The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company’s outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics. Each KPI is monthly associated to a score that summarizes the clinic efficiency for that month. Standard statistical methods are currently used to analyze the BSC data and to give a comprehensive view of the corporate improvements to the top management. We herein propose the Self‐Organizing Maps (SOMs) as an innovative approach to extrapolate information from the FME BSC data and to present it in an easy‐readable informative form. A SOM is a computational technique that allows projecting high‐dimensional datasets to a two‐dimensional space (map), thus providing a compressed representation. The SOM unsupervised (self‐organizing) training procedure results in a map that preserves similarity relations existing in the original dataset; in this way, the information contained in the high‐dimensional space can be more easily visualized and understood. The present work demonstrates the effectiveness of the SOM approach in extracting useful information from the 30‐dimensional BSC dataset: indeed, SOMs enabled both to highlight expected relationships between the KPIs and to uncover results not predictable with traditional analyses. Hence we suggest SOMs as a reliable complementary approach to the standard methods for BSC interpretation.
