Building the knowledge map: an industrial case study

Building the knowledge map: an industrial case study Suyeon Kim, Euiho Suh and Hyunseok Hwang

Abstract Recently, research interest in knowledge management has grown rapidly. Much research on knowledge management is conducted in academic and industrial communities. Utilizing knowledge accumulated in an organization can be a strategic weapon to acquire a competitive advantage. Capturing and representing knowledge is critical in knowledge management. This paper proposes a practical methodology to capture and represent organizational knowledge. The methodology uses a knowledge map as a tool to represent knowledge. We explore several techniques of knowledge representation and suggest a roadmap with concrete procedures to build the knowledge map. A case study in a manufacturing company is provided.

Suyeon Kim is a Researcher in Management Information Systems at the Pohang University of Science and Technology, Korea ([email protected]). Euiho Suh is a Professor and the Director of the Management Information Systems Laboratory at the Pohang University of Science and Technology, Korea ([email protected]). Hyunseok Hwang is a doctoral candidate in Management Information Systems at the Pohang University of Science and Technology, Korea ([email protected]).

Keywords Knowledge management, Knowledge mapping, Knowledge processes

Introduction Knowledge management is an emerging eld that has commanded attention and support from the industrial community. Many organizations currently engage in knowledge management in order to leverage knowledge both within their organization and externally to their shareholders and customers (Rubenstein-Montano et al., 2001). Companies regard intellectual capital as important asset and strive to deploy knowledge management in an organization to gain a competitive edge. Capturing and representing knowledge buried in people and an organization are the fundamental building blocks of knowledge management implementation. Walczak (1998) notes that a signicant and time consuming problem for knowledge-based system developers is how to efciently elicit knowledge from experts and transform this elicited knowledge into a machine usable format. A variety of researches on knowledge representation have been conducted. This paper proposes a practical methodology for capturing and representing organizational knowledge. The methodology is composed of a six-step procedure, from dening an organizational knowledge step to a knowledge map validation step. Practical guidelines and tips are provided in each step. We use a knowledge map as a tool for representing knowledge in these procedures. We rst explore several techniques of knowledge representation and suggest a roadmap for building the

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VOL. 7 NO. 2 2003, pp. 34-45, ã MCB UP Limited, ISSN 1367-3270

DOI 10.1108/13673270310477270

and representing knowledge buried in ‘‘ Capturing people and an organization are the fundamental building blocks of knowledge management implementation.

’’

knowledge map, providing detailed procedures. A case study of a manufacturing company is provided to verify our proposed methodology. Finally, we discuss lessons learned from an industrial case study.

Related works Several techniques of knowledge representation have been devised for knowledgebased applications. These techniques can provide a bridge between human knowledge and machine knowledge. Some of these representational methods are discussed here. Frame Frame is a powerful knowledge representation system that is accessible to both humans and machines. A frame is a collection of information and associated actions that represents a simple concept (Gordon, 2000). Petri net Petri net can depict important knowledge about the structure and dynamic behavior of the system. Rudas and Horvath (1997) used Petri net for process model entities and their evaluation knowledge. Petri net representations of generic manufacturing process entities contain information on all available process variants, and knowledge on their evaluation procedures. The main elements of Petri net models are transitions, for information on the process and its components, and places, for information on evaluation knowledge and status. Semantic network Semantic network is a powerful knowledge representation system, which is easy to understand by human and can be used in automated processing systems. This means that they can also become a vehicle to archive company knowledge (Gordon, 2000). Each node is specic knowledge and links show the interrelationship between knowledge in semantic network. Concept mapping Concept mapping is a type of structured conceptualization used by groups to develop a conceptual framework which can guide evaluation or planning (Trochim, 1989). Trochim considers concept mapping as a structured process, focused on a topic or construct of interest, involving input from one or more participants, that produces a pictorial view of their ideas and concepts and how these are interrelated. Concept mapping can be embodied by a node-link structure (a concept map), in which nodes denote concepts, and links show the relationships between these concepts. Knowledge mapping Speel et al. (1999) dene knowledge mapping as the process, methods and tools for analyzing knowledge areas in order to discover features or meaning and to visualize these in a comprehensive, transparent form, such that the business-relevant features

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are clearly highlighted. Knowledge maps are created by transferring certain aspects of knowledge into a graphical form that is easily understandable. According to Grey (1999), a knowledge map is a navigation aid to explicit and tacit knowledge, illustrating how knowledge ows throughout an organization. The knowledge map portrays the sources, ows, constraints and terminations of knowledge within an organization. Knowledge mapping helps to understand the relationships between knowledge stores and dynamics. Davenport and Prusak (1998) note that developing a knowledge map involves locating important knowledge in the organization and then publishing some sort of list or picture that shows where to nd it. Knowledge maps typically point to people as well as to document and databases.

