Prospects for the 2020 Iron Ore Market. Quantitative Analysis of Market Dynamics and Risk Mitigation Strategies

Transcription

1 Marc P. Bielitza: Prospects for the 2020 Iron Ore Market. Quantitative Analysis of Market Dynamics and Risk Mitigation Strategies Schriften zu MANAGEMENT, ORGANISATION UND INFORMATION, hrsg. von Hagen Lindstädt, Band 36, ISBN , ISBN (e-book pdf), Rainer Hampp Verlag, München u. Mering 2012, 321 S., As the key ingredient for steelmaking, iron ore is integral to the global economy and represents the world's second-largest natural resource market by value. The overall structure and dynamics of the iron ore market have recently undergone various radical changes. The global demand shock caused by the rapid industrialization of China has sent market prices skywards and triggered a collapse of the 40-year-old benchmark pricing system. The newly adopted quarterly index-based pricing mechanism has brought an abrupt end to the traditional stability of the iron ore market, with price dynamics now experiencing greater volatility. Against the backdrop of these disruptive changes, this dissertation proposes a deterministic nine-region spatial equilibrium model of the 2020 iron ore market based on the fundamental characteristics and mechanics of the market. Key features of the model are its bottom-up structure, modeling supply volumes and costs on an individual mine level as well as the simulation of interregional trade flows using a linear optimization approach. In analyzing a number of relevant scenarios, this dissertation projects the key characteristics of the 2020 global iron ore market in terms of regional production volumes, demand, iron ore trade flows and market prices. It quantifies market dynamics based on changes in available production capacity, demand and freight rates. From a demand perspective, the dissertation proposes several strategies to mitigate the risk arising from higher and more volatile iron ore prices. Of these strategies, the captivity of mines and the introduction of iron ore export taxes are analyzed in greater detail with respect to their effectiveness and influence on the iron ore market. Key words: Iron ore, mining, steel, steelmaking, China, market simulation, industry cost curve, pricing, linear optimization, trade flows, market dynamics, volatility, risk, freight, captivity, export taxes Dr. Marc P. Bielitza was born in 1981 in New York, NY. He studied Business Engineering at Universität Karlsruhe (TH) in Germany and Università Commerciale Luigi Bocconi in Milan (Italy) with a focus on Production Technology and Industrial Dynamics. In 2007 he joined McKinsey & Company as a management consultant specializing in basic materials industries. From 2009 to 2011 he took an educational leave to write his doctoral thesis under the supervision of Prof. Dr. Hagen Lindstädt at the Karlsruhe Institute of Technology (KIT).

2 Schriften zu MANAGEMENT, ORGANISATION UND INFORMATION Herausgegeben von Hagen Lindstädt Band 36

3 Marc P. Bielitza Prospects for the 2020 Iron Ore Market Quantitative Analysis of Market Dynamics and Risk Mitigation Strategies Rainer Hampp Verlag München, Mering 2012

4 Bibliographic information published by the Deutsche Nationalbibliothek Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at ISBN (print) ISBN (e-book) Schriften zu Management, Organisation und Information: ISSN DOI / First published 2012 Von der Fakultät für Wirtschaftswissenschaften des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation. Tag der mündlichen Prüfung: 13. Januar 2012 Referent: Prof. Dr. Hagen Lindstädt Korreferent: Prof. Dr. Frank Schultmann 2012 Rainer Hampp Verlag München und Mering Marktplatz Mering, Germany All rights reserved. No part of this publication may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publisher. In case of complaints please contact Rainer Hampp Verlag.

5 Acknowledgments This dissertation was written during my time as a PhD student at the Karlsruhe Institute of Technology (KIT), during an educational leave from McKinsey & Company. A number of people have contributed significantly to this dissertation and I would like to take this opportunity to extend my sincere thanks to them. Foremost, I express my profound gratitude to Prof. Dr. Hagen Lindstädt, for giving me the opportunity to work in such a stimulating research context and for allowing me to learn from his impressive theoretical, methodological and empirical knowledge. The inspiring discussions with him as well as his constructive criticism significantly widened my scientific understanding of oligopolies and market modeling. Also, I would like to thank Prof. Dr. Frank Schultmann for taking on the role of co-referee. My special thanks go to Dr. Ilse Kenis from the McKinsey Basic Materials Institute, for serving as a sounding board for my ideas and for letting me benefit from her vast knowledge of the global iron ore and steel industries. I would also like to thank the McKinsey GEM leadership, particularly Dr. Peter Feldhaus, Dr. Heiner Frankemölle, Dr. Lorenz Jüngling, Dr. Sigurd Mareels, Dr. Stefan Rehbach, Dr. Carsten Sürig and Dr. Benedikt Zeumer, for their guidance and support. Furthermore, I am grateful for the experience of being a part of the vibrant Fellow community in the Munich office. I thank all colleagues for the many helpful discussions as well as their insightful comments. There is no telling how much my dissertation has profited from working in such an environment that is at the same time friendly and professional. I will always remember the good times we had. Great appreciation goes to my parents for their continuous support in many different ways. They have laid the foundation for this dissertation by providing the upbringing and education that make up much of who I am today. Finally, I would like to thank Christina for her relentless encouragement and affectionate support which is so strongly coupled with the success of this dissertation. Marc P. Bielitza Karlsruhe, January 2012

