COMPUTER INTEGRATED TECHNOLOGIES AND KNOWLEDGE MANAGEMENT:KNOWLEDGE MANAGEMENT

KNOWLEDGE MANAGEMENT

Origin and Background

The concept of knowledge management has been used in different disciplines, mostly in knowledge engineering (De Hoog 1997; Schreiber et al. 2000) and artificial intelligence (Go¨bler 1992; Forkel 1994). AI research often reduces the concept of knowledge management to the description of the development and use of expert systems (Go¨dicke 1992) and decision support systems. However, Davenport et al. (1996) found only one expert system application within 30 knowledge work im- provement projects. Analysis of approximately 100 case studies published before February 1998 shows that IT-based approaches towards knowledge management are dominant. IT-based knowledge management approaches focus mainly on the storage (databases, DMS) and distribution (intranet and Internet applications, push and / or pull) of explicit, electronically documented knowledge, thus ig- noring the tacit dimension of knowledge (Mertins 2001).

The improvements in data processing and network technologies have enabled access to data and information via the Internet at every time and every place in the world. Increasing market demands for reduction in time-to-market, more flexibility, and higher quality at lowest costs have contributed to a new discussion of the concept of knowledge management. These approaches differ from the above-mentioned ones in giving more emphasis to critical success factors such as culture and moti- vation of the employees and aiming to combine human-based and IT-based methods and tools for knowledge management (Davenport and Prusak 1998; Probst et al. 1998; Skyrme and Amidon 1997; Wiig 1995, 1997; Willke 1998).

Knowledge

What is knowledge? That is the question most frequently asked by people interested in knowledge management. The discussion about knowledge has a very long tradition. More than 2 thousand years ago, Socrates asked his students, ‘‘Why do we have to know what knowledge is? How can we know what knowledge is? What do we know about knowledge?’’ (see Plato, Theaetetus). Today there are numerous descriptions and definitions of knowledge. Romhardt (1998) finds 40 dichotomies of knowl- edge, such as explicit vs. implicit or tacit and individual vs. collective. Von Krogh and Venzin (1995) create seven categories of knowledge to be used in management and organization theory: tacit, em-bodied, encoded, embrained, embedded, event and procedural. Holsapple and Whinston (1992) dis- cuss six types of knowledge that are important for knowledge management and decision support systems: descriptive, procedural, reasoning, linguistic, assimilative, and presentation. Moreover, Schreiber et al. (2000) ask the question, ‘‘Why bother?’’ because even physicists will often have difficulty giving an exact definition of energy. This does not prevent them, however, producing energy and other products.

The concepts most often mentioned by authors in the context of knowledge management are data, information, and knowledge. Some even add wisdom. This classification, if not properly understood and used, could lead to a philosophical discussion of the ‘‘right’’ distinction between the categories. The transition from one to the other is not always clear-cut. Instead of a hierarchy, a continuum ranging from data via information to knowledge has proved to be the most practical scheme for knowledge management (Probst et al. 1998; Heisig 2000).

Data means the individual facts that are found everywhere in a company. These facts can be easily processed electronically, and gathering of large amounts of data is not problematic today. However, this process alone does not lead to appropriate, precise, and objective decisions. Data alone are meaningless. Data become information when they are relevant and fulfill a goal. Relevant infor- mation is extracted as a response to a flood of data.

However, deciding which knowledge is sensible and useful is a subjective matter. The receiver of information decides whether it is really information or just noise. In order to give data meaning and thus change it into information, it is necessary to condense, contextualize, calculate, categorize, and correct (Tiwana 2000). When data are shared in a company, their value is increased by different people contributing to their meaning.

As opposed to data, knowledge has a value that can be anywhere between true and false. Knowl- edge can be based on assumption, preconception, or belief. Knowledge-management tools must be able to deal with such imprecision (e.g., documentation of experiences).

Knowledge is simply actionable information. Actionable refers to the notion of relevant, and nothing but the relevant information being available in the right place at the right time, in the right context, and in the right way so anyone (not just the producer) can bring it to bear on decisions being made every minute. Knowledge is the key resource in intelligent decision making, forecasting, design, planning, diagnosis, analysis, evaluation, and intuitive judgment making. It is formed in and shared between individual and collective minds. It does not grow out of databases but evolves with experience, successes, failures, and learning over time.’’ (Tiwana 2000, p. 57)

Taking all these aspects into consideration, knowledge is the result of the interaction between information and personal experience. Typical questions for data and information are Who? What? Where? and When? Typical questions for knowledge are How? and Why? (Eck 1997).

