グローバル天然資源産業におけるビックデータ市場（2014 – 2019）...市場調査レポートについてご紹介

1.0 EXECUTIVE SUMMARY 5
2.0 INTRODUCTION 7
2.1 BIG DATA OVERVIEW 7
2.2 BIG DATA IN RESOURCE LOGISTICS AND SCM 8
2.3 BIG DATA AND ANALYTICS IN SUPPLY AND DEMAND MANAGEMENT 9
3.0 BIG DATA IN THE RESOURCE SUPPLY SIDE 11
3.1 BIG DATA IN RESOURCE MANAGEMENT 12
3.1.1 WATER MANAGEMENT 13
3.1.2 BIG DATA IN TIMBER AND FOREST MANAGEMENT 15
3.1.3 BIG DATA IN ENERGY AND ELECTRICITY 16
3.2 BIG DATA IN EXTRACTION AND EXPLORATION 17
3.2.1 BIG DATA IN MINING 19
3.2.2 BIG DATA IN OIL AND GAS 20
4.0 BIG DATA IN THE RESOURCE DEMAND SIDE 23
4.1 PREDICTIVE ANALYTICS TO DETERMINE DEMAND 23
4.1.1 STRUCTURED DATA MODELS VS. BIG DATA 25
4.1.2 SOURCES OF DATA 26
4.2 PREDICTIVE ANALYTICS FOR PRICING 28
4.2.1 PREDICTING OPTIMAL PRICE POINT 30
4.2.2 OPTIMIZING PROFITS VS. SMOOTHING DEMAND 31
5.0 LEADING COMPANIES AND SOLUTIONS 32
5.1 WATER COMPANIES 32
5.1.1 VEOLIA ENVIRONNEMENT 32
5.1.2 SUEZ ENVIRONNEMENT 32
5.1.3 ITT CORPORATION 33
5.1.4 GE WATER 34
5.2 TIMBER COMPANIES 34
5.2.1 WEYERHAEUSER CO. 34
5.2.2 PLUM CREEK TIMBER CO. 35
5.2.3 RAYONIER 35
5.3 MINING COMPANIES 36
5.3.1 ALCOA 36
5.3.2 NEWMONT MINING CORP 37
5.3.3 TECK 37
5.3.4 FREEPORT-MCMORAN 37
5.3.5 RIO TINTO 38
5.3.6 BHP BILLITON 38
5.4 OIL AND GAS COMPANIES 39
5.4.1 SAUDI ARAMCO 39
5.4.2 GAZPROM 39
5.4.3 NATIONAL IRANIAN OIL CO. 39
5.4.4 EXXONMOBIL 40
5.4.5 PETROCHINA 40
5.4.6 BP 40
5.4.7 ROYAL DUTCH SHELL 41
5.4.8 PEMEX 41
5.4.9 CHEVRON 42
5.4.10 KUWAIT PETROLEUM CORP. 42
5.4.11 CONOCO PHILLIPS 42
5.4.12 PETROBRAS 42
6.0 THE FUTURE OF BIG DATA IN PHYSICAL RESOURCES 44
7.0 CONCLUSIONS AND RECOMMENDATIONS 46

Figures

Figure 1: Usage of Big Data in Resources 11
Figure 2: Smart Solution Options in Organizations 12
Figure 3 : Energy Consumption in a Production Line 16
Figure 4: Big Data Exploration 18
Figure 5: Mining Equipment Analytics 19
Figure 6: Big Data in Oil and Gas 20
Figure 7: Big Data Model 24
Figure 8: Mapping the Predictive Analytics 28
Figure 9: Color to Display and Product Differentiation 30
Figure 10: Global Big Data Revenue 2014 – 2019 48
Figure 11: Regional Big Data Revenue 2014 – 2019 48