Project Selection Directed By Intellectual Capital Scorecards

Management of intellectual capital is an important issue in knowledgeintensive organizations. Part of this is the composition of theoptimal project portfolio the organization will carry out in thefuture. Standard methods that guide this process mostly focus onproject selection on the basis of expected returns. However, in manycases other strategic factors should be considered in theirinterdependence such as customer satisfaction, reputation, anddevelopment of core competences.In this paper we present a tool for the selection of a projectportfolio, explicitly taking into account the balancing of thesestrategic factors. The point of departure is the intellectual capitalscorecard in which the indicators are periodically measured against atarget; the scores constitute the input of a programming model. Fromthe optimal portfolio computed, objectives for management can bederived. The method is illustrated in the case of R&D departments.knowledge management;intellectual assets;knowledge capitalization;optimal portfolio

Combining expert knowledge and databases for risk management

Correctness, transparency and effectiveness are the principalattributes of knowledge derived from databases. In current data miningresearch there is a focus on efficiency improvement of algorithms forknowledge discovery. However important limitations of data mining canonly be dissolved by the integration of knowledge of experts in thefield, encoded in some accessible way, with knowledge derived formpatterns in the database. In this paper we will in particular discussmethods for combining expert knowledge and knowledge derived fromtransaction databases.The framework proposed is applicable to widevariety of risk management problems. We will illustrate the method ina case study on fraud discovery in an insurance company.risk management;datamining;knowledge discovery;knowledge based systems

Long-run exchange rate determination: A neural network study

Foreign Exchange;Exchange Rate;Econometrics;Neural Network

General Model for Automated Diagnosis of Business Performance

In this paper, we describe an extension of the methodology for explanation generation in financial knowledge-based systems, offering the possibility to automatically generate explanations and diagnostics to support business decision tasks. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from financial data and business models. A multi-step look-ahead algorithm is proposed that deals with so-called calling-out effects, which are a common phenomenon in financial data sets. The extended methodology was tested on a case-study conducted for Statistics Netherlands involving the comparison of financial figures of firms in the Dutch retail branch. The analyses are performed with a diagnostic software application which implements our theory of explanation. Comparison of results of the classic explanation methodology with the results of the extended methodology shows significant improvements in the analyses when cancelling-out effects are present in the data

Towards a Value-based Method for Risk Assessment in Supply Chain Operations

This paper proposes a risk assessment framework as a research road-map, with the aim of developing a protocol that integrates the risk management requirements from the perspectives of the business and the government. We take the viewpoint of value modeling and interpret the risk management problem as a control problem. Four steps of risk assessment are identified in the framework, forming the risk management cycle

Diagnosis in the Olap Context

The purpose of OLAP (On-Line Analytical Processing) systems is to provide a framework for the analysis of multidimensional data. Many tasks related to analysing multidimensional data and making business decisions are still carried out manually by analysts (e.g. financial analysts, accountants, or business managers). An important and common task in multidimensional analysis is business diagnosis. Diagnosis is defined as finding the “best” explanation of observed symptoms. Today’s OLAP systems offer little support for automated business diagnosis. This functionality can be provided by extending the conventional OLAP system with an explanation formalism, which mimics the work of business decision makers in diagnostic processes. The central goal of this paper is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from multidimensional data and business models. We propose an algorithm that generates explanations for symptoms in multidimensional business data. The algorithm was tested on a fictitious case study involving the comparison of financial results of a firm’s business units