HOW TO SELECT MEASURES FOR DECISION SUPPORT SYSTEMS – AN OPTIMIZATION APPROACH INTEGRATING INFORMATIONAL AND ECONOMIC OBJECTIVES

Abstract

It is still an open issue of designing and adapting (data-driven) decision support systems and data

warehouses to determine relevant content and in particular (performance) measures. In fact, some

classic approaches to information requirements determination such as Rockart’s critical success

factors method help with structuring decision makers’ information requirements and identifying

thematically appropriate measures. In many cases, however, it remains unclear which and how many

measures should eventually be used. Therefore, an optimization model is presented that integrates

informational and economic objectives. The model incorporates (statistic) interdependencies among

measures – i. e. the information they provide about one another –, decision makers’ and reporting

tools’ ability of coping with information complexity as well as negative economic effects due to

measure selection and usage. We show that in general the selection policies of all-or-none or themore-the-better are not reasonable although they are often conducted in business practice. Finally,

the model’s application is illustrated by the German business-to-business sales organization of a

global electronics and electrical engineering company as example.

This document is currently not available here.

Share

COinS