Abstract
The field of Business Process Reengineering (BPR) has recently observed the birth of Decision Support Systems (DSSs) as a solution for overcoming the limitations of previous initiatives. The numerous failures concerning earlier BPR implementations are mostly ascribed to the introduction of best practices from other industrial experiences without proper adaptation to the local specificities, as well as by the inadequate consideration of uncertainty issues within decision making. A considerable amount of DSSs integrates issues dealing with customer opinions and behaviours and takes into account the uncertainties related to the relevance and the implications of the gathered feedbacks. In such a context, the paper describes an algorithmic model (implemented in a computer application) for supporting decision making that quantitatively relates the phases of a business process with its outputs, with reference to the contribution in generating customer value. The proposed decision support method can be advantageously employed especially in those cases characterized by time pressure and impossibility of performing suitable customer surveys. The model sheds light on process value bottlenecks and provides indications about the most beneficial reengineering activities. Context uncertainties are managed by applying Monte Carlo simulation. Such a measure allows evaluating the share of risk ensuing from redesigning certain business process phases.