A model of information technology opportunities for facilitating the practice of knowledge management

The Authors

Rosemary Wild, Orfalea College of Business, California Polytechnic State University, San Luis Obispo, California, USA

Kenneth Griggs, Orfalea College of Business, California Polytechnic State University, San Luis Obispo, California, USA

Abstract

Purpose – There is controversy about the role information technology (IT) should play in knowledge management (KM) spanning extremes that either overvalue or undervalue IT's role. This study recognizes the balance required between the two extremes and aims to present a KM process and a three-dimensional model to assist in identifying opportunities in which IT can effectively facilitate KM practices.

Design/methodology/approach – Through a synthesis of KM literature the paper developed a KM process that consists of identification of classifications of knowledge and their particular value to an organization, sources of knowledge, and application opportunities. It developed a three-dimensional model consisting of KM life cycle, KM level, and KM objectives to assist in identifying the most effective IT application opportunities.

Findings – Current IT infrastructures provide support for the organization, formalization, and distribution of organizational knowledge. However, there are relatively few applications that perform the generation, application, and evolution of organizational knowledge. The paper describes a distributed simulation prototype system that demonstrates the potential ability of IT to perform these important KM activities and contribute to the effective facilitation of KM.

Research limitations/implications – This study is by no means exhaustive, but is intended as a starting point to explore opportunities in which IT may be most effective in realizing the goals and objectives of KM.

Originality/value – The proposed KM process guides knowledge workers toward a systematic understanding of how to view the use of IT in the most effective way to achieve organizational objectives and goals. The model permits them to assess how they are currently using IT for KM and where they may either leverage their current IT infrastructure or develop focused IT applications to achieve benefits through better use of IT in facilitating knowledge management. The Navy planning system described demonstrates one such application.

Article Type:

Research paper

Keyword(s):

Knowledge management; Decision support systems; Simulation; Communication technologies.

Journal:

VINE: The journal of information and knowledge management systems

Volume:

Number:

Year:

2008

pp:

490-506

Copyright ©

Emerald Group Publishing Limited

ISSN:

0305-5728

Introduction

Knowledge management (KM), although not having been termed such, has been practiced for many years in organizations that value the sharing of knowledge (Sieloff, 1999). However, the evolution of what has been referred to as a knowledge-based economy (Drucker, 1995) has forced organizations to evaluate what it means to manage knowledge and has motivated researchers to struggle with questions related to how to define and operationalize the notion of KM. Interest in the subject has exploded, spanning multiple disciplines, somewhat reminiscent of what occurred when artificial intelligence was a “hot” topic. The ABI/INFORM database reveals that the number of new KM articles has, on average, more than doubled each year over the past decade.

One of the difficulties facing organizations and researchers alike is that knowledge management, while being intuitively important is intellectually elusive (Despres and Chauvel, 1999). It is important because:

With rare exceptions, the productivity of a modern corporation or nation lies more in its intellectual and system capabilities than in its hard assets … (Quinn et al., 1996).

It is elusive because:

To define knowledge in a non-abstract and non-sweeping way seems to be very difficult. Knowledge easily becomes everything and nothing (Alvesson, 1993).

Therein lies the conundrum, one of many surrounding this important, and arguably necessary, tenet of present day organizational management.

In this paper we provide a synthesis of relevant literature exploring the meaning of KM and the potential applications of knowledge management systems (KMSs). We tie key emergent themes to the suggested use of information systems and technology as a facilitator of KM. We propose a KM process consisting of knowledge classifications, knowledge sources, and knowledge application opportunities. We present a three-dimensional model that depicts three suggested perspectives for using information technology (IT) to facilitate and promote effective knowledge management. The model dimensions are:

KM objectives, including to support innovation, capture insights and experiences, make it easy to find and re-use resources, foster collaboration, improve the quality of decision making, and increase the effectiveness of intellectual asset.
KM level which defines the role of a KMS system as one that supports or one that performs KM.
The phases in the KM life cycle including the generation, organization, formalization, distribution, application, and evolution of knowledge within an organization.

