Conflicting experiences with DSS

Abstract The concept of DSS is clearly attracting a lot of interest. Although this should be welcomed, it does not signify that the concept is well-defined and well-established. Above, we may observe that empirical evidence on the effectiveness of DSS is not overwhelmingly positive, and is often conflicting. Many DSS focus on problem-solving, not on problem-identification. Many DSS are constructed around aggregated data, separated from primary, transaction processing data. However, it is shown in several studies that use of this type of data may be dangerous for decision-making. With regard to the model component of many decision-making. With regard to the model component ‘equation’ type. These models are only applicable under the assumption that the equations can be filled in and that they give a valid description of reality. This assumption seems to hold only in a very few case. A way out can be found in the application of models of the discrete event or ‘process’ type. A frequently encountered approach for the development of DSS is an evolutionary, step-wise one. However, such an approach is not necessarily converging. Especially personalized DSS demand a strong project management to avoid a chaos of hardware, software, personal files and models. Decentralization of decision-making and computing does not guarantee a better co-ordination of the workstations of individual information workers. We propose to use the concept DSS in a more narrow sense of an environment to support decision making, encompassing a language system, a knowledge system and a problem processing system. The language system is based on an object-oriented representation form. The problem processing system and the knowledge system bear heavily on the process type system specification.