One important differentiation is often made between tacit and explicit knowledge. Tacit knowledge is stored in the minds of employees and is difficult to formalize (Polanyi 1962; Nonaka and Takeuchi 1995). Explicit knowledge is the kind that can be codified and transferred. Tacit knowledge becomes explicit by means of externalization. With the introduction of CNC machines in mechanical work- shops, experienced and highly skilled workers often felt insecure about their ability to control the process. They missed the ‘‘right sound’’ of the metal and the ‘‘good vibrations’’ of the machine. These signals were absorbed by the new CNC machines and hence workers were not able to activate their tacit knowledge in order to produce high-quality products (Martin 1995; Carbon and Heisig 1993). Similar problems have been observed with the introduction of other CIM technologies, such as CAD / CAM in the design and process-planning department and MRP systems for order manage- ment. The information supply chain could not fully substitute the informal knowledge transfer chain between the different departments (Mertins et al. 1993; Fleig and Schneider 1995). A similar obser- vation is quoted from a worker at a paper manufacturing plant: ‘‘We know the paper is right when it smells right’’ (Victor and Boynton 1998, p. 43) However, this kind of knowledge is not found only in craftwork or industrial settings. It can be found in high-tech chip production environments (Luhn 1999) as well as social settings. From the noise of the pupils, experienced teachers can distinguish what they have to do in order to progress (Bromme 1999).

Knowledge Management Is Business and Process Oriented

Nearly all approaches to knowledge management emphasize the process character of interlinked tasks or activities. The wording and number of knowledge-management tasks given by each approach differ markedly. Probst (1998) proposes eight building blocks: the identification, acquisition, development, sharing, utilization, retention, and assessment of knowledge and the definition of knowledge goals. Another difference is the emphasis given by authors to the steps of the process- or knowledge- management tasks. Nonaka and Takeuchi (1995) describe processes for the creation of knowledge, while Bach et al. (1999) focus on the identification and distribution of the explicit, electronically documented objects of knowledge.

The Core Process of Knowledge Management

The analysis of different knowledge-management approaches (Probst et al. 1998; Davenport and Prusak 1998; Nonaka and Takeuchi 1995; Bach et al. 1999; Bukowitz 1999; Weggemann 1998) and the empirical results (Heisig and Vorbeck 2001) lead to the design of an integrated core process in which all activities are supported by organizational, motivational, and technical aspects. The core process can be further broken down into the core activities ‘‘define the goals of knowledge,’’ ‘‘identify knowledge,’’ ‘‘create (new) knowledge,’’ ‘‘store knowledge,’’ ‘‘distribute knowledge,’’ and ‘‘apply knowledge.’’ The quality of these stages is guaranteed by certain design fields for knowledge man- agement. These fields include a company’s process organization, available information technology, management systems, corporate culture, management of human resources, and control.

• Create (new) knowledge: Measures and instruments that promote the creation of knowledge include the acquisition of external knowledge (mergers, consultants, recruiting, patent acquisi- tion), the setting up of interdisciplinary project teams that include the customers, and the ap- plication of lessons learned and methods to elicit tacit knowledge.

• Store knowledge: The stored knowledge in manuals, databases, case studies, reports, and even corporate processes and rules of thumb makes up one column of the other core activities. The other column consists of the knowledge stored in the brains of thousands of employees who leave their respective organizations at the end of each working day.

• Distribute knowledge: Provision of the right knowledge to the right person at the right time is the aim of the core task of distribution of knowledge. The methods and tools are dominated by IT applications such as the Internet or intranet. However, these tools provide added value only if trust and mutual understanding pervade the atmosphere of the entire company as well as project teams. The development of a common language is an important task. Other aspects of the distribution of knowledge are the transfer of experiences to new employees by training on the job, mentoring, or coaching techniques.

• Apply knowledge: According to our survey, the application of knowledge is the most essential task of knowledge management. Knowledge management mainly provides methods to overcome the barriers of the ‘‘not invented here’’ syndrome: the one-sided thinking and the development of preferred solution by existing information pathologies.