We then discuss different IT required to support both the infrastructure and the application requirements for KMSs. We conclude with an illustrative KMS application for the US Navy. The system is a prototype KMS which enables the generation, application and evolution of manpower knowledge that has the potential to significantly improve manpower management decisions, integrate “stove piped” communities within the Navy, and ultimately and most importantly, help achieve the overriding strategic Naval goal of fleet readiness. Our research goal was to contribute to the literature on KM by focusing on the development of a KM process which clarifies the role IT can and does play in facilitating KM practices.

Overview of KM

Drucker (1993) said the only – or at least the most important – source of wealth in the contemporary post-capitalist society is knowledge and information. He also states that the most important challenge facing our knowledge-based economy is to find a methodology, a discipline, or a process with which information can be made productive. It is difficult to specify a process for managing knowledge, and potentially lethal to throw technology at a process that is not understood, without agreement about the term KM.

A plethora of definitions of KM exist in the literature, for example see Bollinger and Smith (2001); Hedlund (1994); Malhotra (1998); Mullin (1996); Nonaka (1994); Pohs et al. (2001); and Wiig (1997). At one end of the spectrum definitions are somewhat lofty:

Knowledge management is the art of creating value from an organization's intangible assets (Sveiby, 2001).

At the other end of the spectrum we find a cynical view dismissing KM as a passing fad, claiming knowledge cannot be managed and therefore knowledge management is nonsense (Wilson, 2002). Somewhere in the balance we find more focused and practical definitions such as:

Knowledge management is the coordination and exploitation of organizations' knowledge resources, in order to create benefit and competitive advantage (Drucker, 2002).

Knowledge management is arranging and managing the operational processes in the knowledge value chain in such a way that realizing the collective ambition, the targets and the strategy of the organization is being promoted (Weggeman, 1997).

Weggeman defines the knowledge value chain as four successive constituent processes. First, the strategic knowledge requirements need to be identified. Second, the knowledge gap (the quantitative and qualitative difference between the knowledge needed and that available in the organization) needs to be determined. Third, the knowledge gap needs to be closed either by developing new knowledge, buying knowledge, improving existing knowledge, or getting rid of out-of-date or irrelevant knowledge. Finally, the available knowledge needs to be disseminated and applied to serve the interest of customers and other stakeholders.

Beijerse's (1999) definition states:

Knowledge management is achieving organizational goals through the strategy-driven motivation and facilitation of (knowledge-) workers to develop, enhance and use their capability to interpret data and information (by using available sources of information, experience, skills, culture, character, personality, feelings, etc.) through a process of giving meaning to these data and information.

Weggeman and Beijerse's definitions concur with Drucker's observation that there is a need for a process to make information productive; thus the challenge to managing knowledge successfully is defining a workable process by which people (knowledge workers) can interpret and find meaning in organizational data and information.

This process does not necessarily require the use of IT, however, throughout the literature there seems to be agreement that IT plays a dominant role in facilitating or enabling KM, even if a clear identification of IT's most effective role is not yet understood (Holsapple, 2005). As noted by Nissen and Espino (2000):

[…] until recently, integration of knowledge process design with information system design has been strangely missing from the knowledge management literature and practice.

From this discussion the following proposition emerges:

P1. A KM process in which IT plays a role in facilitating KM needs to be defined.

In the following sections we discuss three perspectives of KM and then propose a KM process for identifying opportunities in which IT can enable KM practices. We then describe a KM prototype system developed for US Navy manpower management that illustrates IT's role in facilitating knowledge generation, application, and evolution.

Three perspectives of KM

From a study conducted by Alavi and Leidner (1999) to ascertain the meaning managers ascribed to the concept of KM, three perspectives emerged: an information-based perspective, a technology-based perspective, and a culture-based perspective. Figure 1 depicts these three perspectives.

