In order to do justice to the increased requirements on project and business controlling, there is a need for a systematic approach tailored to the corporation involved. Many corporations have been shown to prioritize improvement measures insufficiently, if at all, despite increased requirements on the economic viability of projects. Since resources are limited in all companies, improvement measures with the highest relative output should be given preference. The usual methods used up to now have been either inappropriate or are no longer sufficient.
The Value Analyzer is a method-supported tool for estimating the expected improve¬ment of key performance indicators as a part of a general improvement in corporate performance.
This makes it easy to focus on worthwhile measures and supports a general economic calculation to base a go/no go decision on before the start of an implementation project.
Based on general key performance indicators, specific scenarios can be created and then altered and/or simulated by changing the parameters or factors of influence. This is all based on a three-level principle in the calculation model:
• Definition of a primary goal, KPI (key performance indicator, for example inventory level)
• Breakdown into part goals, KPI elements (performance indicator element, for example finished products inventory level)
• Determination of appropriate measures for improving the part goal
In the calculation of business scenarios that follows, the exact impact of enablers and the optimal combination of appropriate measures can be revealed, or possible measures can be prioritized according to specific aspects (e.g. effort in implementation).
The more complex the situation, the higher the value added from the Value Analyzer’s structured solution.
Inhaltsverzeichnis (Table of Contents)
- THESIS:
- Management summary.
- INTRODUCTION
- Prerequisites.
- Selecting measures.
- OTHER METHODS
- Cash-value method, net present value (NPV).
- Area of application.
- Frequency of use.
- Preconditions.
- Implementation.
- Advantages.
- Disadvantages.
- Variations of this method.
- Real Options.
- Evaluation of flexibility with real options.
- Cash-value method, net present value (NPV).
- TRANSFERRING TO VALUE ADDED NETWORK DESIGN
- Monte Carlo Simulation.
- Introduction To Monte Carlo Simulation.
- How Does Monte Carlo Simulation Work?
- Monte Carlo: Based On Probability & Chance.
- What Is Monte Carlo Simulation Good For?.
- Evaluating the methods:
- Monte Carlo Simulation.
- WHAT IS MY SUGGESTION AND WHY?
- GENERAL NOTES ABOUT KPIS
- Targets and customer benefit for KPI implementation and controlling.
- General comments and guidelines on KPIs.
- METHODOLOGY AND APPROACH
- KPI Diagnosis.
- KPI Design.
- KPI implementation.
- KPI Controlling.
- Embedding KPIs in organization processes.
- As an example:.
- Plan.
- Source.
- Make.
- Deliver.
- THE LIMITS TO USING INDICATORS
- Current indicator types in various corporations and industrial sectors.......
- THE CLASSICAL INDICATOR SYSTEM
- The Du Pont indicator system.
- The ZVEI indicator system.
- The RL indicator system.
- Measures portfolio.
- Plan: Measures in the "plan" area.
- Source: Measures in the "source" area.
- Make: Measures in the "make" area.
- Deliver: Measures in the "deliver" area.
- THE CONTEXT BETWEEN IMPROVEMENT MEASURES (ENABLERS) – KPI AND KPI
- ELEMENTS.
- THE VALUE ANALYZER.
- Basics:
- KPI data input mask.
- KPI element data input mask.
- Industry data input mask.
- Enabler data input mask:.
- Category data input mask.
- Improvement range data input mask.
- Share range data input mask.
- Calculation form:.
- Allocation factor:.
- Improvement factor:.
- Calculate.
- KPI values for Customer.
- Calculation for Customer Form.
- The Reports.
- Show improvement report.
- Show share report.
- Show allocation report.
- Show value added report (KPI).
- Show KPI report.
- Refresh.
- CALCULATION EXAMPLE
- Sample Calculation KPI: Account Executive Push.
- The change of KPIs value.
- Input Value Validation 1:.
- Input Validation 2:
- Value Analyzer.
- Calculation Form.
- Industry:
- Customer:
- KPI:
- KPI Element:.
- Category:
- Enabler:
- Calculation Matrix Mask.
- THE REPORTS.......
- Improvement Report.
- Allocation Report.
- Prioritising Measures
- Developing the mathematic formulas.
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
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.
- Relationship between improvement measures (enablers) and KPIs
- Developing a methodology for calculating the influence of improvement measures
- Prioritizing measures based on their impact on KPIs
- Utilizing Monte Carlo simulation for assessing uncertainty and risk
- Application of the Value Analyzer tool for data input and analysis
Zusammenfassung der Kapitel (Chapter Summaries)
- Introduction: This chapter provides a foundation for the thesis, outlining the importance of selecting appropriate measures for improving KPIs and the challenges involved in this process.
- Other Methods: This chapter examines various methods used to assess the value of investments, including cash-value methods, net present value (NPV), and real options.
- Transferring to Value Added Network Design: This chapter explores the application of Monte Carlo Simulation for assessing uncertainty and risk in the context of improving KPIs.
- General Notes about KPIs: This chapter discusses the importance of setting targets and ensuring customer benefit when implementing and controlling KPIs. It also provides general comments and guidelines on KPIs.
- Methodology and Approach: This chapter outlines a comprehensive methodology for KPI diagnosis, design, implementation, controlling, and embedding KPIs within organizational processes.
- The Limits to Using Indicators: This chapter discusses the limitations of using traditional indicator systems and explores current practices in various corporations and industrial sectors.
- The Classical Indicator System: This chapter examines different classical indicator systems, including Du Pont, ZVEI, and RL, and analyzes their respective strengths and weaknesses.
- The Context Between Improvement Measures (Enablers) – KPI and KPI Elements: This chapter emphasizes the crucial connection between improvement measures (enablers) and their impact on KPIs and KPI elements.
- The Value Analyzer: This chapter introduces the Value Analyzer tool, a valuable resource for data input, analysis, and reporting. It details the various data input masks and calculation functions offered by the tool.
- Calculation Example: This chapter presents a sample calculation of a KPI, demonstrating the application of the Value Analyzer tool and illustrating the methodology developed in the thesis.
- The Reports: This chapter explains the various reports generated by the Value Analyzer, including the Improvement Report, the Allocation Report, and the Prioritization Report.
Schlüsselwörter (Keywords)
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.
- Quote paper
- Andreas Müllner (Author), 2004, Calculating the influence of improvement measures on corporate KPIs, Munich, GRIN Verlag, https://www.grin.com/document/44959
