing, selling, or distributing a product or service for profit (Kukalis and Jungemann, 1995). JVs involving international competitors have attracted growing interest among both researchers and participants (Richter and Vettel, 1995). Many scholars and researchers are working on the issues related to enterprises engaging in JVs. The literature on this subject can be sorted into five categories: strategic issues, relationship management issues, enterprises modeling issues, performance measurement issues, and KM issues (Table 1). 2.2. EO and KM issues for JVs project Gruber (1993) defined an ontology as a specification of a representational vocabulary for a shared domain of dis-
course – including definitions of classes, relations, functions, and other objects. The use of the term has a long history in philosophy, where it refers to the nature of existents and existence. In a broad sense ontologies form the basis of what is relevant, what can be referred to and discussed, and what can be modeled in a particular domain. Research into ontologies has become increasingly important to knowledge-based systems and KM. In the context of modern organizations it has been argued that any ontology must take account of activities, agents, roles, positions, goals, communication, authority, and commitment (Fox, Barbuceanu, and Gruninger, 1996). In this context, EO is a collection of terms and definitions relevant to enterprises (Uschold, King, Moralee, and Zorgios, 1998). Much of the previous research has focused on developing ontologies for particular organizations. Recent studies on EO and KM for JV projects are shown as follows: Kidd in 2000 proposed a novel development in knowledge brokering based on a ‘trusted agent’ located off-shore. In this agent-based model, the data warehouse concept is adopted as the basis of a search for prospects and an attempt to gain benefits from a JV. This study first raised issues concerning KM in the context of collaboration among JV. Madni, Lin, and Madni (2001) presented the IDEONTM extensible EO for enterprise design, management and control processes. This work first provides a reference model for the interoperability between new and legacy business applications to integrate and adapt business strategies and ongoing operations to external and internal environmental changes. Jolly (2002) conducted an investigation to identify decision and knowledge sharing problems in JVs. This study showed the importance of KM and trust for inter-firm collaboration. Moreover, Wong, Maher, and Luk (2002) outlined the development and attraction of the ‘‘JV’’ approach to foreign investment in China. Attempting to discover what strategic management knowledge was transferred from the Western partner. This study examines the issue of international KM for JV projects.
Table 1 Literature review of JVs research Strategy
Relationship management
Enterprises modeling
Performance measurement
Knowledge management
Kukalis and Jungemann (1995), Mills and Chen (1996), Naylor and Lewis (1997), Vanhonacker (1997), Maccoby (1997), Chen and Chen (2002), Rigby and Zook (2002), Yasuda (2005)
Shaughnessy (1995), Littler and Leverick (1995), Martinsons and Tseng (1995), Beamish and Inkpen (1995), Gifford (1998), Norwood and Mansfield (1999), Fey and Beamish (2000), Hobbs and Andersen (2001), Steier (2001), Meyerson (2001), Ghosn (2002), Buckley et al. (2002), Walker and Johnnes (2003), Bayona et al. (2006)
Mesak and Mayyasi (1995), Richter and Vettel (1995), Nakamura et al. (1996), Williams et al. (1998), Wang et al. (2004), Storey (2005)
Luo (1996), Park and Kim (1997), Pearce and Hatfield (2002), Beamish and Berdrow (2003), Swierczek and Dhakal (2004), Mohr and Puck (2005)
Abecker et al. (1998), Kidd (2000), Jolly (2002), Wong et al. (2002), Tsang (2002), Walker and Johannes (2003), Li et al. (2003), Gerwin and Ferris (2004), Chadam and Pastuszak (2005), Revilla et al. (2005), Wong (2005)
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Other research regarding the research JV topic, such as that of Li, Hsieh, and Sun (2003), has provided an ontology-based KM system for the metal industry to study knowledge sharing and development from the macro view in traditional industry. Li, Wu, and Yang (2004) presented an ontology-oriented approach that uses logical description to formally represent concepts and roles (relations) relating to the partner view of domain knowledge. This work extends this approach to the high technology industry to further study the application of KM. This study summarizes previous research and reviews KM from both the macro and micro perspectives by using the ontology-based approach to analyze complex knowledge sharing and development in the high technology industry.