From an informational perspective, the managers involved in the study viewed KM as a means for tracking who holds knowledge and how to locate them, rather than a system for keeping track of the knowledge itself. They identified the information-based capability of a KMS to include external knowledge such as knowledge about clients, competitors, and customers, as well as internal knowledge such as financial, human resource, and product/service knowledge.

From a technology perspective, the managers associated KM with existing technology that comprised their organizational technology infrastructure such as data warehouses, intranets, and the world wide web as well as existing tools including search engines and multi-media and decision-making tools. Interestingly, a clear identification of a new type of technology specifically dedicated to knowledge management did not emerge. The technology capabilities identified included a global IT infrastructure, integrated databases, interoperability of existing systems, greater bandwidth, intelligent agents, and a consistent suite of e-mail and web products.

From a culture-based perspective, the participants identified organizational learning, communication, and intellectual property cultivation as the elements of KM. They attributed cultural and managerial issues as accounting for the bulk of KM issues, however, were not specific about the cultural implications of KM. The culture capabilities identified included teamwork and knowledge sharing.

Despite the fact that many critical-mass technologies such as e-mail, groupware, and the world wide web often act as catalysts to bring about culture changes required for information sharing, the ongoing debate about the role IT does and should play in KM continues with many stating the emphasis should be more on the “I” than the “T”. Some have issued a warning that the social and cultural aspects of knowledge management should not be overshadowed by the application of IT to the practice of KM (Call, 2005; Davenport and Prusak, 1998; O'Dell and Grayson, 1998; Park et al., 2004; Sasson and Douglas, 2006). The question that remains is how can IT most effectively enable and facilitate the practice of KM. Alavi and Leidner (2001) offer a detailed discussion of the research issues related to defining the role of IT in enabling KM practices. One of the keys appears to be the formulation of a KM process.

A KM process

While culture issues play a critical and necessary role in KM, our focus is on the effective use of IT to facilitate KM and possibly act as a catalyst to bring about some of the culture changes, such as knowledge sharing, required for KM to truly benefit an organization. For knowledge to be shared its value needs to be understood, its sources must be identified, and it needs to be extracted from these sources and stored in such a way that it can be accessed and used by the appropriate people.

We have developed a KM process which consists of identification of:

classifications of knowledge and their particular value to an organization;
sources of knowledge; and
application opportunities.

In concert, these process characteristics lead to a framework for identifying key KM enabling capabilities of existing IT and opportunities for developing specific IT applications to facilitate effective KM. The proposed KM process addresses P1.

Classifications of knowledge

Nonaka and Takeguchi (1995) differentiate between two classifications of knowledge, tacit knowledge, which is deep, intuitive knowledge generally accrued through experience, and explicit knowledge, which is factual knowledge that can be easily expressed and transferred (Table I). According to Fry (2001), tacit knowledge is the key to what corporate clients pay for and what corporate KMSs should ultimately strive to communicate. KM can then permit the capture and dissemination of key earners' insights and expertise through audio, video, and textual media.

Explicit knowledge such as knowledge about new markets, customers, suppliers, product features, as well as process features and technology does help workers perform their jobs better to improve the products and services offered to customers. Although tacit knowledge appears to have the greatest value in most organizations, explicit knowledge is also valuable as long as it contributes to the strategic knowledge requirements of a firm. From this discussion the following proposition follows:

P2. For IT to contribute value to an organization's KM effort, it should facilitate the transfer of both tacit and explicit knowledge.