The close relationship between process and knowledge management is underscored by the critical success factors named by companies in the Europe-wide survey. Nearly one out of four companies (24%) mentioned aspects of the design of structures and processes as a critical factor in the success of knowledge management. Knowledge management is understood by practitioners from manufac- turing and the service industry mainly as part of corporate culture and a business-oriented method: ‘‘The sum of procedures to generate, store, distribute and apply knowledge to achieve organizational goals’’ (Heisig and Vorbeck 2001).

Furthermore, the survey results indicate that companies focus on specific business processes to implement knowledge management. One out of every two companies starts its KM initiatives in the R&D area, two out of five focus on the process ‘‘Understanding Markets and Customers,’’ and more than one out of every three of the companies begins in the area ‘‘Production and Delivery of Products and / or Services.’’ The process ‘‘Manage Information’’ is ranked fourth in our overall sample and second in the service industry sample (Heisig and Vorbeck 2001). The companies locate their core competencies in these business processes too (Figure 27). Knowledge-management activities are started mainly within the areas identified as core competencies.

Design Fields of Knowledge Management

The second important step is to set up the link between knowledge management and the general organizational design areas, such as business processes, information systems, leadership, corporate culture, human resource management, and control (Figure 28).

• The business processes are the application areas for the core process of knowledge management. Existing knowledge has to be applied and new knowledge has to be generated to fulfill the needs of internal and external customers. The core activities have to be permanently aligned with the operating and value-creating business processes. Furthermore, knowledge-management activities could be linked with existing process-documentation programs (e.g., ISO certification) and integrated into business process reengineering approaches.

• Information technology is currently the main driving factor in knowledge management. This is due to considerable technological improvements in the field of worldwide data networking through Internet / intranet technologies. IT builds the infrastructure to support the core activities of storing and distributing knowledge. Data warehouses and data mining approaches will enable

companies to analyze massive databases and therefore contribute to the generation of new knowledge.

• The success of knowledge-management strategies is to a large degree determined by the support through top and mid-level managers. Therefore, leadership is a critical success factor. Each manager has to promote and personify the exchange of knowledge. He has to act as a multiplier and catalyst within day-to-day business activities. Special leadership training and change pro- grams have to be applied to achieve the required leadership style.

• If the knowledge-management diagnosis indicates that the current corporate culture will not sustain knowledge management, wider change-management measures have to be implemented.

The required company culture could be characterized by openness, mutual trust, and tolerance of mistakes, which would then be considered necessary costs of learning.

• Personnel-management measures have to be undertaken to develop specific knowledge- management skills such as the ability to develop and apply research and retrieval strategies as well as adequately structure and present knowledge and information. Furthermore, incentives for employees to document and share their knowledge have to be developed. Career plans have to be redesigned incorporating aspects of knowledge acquisition of employees. Performance- evaluation schemes have to be expanded towards the employees’ contribution to knowledge generation, sharing, and transfer.

• Each management program has to demonstrate its effectiveness. Therefore, knowledge- controlling techniques have to be developed to support the goal-oriented control of knowledge creation and application with suitable control indicators. While strategic knowledge control supports the determination of knowledge goals, operative knowledge control contributes to the control of short-term knowledge activities.

Empirical results confirmed the great potential for savings and improvements that knowledge management offers (Figure 29). Over 70% of the companies questioned had already attained notice- able improvements through the use of knowledge management. Almost half of these companies had thus saved time and money or improved productivity. About 20% of these companies had either improved their processes, significantly clarified their structures and processes, increased the level of customer satisfaction, or facilitated decisions and forecasts through the use of knowledge management (Heisig and Vorbeck 2001).

However, some differences were apparent between the answers provided by service and by man- ufacturing companies. Twenty-eight percent of the service firms indicated an improvement in cus- tomer satisfaction due to knowledge management, as compared with only 16% of the manufacturing companies. Twenty-three percent of manufacturing companies stressed improvements in quality, as compared to only 15% of the service companies. Answers to questions about the clarity of structures and processes showed yet another difference. Twenty-six percent of the service companies indicated improvement with the use of knowledge management, as opposed to only 14% of manufacturing companies.

