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.
Table of Contents
1. THESIS:
1.1 Management summary
2. INTRODUCTION
2.1 Prerequisites
2.2 Selecting measures
3. OTHER METHODS
3.1 Cash-value method, net present value (NPV)
3.1.1 Area of application
3.1.2 Frequency of use
3.1.3 Preconditions
3.1.4 Implementation
3.1.5 Advantages
3.1.6 Disadvantages
3.2 Variations of this method
3.3 Real Options
3.3.1 Evaluation of flexibility with real options
4. TRANSFERRING TO VALUE ADDED NETWORK DESIGN
4.1 Monte Carlo Simulation
4.2 Introduction To Monte Carlo Simulation
4.2.1 How Does Monte Carlo Simulation Work?
4.2.2 Monte Carlo: Based On Probability & Chance
4.2.3 What Is Monte Carlo Simulation Good For?
4.3 Evaluating the methods:
5. WHAT IS MY SUGGESTION AND WHY?
6. GENERAL NOTES ABOUT KPIS
6.1 Targets and customer benefit for KPI implementation and controlling
6.2 General comments and guidelines on KPIs
7. METHODOLOGY AND APPROACH
7.1 KPI Diagnosis
7.2 KPI Design
7.3 KPI implementation
7.4 KPI Controlling
7.5 Embedding KPIs in organization processes
7.6 As an example:
7.7 Plan
7.8 Source
7.9 Make
7.10 Deliver
8. THE LIMITS TO USING INDICATORS
8.1 Current indicator types in various corporations and industrial sectors
9. THE CLASSICAL INDICATOR SYSTEM
9.1 The Du Pont indicator system
9.2 The ZVEI indicator system
9.3 The RL indicator system
9.4 Measures portfolio
9.5 Plan: Measures in the “plan” area
9.6 Source: Measures in the “source” area
9.7 Make: Measures in the “make” area
9.8 Deliver: Measures in the “deliver” area
10. THE CONTEXT BETWEEN IMPROVEMENT MEASURES (ENABLERS) – KPI AND KPI ELEMENTS
11. THE VALUE ANALYZER
11.1 Basics:
11.2 KPI data input mask
11.3 KPI element data input mask
11.4 Industry data input mask
11.5 Enabler data input mask:
11.6 Category data input mask
11.7 Improvement range data input mask
11.8 Share range data input mask
11.9 Calculation form:
11.10 Allocation factor:
11.11 Improvement factor:
11.12 Calculate
11.13 KPI values for Customer
11.14 Calculation for Customer Form
11.15 The Reports
11.15.1 Show improvement report
11.15.2 Show share report
11.15.3 Show allocation report
11.15.4 Show value added report (KPI)
11.15.5 Show KPI report
11.15.6 Refresh
12. CALCULATION EXAMPLE
12.1 Sample Calculation KPI: Account Executive Push
12.2 The change of KPIs value
12.3 Input Value Validation 1:
12.4 Input Validation 2:
12.5 Value Analyzer
12.6 Calculation Form
12.6.1 Industry:
12.6.2 Customer:
12.6.3 KPI:
12.6.4 KPI Element:
12.6.5 Category:
12.6.6 Enabler:
12.6.7 Calculation Matrix Mask
13. THE REPORTS
13.1 Improvement Report
13.2 Allocation Report
13.3 Prioritising Measures
13.3.1 Developing the mathematic formulas
14. CONCLUSION
Objectives & Research Focus
This thesis aims to provide a systematic methodology for companies to accurately calculate and prioritize improvement measures based on their influence on specific corporate Key Performance Indicators (KPIs). The research addresses the common shortcoming of insufficient quantification in performance improvement projects by introducing the "Value Analyzer" tool, which enables the simulation of business scenarios to achieve an optimal mix of enablers for defined performance targets.
- Development of a three-level calculation model involving primary KPI goals, subordinate KPI elements, and improvement measures (enablers).
- Implementation of matrix-based calculations to manage cross-functional impacts and dependencies between various improvement measures.
- Evaluation of traditional investment methods (NPV, Real Options, Monte Carlo) and their limitations in a dynamic business context.
- Prioritization of improvement measures based on effort and expected relative output.
- Creation of a software-based (VBA/Access) solution to support management decision-making in complex environments.
Excerpt from the Book
12.6 Calculation Form
The following figure shows the calculation form with all of its individual fields. The individual “participants” in the calculation can be selected in the upper area—which KPI should be calculated, which KPI elements should be used to make up the KPI, and which enablers influence these KPI elements.
Likewise, the project is also defined in a customer-specific manner here; in this case, which customer, which country (since this is a global rollout scenario), and not least, which category. This is therefore a project from the production, logistics, or, as in this case, the CRM field.
12.6.1 Industry:
In order to form a basis of comparison foe the specific figures and results for the corresponding area, you can also enter various industries here. Assuming that the values in the various industries are in completely different scales (machine engineering has a very high inventory level compared to software development), the results can immediately be benchmarked.
12.6.2 Customer:
Within the corresponding industry, you can enter a new customer. The “Customer” input field can also be used to create different business scenarios for the same customer. The data remain intact and the results are comparable.
