Commodity Procurement with Operational and Financial Instruments (eBook)

Jan Arnold (Autor)

eBook Download: PDF

2010 | 2010
XIII, 152 Seiten
Betriebswirtschaftlicher Verlag Gabler
978-3-8349-8654-2 (ISBN)

Infos

Jan Arnold integrates financial and operational aspects into a holistic approach to commodity procurement. He shows how to combine operational strategies considering just-in-time procurement, inventory holding and backlogging with financial strategies considering derivative instruments into an optimal procurement plan under volatile procurement prices.

Dr. Jan Arnold completed his doctoral thesis under the supervision of Prof. Dr. Stefan Minner at the Chair of Business Administration and Logistics at the University of Mannheim. He works as a management consultant.

Inhalt

Acknowledgement 6
Contents 7
List of Figures 10
List of Tables 12
1 Introduction 13
1.1 Motivation 13
1.2 Research Questions 17
1.3 Structure and Overview 18
2 Fundamentals and Literature Review 19
2.1 Financial Fundamentals 19
2.1.1 Financial Markets 19
2.1.2 No-Arbitrage 20
2.1.3 Option Contracts and Option Pricing 21
2.2 Commodities 24
2.2.1 Convenience Yield 24
2.2.2 Commodity Prices 25
2.3 Literature 27
2.3.1 Operations Literature 27
Continuous Time Models 27
Discrete Time Models 29
2.3.2 Industrial Organization Literature 34
Cournot Competition in Terms of Quantity Commitment 34
Models Combining Aspects from Cournot and Bertrand Competition 36
2.3.3 Literature on the Interface of Operations and Finance 38
Uncertain Prices 39
Uncertain Demands 39
Uncertain Prices and Uncertain Demands 40
3 Procurement with Deterministic Costs 42
3.1 Introduction 42
3.2 Manufacturing Model 43
3.2.1 Model Formulation 43
3.2.2 Model Solution 46
Feasible Combinations of Control and State Variable 46
Feasible Sequences of Policies 47
Initial and Terminal Inventories 49
Solution Algorithm 51
Numerical Example 52
Interpretation of Shadow Prices 52
3.3 Procurement Model 53
3.3.1 Model I: Uncapacitated Storage, no Backlogging 56
Initial and Terminal Inventories 59
Solution Algorithm 59
Numerical Examples 60
Interpretation of Shadow Prices 60
3.3.2 Model II: Uncapacitated Storage and Backlogging 62
Model II.1 62
Numerical Example 64
Model II.2 64
Solution Algorithm 66
3.3.3 Model III: Capacitated Storage, no Backlogging 66
Numerical Example 68
Solution Algorithm 69
3.3.4 Model IV: Capacitated Storage and Backlogging 70
Numerical Example 70
3.4 Conclusions and Outlook 70
4 Procurement with Stochastic Costs 73
4.1 Risk Management for Operational and Financial Commodity Procurement 73
4.1.1 Introduction 73
4.1.2 Notation and Assumptions 75
4.1.3 Expected Profit Optimization 76
Analysis of the Salvage Value 79
4.1.4 Mean-Variance Optimization 82
Isolated Analysis of Mismatch Risk and Price Risk 83
Influence of Correlation Between Price and Demand 86
Comparison to Newsvendor Models 87
Analysis of the Salvage Value 90
4.1.5 Expected Utility Optimization 90
Analysis of the Salvage Value 93
4.1.6 Conclusions and Outlook 94
4.2 Operational and Financial Commodity Procurement in Competition 95
4.2.1 Introduction 95
4.2.2 Notation and Assumptions 96
4.2.3 Procurement Strategy in a Monopoly 98
4.2.4 Procurement Strategy in a Cournot Duopoly 101
The Influence of Salvage Value 104
4.2.5 Procurement Strategy in a Bertrand Duopoly 107
4.2.6 Conclusions and Outlook 113
5 Conclusions and Outlook 115
5.1 Conclusions 115
5.2 Outlook 117
A Risk Management 118
A.1 Expected Profit Optimization 118
A.1.1 Analysis of the Expected NPV of the Profit 118
A.1.2 The Influence of the Salvage Value 123
A.2 Mean-Variance Optimization 124
A.2.1 Analysis of the Variance of the Profit 124
A.2.2 Isolated Analysis of Mismatch and Price Risk 127
A.3 Expected Utility Optimization 130
B Monopoly 131
B.1 Analysis of the Second Period 131
B.2 Analysis of the First Period 133
C Cournot Duopoly 137
C.1 Analysis of the Second Period 137
C.2 Analysis of the First Period 138
C.3 Salvage Value 144
D Bertrand Duopoly 145
D.1 Analysis of the Second Period 145
D.2 Analysis of the First Period 147
References 150

Textauszug

5 Conclusions and Outlook (S. 103-104)

5.1 Conclusions

This thesis considered deterministic commodity procurement and production problems and stochastic commodity procurement problems in presence of a ?nancial market in order to isolate dynamic and stochastic effects. The deterministic problems investigated dynamic manufacturing and procurement models in continuous time (Sections 3.2 and 3.3) and were solved using an optimal control approach. The key result is a crucial trade-off for the decisions of timing the production/procurement and determining the production/procurement quantity which holds for general price, cost, and demand functions and is as follows.

• In the dynamic manufacturing problem, the production decision results from the trade-off between marginal production costs, inventory holding costs, and costs on capital tied in the inventory. Production though inventories are available can be optimal if the manufacturing costs are strongly increasing in time as the production rate is subject to limited capacity.

• In the dynamic procurement problem, the trade-off between marginal procurement costs, inventory holding costs, and costs on capital tied in the inventory is responsible for the procurement decision. Additional procurement in presence of positive inventories can be optimal if the warehouse capacity is limited.

These optimality properties have been derived analytically and are exploited in solution algorithms to solve the trade-off. The stochastic models shed light on the in?uence of risk and competition aspects. The interface of operations and ?nance comes up when the restrictive assumptions of a perfect capital market are relaxed. The aspects of risk-aversion and a competitive sales markets in presence of a perfect capital market have been investigated.


• A risk-neutral ?rm does not bene?t from advance procurement.
• For a risk-averse ?rm optimizing a mean-variance criterion, a mixture of inventories, option contracts, and JIT-procurement can be optimal.
For an exponential utility function which is among the class of CARA utility functions, the following numerical results were found.

• Increasing risk-aversion increases the optimal level of advance procurement.
• Increasing price uncertainty increases the optimal level of inventory holding and decreases the optimal amount of option contracts.
• Increasing demand uncertainty decreases the optimal level of advance procurement.

• An increasing salvage value implying a lower degree of perishability of the commodity increases the optimal level of advance procurement.
Section 4.1 considered the risk management aspect when a ?rm has access to both operational and ?nancial procurement instruments under arbitrage-free conditions on the procurement market for various optimization objectives (riskneutrality and risk-aversion).

Produkt-Details

• Erscheint lt. Verlag: 10.5.2010
• Zusatzinfo: XIII, 152 p.
• Verlagsort: Wiesbaden
• Sprache: englisch
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Schlagworte: Hedging • Inventory • Just-in-Time • Operations Research • Rohstoffbeschaffung • Rohstoffe
• ISBN-10: 3-8349-8654-2 / 3834986542
• ISBN-13: 978-3-8349-8654-2 / 9783834986542