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Design Time Enterprise A
Enterprise B
Domain Knowledge
Build Time
Infer
Domain Knowledge
Infer
Own
Domain Experts
Own
Execute
Enterprises Ontology
Execute
Domain Experts
Produce
Link Time
3. Framework of ontology-based KM
Joint Ventures Dictionary
Use
3.1. Key concept of the framework Ontologies are increasingly considered a key technology for enabling semantics-driven knowledge processing (Maedche, Motik, Stojanovic, Studer, and Volz, 2003). McKeown (1992) described Semantics-driven approaches as ‘‘using knowledge about the case frames of verbs to drive interpretation’’. Enterprise KM entails formally managing knowledge resources to facilitate access and reuse of knowledge, typically through the use of advanced information technology (O’Leary, 1998). The development of EO for JVs will typically require activities that involve knowledge sharing, knowledge negotiation, knowledge creation and the resolution of knowledge conflicts. This study develops an EO for a JV, a process for EO development and a dictionary for JV EO components. The development of an EO for a JV can be divided into four phases: Design, Building, Linking, and Running. During the design phase the two enterprises that are entering into a JV represent two, essentially independent, knowledge repositories. The two jointing enterprises have independent domain experts owns its domain knowledge. After triggering the JV project, both sides began to prepare and design the knowledge sharing and developing processes. Knowledge sharing and development start from the communication and collaboration processes during the Build phase. Two independent domain expert groups begin to create the new knowledge base via sharing and inferring their own domain knowledge. In the Link Time phase, the EO plays a key role in integrating the joint enterprises. Domain experts from different enterprises have to work out the EO together to produce the JVs dictionary for both enterprises in the following collaborative JV project. Finally, in the run time phase, the knowledge workers use the JVs dictionary to complete collaborative missions. The overall processes continues until the termination of the JV relationship. Fig. 1 shows the key conceptual framework use in this study.
Use
Run Time
Knowledge Workers
Collaborate
Knowledge Workers
Fig. 1. Key concept of framework.
3.2. Ontology-based KM This study provides a model of ontology-based KM for JVs. The ontology-based KM process model consists of seven important steps for reference by managers when implementing JV projects, including: Knowledge acquisition, knowledge identification, ontology analysis, ontology implementation, ontology verification, knowledge reposition, and knowledge sharing/development. Fig. 2 shows the model of ontology-based KM for JVs project. Each of the seven steps is described in detail below. Knowledge acquisition: Domain experts provide the domain knowledge for the JV which can involve the overall business processes and specific technological transfer. The knowledge acquisition process contains the joint domain knowledge from individual enterprises increase the complexity of KM. Poor knowledge acquisition procedure can lead to incomplete EO result. Knowledge identification: Domain experts identify what knowledge needs to be shared and what knowledge does not need to be shared in this step. This step prevent the leakage of knowledge and avoids arguments regarding knowledge sharing. Ontology analysis: This step analyzes the collected data and information. Based on the analysis, the ontology can be divided into information ontology and domain ontology (Abecker, Bernardi, Hinkelmann, Ku¨hn, and Sintek, 1998).
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1996). The verification of ontology for JVs projects involves more complicated processes and is further discussed in Section 3.3. Knowledge reposition: The new created ontologies from joint enterprises are stored in the public database for reposition. The public database provides a channel for accessing the new ontologies and supports subsequent knowledge sharing and development. Knowledge sharing/development: The joint venture dictionary is generated in this step. The joint venture dictionary consists of the new definitions and axioms for the JVs project. The enterprises entering into the JV share and develop the knowledge required for the JV through the joint ventures dictionary, which acts as the basis for improved collaboration.
Knowledge Acquisition
Knowledge Identification
Ontology Analysis
3.3. Verification of EO
Ontology Implementation
Ontology Verification
Knowledge Reposition
Knowledge Sharing/ Development
Fig. 2. Ontology-based KM process for JVs project.
