Arnold Commodity Procurement with Operational and Financial Instruments

2010
ISBN: 978-3-8349-8654-2
Verlag: Betriebswirtschaftlicher Verlag Gabler
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 152 Seiten, eBook

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.

Zielgruppe

Research

Autoren/Hrsg.

Arnold, Jan

Inhaltsverzeichnis

1;Acknowledgement;6
2;Contents;7
3;List of Figures;10
4;List of Tables;12
5;1 Introduction;13
5.1;1.1 Motivation;13
5.2;1.2 Research Questions;17
5.3;1.3 Structure and Overview;18
6;2 Fundamentals and Literature Review;19
6.1;2.1 Financial Fundamentals;19
6.1.1;2.1.1 Financial Markets;19
6.1.2;2.1.2 No-Arbitrage;20
6.1.3;2.1.3 Option Contracts and Option Pricing;21
6.2;2.2 Commodities;24
6.2.1;2.2.1 Convenience Yield;24
6.2.2;2.2.2 Commodity Prices;25
6.3;2.3 Literature;27
6.3.1;2.3.1 Operations Literature;27
6.3.1.1;Continuous Time Models;27
6.3.1.2;Discrete Time Models;29
6.3.2;2.3.2 Industrial Organization Literature;34
6.3.2.1;Cournot Competition in Terms of Quantity Commitment;34
6.3.2.2;Models Combining Aspects from Cournot and Bertrand Competition;36
6.3.3;2.3.3 Literature on the Interface of Operations and Finance;38
6.3.3.1;Uncertain Prices;39
6.3.3.2;Uncertain Demands;39
6.3.3.3;Uncertain Prices and Uncertain Demands;40
7;3 Procurement with Deterministic Costs;42
7.1;3.1 Introduction;42
7.2;3.2 Manufacturing Model;43
7.2.1;3.2.1 Model Formulation;43
7.2.2;3.2.2 Model Solution;46
7.2.2.1;Feasible Combinations of Control and State Variable;46
7.2.2.2;Feasible Sequences of Policies;47
7.2.2.3;Initial and Terminal Inventories;49
7.2.2.4;Solution Algorithm;51
7.2.2.5;Numerical Example;52
7.2.2.6;Interpretation of Shadow Prices;52
7.3;3.3 Procurement Model;53
7.3.1;3.3.1 Model I: Uncapacitated Storage, no Backlogging;56
7.3.1.1;Initial and Terminal Inventories;59
7.3.1.2;Solution Algorithm;59
7.3.1.3;Numerical Examples;60
7.3.1.4;Interpretation of Shadow Prices;60
7.3.2;3.3.2 Model II: Uncapacitated Storage and Backlogging;62
7.3.2.1;Model II.1;62
7.3.2.2;Numerical Example;64
7.3.2.3;Model II.2;64
7.3.2.4;Solution Algorithm;66
7.3.3;3.3.3 Model III: Capacitated Storage, no Backlogging;66
7.3.3.1;Numerical Example;68
7.3.3.2;Solution Algorithm;69
7.3.4;3.3.4 Model IV: Capacitated Storage and Backlogging;70
7.3.4.1;Numerical Example;70
7.4;3.4 Conclusions and Outlook;70
8;4 Procurement with Stochastic Costs;73
8.1;4.1 Risk Management for Operational and Financial Commodity Procurement;73
8.1.1;4.1.1 Introduction;73
8.1.2;4.1.2 Notation and Assumptions;75
8.1.3;4.1.3 Expected Profit Optimization;76
8.1.3.1;Analysis of the Salvage Value;79
8.1.4;4.1.4 Mean-Variance Optimization;82
8.1.4.1;Isolated Analysis of Mismatch Risk and Price Risk;83
8.1.4.2;Influence of Correlation Between Price and Demand;86
8.1.4.3;Comparison to Newsvendor Models;87
8.1.4.4;Analysis of the Salvage Value;90
8.1.5;4.1.5 Expected Utility Optimization;90
8.1.5.1;Analysis of the Salvage Value;93
8.1.6;4.1.6 Conclusions and Outlook;94
8.2;4.2 Operational and Financial Commodity Procurement in Competition;95
8.2.1;4.2.1 Introduction;95
8.2.2;4.2.2 Notation and Assumptions;96
8.2.3;4.2.3 Procurement Strategy in a Monopoly;98
8.2.4;4.2.4 Procurement Strategy in a Cournot Duopoly;101
8.2.4.1;The Influence of Salvage Value;104
8.2.5;4.2.5 Procurement Strategy in a Bertrand Duopoly;107
8.2.6;4.2.6 Conclusions and Outlook;113
9;5 Conclusions and Outlook;115
9.1;5.1 Conclusions;115
9.2;5.2 Outlook;117
10;A Risk Management;118
10.1;A.1 Expected Profit Optimization;118
10.1.1;A.1.1 Analysis of the Expected NPV of the Profit;118
10.1.2;A.1.2 The Influence of the Salvage Value;123
10.2;A.2 Mean-Variance Optimization;124
10.2.1;A.2.1 Analysis of the Variance of the Profit;124
10.2.2;A.2.2 Isolated Analysis of Mismatch and Price Risk;127
10.3;A.3 Expected Utility Optimization;130
11;B Monopoly;131
11.1;B.1 Analysis of the Second Period;131
11.2;B.2 Analysis of the First Period;133
12;C Cournot Duopoly;137
12.1;C.1 Analysis of the Second Period;137
12.2;C.2 Analysis of the First Period;138
12.3;C.3 Salvage Value;144
13;D Bertrand Duopoly;145
13.1;D.1 Analysis of the Second Period;145
13.2;D.2 Analysis of the First Period;147
14;References;150

Fundamentals and Literature Review.- Procurement with Deterministic Costs.- Procurement with Stochastic Costs.- Conclusions and Outlook.

Leseproben

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).

Über Autor(innen)

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.