Rosemary H. Wild

A framework for e‐learning as a tool for knowledge management

A common thread among the plethora of definitions of knowledge management is that its objective is to identify and leverage the collective knowledge in an organization to help organizations compete and survive. One potential lever is e‐learning, the creation and distribution of organizational knowledge through the online delivery of information, communication, education, and training. John Chambers, of Cisco Systems, said: “The two great equalizers in life are the Internet and education”, so why not fuse the two to provide an efficient way to empower a workforce with the skills and knowledge it needs to compete amid the rapid pace of change in business? In this paper we discuss the relationship between knowledge management and e‐learning and present a framework for employing e‐learning as a valuable tool for knowledge management. The framework encompasses the planning and implementation elements necessary for organizations to leverage existing technologies and implement new ones to promote organizational learning and contribute to the management of organizational knowledge.

An experimental design strategy for designing robust systems using discrete-event simulation

Well designed experiments can dramatically improve the statistical accuracy of simula tion output data and facilitate the statistical analysis. In this paper we propose an experimental design strategy for designing robust systems using discrete-event simulation. The proposed strategy is motivated by Genichi Taguchis strategy for improving product and process quality in manufacturing. Robust systems are systems that not only satisfy performance criteria, but also are not sensitive to uncontrollable environmental conditions or noise in the systems environment. We demonstrate the proposed experimental design strategy with a jobshop example and discuss the potential benefits of employing such a strategy for designing robust systems in general.

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

Purpose – There is controversy about the role information technology (IT) should play in knowledge management (KM) spanning extremes that either overvalue or undervalue ITs 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 t...

A web portal/decision support system architecture for collaborative intra-governmental planning

In this paper, we focus on the US intra-governmental initiative to improve internal efficiency and effectiveness (IEE) using information technology. We propose a web portal architecture that permits government planning teams to collaborate in an effective way to make more informed and better decisions. The architecture incorporates a simulation decision support system, which provides remote teams with the ability to experiment with a variety of planning scenarios to gain an understanding of the effect of policies and rules on team coordination and overall performance. The web portal architecture allows government agencies to share simulation results and discuss potential plans before implementing them.

An architecture for distributed scenario building and evaluation

A Web portal can support collaborative learning and help individuals and organizations plan for an uncertain future.

A funding choice decision model for financing promising e-government services

The evolution of electronic government (e-government) initiatives poses a myriad of challenges that involve complex technology, social and managerial decisions. One of the most overwhelming challenges, however, is identifying funding sources for promising e-government services. In this paper, we explore issues associated with the shortcomings of traditional government budgeting that prevent the successful use of information technology to enable e-government processes and services. We present diverse and innovative approaches local, state and federal government agencies are experimenting with to counter governmental budgeting constraints and create cost effective and properly funded e-government services. Finally, we develop a decision model to help guide the choice of an appropriate non-traditional funding approach in light of the risks associated with each alternative and the potential policy, social and political ramifications of each choice.

A Knowledge Capture Distributed DSS Architecture to Support Planning and Policy Decision Making

In this paper we propose a Web portal architecture that supports collaborative planning and policy decision making efforts through remote, joint experimentation with a distributed simulation model of the planning and policy process. The architecture also contains a knowledge management component that enables knowledge capture and representation. Thus the architecture facilitates the planning process by providing a transparent experimentation environment in which planners can work together to assess the impact of different sets of planning policies and model parameters on important organizational performance measures. The knowledge generated from collaborative experimentation with the planning model is captured and can be shared to improve subsequent planning and policy decisions. We illustrate the proposed architecture with a U.S. Navy manpower planning application, but highlight its applicability to planning and policy formulation processes in general, such as planning for new product development or disaster recovery.

A methodology for modelling and designing robust information systems in an uncertain use environment

Typical modelling techniques for information system analysis and design treat key system requirements parameters as deterministic. In addition, system dynamics reflected in time-path behaviour, such as queues and bottlenecks, are not captured in traditional information process models; thus it is difficult to gain insights into the performance of an information system (IS) within the context of the uncertainty in its use environment. This is increasingly important for systems such as supply chain management and customer relationship management systems that operate within a global milieu. In this study we propose an experiment design methodology that, unlike conventional simulation experimentation, allows robust information system designs to be identified. We demonstrate the risks associated with design decisions in which uncertainty is ignored and provide results that illustrate how the proposed experiment design methodology reduces the ad hoc character of simulation experimentation and provides insights into the best design alternatives in light of uncertainty in the IS use environment.

A response to Kleij nen's note on Wild and Pignatiello's experimental design strategy:

designs, methods of analysis, and so on. Distinctions have been made between Taguchi’s tactics and strategy [2]. A balanced assessment of Taguchi’s contributions and technical flaws with his methods can be found in [3]. One of the purposes of our paper was to bring the technology of robust design of manufactured products to the simulation community so that the systems (&dquo;products&dquo;) that they design using simulation will be more robust to uncontrollable environmental factors. As Kleijnen points out, the basic approach that we discussed can be easily generalized to systems that do not have any probabilistic components. We did not state this explicitly in our paper and thank Professor Kleijnen for catching our oversight. Two examples of the application of this basic approach involving nonprobabilistic models can be found in [6,7]. We also appreciate Professor Kleijnen’s comments about the applications of our method to system design areas other than manufacturing such as, for example, financial, ecological, and nuclear safety systems. We wholeheartedly agree with Professor Kleijnen that if the real system to be simulated already exists and is observable, actual system data should be used to obtain information on various environmental variables. We