Masterarbeit, 2004
74 Seiten, Note: A+
This thesis aims to develop a methodology for calculating the influence of improvement measures on corporate Key Performance Indicators (KPIs). The focus is on determining the impact of various measures on specific KPIs, ultimately leading to improved decision-making regarding resource allocation and prioritization.
This thesis revolves around the critical concepts of Key Performance Indicators (KPIs), improvement measures (enablers), and their complex relationship. It explores the use of Monte Carlo simulation for evaluating uncertainty, the Value Analyzer for data analysis, and the development of a comprehensive methodology for calculating the impact of enablers on KPIs. The thesis highlights the importance of aligning KPI implementation with customer benefit and organizational processes.
The Value Analyzer is a method-supported tool used to estimate the expected improvement of corporate Key Performance Indicators (KPIs) through specific measures.
Measures can be prioritized by calculating their relative output and impact on KPIs using structured calculation models and prioritization reports.
Monte Carlo Simulation is used to assess uncertainty and risk by basing scenarios on probability and chance to evaluate the flexibility of network designs.
It involves defining a primary goal (KPI), breaking it down into part goals (KPI elements), and determining appropriate measures to improve those part goals.
Enablers are the specific improvement measures or factors that drive the change and enhancement of KPI elements within an organization.
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