An integrated technology infrastructure, including secure networks, intranets, web-based technologies, database systems, and a wide variety of communication, messaging, browsing and retrieval tools, can provide access to explicit knowledge. The challenge for most organizations is capturing and disseminating tacit knowledge, what corporate clients pay for and what has the greatest value-added potential for an organization. KnowledgeX, before being acquired by IBM, was an early KMS that attempted to address this issue by clarifying the meaning of information through the analysis and presentation of the relationship between facts. The system uncovered knowledge-relationships users may not have known about that could be both tactical and strategic in nature. Initial applications for the product were very specific: competitive intelligence and governmental intelligence agencies. OLAP and data mining tools attempt to “discover” knowledge, not just information, deeply embedded in transactional data through pattern recognition or correlations that may not be readily apparent to the knowledge worker. Many argue that tacit knowledge is information processed by the human brain and can therefore reside only in the minds of individuals (Wilson, 2002); however, data mining has shown to have the potential to uncover tacit knowledge that is embedded in organization processes and data, not in the minds of individuals. Stenmark (2000) provides other examples of how tacit knowledge can be made tangible and Faucher et al. (2008) provide an interesting discussion of the knowledge hierarchy using a tacit/explicit continuum.

Sources of knowledge

Organizational knowledge may be acquired or purchased from external sources or generated internally. Regardless of whether knowledge becomes available from external or internal sources, it generally originates within individuals, teams, or organization processes. Once extracted it may be stored in a repository to be accessed and shared by other individuals or groups within an organization. Figure 2 depicts the typical sources of knowledge that can be tapped within the KM process.

Alavi and Leidner (1999) posit that knowledge is not a radically different concept than information, but rather that information becomes knowledge once it is processed in the mind of an individual (tacit knowledge). This knowledge then becomes information again (explicit knowledge) once it is communicated to others. The recipient can then cognitively process and internalize the information so it is converted back to tacit knowledge. Thus the source of all knowledge seems to be people, however, we suggest that some organizational knowledge is not possessed by individuals but instead is embedded within the system itself. Through the effective use of information technology this knowledge can be extracted, stored, and shared by others in the organization. Extracting knowledge poses the greatest challenge here. The manner in which knowledge is stored varies but it is generally embedded within: documents such as manuals, briefs, reports; data warehouses that have been constructed from a variety of organizational databases; applications, particularly those that are closely tied to strategic and competitive objectives; best practices of the firm and its knowledge partners as well as lessons learned from projects and other organizational activities; and discussions among knowledge workers. The previous observations led to the following proposition:

P3. If IT is to make a meaningful contribution to KM it must enable the extraction of knowledge from individuals as well as from processes of the system itself, and provide the ability to store knowledge for others to access and share.

Again, an organization's IT infrastructure should provide the capability to store knowledge and provide access to it by knowledge workers. The challenge is to find effective ways in which IT can be used to extract knowledge from individuals, groups such as project teams, and the organization's processes. As mentioned, data mining tools have demonstrated the greatest promise in this area thus far.

A model of potential KM application opportunities

Through a review of current literature one can observe several perspectives taken to describe a knowledge design process. These include, for example, the notions of a KM life cycle, KM objectives, KM level, and KM span. We suggest that some of the intersections of these dimensions to KM practice capture the current use of IT in KM and may serve as a guide for identifying opportunities for the development and application of KMSs. To illustrate these dimensions we offer a model that incorporates the following three dimensions: KM life cycle, KM level, and KM objectives (Figure 3). The dimension KM span (individual, group, enterprise) is subsumed in all three levels and was therefore not explicitly represented. The model reflects a compilation of individual approaches found in the KM literature.

KM life cycle

The elements of the knowledge management life cycle represented (not necessarily linear) highlight the phases involved in the generation and dissemination of knowledge throughout an organization. Not all researchers agree on these precise phases, but there is general agreement on aspects of most of them (Bhatt, 2000; Despres and Chauvel, 1999; Davenport and Prusak, 1998; Nissen and Espino, 2000). For example, we have specified knowledge generation as the first phase, whereas others have termed this phase “knowledge creation”, “knowledge capture”, or “knowledge adoption”. We think the term knowledge generation aptly conveys all three concepts.