A conceptual framework We employ a knowledge map approach to represent explicit and tacit knowledge within an organization. We dene a knowledge map as a diagrammatic representation of corporate knowledge, having nodes as knowledge and links as the relationships between knowledge, and knowledge specication or prole. Figure 1 depicts a conceptual model of knowledge map. As shown in Figure 1, knowledge map consists of two components: (1) Diagram: Graphical representation of knowledge, having node and linkage: Node: Rectangular object denoting Knowledge captured from business process; Linkage: Arrow between nodes implying relationships among knowledge; and (2) Speci cation: Descriptive representation of knowledge. Knowledge map provides a knowledge worker with a robust cornerstone to capture, share, and use organizational knowledge. Advantages gaining from building the knowledge map can be summarized as follows: formalization of all knowledge inventories within an organization; perception of relationships between knowledge; efcient navigation of knowledge inventory; and

Figure 1 A conceptual model of knowledge map

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promotion of socialization/externalization of knowledge by connecting domain experts with knowledge explorers. The knowledge map plays important roles in implementing knowledge management. All captured knowledge can be summarized and abstracted through the knowledge map. The knowledge map also gives a useful blueprint for implementing a knowledge management system (KMS). Figure 2 shows a knowledge management framework based on the knowledge map. Corporate culture and leadership form the foundation for successful knowledge management implementation. Infrastructure such as IT and the Internet is also indispensable. Organizational knowledge is inherent in diverse locations, including written documents, corporate databases, the human brain, and corporate memory. Explicit knowledge is chiey extracted from documents or databases and tacit knowledge from human resource while executing business processes. Explicit and tacit knowledge can be transformed into objects in the knowledge map. Knowledge map not only represents knowledge and knowledge streams within an organization, but also provides a well-organized basis for KMS. The knowledge map plays a key role in the KM project because it gives a knowledge prole (knowledge warehouse), knowledge link (navigation aids among knowledge), and expert nder.

Procedures of building the knowledge map According to the knowledge management framework, we proposed procedures for building the knowledge map. Figure 3 shows procedures of building the knowledge map. The procedure consists of six steps: dening organizational knowledge, process map analysis, knowledge extraction, knowledge proling, knowledge linking and knowledge map validation.

Figure 2 A knowledge management framework

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Figure 3 Procedures of building the knowledge map

Dening organizational knowledge This step starts with dening knowledge ontology. Ontology is a formal specication of the vocabulary to be used in specifying knowledge. The purpose of the ontology is to provide a uniform, text-based intermediate representation of the knowledge types specic to a development effort that is comprehensible by either humans or machines. The intermediate representation provides a means of describing knowledge, at any level of granularity, without expert knowledge of the specic technologies that will be used to implement that knowledge (Kenyon, 1998). This step covers dening knowledge and baseline taxonomy within an organization. The scope and level of detail of the knowledge map are also determined by using questionnaires and interview techniques. The scope of the knowledge map decides whether the knowledge map is constructed throughout an entire company or a specic organization. After deciding the scope, we determine the detail level (level of granularity) of knowledge analysis. When deciding the level of granularity, we should take into account the trade-off of granularity. Highly decomposed knowledge may impede rapid knowledge retrieval and knowledge map construction due to knowledge-glut. Therefore, it is important to determine the proper level of detail to meet organizational knowledge demand. When analyzing the source of knowledge within an organization, we can use operation manuals, meeting minutes, external data, project deliverables, and customer contact records. Process map analysis In this study we extract organizational knowledge based on the business process. We nd experience and know-how acquired during business execution. By capturing and managing knowledge involved in business processes, we can nd solutions to problems easily.

