Building A Knowledge Model: A
Decision-Making Approach
Sung-kwan Kim, University of
Arkansas-Little Rock, Sengbae Lim, State University of New York-Geneseo,
& Robert B. Mitchell, University
of Arkansas-Little Rock
ABSTRACT:
Knowledge is a key word in the information age. Organizational knowledge provides businesses with a way to compete effectively and efficiently in the market. The performance of many organizations is determined more by their knowledge than their physical assets. Thus knowledge management (KM) is a critical concern of the businesses. This paper presents a method for building the knowledge requirements. Knowledge requirements are the architecture for the effective KM systems. The method is decision making oriented. First, the rationale of the method is introduced. Second, the notations, grammars and processes of the method are presented. Third, then the method is applied to building a knowledge model for a shipping company.
1. Introduction
In today’s economy knowledge has become a key word. With fierce global competition, business organizations are looking for new ways to compete effectively. Organizational knowledge provides the capability to understand the market, assess the customer's needs, and translate them into products and services by combining organizational resources. Organizational knowledge provides opportunities to cut costs, reduce time to market, and increase revenue opportunities (Quintas, Lefere, & Jones, 1997); it guides business action. Thus the successes and failures of many organizations depend more on knowledge power than physical assets. Organizational knowledge is the ultimate source of competitive advantages (Nonaka & Takeuchi, 1995; Teece, 1998); it lies at the center of modern business operations. A major challenge facing business organization is to make effective use of the knowledge stored in their diverse knowledge sources.
Knowledge management (KM) is the systematic way of managing this precious resource. KM promotes an integrated approach to identifying, capturing, structuring, organizing, retrieving, sharing, and evaluating an enterprise’s knowledge assets. Knowledge management systems (KMS) offer an environment for organizations to manage knowledge. Therefore, building an efficient and effective KMS is a critical concern. The problem, however, is how and where to begin. Most KMS designers focus on the process rather than the knowledge that needs to be managed (Cooks, 2000). Organizations are assuming that they understand what they need to know, though often it is not the case. Organizations must analyze knowledge needs before designing any KMS. This paper presents the rationale for knowledge modeling as a foundation for successful KMS projects and how the task of knowledge modeling can be accomplished. A method is proposed for building an effective knowledge model which can help businesses analyze and specify knowledge requirements.
2. Need
For A Knowledge Model
Currently heavy emphasis is placed on technology for managing knowledge. When a firm builds KMS, it frequently begins with technology. For example, according to Ruggles (1998) the four most popular KM projects are related to IT (i.e., Intranet, data warehouse and knowledge repository, decision support tool, and groupware). Many studies report that technology is one of the critical factors for successful KM projects (Junnarkar and Brown, 1997; Syed, 1998, Skyrme, 1999; Bahatt, 2001, Kankanhalli, 2003). “However, this technology-oriented mind narrows our perspective . . . No matter how superior it is, technology is meaningless unless it provides what we need to run businesses. The main business objective is not to administrate technology . . .” (Cooks, 2000).
Technology is a tactical tool for KM. It is much less important than the clear understanding of the knowledge contents needed. Without this understanding businesses cannot maintain a framework to quickly create and update knowledge. Cooks (2000) cautions business against losing control of knowledge contents by focusing on capabilities of new technologies. The essence of KM is to provide a mechanism whereby knowledge contents are created, shared, and utilized in an efficient and effective manner. The success of a KMS is dependent upon its contents -- contents to answer users’ questions, help solve their problems, or expedite their decision making processes.
A clear definition of business requirements is critical in designing a KMS. But as Gemino (2003) stated, “it is not apparent how that task is best accomplished.” A tool—business model—with which to analyze knowledge requirements is needed. The knowledge model will help specify knowledge contents and show their flows into the business processes.
A model is a simplified view of a complex reality; it is a means of creating abstraction. It enables one to better understand the domain reality (Ericksson and Penker, 2002). This knowledge model will provide the basis for business control over requirements by identifying and describing knowledge contents and their flows around the business processes. It provides a holistic and integrated view of organizational knowledge contents.