Knowledge creation implies the origination of novel and useful ideas and solutions (Marakas, 1999). The knowledge creation process is not a systematic process that can be planned and controlled and is, rather, continuously evolving and emergent (Lynn et al., 1996). Some researchers argue that knowledge creation is an individual thought process (Crossan et al., 1999), however, others have demonstrated that creativity can be learned and taught (Marakas and Elam, 1997). Mohamed et al. (2006) question whether or not knowledge can be created or only discovered, and whether or not tacit knowledge can be captured at all. They say the answer is “yes” knowledge can be created and tacit knowledge can be captured, but it is a matter of degree. Thus KMSs may be used, to a degree, to foster the knowledge creation process. But knowledge creation is only one aspect of knowledge generation. Created knowledge should also be coupled with existing knowledge that may be stored in organizational documents, the personal knowledge base of individuals, or deeply embedded in the processes of the system itself. This type of knowledge, whether tacit or explicit, is generated through knowledge capture, an area in which information systems are currently employed. In addition, organizations acquire or adopt knowledge from other sources. Knowledge adoption strategies may combine imitation, replication, and/or substitution. Imitation involves the adoption of knowledge from the source being imitated. Replication involves the adoption of knowledge associated with duplicating “best practices”. And substitution involves the adoption of knowledge associated with similar, but not identical, products, processes, and practices of outside industry players. For many firms, knowledge adoption is a necessity if they do not rely on inventing knowledge but on interpreting past knowledge in a new light (Bhatt, 2000).

The knowledge organization phase involves bundling and mapping knowledge. Different terms are used for the third phase but they all involve formalizing knowledge through codification in some formal or explicit manner. This may mean converting generated knowledge from tacit to explicit form or simply creating a formal representation for existing knowledge. Phase four involves the distribution and sharing of knowledge so it can be exploited throughout the organization. For KM to achieve the overarching goal of helping organizations compete and survive it must be used and applied to organizational problems or opportunities; thus, phase 5. The last phase, knowledge evolution, involves the review and replenishment of knowledge clusters continually in the organization. Obviously all phases are interdependent but viewing them as separate phases may help organizations identify where information technology may provide the greatest benefits, strategic or otherwise, in facilitating knowledge management. Elements of both an organization's IT infrastructure and specific software applications can be used to assist in the various phases of the KM life cycle.

KM level

Knowledge level refers to the role of a KMS as either to support or perform KM. Nissen and Espino (2000) observed that the activities organize, formalize, and distribute knowledge are enabled by existing IT and IT architectures and are inherently supportive in nature. That is, systems that enable the organization, formalization, and distribution of knowledge tend to support people who in turn generate, apply, and evolve knowledge in the organization. The activities of knowledge generation, application, and evolution, ironically, are not well supported by existing IT. IT and applications that are able to generate, apply, and evolve knowledge represent promising opportunities in which IT actually performs KM activities.

KM objectives

A third dimension in our model, KM objectives, as outlined by KPMG, gives form to Drucker's notion of “productive information”. Knowledge is productive if it ensures an organization is efficient and effective in using its assets to support its strategy. The specific organizational objectives of KM include, but are not limited to, the following:

To support innovation through the generation of new ideas and the exploitation of the organization's thinking power.
To capture insights and experiences to make them available and usable when, where, and by whom required.
To make it easy to find and reuse sources of know-how and expertise, whether recorded in a physical form or held in someone's mind.
To foster collaboration, knowledge sharing, continual learning, and improvement.
To improve the quality of decision making and other intelligent tasks.
To understand the value and contribution of intellectual assets and increase their worth, effectiveness and exploitation.