After processing the EO for enterprises entering into a JV, ontology verification can ensure the correctness of the joint venture dictionary and thus enhance collaboration during JV evolution. Fig. 3 shows the EO verification process. First, the definitions and axioms of the domain knowledge are collected from the knowledge bases. The other definitions from the jointing ontologies can be imported to the collecting knowledge bases. The verification screens the two parts of definitions and axioms. The clear part is sent to the JV dictionary for knowledge workers. Meanwhile, the other parts are sent to the inference engine to distinguish the inferable axioms using the definitions and axioms in the knowledge bases. Some axioms may not be able to be inferred owing to incomplete content preventing the inference engine from distinguishing them. These axioms are stored in the other knowledge base for later manual analysis. 3.4. Domains of the enterprises entering into the JV EO
The information ontology is a meta model that describes knowledge objects and contains generic concepts and attributes of all information about knowledge objects. The domain ontology consists of the concepts, attributes, and instances of the enterprises involved in the JV. Domain ontology is designed to achieve semantic matching when searching for knowledge objects (Li et al., 2003). The ontology analysis can also include verifying that the party possessing some technology agrees to that technology being transferred to another party. Ontology implementation: The ontology implementation step executes the ontology process and ensures the completion of information and domain ontology after the ontology analysis. Ontology verification: Ontology verification includes verification of: (1) Each individual definition and axiom. (2) Collection of definitions and axioms that are stated explicitly in the definitions of the ontology. (3) Definitions that are imported from other ontologies. (4) Axioms that can be inferred using other definitions (Go´mez-Pe´rez,
The domains of EO in a JV may involve major business processes and related information. This study categorizes Axioms can be inferred using other Definitions and Axioms
Collecting Definitions & Axioms
Yes
Inference
Not Clear
Verification Check
No
Clear
Store
Joint Ventures Dictionary
Fig. 3. EO verification process.
Import Definition fromjointing Ontologies
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the major knowledge domains as the Technology domain, Finance domain, Marketing domain, Production domain, and Human resources domain. The Technology domain serves as an example demonstrating the JV EO development processes (Fig. 4). During JVs, the Technology 1 domain represents the Technology domain ontology in company A to JV. The Technology 1 domain may consist of the Product 1 domain, Process 1 domain, Equipment 1 domain, Information Technology 1 domain, and Quality 1 domain, which represent the domains of product, processes, equipments, information technology, and quality, respectively, in company A. The Technology 2 domain represents the technology domain ontology from company B. The Technology 2 domain consists of Product 2 domain, Process 2 domain, Equipment 2 domain, Information Technology 2 domain, and Quality 2 domain, which represent the domains of product, processes, equipment, information technology, and quality, respectively, from the company B. The Product 1 domain consists of four sub-product domains from company A, namely: Sub-Product 1A, Sub-Product 1B, Sub-Product 1C, and Sub-Product 1D (Fig. 5). Meanwhile, the Product 2 domain consists of four sub-product domains from company B: Sub-Product 2A, Sub-Product 2B, Sub-Product 2C, and Sub-Product 2D from company B. During the JV, the domains of EO are combined, resulting in the creation of new ontological domains, namely technology joint venture domain, product joint venture domain, Sub-Product joint venture domain, which represent the technology domain of joint ventures,
Product 1
Product 2
Process 1
Equipment 1
Process 2
Technology 2
Technology 1
Information Technology 1
Quality 1
Equipment 2
Information Technology 2
Finance 1
Joint Ventures Project
Finance 2
Marketing 1
Marketing 2
Production 1
Production 2
Human Resource 1
Human Resource 2
Fig. 4. Domains of EO in JVs project.