A knowledge model is an overall picture of a company’s knowledge
architecture. It provides what is
needed, not what is currently available, and it sets the environment for
formulating strategy for obtaining knowledge not currently available. The model will communicate what needs
to be accomplished to the project stakeholders at the conceptual level. Since
it is a high level model, it is stable and doesn’t change frequently since it
is built around business processes and the requisite knowledge.
3. Building
A Knowledge Model: A Knowledge
Modeling Method
A modeler requires a good method for developing and maintaining a successful knowledge model. “The method should encourage and promote a fresh examination of business. The method therefore must begin at a very fundamental level, providing the opportunity and encouragements to rethink the organization’s basic concepts” (Curtice, 1987). The method introduced in this article is decision-making oriented.
3.1. Model
Based On Decision-Making Processes
Decision making is one of the fundamental processes for any business. A company has to make many decisions quickly and continuously. Organizations are filled with decision making at various levels: How do we acquire and retain customers? How much capacity do we add to the current production capability? How do we respond to the competitor’s move? What new services do we need to offer? How do we disinvest or invest? The list is endless. All managerial activities revolve around decision making. For managers to make decision, they need knowledge. An individual’s problem solving and decision making capability is limited by the knowledge available. Having knowledge available to decision makers is crucial to improving individual and organizational performance. Therefore, the decision-making oriented approach is a valid way of identifying knowledge requirements.
3.2. Model
Validated As Rigorous
A good model will represent the domain with accuracy and completeness; it
should be validated as rigorous (Shanks, Tansley, and Weber, 2003). How rigorous is our method? One of the ways of validating any model
is to test how well the model conforms to a meta model. A meta model is a model with a set of
generic standards and modeling constructs. It is a definition and specification of a model; it is
independent of the domain.
According to Henderson-Seller (2003), one of the popular meta models at
the conceptual level is the OPF (Open
Process Framework) Meta Model. The meta model specifies five
elements of a conceptual model:
work product, producers, work unit, language, and stages. The method presented in this paper is
analyzed for conformity to the meta model.
3.3. Model Components Identified
According to the OPF Meta Model, the first element of a conceptual model is a work product. “A work product is anything of value produced during the development process. Work products are the results of producers executing work units and are used either as input to other work units or delivered to clients” (Henderson-Seller, 2003). Our method contains two work products. One is graphical; the other is textual. The graphical model is a Knowledge Component Diagram (KCD); the textual model is a Knowledge Catalog (KCG). KCD is the graphical representation that shows locations, flows, and relationships between decision points and knowledge components. KCG is a detailed description of KC. In KCG, the KC is decamped and described in detail. These products will be presented with more explanations in depth in the following section of the paper.
The most important element of a conceptual model is the modeling language. Any conceptual model should provide a
modeling language. The language is
used to document work products and should contain a set of basic constructs
expressing what should be modeled and a grammar. A set of constructs are typically graphical representations. A grammar is a set of rules for creating representation of
domains. A grammar should guide a
modeler in combining the constructs into meaningful statements about the
domain. Grammars and constructs
are used in creating a model that is a representation of a domain, and
obtaining information about domain by reading the model written with the
grammar (Gemino, 2003).
We use four constructs in the method. The first one is a decision point (DP). A DP is a node of a business process where a key decision is made. The DP is represented by an oval circle. The second notation is a rectangle. It represents the knowledge component (KC). At every DP people will need a collection of related knowledge to make the decision. The KC represents the group of knowledge required to make decisions at the specific DP. The third notation is a line with an arrow. The solid arrow represents a decision making process that continues as a result of the previous decision making and moves to a new DP. The dotted line with an arrow represents a decision making process that has been terminated as a result of decision making at the previous DP. Table 1 summarizes the constructs and graphical representations of the constructs.