12.6.3 KPI:
This field contains a selection of the KPIs already entered. KPIs already in the system from earlier calculations can be selected or new ones created for the specific requirements or projects from the dropdown menu.
12.6.4 KPI Element:
This field contains a list of KPI sub elements. The “inventory level” KPI, for example, can be divided in to “raw materials inventory” and “finished goods inventory.” KPI elements already in the system from earlier calculations can be selected or new ones created for the specific requirements or projects from the drop-down menu.
Summary of Chapters
THESIS: This chapter highlights the problem that companies often fail to sufficiently calculate and prioritize the impact of improvement measures on corporate performance indicators.
INTRODUCTION: The introduction establishes the necessity of transparency and systematic management when implementing IT and business improvements, citing challenges such as wasting investment capital.
OTHER METHODS: This section reviews traditional evaluation methods like Net Present Value (NPV), Real Options, and Monte Carlo simulations, noting their specific limitations in addressing complex interdependencies.
TRANSFERRING TO VALUE ADDED NETWORK DESIGN: This chapter discusses project flexibility and how stochastic techniques like Monte Carlo simulation can be applied to handle changing environmental influences.
WHAT IS MY SUGGESTION AND WHY?: The author introduces the need for a three-level calculation method that links primary goals, KPI elements, and improvement measures to overcome the flaws of existing evaluation systems.
GENERAL NOTES ABOUT KPIS: This chapter outlines essential guidelines for KPI implementation, emphasizing that indicators must be embedded in organizational processes and serve as tools for informed improvement, not as static league tables.
METHODOLOGY AND APPROACH: The author presents a structured, four-phase approach covering KPI diagnosis, design, implementation, and controlling to ensure consistency and quality.
THE LIMITS TO USING INDICATORS: This section discusses potential pitfalls in indicator usage, such as "indicator inflation" and the dangers of treating metrics as an autopilot for corporate success.
THE CLASSICAL INDICATOR SYSTEM: This chapter provides an overview of traditional frameworks like the Du Pont, ZVEI, and RL indicator systems, noting their individual strengths and weaknesses regarding target alignment.
THE CONTEXT BETWEEN IMPROVEMENT MEASURES (ENABLERS) – KPI AND KPI ELEMENTS: This chapter explores the complex relationships between KPIs and enablers, where a single measure can impact multiple elements and vice versa.
THE VALUE ANALYZER: This chapter details the technical and functional components of the Value Analyzer tool, including its input masks and data structure for simulating performance impacts.
CALCULATION EXAMPLE: The final content chapter provides a step-by-step example of how to use the Value Analyzer, covering allocation factors, improvement factors, and the generation of reports.
THE REPORTS: This chapter describes the various output formats of the Value Analyzer, including improvement, allocation, and KPI reports for management review.
CONCLUSION: The conclusion reaffirms that a systematic approach and transparency are essential for prioritizing improvement measures and that the Value Analyzer offers a priority-worthy solution for navigating complex corporate scenarios.
Keywords
Key Performance Indicators, KPI, Value Analyzer, Improvement Measures, Enablers, Corporate Performance, Business Scenarios, ROI, Monte Carlo Simulation, Strategic Management, Performance Management, Decision Support, Supply Chain Management, Simulation, KPI Controlling
Frequently Asked Questions
What is the primary focus of this work?
The work focuses on the correct calculation and prioritization of improvement measures in relation to their influence on predefined Key Performance Indicators (KPIs) within corporate settings.
Which central themes are addressed?
Key themes include performance measurement transparency, the breakdown of KPIs into actionable elements, the simulation of improvement impacts, and the prioritization of investment measures in complex organizational structures.
What is the primary goal of the research?
The primary goal is to provide a systematic methodology and a specialized tool (the Value Analyzer) that allows management to assess how specific improvement measures affect corporate KPIs before committing resources to implementation.
What scientific methods are used?
The author uses a structured, three-level calculation model (Goal, Element, Enabler) combined with matrix-based math to account for cross-functional dependencies and impact factors.
What does the main body of the book cover?
It covers the diagnosis and design of KPI systems, an analysis of traditional evaluation methods (NPV, Real Options), the technical architecture of the Value Analyzer, and a detailed practical application through a calculation example.
Which keywords best characterize this thesis?
The most relevant keywords include KPI, Value Analyzer, Enablers, Strategic Management, Simulation, Business Scenarios, and Performance Measurement.
How does the "Value Analyzer" prevent double counting of improvements?
The tool uses an allocation factor to ensure that the total influence of multiple enablers on a single KPI element remains constrained and logically distributed.
Can the model handle different currencies or measurement units?
Yes, the author stresses that the currency or unit of measure must be consistent throughout the relationship network. If non-monetary data is used, conversions must be applied to ensure compatibility in the final calculation.
Is this method applicable to all industries?
The framework is presented as a generic, systematic approach. The author suggests that it can be tailored to various corporate environments, including production, logistics, and CRM, by adjusting the industry-specific parameters in the Value Analyzer.
- Quote paper
- Andreas Müllner (Author), 2004, Calculating the influence of improvement measures on corporate KPIs, Munich, GRIN Verlag, https://www.grin.com/document/44959