Quality 2
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product domain of joint ventures, and sub-product domain of joint ventures, respectively. 4. Example 4.1. Introduction to the IC foundry industry Fig. 6 shows how IC devices are mostly produced by Integrated Device Manufacturer (IDM) and Application Specific Integrated Circuit (ASIC) in the initial phase of the IC business (Tseng, 2002). The IDM and ASIC companies include functions of system/IC design, wafer manufacturing, assembly and testing. After the emergence of IC design companies (Fabless company), IC Foundries started to play a very important role in the business. Foundries manufacture ICs for design companies or other IDM and ASIC companies. Foundries have expanded their technical support for Intellectual Property (IP) design companies by integrating design services and wafer manufacturing. Each IC foundry company usually has several wafer fabrication foundries located in different areas, and even different countries. Wafer fabrication usually requires 100–500 processes over several weeks. Besides the time-consuming operations, complex manufacturing characteristics such as reentry process, lot splitting, batch operation, and maximum lot waiting time complicate capacity planning for wafer fabrication’’ (Chen, Chen, Lin, and Rau, 2005, p. 710). Features of the IC operation life cycle, independent of the JV itself, include Design, Engineering, and Logistics Collaboration, as shown in Fig. 7 (Tseng, 2002). Capacity expansion in IC industries is determined during design collaboration through interactions among the IP design selection, wafer layout design and wafer mask making process. Engineering collaboration begins with manufacturing evaluation and continues until logistic arrangement. Meanwhile, logistics collaboration begins with capacity booking and ordering process continuing through to the logistic management process. These collaborations may happen concurrently through different organization units and functions. 4.2. Trend of IC foundry JVs To remain competitive in the evolving semiconductor industry environment, some IDM/ASIC companies, system companies, and Fabless companies have begun to enter into joint ventures with IC Foundry companies. Table 2 shows a clear increase in JVs involving IC Foundry companies in the semiconductor industry over the past five years. The strategic purposes of these JVs include new technology sharing, capacity sharing, and gaining increased support for production. The above table demonstrates that most strategic JV projects require technological collaboration and knowledge sharing/development. Specifically, IC Foundry companies must integrate complex business processes, respond to the
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Human Resource 1
Production 1
Production 2
Marketing 1
Marketing 2
Quality 2
Joint Ventures Project
Quality 1 Finance 1
Finance 2
Information Technology 2
Information Technology 1 Technology 1 SubProduct 1.D
Equipment 1
SubProduct 1.C
Process 1 Product JV
SubProduct 1.B
SubProduct 1.A
Technology 2
Technology JV
Process JV
Equipment JV
Information Technology JV
Product 1
Quality JV
Equipment 2
SubProduct 2.D
Process 2
SubProduct 2.C
Product 2
SubProduct 2.B
SubProduct 2.A
Sub-Product JV
Fig. 5. Domains of jointing EO.
System Co.
IDM/ASIC
IP System Design IC Design
Fabless Co. System Design IC Design
Design Service Fab Foundry
Assemble Test
Contract Assemble Test
Fig. 6. IC foundry business.
challenge of collaborating with respect to different technologies, and engage in complex KM. Poor KM and ineffective communications can cause JV project failure or even result in lost business in the competitive semiconductor industry.
4.3. Case study of the IC foundry industry 4.3.1. Example of processing EO for IC foundry industry Considering the case of two IC Foundry companies establishing a strategic joint venture to share advanced technology, the jointing companies first perform technology transfer and sharing during the JV project. As discussed in the previous section, the ontology-based KM for the JV project in this study consists of six steps: knowledge acquisition, ontology analysis, ontology implementation, ontology verification, knowledge reposition, and knowledge sharing/development. In this case, the advanced technological product, process, and equipment in the IC industry are used as an example to describe the ontology processes for managing knowledge sharing and development in the JVs project. Fig. 8 shows the example of processing EO for the jointing IC Foundry companies. Knowledge acquisition may occur in one of the advanced technologies of the 0.13 lm process and the product type of Logic provided by the product engineers from company A. The product engineers of company B may also provide experience in running 0.13 lm process and product type of Logic during collaborative meetings
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IC Operation Life Cycle Points of View
Manufacturing Evaluation
Capacity Booking & Ordering
Design IP Selection
Layout
Mask Making
Manufacturing
Manufacturing Quality
Logistic Management
Design Collaboration
Engineering Collaboration
Logistic Collaboration
Fig. 7. IC operation life cycle resource from
Table 2 Major IC foundry JV events in past five years Year
Joint ventures (JVs)
Rationale
2005
Advanced Micro Devices Inc. (AMD) and United Microelectronics Corp. (UMC)
Establish a joint 300-mm wafer foundry venture in Singapore for high-volume production of PC processors and other logic products starting with 65-nm technology
2004
Fujitsu and Sumitomo Electric
Increase sales of a wide variety of compound semiconductor devices through development and manufacturing
2003
Jazz Semiconductor and Hua Hong NEC (HHNEC) Taiwan Semiconductor Mfg Co. Ltd (TSMC) and OmniVision Technology, Inc. Infineon Technologies AG and United Expitaxy Company (UEC)
Increases current Jazz capacity for digital, analog and RF CMOS processes as well as SiGe Bi-CMOS Provides integrated back-end manufacturing services for image sensors, including color filters Matches Infineon’s optoelectronic chip technology with UEC, its back-end expertise
2001
Micron Technology Inc. and Hynix Semiconductor Inc.