|
Notation |
Title |
Meaning |
||||
|
|
Decisions making point |
It is a point where the key
decision should be made. |
||||
|
|
Knowledge component |
It describes the knowledge
required to make decisions at each decision making points. |
||||
|
|
Decision making process
flow |
It indicates that the
decision making process moves to a next decision making point |
||||
|
|
Decision making process
flow terminated |
It indicates that the
decision making process has been terminated as a result of previous decision
making. |
Table 1:
Constructs and Notations
The other elements of conceptual model are producers, work units, and stages. “A producer is responsible for creating, evaluating, iterating and maintaining work products” (Henderson-Seller, 2003). In the proposed method a producer is the knowledge modeler. Modelers must have a high knowledge of the business or access to the people with such knowledge. “A work unit is a functionally cohesive operation performed by a producer” (Henderson-Seller, 2003). It is the activity, task or technique performed by the modeler. “A stage is an identified and managed duration within a point in time at which some achievement is recognized” (Henderson-Seller, 2003). In our method each stage defines the work unit that should be performed during each stage. Stages and work units are explained in detail with examples in the next section of the paper.
4. Application
to Model: Vessel Purchase
Decision-Making Process In A Shipping Company
The proposed method will be described using an example in a shipping company. The method is named Decision making Oriented Knowledge Modeling Method (DOKM). DOKM consists of four stages: initiation, analysis, documentation, and evaluation. We will identify what work units are to be performed and how they are performed at each stage. As an example, a vessel purchase decision-making process of a maritime shipping company will be used. The company (SYS) is located in Seoul, Korea. It is one of the subsidiaries of a Korean conglomerate business group. SYS is mainly engaged in domestic and near sea shipping (i.e., Japan and China). SYS is one of the largest domestic cement transportation carriers in Korea; the main cargo of the company is bulk cement. The company was established to transport bulk cements produced by its sister company (SYC). This example is not designed to accurately and completely represent the ship purchase decision-making process. Rather, it illustrates how the notations and procedures suggested can be applied to actual business processes.
4.1. Stage
1: Initiation
In the first stage a business process for which we want to build a knowledge model is identified. The method can be applied to a single business process, to multiple processes, or even to the entire firm. A business process refers to the manner in which work is organized and coordinated to produce a product or service. It is a concrete workflow of materials, information, and knowledge. Identifying a major business process defines a conceptual framework for which we build knowledge architecture. It also defines the scope of the project. The knowledge flows will be formed around the process.
As in other shipping companies, there are several major business processes in SYS. The key business processes include purchasing vessels, scheduling vessels, making long-term cargo contracts, and managing crews. One of the most important business activities is the vessel purchase decision-making process, which is essentially fleet expansion. This decision is critical for several reasons. A vessel in the shipping industry is a production facility; it is like a plant in other manufacturing companies. It requires a huge upfront investment. For a specialized cement carrier, i.e., size of 10,000 ~ 20,000 DWT: (Dead Weight Tonnage), the purchase may cost several millions dollar depending on the types of loading and discharging equipment needed and the shipbuilding market. Since the vessels must run for up to 20 years or even longer, a mistake in this decision impacts a shipping company for the long run. Since the ship purchase decision is one of the most critical decisions for any shipping company, very sophisticated knowledge is required for the decision. Thus the decision task force must thoroughly understand the business process: objectives, procedures, significance, etc.
4.2. Stage
2: Analysis
4.2.2. Task 1: Identify Key Decision Points
The first task in stage 2 is to analyze key decision points (DP). After choosing a business process, the modeler reviews and analyzes it to identify key DP of the process. A business process will consist of a series of DP, points where the key decisions should be made. For example, the vessel purchase decision-making process consists of several critical DP. The fundamental question is to decide whether or not to add a vessel. For example, the company is experiencing a shortage of tonnage; sometimes it does not meet the peak time demand. Is the situation temporary or permanent? If it is temporary, tonnage should not be added. Adding tonnage to meet temporary shortage may result in tonnage surplus for the following years. If the decision is not to add a vessel, the decision-making process is terminated. However, if the decision is to add vessels, then the next decision point is to ask how much tonnage to add. Next the decision maker must decide whether to build or charter. If the decision is to build the new vessel, then a decision must be made on the proper shipbuilder, the next decision point. If the decision is to charter, then ship brokers who have information and knowledge on used ships in the market must be sought. The process is like a decision made when obtaining a car: buy or rent.