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is important to determine the proper level of ‘‘ Itdetail to meet organizational knowledge demand. ’’ Business process is analyzed using a process map technique. Process is a systematic series of actions directed to some end (customer or next process). Process is triggered when a certain event happens. A process map is composed of process, ow (dependency), event, and external object. A horizontal axis in a process map denotes a related organization and processes are laid out vertically by an execution order. The relationship between preand post-process is represented by an arrow. External object and event are also drawn, if any. Figure 4 shows a simple process map of issuing a membership card. When many organizations are involved in a process, we should keep the process consistent over related organizations. Knowledge extraction In this step, knowledge is extracted through a process map. The extracted knowledge is of three types: prerequisite knowledge before process execution, used knowledge during execution, and produced knowledge after execution. Knowledge irrelevant to any process may exist. It can be general knowledge or external knowledge. After identifying knowledge through the process map, we extract knowledge independent of the business process. The following techniques are available in knowledge extraction: Interviewing (structured, unstructured, semi-structured): to extract knowledge from domain experts using a prepared questionnaire. Document analysis: to extract knowledge from documents, such as operation manual, organization chart, training material and external documents. System analysis: to extract knowledge based on information stored in database, including system log and le structure.

Figure 4 An example of a process map

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Knowledge workshop (KW): to capture and analyze knowledge in a specied knowledge area. KW is highly interactive. Components of KW are general presentation about KM project, ontology denition, building knowledge consensus among participants, and knowledge extraction session. We can use another techniques such as traditional brainstorming, nominal group techniques, focus groups, qualitative text analysis, and task environment analysis to extract knowledge. Knowledge proling We produce a knowledge prole of extracted knowledge. We describe knowledge with pre-dened items (attributes) and derive relationships with process. Junnarkar (1997) notes that knowledge management has two aspects: connecting people with information and connecting people with people. Knowledge proling can support these two aspects by providing informational attributes, such as keywords, description, importance, and people-nder attributes such as an expert or author. Sample attributes of knowledge prole are listed in Table I. Each attribute may be mandatory, optional, or system-generated. Knowledge linking We identify the knowledge link after completing the knowledge prole. The knowledge link is rst indicated when producing knowledge prole, and is later conrmed. We identify new links and examine and conrm existing links. Knowledge link is represented as an arrow in a knowledge map. The knowledge map shows a navigation path of knowledge. The knowledge map is a type of directed graph and consists of nodes and links, each node denoting knowledge item and link denoting pre- and post-relationship between knowledge. Figure 5 shows an example of a knowledge link. Knowledge map validation User validation is performed on knowledge map. A structured walkthrough is conducted with domain experts, business managers, and knowledge map producer. The following are review checkpoints:

Table I Sample attributes of knowledge prole

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Category

Item

General

Knowledge ID Title Type (tacit/explicit) Creating date Last modied Expired date

Storage

Format Location

Ownership

Author Organization Access right

Contents

Keyword Description

Evaluation

Importance Rating

Link

Prerequisite Expert

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Mandatory

Optional

System

Figure 5 An example of a knowledge link

Are all knowledge extracted? (Unidentied knowledge should be found) Is any knowledge redundant over organizations? Are proles and links fully described for all knowledge? Are the knowledge prole and knowledge map consistent? When completing knowledge mapping procedures we can acquire the following deliverables (Speel and Shadbolt, 1999): Knowledge mapping deliverables: a standardized terminology, new knowledge creation, knowledge maps, knowledge gaps. Knowledge dissemination deliverables: nal report, an electronic system containing the captured knowledge. People-oriented deliverables: a network of experts.

Case study: P steel company Overview The P steel company is the most competitive company in the steel industry. Annual sales and net prot amounts to more than ten billion dollars and one billion dollars, respectively. As the steel industry requires various kinds of technologies and knowhow, we select a steel company to apply the suggested methodology. P steel company accumulates its own knowledge of fabricating crude steel for 30 years. A knowledge map of hot rolling process, core part of steel production process, is constructed. The procedure to build the knowledge map follows a six-step procedure. Dening organizational knowledge We rst dene knowledge which occurs in a hot rolling mill. The rolling mill reduces a hot slab into a coil of specied thickness; the whole processing procedure occurs at a relatively high temperature. A knowledge workshop on ontology was held to specify the knowledge requirements, analyze input sources, and develop basic taxonomy. We prescribed segment knowledge under ve categories: mechanical, electrical, instrumental, information system, and control.