Obviously not all intersections of the three dimensions of the model provide an opportunity for IT to enable KM practices. In fact, an overview of existing KMSs shows the coverage to be quite patchy. Many existing IT systems facilitate organizing, formalizing and distributing information that may be converted to knowledge using current components of an organization's IT infrastructure such as databases and communication technologies. For example, the internet, corporate extranets, and company portals primarily support the objective of making it easy to find and re-use sources of know-how and expertise. Using KMSs to analyze, codify and distribute best practices not only helps to capture insights and lessons learned, but also has the potential to improve decision making. Groupware products such as Lotus Notes™ support collaboration and knowledge sharing. A useful application employed by many organizations including Microsoft and Hewlett Packard (HP) to find know-how and expertise involves the development of a system to guide employees to human knowledge within the firm. HP's Product and Processes organization developed three knowledge sharing products:

competition information;
research information; and
marketing intelligence (Davenport, 1998).

It can be argued that these types of systems are still “information” sharing systems and the knowledge component involves the interpretation and application of the information. Interpretation and application represent areas with the least IT support and the greatest potential for providing measurable benefits to organizations.

The use of data warehouses and data mining tools to help organizations apply their knowledge to make better decisions is one of the few applications geared toward practicing KM. Ernst & Young (McCampbell et al., 1999) developed a KMS that involves the rapid application of knowledge, models and approaches to client situations that is used to practice KM. In general, however, there are relatively few existing KMSs that are used to perform KM, especially in the knowledge generation, application, and evolution phases of the KM life cycle.

This discussion is by no means exhaustive but is intended as a starting point to explore opportunities in which IT may be most effective in realizing the goals and objectives of knowledge management. The greatest challenges posed are finding ways in which IT can facilitate the extraction and transfer of tacit knowledge ideally through “push” technologies, support innovation through the development of KMSs that assist in the generation, application, and evolution of knowledge, and be exploited not only to support KM, but also to perform KM. The preceding discussion leads to the following proposition:

P4. If IT is to make a meaningful contribution to KM it should both support organizational objectives and perform important knowledge functions through focused KMS applications.

In the following section we describe a prototype KMS that addresses P2, P3, and P4 and focuses on several of the intersecting cubes of our model in Figure 3. In particular, the system is intended to perform KM by generating tacit knowledge embedded in the US Naval manpower management system and converting it to explicit knowledge that can then be distributed and applied to make better manpower policy decisions. In addition, the system demonstrates the potential to capture insights, foster collaboration, and make it easy to find and reuse sources of know-how and expertise. This prototype system illustrates a novel representation of a focused and strategic IT-enabled KM application.

A prototype IT enabled KMS

One of the most common activities in any organization, whether it be government, business, non-profit, or educational, is planning. Planning involves the evaluation of a variety of potential policies that impact the success of an organization in achieving its goals and making effective decisions related to the resources, manpower, and processes needed to implement selected policies. Whether the objective is to develop a strategic, tactical, or operational plan, planning sets the direction an organization should take to achieve its goals and guides it to follow that direction.

Although there are infinitely many planning and policy examples in business and government, especially in long-term planning, there are characteristics of the planning process that are common across planning domains. First, the complexity of high level planning is exacerbated by the geographic dispersion of key personnel involved in the planning process. Second, since the success of long-term planning relies on an unknown future, the ability to assess performance in light of various plausible future scenarios is essential to the success of the planning process. Third, planning teams are seldom knowledgeable of the policies individual planners implement, and fourth, knowledge is accrued during the planning process. Perhaps most importantly, remote planning teams do not possess the same “world view” required to guide the planning process to success and do not always capture and share the knowledge generated during the planning process.