Approaches to the Design of Business Process and Knowledge Management One primary design object in private and public organizations are the business processes that structure work for internal and external clients. Known as business process reengineering (BPR) (Hammer 1993), the design of business processes became the focus of management attention in the 1990s. Various methods and tools for BPR have been developed by research institutes, universities, and consulting companies. Despite these developments, a comparative study of methods for business process redesign conducted by the University of St. Gallen (Switzerland) concludes: ‘‘To sum up,

we have to state: hidden behind a more or less standard concept, there is a multitude of the most diverse methods. A standardized design theory for processes has still not emerged’’ (Hess and Brecht 1995, p. 114).

BPR‘s focus is typically on studying and changing a variety of factors, including work flows and processes, information flows and uses, management and business practices, and staffing and other resources. However, most BPR efforts have not focused much on knowledge, if at all. This is indeed amazing considering that knowledge is a principal success factor—or in many judgment, the major driving force behind success. Knowledge-related perspectives need to be part of BPR. (Wiig 1995, p. 257)

Nearly all approaches to knowledge management aim at improving the results of the organization. These results are achieved by delivering a product and / or service to a client. This again is done by fulfilling certain tasks, which are linked to each other, thereby forming processes. These processes have been described as business processes. Often knowledge is understood as a resource used in these processes. Nevertheless, very few approaches to knowledge management have explicitly ac- knowledged this relation. And even fewer approaches have tried to develop a systematic method to integrate knowledge-management activities into the business processes. The following approaches aim to support the analysis and design of knowledge within business processes:

• CommonKADS methodology (Schreiber et al. 2000)

• The business knowledge management approach (Bach et al. 1999)

• The knowledge value chain approach (Weggemann 1998)

• The building block approach (Probst et al. 1998)

• The model-based knowledge-management approach (Allweyer 1998)

• The reference model for knowledge management (Warnecke et al. 1998).

None of the approaches presented to knowledge management has been developed from scratch. Their origins range from KBS development and information systems design to intranet development and business process reengineering. Depending on their original focus, the approaches still show their current strengths within these particular areas. However, detailed criteria for the analysis and design of knowledge management are generally missing.

Due to their strong link to information system design, all approaches focus almost exclusively on explicit and documented knowledge as unstructured information. Their design scope is mainly limited to technology-driven solutions. This is surprising because the analysis of 30 knowledge work- improvement projects suggests a modified use of traditional business process design approaches and methods including nontechnical design strategies (Davenport et al. 1996). Only the business knowl- edge management approach (Bach et al. 1999) covers aspects such as roles and measurements.

A Method for Business Process-Oriented Knowledge Management Since the late 1980s, the division of Corporate Management at the Fraunhofer Institute for Production Systems and Design Technology (Fraunhofer IPK) has developed the method of integrated enterprise modeling (IEM) to describe, analyze, and design processes in organizations (Figure 30) (Spur et al. 1993). Besides traditional business process design projects, this method has been used and customized for other planning tasks such as quality management (Mertins and Jochem 1999) (Web and process- based quality manuals for ISO certification) for the design and introduction of process-based con- trolling in hospitals and benchmarking. The IEM method is supported by the software tool MO2GO (Methode zur objektorientierten Gescha¨ftsprozessoptimierung—method for object-oriented business process optimization).

The method of integrated enterprise modeling (IEM) distinguishes between the three object classes ‘‘product,’’ ‘‘order,’’ and ‘‘resource.’’ These object classes are combined by the construct ‘‘Action’’ within a generic activity model. Five elements are provided to link the objects to the actions (Figure 31). The IEM approach offers the possibility of describing knowledge as an object within the process model. According to the overall modeling task, knowledge can be modeled as a subclass of the superordinated class ‘‘resource’’ and broken down into different sub-subclasses in the form of knowl- edge domains. The subclass ‘‘knowledge’’ can be linked to other ‘‘resource’’ subclasses such as ‘‘staff,’’ ‘‘EDP-Systems,’’ ‘‘Databases,’’ ‘‘Documents,’’ and so on that are relevant for the analysis and improvement of the business process. The final objective of every business process consists of the fulfillment of the internal and / or external customer demand with a product and / or service. Knowl- edge is required to produce and / or deliver this service or / and product and thus becomes implemented in the object ‘‘product.’’ This implemented knowledge could be divided into subclasses as well. The object ‘‘order’’ that triggers the actions could be translated into knowledge goals if appropriate.