4.2.3. Task 2:
Identify Knowledge Components
The second task in Stage 2 is to analyze key knowledge components (KC) for each DP. A KC is a collection of related knowledge necessary to make a decision. The KC for key decision making at each DP must be identified. It is a description of a company’s knowledge requirements for the business process chosen. The choice of KC is a major factor that influences knowledge architecture. The challenge is to clearly define KC granularity. If the components are defined too narrowly, too many KC will appear on the diagram. The diagram will be too complex to communicate and be understood. On the other hand, too broadly defined KC are difficult to implement and are often more confusing even though they are flexible.
In the example process, the first DP is to decide whether or not to add a vessel. To make such decisions, the purchasing manager will need various types of knowledge. For example, the manager will need to know demand-side knowledge (i.e., cement consumption rate, housing and construction market, sister company’s cement production capability, industry cement production capability, cement export levels to and import levels from Japan and China). Also needed are tonnage supply side-information (cement carrier tonnage in the industry, tonnage worldwide, etc.). Knowledge on cement demand and knowledge on tonnage supply are two examples of KC.
Another DP is to determine the type and size of vessel (i.e., 6,000 DWT, 10,000 DWT, or even bigger vessel) to add. To make such decision, the business will need to know about ports where the new vessel is supposed to visit and specific characteristics of each port: silo size, loader and discharger capabilities, berth length, water depth, flux and reflux of sea water, etc. Knowledge of these factors critically affects the vessel type and supply shortage.
The next DP is how to add tonnage. The business can build it or charter it from other ship owners. To make this decision, the business will need various type of knowledge. Above all, the new vessel price is key knowledge. Understanding how pricing works is a very knowledge intensive task. A shipping company needs very sophisticated knowledge about shipping and the shipbuilding market: ship supply and demand, shipbuilders’ capacity and work in inventory, world economy, the ages of current fleet world wide, etc. Depending on these factors, the vessel price can vary significantly. For example, the shipbuilding industry is competing worldwide. If the dock is full with a few years of work, then ship builders would maintain a high price—they are not pressed to reduce the ship price because they have enough work in progress. On the other hand, if shipbuilders are running out of work in their docks, they would be more agreeable to cut the price to secure the business.
If the decision is to build a new ship, then the decision maker moves to the next DP: which shipbuilder to choose. The quality, technology, reputation, viability, delivery time, after sale service, financing terms, skill, and legal constraints are a few examples of knowledge needed to make such a decision. If the decision is to charter, knowledge about ship brokers is needed. And the process continues.
This step of identifying knowledge components involves analyzing the business process carefully. The analysis should be performed by one knowledgeable about the process or by one who has access to knowledgeable people. The top down approach is recommended for developing and implementing enterprise knowledge architecture, since management generally better understands the critical decision-making processes that need knowledge. The step should not be delegated to purely technical staff or lower-level employees. The important issue is what knowledge is required in the decision-making process, not simply what we have currently. In this architecture the objective is to specify knowledge requirements, whether or not the knowledge is supplied to the company.
One should note that the KC have lasting value. Data tend to be stable over long periods of time, more stable than the particular process to which the data will be subject. Knowledge is even more stable. Certainly the decision rules will change regularly to reflect environmental changes. The content of knowledge may change, but the type of KC located should not change.
4.3. Stage 3: Documentation
4.3.1. Task 1: Knowledge
Component Diagram
During the third stage findings of the previous stage are documented. With DP and KC identified, we build the KCD. Together with the knowledge catalog (KCG) discussed in the next step, the KCD constitutes the knowledge architecture of an organization. A KCD shows the critical DP in the process and what knowledge is required for the decision making. We express all DP and KC identified in the previous stage using the four graphical notations. Figure 1 is an arrangement of the findings of stage 2 analysis in graphical representation. It shows the knowledge context diagram constructed for the company’s vessel procurement process.