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7 vii Contents List of Figures List of Tables Abbreviations Symbols xi xv xvii xix 1 Introduction Motivation Ambition Structure of the present paper Relevant previous research on the iron ore market Technical introduction to iron ore Nature and occurrence of iron and iron ores Natural iron resources and iron ore reserves Depletion of iron ore Differentiating factors of natural iron ores Iron ore value chain from mine to market Discovery of iron ore deposits Buildup of mining infrastructure Mining of iron ore Beneficiation and agglomeration of iron ore Transportation of iron ore to the market Significance of iron ore for steelmaking Overview of iron- and steelmaking process routes Quality of iron ores with respect to their use for steelmaking 26 3 Structure and characteristics of the 2009 iron ore market Introductory comments regarding iron ore market data and statistics Units of measure for volume data

8 viii Contents Units of measure for price data Iron ore demand Geography of iron ore demand Chinese steel industry as key source of iron ore demand Iron ore supply Geography of iron ore supply Corporate control in iron ore mining Dry bulk freight rates Iron ore trade flows and market mechanism Global iron ore trade flows Iron ore market mechanism Public policy Analysis of recent market developments and derivation of risk mitigation strategies Recent developments and trends in the iron ore market Increased iron ore market price Increased amount of iron ore capacity expansion projects Increased iron ore market price volatility Increased freight rates and freight rate volatility Impact of iron ore market developments and trends on the steel industry Impact of increased iron ore prices Impact of increased iron ore price volatility Potential risk mitigation strategies for demand-side players A model of the 2020 iron ore market Anatomy and mechanisms of the model Definition of geographic regions Key input components Market mechanism Output Technical implementation of the model Validation of the model vs actuals Validation of the allocation mechanism Validation of the pricing mechanism Quantitative analysis of the 2020 iron ore market and risk mitigation strategies Definition of scenarios based on research questions Definition of 2020 base case and determination of scenarios for sensitivity analysis

9 Contents ix Definition of 2020 scenarios for evaluation of selected risk mitigation strategies Description of methodology and scope of analyses Results of quantitative analyses Quantitative analysis of structure and dynamics of the 2020 iron ore market Evaluation of risk mitigation strategies for demand-side players in Discussion of results Discussion of results of quantitative analysis of structure and dynamics of the 2020 iron ore market Discussion of evaluation of selected risk mitigation strategies Discussion of results from a European perspective Wrap-up and outlook Review of goal attainment and conclusion Critical assessment and areas for future research Appendix 265 I Alphabetical list of countries/territories and geographic regions 265 II Model input data by scenario III Overview of formulae and mechanisms of the market model Bibliography 275

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11 xi List of Figures 2.1 Classification of natural and marketable iron ores Process routes for the production of crude steel Framework defining the iron ore market structure Geographic distribution of top ten iron ore consuming countries, Chinese crude steel production and share of world total, Geographic distribution of top ten iron ore producing countries, Top ten interregional iron ore trade flows, Iron ore price levels under changing pricing regimes, Key players active in the development and trading of iron ore derivatives Global crude steel production and average annual iron ore market price, Global crude steel production and average annual iron ore market price, Estimation of iron ore derivatives trading volumes vs. physical trading volumes Monthly Capesize voyage freight rates for routes from Tubarão and Port Hedland to Qingdao, Development of hot-rolled coil raw materials basket for Western European steelmaker, Shift in EBITDA profit pools from steelmaking to iron ore mining, Sensitivity of European steel industry s profitability to iron ore price Structure of proposed global iron ore market model Definition of geographic regions and ports Values of parameters defining 2020 iron ore production capacity levels Example of standardization of production capacity, Minas do Meio (Brazil)

12 xii List of Figures 5.5 Split of FOB cost along the iron ore value chain Split of mining cost by cost type for seven mines Example of standardized FOB cost calculation from mining cost, Corumbá mine (Brazil) Derivation of 2009 average Capesize freight rate per route using linear OLS regression Exemplary derivation of 2020 Capesize freight rate scenarios for route Oceania China Top-down methodology of long-term apparent finished steel demand modeling Top-down methodology of long-term iron ore demand modeling Network representation of the allocation mechanism Illustrative iron ore mining cost curve Illustrative overview of market mechanism Comparison of iron ore trade flows from 2009 model run to 2009 actuals Comparison of actual spot market price CFR China in 2009 with calculated price from 2009 model run Definition of 2020 base case and scenarios Structure of results of quantitative analyses Scenario A: 2020 base case Analysis of production, net trade and demand by region Scenario A: 2020 base case Analysis of interregional trade flows Scenario A: 2020 base case Analysis of market prices, procurement cost and profit contribution of miners by region Scenario A: 2020 base case Analysis of individual profit contributions of miners Scenario B: 2020 low capacity Analysis of production, net trade and demand by region Scenario B: 2020 low capacity Analysis of interregional trade flows Scenario B: 2020 low capacity Analysis of market prices, procurement cost and profit contribution of miners by region Scenario B: 2020 low capacity Analysis of individual profit contributions of miners Scenario C: 2020 high capacity Analysis of production, net trade and demand by region Scenario C: 2020 high capacity Analysis of interregional trade flows Scenario C: 2020 high capacity Analysis of market prices, procurement cost and profit contribution of miners by region