To address some of these difficulties we developed a web portal architecture that enables enterprise planning teams to experiment with a plethora of policies using a simulation decision support system (DSS) model of key planning processes in a virtual community environment (Wild and Griggs, 2007). The architecture, depicted in Figure 4, contains a KM component that uses both traditional and “new media” technologies to capture and distribute knowledge that surfaces as a result of experimentation with simulated planning scenarios. Through regular experimentation with the distributed planning simulation, knowledge evolves and can be captured and shared through successive experimentation with the simulation. Our proposed architecture combines collaboration, DSS, KM, and web portal technologies. The potential benefits of such an approach to enterprise-wide policy and decision making are promising. For example, experimentation with a DSS model enables knowledge generation, generally knowledge embedded in the modeled system rather than in the minds of individuals. Planning teams experimenting with the planning simulation can gain an understanding of the potential effects of policy implementations on key performance measures before actually implementing policies. A web portal enables teams that often operate in a stove-piped fashion to collaborate in an integrated electronic environment and share results of scenario planning experiments. If designed properly, a web portal can assist teams collaborating on the planning process to develop and share a world view of the organization and maintain a repository or history of the planning process. Distributed experimentation with a simulation model of the planning environment uncovers rules and processes not previously known to all planners and policy makers that, in the aggregate, may have detrimental effects on the ultimate plan.

US Navy manpower planning and management

An initiative has been underway for some time to create a “networked Navy”, one in which information systems are integrated and communication technologies are exploited to distribute critical information and encourage the sharing of knowledge. KM is a critical component of this initiative.

The most important strategic goal of the Navy is fleet readiness. Fleet readiness involves a complex array of activities but can be defined as having the right people with the necessary skills and knowledge in the right place at the right time. Manpower management is a key to achieving this goal. However, the US Navy lacks a comprehensive model of the enlisted manpower management system that supports analysis of policies, constraints, and external factors on portions of the manpower system or on the readiness of the Navy to support national objectives. Navy manpower, personnel, and training (MPT) organizations must deal with the knowledge gap between the level of expertise needed to make informed decisions and the subject matter expertise of the staffs making those decisions. The organizational structure of the MPT system results in less than optimal sharing of information between functional areas resulting in “stove-piping” of information and knowledge. In addition, the continuous turnover of uniformed personnel results in erosion of expertise and corporate knowledge of the business functions that support the Navy's MPT processes. Many of these functions require extensive knowledge and analytical ability gained only from years of experience. Without this core knowledge and the benefit of past trial and error, process managers make less informed and potentially costly decisions.

Another very critical factor is that the impacts of uninformed decisions generally take a long time to mature. Accurate accession plans, strength plans, reenlistment bonus levels, promotion flow points or retention goals can ensure sufficient, qualified personnel to man the Navy. But, if inaccurate, the implemented plans can cause shortages or excesses that require years to counteract. The consequence of such decisions is that the Navy's readiness levels are severely impacted and uninformed decisions may lead to excessive personnel spending, reduced morale, and promotion bottlenecks that will ripple through the community for years.

An accurately modeled MPT system with the ability to experiment with manpower policies offered the potential to close this knowledge gap. In addition to fostering collaboration among sub-systems of the manpower system, the goal of our proposed KMS was to capture insights related to manpower management and generate tacit knowledge about the impact of manpower policies on key readiness measures. In a system as complex as the Naval MPT system, tacit knowledge is often embedded in the underlying processes of the system itself and does not necessary reside within the personal knowledge bases of individuals or groups. The tacit knowledge extracted from the KMS is then converted to explicit knowledge, transferred to knowledge workers and subsequently applied to manpower policy decisions. The characteristics of an effective and integrated manpower management system led us to conclude that a hybrid simulation model coupled with knowledge discovery tools and a knowledge repository in a portal environment would support the knowledge generation, distribution, and evolution requirements of the problem. Our prototype simulation model permitted knowledge workers to accomplish the following:

Demonstrate the relationship between various policies and readiness levels attained over a specified time period, for example, each month for the next eight years.
Permit managers to evaluate manpower schemes and their effect on the distribution and assignment process before making policies.
Permit the exploration of policy impacts on retention.
Compare costs to evaluate the trade-offs between retention, training, travel, and other pertinent issues.
Provide better projections for the number of permanent change of station (PCS) moves required over subsequent years to support planning throughout the MPT system.