Micron Technology Inc. uses the Korean chip maker as a foundry for DRAMs
2000
Hitachi, Ltd. and United Microelectronics Corporation (UMC) Amkor Technology and Toshiba Corporation’s Semiconductor Company IBM China Company Limited (IBM China) and China Great Wall Shenzhen Co., Limited Chartered Semiconductor Manufacturing and Lucent Technologies’ Microelectronics Group Malaysia’s Mimos Bhd and Integrated Silicon Solution Inc (ISSI)
Achieves maximum production capacity for UMC 300 mm wafer facility
Philips, the Taiwan Semiconductor Manufacturing Company and the Economic Development Board of Singapore
Produces chips at sizes of 0.25 lm, 0.18 lm and smaller
1999
Collaborates with Iwate Toshiba’s assembly and test operations, will allow for manufacturing operations at two independent subcontract assembly houses Provides electronic manufacturing services for Nokia JV manufacturing operations Forged a $700 m R& D agreement for next-generation IC communications Establishes a fabless design and marketing organization for non-volatile memory products
and discussions. The equipment used in the process consists of the photo, thin film, etch, and diffusion for both companies. Furthermore, the related equipment engineers help to provide related equipment information. The ontology analysis process starts from analyzing the collected definition and related information of the 0.13 lm process, Logic product, which may include the design IP, the layout to produce masks, mask layers, manufacturing issues, quality requirement, and shipping procedures. The information of equipment may include the recipes of each machine, maintenance schedules, machine vendors, spare parts, machine dimensions, etc. Some information may be reserved for business certain issues and is also discussed in the process.
The ontology implementation process is executed after the analysis of ontology. Certain information ontology (such as process types, components of layout, mask information, manufacturing information, etc.) and domain ontology (IP number, dimension of each mask layer, wafer specification, wafer acceptance testing specifications, inspection specifications, etc.) are created for the JV project. The example of the jointing domain ontology process is detailed in Section 4.3.2. To ensure the correctness of the ontology, the ontology verification process is continuously executed after each of the ontology implementation process. To further understand the importance of the ontology verification process, the
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Product Engineer from A company
Knowledge
Knowledge
Acquisition
Identification
A COMPANY 0.13μMPROCESS 1.PATTERN SELECTION 2.EQUIPMENT REQUIREMENT 3.MASK PREPARATION
B COMPANY 0.13 μM PROCESS 1.PATTERN SELECTION 2.EQUIPMENT REQUIREMENT 3. MASK PREPARATION
Product Engineer from B company
Discussion Meeting
Ontology Verification
Comparing Definition of 0.13μM process
No
Verification
No
Ontology
Term Usable ?
No
Term Termination
Analysis
Knowledge Reposition
Knowledge Sharing/
Yes
OK
Store Information
Assign New Term
Ontology
Yes
Implementation
Documentation & Announcement
Development Joint Ventures Dictionary
Process Engineer of A Company
Process Engineer of B Company
Fig. 8. Example of processing EO for jointing IC foundry companies.