13 List of Figures xiii 6.14 Scenario C: 2020 high capacity Analysis of individual profit contributions of miners Scenario D: 2020 low demand Analysis of production, net trade and demand by region Scenario D: 2020 low demand Analysis of interregional trade flows Scenario D: 2020 low demand Analysis of market prices, procurement cost and profit contribution of miners by region Scenario D: 2020 low demand Analysis of individual profit contributions of miners Scenario E: 2020 high demand Analysis of production, net trade and demand by region Scenario E: 2020 high demand Analysis of interregional trade flows Scenario E: 2020 high demand Analysis of market prices, procurement cost and profit contribution of miners by region Scenario E: 2020 high demand Analysis of individual profit contributions of miners global capacity surplus and utilization per scenario Scenarios A E Split of supply to China by source Scenarios A E Market prices by region Scenarios A E Split of total iron ore procurement cost Scenarios A E Split of miner s profit contribution by player Scenario F: 2020 low freight rates Analysis of production, net trade and demand by region Scenario F: 2020 low freight rates Analysis of interregional trade flows Scenario F: 2020 low freight rates Analysis of market prices, procurement cost and profit contribution of miners by region Scenario F: 2020 low freight rates Analysis of individual profit contributions of miners Scenario G: 2020 high freight rates Analysis of production, net trade and demand by region Scenario G: 2020 high freight rates Analysis of interregional trade flows Scenario G: 2020 high freight rates Analysis of market prices, procurement cost and profit contribution of miners by region Scenario G: 2020 high freight rates Analysis of individual profit contributions of miners Scenarios F, A, G Split of supply to China by source Scenarios F, A, G Market prices by region Scenarios F, A, G Split of total iron ore procurement cost Scenarios F, A, G Split of miner s profit contribution by player. 211

14 xiv List of Figures 6.40 Scenario H: 2020 no captivity ArcelorMittal Analysis of production, net trade and demand by region Scenario H: 2020 no captivity ArcelorMittal Analysis of interregional trade flows Scenario H: 2020 no captivity ArcelorMittal Analysis of market prices, procurement cost and profit contribution of miners by region Scenario H: 2020 no captivity ArcelorMittal Analysis of individual profit contributions of miners Scenario H: 2020 no captivity ArcelorMittal Analysis of procurement cost of ArcelorMittal Scenario I: 2020 no Chinese captivity abroad Analysis of production, net trade and demand by region Scenario I: 2020 no Chinese captivity abroad Analysis of interregional trade flows Scenario I: 2020 no Chinese captivity abroad Analysis of market prices, procurement cost and profit contribution of miners by region Scenario I: 2020 no Chinese captivity abroad Analysis of individual profit contributions of miners Scenario I: 2020 no Chinese captivity abroad Analysis of procurement cost of China Scenario J: 2020 no export taxes India Analysis of production, net trade and demand by region Scenario J: 2020 no export taxes India Analysis of interregional trade flows Scenario J: 2020 no export taxes India Analysis of market prices, procurement cost and profit contribution of miners by region Scenario J: 2020 no export taxes India Analysis of individual profit contributions of miners Scenario K: 2020 introduction export taxes Brazil Analysis of production, net trade and demand by region Scenario K: 2020 introduction export taxes Brazil Analysis of interregional trade flows Scenario K: 2020 introduction export taxes Brazil Analysis of market prices, procurement cost and profit contribution of miners by region Scenario K: 2020 introduction export taxes Brazil Analysis of individual profit contributions of miners Influence of 2020 Chinese and South American iron ore demand on European market price Influence of 2020 Brazilian and Indian iron ore export tax on European market price

15 xv List of Tables 1.1 Chronological overview of relevant previous research on the iron ore market World iron ore reserves by country as of January Iron ore demand by country, Iron ore production by country, Iron ore production by controlling entity, Iron ore exports and imports by country, Interregional iron ore trade flows, Overview of three main spot-based iron ore indices Overview of tax types affecting iron ore mining I.1 Alphabetical list of countries/territories and geographic regions II.1 Input data 2009 capacity and demand II.2 Input data 2020 capacity II.3 Input data 2020 demand II.4 Input data 2009 freight rates II.5 Input data 2020 freight rates