Figure
1: Knowledge Component Diagram in Vessel Purchase Decision Making Process
4.3.2. Task 2:
Knowledge Catalog
The second task is to document KC in the knowledge catalog (KCG). KC are an aggregate knowledge, which is a collection of related knowledge. We decompose KC into sub components and describe them in details in the KCG. Basically the idea is to analyze and describe the knowledge components in increasing levels of detail. For each knowledge sub component, the source of the knowledge and how the knowledge is supplied must be identified. The knowledge can be manufactured by internal sources; however, organizations are not limited to their own knowledge shops. Is the piece of knowledge manufactured inside or purchased from outside? If it is manufactured internally, describe where it is produced and who/which department manages it. Also describe the format of the knowledge. Knowledge can be in the form of a document (report, manual, white paper, government statistics etc.) or in electronic format (database, hypertext, etc.). It may be intangible and stored in someone’s head.
There are basically four types of knowledge sources. The first type is knowledge provided by an internal data manufacturer in the electronic format (i.e., database). The second type of knowledge is that embedded in the internal employee’s head, such as experience, intuition, or other intangible know-how. The third type exits in the form of text but is supplied from outside (i.e., government analysis of economy, demographic analysis, market report, etc.). Finally, the knowledge may be supplied by people outside (i.e., consultant, lawyer, accountant, etc.). The symbols for these knowledge sources are illustrated in Table 2.
|
Notations |
Meaning |
|||
|
|
Internal
Report (i.e., manuals, policies,
best practices, and any reports produced inside a company) |
|||
|
|
External
Report (i.e., market forecast, white paper, governmental statistics) |
|||
|
|
Internal
information system (i.e., databases, data warehouse and data mining) |
|||
|
|
External
Information System (i.e., suppliers’ or customers’ system, Internet, or other
commercial database |
|||
|
|
Internal
employee (i.e., employee experts’ opinion, expertise or experience) |
|||
|
|
External
expert (i.e., outside experts’ expertise or solution – consultants, lawyers,
accountants, customers, or suppliers) |
Table 2. Notations Knowledge Source
In the example process consider the cement demand knowledge component
(KC1). Cement demand KC are
composed of several pieces of knowledge.
To have accurate understanding of cement demand, one must know about
cement consumption with seasonal adjustments including peak time demand. One would also need to know economic
conditions in the housing and construction market and to be knowledgeable about
import and export movements.
Sometimes imports and exports to Japan and China result from strategic
and political forces regardless of supply and demand. These examples of subcomponents of cement demand knowledge
component (KC1) must be identified.
Using the template, describe and make comments
on each of the subcomponents.
4.4. Stage
4: Evaluation
Any decision making can be strategic, tactical, or operational. For example, operational decision may have an impact only on a small portion of a department or division. It will affect the company for a very short period of time (i.e., day by day). Decision making on recruiting a lower-level employee (i.e., part time clerk) is considered as operation decision. Any knowledge component used for this decision making has operational importance.
On the other hand, strategic decision making will have an impact for longer periods of time (i.e., 5 or 10 years) across multiple functional areas (i.e., production, accounting, and sales departments). Any knowledge required to make such a decision has strategic meaning. Any exclusive possession of this type of knowledge may change the industry structure and industry competition. As managers move up the organizational hierarchy, they become responsible for a wider range of activities and are charged with planning the future of the organization, which requires knowledge from external sources on long term economic, demographical, political, and social trends. Evaluating and classifying KC and their subcomponents are critical tasks of this stage. An example of KCG is shown in Figure 2.
|
Knowledge
Component |
Cement
Demand |
||||||||||||||||
|
Identification
Number |
KC1 |
||||||||||||||||
|
Descriptions |
This
component contains information and knowledge about cement demand and is used
for vessel addition decision. |
||||||||||||||||
|
Sub
components |
KC1-1 |
KC1-2 |
KC1-3 |
||||||||||||||
|
Name Source Evaluation Management |
Cement
consumption |
Housing
and construction market |
SCC’s
cement export |
||||||||||||||
|
Government
Statistics
Trade
association publication
SCC
Production System
|
Government
Statistics
|
SCC
Production System
|
|||||||||||||||
|
- Strategic - This knowledge is attained by analyzing external
documents and SYC’s database - Seasonal adjustment is a key skill. - This adjustment can be done by people with at least
5 years experience. |
- Strategic - This knowledge is obtained by analyzing governmental
statistics |
- Strategic - The export to Japan and China is often made due to
strategic and some political reason - One key is to know export demand. This knowledge is
attained mostly through communication with the branches in Japan and
China. |
|||||||||||||||
|
Planning
Dept |
Bulk
Cement Department |
Bulk
Cement Department |
|||||||||||||||
|
Others |
|
||||||||||||||||
Figure 2. Knowledge Catalog
Knowledge evolves and so must be continuously updated. Regular audit and evaluation is a must for maintaining effective knowledge architecture. Environmental changes must be incorporated into the architecture. Any new knowledge components due to technological advance may be incorporated into the architecture.