The business-oriented knowledge-management approach starts with the selection of the business process to be improved. Davenport (1996) characterizes knowledge work processes as possessing a

high degree of variety and exception rather than routine and requiring a high level of skills and expertise. The description of the real-world business process is carried out with the modeling con- structs of the IEM method. After the description of the real-world business process, the analysis starts with the evaluation of each business task. The result is a knowledge activity profile that shows the level and quality of support provided by the current operational task towards the individual core

tasks of knowledge management. The scope is then extended towards the analysis of the relations between the knowledge-processing tasks within the business process. This step contains the evaluation of the degree of connectivity inherent in the core process activities of knowledge management within the selected business process. The result shows whether the business processes supporting knowledge management are connected in a coherent manner. The optimization and new design of business processes aim at closing the identified gaps within the underlying core processes and sequencing the core tasks of knowledge management. One design principle is to use available procedures, methods, tools, and results from the process to design the solution. In the last step of the analysis, the focus shifts from the actions towards the resources used and the results produced within the process. The results of the analysis not only demonstrate which kind of knowledge is applied, generated, stored, and distributed but also the other resources, such as employees, databases, and documents. Due to the project aim of the improvement, the user will be able to evaluate whether the required knowledge is explicitly available or available only via the internal expert using the expert’s implicit or tacit knowledge. The identified weaknesses and shortcomings in the business process will be addressed by knowledge-management building blocks consisting of process structures. The improvement mea- sures have to integrate not only actions directed to a better handling of explicit knowledge but elements to improve the exchange of implicit knowledge.

Knowledge-Management Tools

Information technology has been identified as one important enabler of knowledge management. Nevertheless, transfer of information and knowledge occurs primarily through verbal communication. Empirical results show that between 50% and 95% of information and knowledge exchange is verbal (Bair 1998). Computer-based tools for knowledge management improve only a part of the exchange of knowledge in a company. The richness and effectiveness of face-to-face communication should not be underestimated. Computer tools promote knowledge management. The access to knowledge they enable is not subject to time or place. A report can be read in another office a second or a week later.

Therefore, a broad definition of knowledge-management tools would include paper, pencils, and techniques such as brainstorming. According to Ruggles (1997, p. 3), ‘‘knowledge management tools are technologies, which automate, enhance and enable knowledge generation, codification and trans- fer. We do not look at the question if tools are augmenting or automating the knowledge work.’’

E-mail and computer videoconference systems can also be understood as tools for knowledge management. However, we consider this kind of software to be the basic technology, that is, the building blocks for a knowledge-management system. Initially, groupware and intranets are only systems for the management of information. They become knowledge-management tools when a structure, defined processes, and technical additions are included, such as a means of evaluation by users.

This is not the place for a discussion about whether software can generate, codify, and transfer knowledge alone or can only aid humans in these activities. For the success of knowledge manage- ment, the social aspects of its practical use are very important. For example, a sophisticated search engine alone does not guarantee success as long as the user is not able to search effectively. It is not important for this study whether employees are supported in their knowledge management or whether the tool generates knowledge automatically on its own. This is an important point in the artificial intelligence discussion, but we do not need to go into detail here.

Syed (1998) adopts a classification from Hoffmann and Patton (1996) that classifies knowledge techniques, tools, and technologies along the axes complexity–sophistication and intensity along the human–machine continuum, indicating whether certain tools can handle the complexity of the knowl- edge in question and what kind of workload this means for the user (Figure 32).

5.6.1. Technologies for Knowledge Management

The following is an overview of the basic technologies used in every knowledge-management solu- tion. The following explanation of the basic technologies helps to examine and classify tools more precisely. These are the technologies that we find today in knowledge management (Bottomley 1998). Different knowledge management tasks can be processed using these basic technologies.

Intranet technology: Intranets and extranets are technologies that can be used to build a knowl- edge-management system. The unified surface and access to various sources of information make this technology perfect for the distribution of knowledge throughout a company.

Groupware: Groupware is a further substantial technology that is used for knowledge-management systems (Tiwana 2000). Groupware offers a platform for communication within a firm and cooperation between employees.