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Process map analysis We then produced process maps based on task ow. Figure 6 shows a series of procedures for the raw material to be transformed from the input yard through several processes to the next step or customer. Knowledge extraction Knowledge is extracted based on each process dened in a process map. Knowledge is organized according to the operation manual, and domain knowledge of onsite technician. Knowledge is extracted using interview and questionnaires with domain experts having 20 years eld experience. Most experts are required to describe their own knowledge at least ve items. They also describe characteristics of knowledge and evaluate the knowledge. An example of knowledge extraction in roughing mill process is shown in Figure 7. Knowledge proling We matched the knowledge with process map based on interview results and reviewed the knowledge list with domain experts. After rening unidentied and duplicated knowledge, we conrm the nal knowledge prole. The knowledge prole is composed of several attributes, such as title, creating date, author, expert, location, and a brief description. Figure 8 is an example of a knowledge prole. Knowledge linking A knowledge link is built for automatic input and roughing mill process, typical processes of a hot rolling mill. It is very helpful in identifying knowledge ow and association. An example of a knowledge link is shown in Figure 9. Knowledge map validation After completing the production of the knowledge map, a structured walkthrough is conducted with domain experts to validate the knowledge map.

Conclusion Management of intellectual assets can improve business performance by extracting, sharing, and reusing experience and know-how. Knowledge representation methods are crucial to manage knowledge inventory in an organization. They should be easy to

Figure 6 Process map of hot rolling mill

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Figure 7 An example of knowledge extraction

Figure 8 An example of a knowledge prole

understand by humans and used in automated processing systems. We employ a knowledge map approach to represent organizational knowledge. In this paper we dene knowledge ontology to represent organizational knowledge, and analyze business process using a process map technique. After extracting knowledge based on the business process, a detail prole of each knowledge is produced. Finally, we construct a knowledge map representing a knowledge link by investigating the pre- and post-relationship.

knowledge is more dif cult than ‘‘ Maintaining creating knowledge; promotion of knowledge sharing culture is indispensable. ’’

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Figure 9 An example of a knowledge link

The suggested methodology is applied to a manufacturing company which, by nature, possesses much technology, many skills and specialized knowledge. Domain experts describe and evaluate their own knowledge, and nally conrm the knowledge map. The knowledge map helps to replace knowledge lost by ‘‘brain drain’’ and to transfer knowledge from experts to novices. Some lessons were learned from the P steel company: maintaining knowledge is more difcult than creating knowledge, promotion of knowledge sharing culture is indispensable, top management support on knowledge management project is inevitable, reward system is clearly declared to enhance knowledge sharing, knowledge management system should satisfy knowledge requirement.

Acknowledgement The authors would like to thank the Ministry of Education of Korea for its nancial support toward the Electrical and Computer Engineering Division at POSTECH through its BK21 program.

References Davenport, T.H. and Prusak, L. (1998), Working Knowledge, Harvard Business School Press. Gordon, J.L. (2000), ‘‘Creating knowledge maps by exploiting dependent relationships’’, Knowledge-Based Systems, Vol. 13, pp. 71-9.

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Grey, D. (1999), ‘‘Knowledge mapping: a practical overview’’, SWS Journal, available at: http://smithweaversmith.com/knowledg2.htm, March. Junnarkar, B. (1997), ‘‘Leveraging collective intellect by building organizational capabilities’’, Expert Systems with Applications, Vol. 13 No. 1, pp. 29-40. Kenyon, J.D. (1998), ‘‘A process for knowledge acquisition and management’’, 11th Workshop on Knowledge Acquisition Modeling and Management, Alberta, Canada, April. Rubenstein-Montano, B. et al. (2001), ‘‘A systems thinking framework for knowledge management’’, Decision Support Systems, Vol. 31, pp. 5-16. Rudas, I.J. and Horvath, L. (1997), ‘‘Modeling of manufacturing processes using a Petri net representation’’, Engineering Applications of Articial Intelligence, Vol. 10 No. 3, pp. 243-55. Speel, P.-H., Shadbolt, N., de Vries, W. Van Dam, P.H. and O’Hara, K. (1999), ‘‘Knowledge mapping for industrial purposes’’, 12th Workshop on Knowledge Acquisition Modeling and Management, Alberta, Canada, October. Trochim, W.M. (1989), ‘‘An introduction to concept mapping for planning and evaluation’’, Evaluation and Program Planning, Vol. 12 No. 1, pp. 1-16. Walczak, S. (1998), ‘‘Knowledge acquisition and knowledge representation with class: the object-oriented paradigm’’, Expert Systems with Applications, Vol. 15, pp. 235-44.

Further reading Gruber, T. (1991), ‘‘The role of common ontology in achieving sharable, reusable knowledge bases’’, in Allen, J.A., Fikes, R. and Sandewall, F. (Eds), Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, Morgan Kaufmann, San Mateo, CA.

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