example of ontology verification will be discussed in detail in Section 4.3.3. The knowledge reposition process starts after the completion of ontology processing. The identified terms are stored in the database for further retrieval. The knowledge sharing/development process provides information and knowledge for the other related knowledge workers to access. The joint venture dictionary is created in this process. Managers and knowledge workers, such as project managers, process engineers, manufacturing supervisors and quality engineers, can use the JV dictionary to efficiently retrieve required information and common definitions for communication and collaboration with partners. 4.3.2. Example of domains of jointing EO In the domains of joined EOs (Fig. 9), the process domains of company A are 0.13 lm, 0.15 lm, 0.18 lm,
and 0.22 lm. Meanwhile, the process domains of company B are 90 nm, 0.13 lm, 0.25 lm, and 0.35 lm. Moreover, the ontological domains merged in the JV are the new ontology for the 0.13 lm process is created after processing the JV EO. Company A can also receive technological support for 90 nm from company B to increase competition in advanced technology. The new ontological domain for the 90 nm processes is fully transferred from company B during the JV. The product domains of company A consist of OR comprise logic, DRAM, and high voltage. The product domains of company B consist of logic, NVM, SiGe, and color filter. The new ontology for the logic product is created after processing the EO for the JV. The equipment domains of companies A and B consist of photo, thin film, etch, and diffusion. The new ontologies for photo, thin film, etch, and diffusion are created after processing the EO for the JV.
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Diffusion B
Diffusion A
Etch B
Etch A
Photo JV
Thin Film JV
Etch JV
Diffusion JV
Thin Film B
Equipment B
Thin Film A
Photo B
Equipment A
Color Filter B
Photo A Equipment JV
SiGe B
High Voltage A
NVM B
DRAM A
Joint Ventures Project
Product A
Logic A
Technology A
0.18 μm A
0.13 μm A
Logic B
Technology B Technology JV
0.22 μm A
0.15 μm A
Product B
Process JV
0.35 μm B
0.25 μm B
Product JV
Process A
Process B
Logic JV
0.13 μm B
0.13 μm JV 90 nm B
90nm JV
Fig. 9. Example of domains of jointing EO.
4.3.3. Example of EO verification Fig. 10 shows an example of verification of EO for 0.13 lm process. First, the definitions and axioms of 0.13 lm process are collected from company A. Then, the definitions of 0.13 lm process from company B are imported to the ontology of company A. In the product domain of 0.13 lm, in both company A and B, there may be 1.0 V, 1.2 V and 1.5 V core options, and I/Os of 2.5 V and 3.3 V. the 1.0 V, 1.2 V and 1.5 V are verified and sent
to the JV dictionary for reference. The domain ontology of 6T SRAM cell size (2.43 lm2) for various system-onchip (SOC) applications in the networking, computing and consumer market segments cannot be verified. After the inference procedure, the definition of SOC is found a new term for the JV project. Moreover, the new term is not involved in the present project. The definition of SOC is first stored in the other database for reasons of security OR owing to security concerns.
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Axioms can be inferred using other Definitions and Axioms
Collecting Definitions & Axiomsof 0.13 μm process
Import Definition from jointing Ontologies of 0.13 μm process
Yes
Inference (SOC)
No
Store -system-on-chip (SOC)
Not Clear
Verification Check (1.0V,1.2V, 1.5 V,SOC)
Clear Joint Ventures Dictionary - 1.0 V -1.2 V -1.5 V
Fig. 10. Example of verification of EO.
In the IC foundry industry, the collaboration of two jointing companies may involve only partial technology transfer but not full support for some strategic security issues. The verification of EO for the JV project has to protect the intellectual property of the companies. On the other hand, EO verification also helps the collaboration for both companies to run the project smoothly. Therefore, poor verification process can cause serious problems and impact JV project success. 5. Conclusion This study provides a reference framework for managers during knowledge sharing and development in a JV project. This work adopts an ontological approach to analyze KM processing in JVs. The IC Foundry industry is used as an example to study the practicality of the proposed approach. The characteristics of complex business processes and high technological issues in the IC Foundry industry increase the challenge of this research. This study aimed to provide managers with assistance to increase their changes of successfully dealing with complicated JV projects. The inaccuracy and discrepancy of the original information provided by the domain experts represents an important limitation of this study. The information of specific technology domains can provide a reference for improving information on this area. This study focuses on the KM of the technology domain. Other domains, like finance, marketing, production, and human resources, would also make interesting topics for future research. Meanwhile, future studies can examine other forms of real world strategic alliances, such as mergers or acquisitions. After we identified the process of knowledge sharing and development for the collaborative companies, knowledge access and cultural differences represent another two key issues of concern. Some previous studies concern access to knowledge from some different perspectives, including: access control (Bertino, 1998; Ray, 2004), modeling knowl-
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