5. Conclusion
Using the Decision-making Oriented Knowledge Modeling Method (DOKM), one can build knowledge architecture, as demonstrated through the example of the vessel purchase decision making process. The method has several strengths. First, the focus is on what to manage, not on how to manage it. We model what is required, not what we have. Therefore, future knowledge can be integrated and managed. If we don’t have what knowledge is required, we find a way to acquire it. Building knowledge architecture parallels with performing high-level knowledge requirement analysis.
Our modeling language, recommended by Ericksson and Penker (2000), is simple. It has only four constructs and a few rules to combine them. Users can easily see the major knowledge requirements of the business process: what the knowledge requirements are, how they are provided, how they are managed, etc. We use simple notations. They can be created with any basic software. The knowledge architecture can be used as a road map to information system planning.
The information system should be a part of the knowledge management system because information is part of knowledge. The final product of information systems should be knowledge which can be used for decision making. For example, if any knowledge component has a strategic impact on the business and it is purchased outside, the organization may want to build an information system to generate such knowledge internally. Control over strategic knowledge should not be lost.
Finally, the method highlights key decision-making points. Focusing on critical decision points avoids distraction that can result with a detailed process.
The method, however, needs more empirical testing. A modeler needs to know if the method faithfully represents the domain. “Otherwise, defects in the model might propagate to subsequent system implementation activities. If these defects are not discovered until late in the development process, they are often costly to correct” (Shanks, Tansley, and Weber, 2003). As recommended by Shanks, Tansley, and Weber (2003), a modeling method can be tested by reviews via focus group, questioning by stakeholders, or real problem solving. In this paper the method was applied to problem solving in a shipping company. Improvement in the method can occur as it is applied to more situations.
As businesses move further into the intelligence age, knowledge continues to be a key competitive weapon. Thus knowledge management is a critical concern. Managers and organizations, however, need to focus on the knowledge requirements definition before implementing a system. This architectural approach will help them define their knowledge requirements.
6. References
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Curtice, Robert M. (1987), Strategic Value Analysis: A Modern Approach to Systems and Data Planning, Prentice Hall, Eaglewood Cliffs: New Jersey.
Eriksson, H. and Penker, M (2000), Business modeling with UML: Business patterns at Work, John Wiley & Sons, Inc., New York: New York
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Ruggles, R. (1998), The state of the notions: Knowledge management in practice, California Management Review, Vol. 40, No. 3, pp. 80-89.
Skyrme, D. J. (1999), Knowledge networking: Creating the collaborative enterprise, Massachusetts, Butterworth and Heinemann, Massachusetts: PA.
Shanks, G., Tansley, E., and Weber, R. (2003), Using Ontology to validate conceptual models, Communications of ACM, Vol. 46, No. 10, pp.85-89.
Syed, J. R. (1998), An adoptive framework for knowledge work, Journal of Knowledge Management, Vol. 2 No. 2, pp. 59-69.
Teece, D. J. (1998), Capturing value from knowledge assets: The new economy, markets for know-how, and intangible assets, California Management Review, Vol. 40, No. 3, pp. 59-79.
Contact The Authors:
Sung-kwan Kim, Ph. D., Assistant Professor, University of Arkansas-Little Rock; (Email) sxkim@ualr.edu; (Phone) 501-569-8859, (Fax) 501-569-8855
Sengbae Lim, Ph. D., Assistant Professor, State University of New York-Geneseo; (Email) lim@geneseo.edu; (Phone) 585-245-5986
Robert B. Mitchell, DBA, Professor, University of Arkansas-Little Rock; (Email) rbmitchell@ualr.edu; (Phone) 501-569-3383, (Fax) 501-569-8855