Price and Product-Mix Decisions Under Different Cost Systems (eBook)

Philipp Plank received his doctoral degree from the University of Cologne under the supervision of Prof. Dr. C. Homburg. He now works as a project manager und data scientist.

Foreword 6
Acknowledgment 8
Table of contents 10
Abbreviations 13
Symbols 14
Tables 21
Figures 23
1. Introduction 24
1.1 On the adequacy of full-costing for decision making 24
1.2 From cost system design to pricing and portfolio decisions 25
2. Simulation research framework 29
2.1 A basic introduction to simulations 29
2.2 On the adequacy of simulations in management accounting research 32
2.3 The underlying research framework 34
2.4 Relevant simulation models in management accounting – A theory review 39
2.4.1 The Grand Model 39
2.4.2 Cost Model 44
2.4.2.1 First-stage heuristics 47
2.4.2.2 Second-stage heuristics 49
2.4.2.3 Simulation approach 49
2.4.2.4 Results 54
2.4.3 Margin Model 55
2.4.3.1 Simulation approach 56
2.4.3.2 Results 60
3. Price and Product-Mix Decisions Under Different Cost Systems 62
3.1 Model Details 65
3.1.1 Model design choices 66
3.1.2 The Benchmark Model 67
3.1.3 The Noisy Model 70
3.1.3.1 First-stage heuristics 71
3.1.3.2 Second-stage heuristics 72
3.1.4 Alternatives of modeling capacity in the NM 72
3.2 The Simulation Approach 73
3.2.1 Market & production parameters
3.2.2 Cost system design & parameters
3.2.3 Information & simulation flow and data
3.2.4 Simulation Example 79
3.2.4.1 Stage one: BM joint price and capacity decision 79
3.2.4.2 Stage two: NM price decision 80
3.2.4.3 Stage three: NM capacity decision 81
3.3 Hypotheses Development 82
3.4 Key metrics and analysis method 84
3.4.1 Derivation of Profit Error Types 84
3.4.2 Regression Specification 88
3.5 Results 90
3.5.1 Descriptive Results 90
3.5.2 Cost-System Complexity (H1) 91
3.5.2.1 Mean Profit Errors across Different Cost Systems 91
3.5.2.2 Regression Results for Profit Errors across Different Cost Systems 92
3.5.3 Market Growth (H2, H2a & H2b)
3.5.4 Profit Error Types 102
3.6 Robustness 104
3.6.1 Parameter fit and sensitivity against alteration of parameter choice 104
3.6.2 Mean Cost Errors across Different Cost Systems 106
3.6.3 Distributions of Cost and Profit Errors: Graphical Analysis (Box Plots) and Formal Tests (Pearson Correlation, Rank Test and Regression) 107
3.6.4 Introducing Batch Resources 109
3.7 Preliminary conclusion of cost system influences on decision making 110
4. Extended Decision Model Incorporating Cost- Stickiness 112
4.1 A Side Note on Cost-Stickiness and its model implications 114
4.2 The Extended Model Approach 116
4.2.1 The BM adjustment 117
4.2.2 The NM adjustment 120
4.2.3 Information & simulation flow and data
4.3 Hypothesis Development 127
4.4 Research framework: Key metrics and analysis method 128
4.4.1 Variance of Profits 128
4.4.2 Regression Specifications 129
4.5 Results 130
4.5.1 Descriptive Results (H3) 130
4.5.2 Regression Results (H1 & H2)
4.5.3 The influence of cost structure 134
4.6 Discussion of the limitations of the new approach 135
5. Conclusion 137
6. References 139
7. Appendix 147
A. Error Derivation 147
B. Variable and Parameter declaration 148
C. Robustness Tables 150