The combination of user input values, including setting limits on the maximum number of moves allowed for a specified time period, setting priorities for assigning personnel to billets (jobs), providing estimates for attrition rates and advancement rates, setting class sizes for training and adjusting training start dates, and adding activities (commissioned) and deleting activities (de-commissioning) based on forecasted changes in force structure, provided a rich experimentation environment for managers from all planning communities to assess the impact of potential policy implementations on fleet readiness.

Knowledge generated

The prototype was designed for proof of concept purposes and to gain an understanding of the underlying process and the ability of a simulation model to emulate the MPT process. Because of the complexity of the actual MPT system few people, if any, really understand the whole picture. Through experimentation with the simulation DSS, planning teams such as Community Management and Training, for example, were for the first time provided an opportunity to explore, understand, and deal with the relationships that exist between the various components and functions of the MPT system. Relationships and trade-offs between re-enlistment bonuses and training dollars and PCS dollars, etc., were explored and analyzed more effectively than ever before. Experimentation with the system generated knowledge that can be stored and shared. If poor assignments are made between personnel and billets (jobs), the simulation is able to trace the cause (the policy specified) of such mismatches. The following exemplifies the type of knowledge that surfaced as a result of experimentation with the simulation:

For several sets of input policies and rules, personnel were sent to assignments without the requisite skills; it was discovered there were no available seats in the necessary training sessions to accommodate the personnel requiring specific skills training.
For some input scenarios the training required to fulfill billet requests was not available until a date later than the on-time arrival dates required for the billets. This caused degradation in the ability to satisfy on-time arrival targets for available billets.
Too many PCS moves impacted the ability to recruit and retain personnel.
It was discovered that some of the constraints imposed by the Enlisted Community Management organization's policies to maintain rank, occupation, and geographical balances were too stringent to permit assignments that would better satisfy fleet readiness objectives. Experimentation with the modification or relaxation of existing policies and rules provided insight into the consequent benefits and potential trade-offs.
Most importantly, the system made visible the rules and policies enforced by different planning groups and, for the first time, planners became aware of the potential positive or negative impacts some of these rules and policies had on effective manpower planning decisions. The modeling process alone shed light on many of the problems with the current manpower planning process, permitting planners to re-evaluate the need to change this important process.

In its current state the simulation model can be run in training mode to instruct organizations within the manpower system on how the various portions of the MPT system fit together, providing help files and links to other online documentation that offers detailed discussion of the system. It can also be run in analysis mode to help planners understand, for example, how a reduction in the PCS budget will affect a particular community. By using a common tool that is accessible through the corporate intranet, where the combined knowledge of the system is incorporated and used by analysts across the enterprise, planners can generate knowledge about the impact of a decision (a scenario defined by the combination of simulation input factors and parameters) on critical success factors for the Navy. Because there are infinitely many conceivable scenarios, each experiment run has the potential to add to the knowledge base generated through simulation experimentation and to evolve process knowledge over time.

Summary and concluding remarks

KM is an important tenet of modern day organizational management. There are many questions related to implementing KM and the role IT does and should play in enabling and facilitating KM practice. We have discussed some of the issues prevalent in the literature and offer a KM process to highlight areas of concern for IT-facilitated KM practice. We have also presented a three-dimensional model that provides a guide for assessing where IT may effectively contribute to the practice of KM and underscored areas that show promise for future research. We discussed a representative prototype KMS that generates, distributes, and evolves knowledge to improve US Navy manpower management decisions. Focused KMS applied toward strategic concerns and incorporated into an organization's IT infrastructure may have the added benefit of being catalysts to change organizations from knowledge hoarding to knowledge sharing cultures.

Rosemary Wild

Kenneth Griggs

Figure 1Perspectives on knowledge management

Figure 2Sources of knowledge

Figure 3Potential application opportunities for IT-enabled knowledge management systems

Figure 4Architecture for distributed DSS and KM system

Table IKnowledge classifications

References

Alavi, M., Leidner, D.E. (1999), "Knowledge management systems: issues, challenges, and benefits", Communications of the AIS, Vol. 1 No.2, pp.1-36.