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

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

Project Selection Directed By Intellectual Capital Scorecards

Management of intellectual capital is an important issue in knowledge intensive organizations. Part of this is the composition of the optimal project portfolio the organization will carry out in the future. Standard methods that guide this process mostly focus on project selection on the basis of expected returns. However, in many cases other strategic factors should be considered in their interdependence such as customer satisfaction, reputation, and development of core competences. In this paper we present a tool for the selection of a project portfolio, explicitly taking into account the balancing of these strategic factors. The point of departure is the intellectual capital scorecard in which the indicators are periodically measured against a target; the scores constitute the input of a programming model. From the optimal portfolio computed, objectives for management can be derived. The method is illustrated in the case of R&D departments

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

Combining expert knowledge and databases for risk management

Correctness, transparency and effectiveness are the principal attributes of knowledge derived from databases. In current data mining research there is a focus on efficiency improvement of algorithms for knowledge discovery. However important limitations of data mining can only be dissolved by the integration of knowledge of experts in the field, encoded in some accessible way, with knowledge derived form patterns in the database. In this paper we will in particular discuss methods for combining expert knowledge and knowledge derived from transaction databases.The framework proposed is applicable to wide variety of risk management problems. We will illustrate the method in a case study on fraud discovery in an insurance company