Global Big Data Market With Focus on Supply Chain Management

IndustryARC

GLOBAL BIG DATA MARKET WITH FOCUS ON SUPPLY CHAIN MANAGEMENT

Key Trends, Competitive Landscape, Geographic & End-user Segment Analysis

(2012 - 2018)

TABLE OF CONTENTS

1 Introduction

1.1 Key Takeaways 1.2 Report Description 1.3 Scope & Markets Covered 1.4 Stakeholders

2 Executive Summary

3 Market Overview

3.1 Introduction 3.2 Definition

3.2.1 Big Data

3.2.2 Supply Chain Management (SCM)

3.3 Global Market Overview – Need for Big Data in SCM 3.3.1 Current Transactional Systems have High System Complexity

3.3.2 Growing Data Creates Problem of Plenty

3.3.3 Provision of Structured Data in Big Data

3.4 Current scenario of big data in SCM 3.5 Use Cases for Big Data in Supply Chain Management

3.5.1 Overview

3.5.2 Big Data in Travel and Transportation Industry

3.5.2.1 Improving Customer and Operations Insights

3.5.2.2 Predictive Maintenance Optimization

3.5.2.3 Capacity and Pricing Optimization

3.5.3 Big Data in Automotive Industry

3.5.4 Big Data in Consumer Products or manufacturing Industry

3.5.5 Big Data in Retail Industry

4 Market Analysis

4.1 Market Dynamics 4.1.1 Market Drivers

4.1.1.1 Usage of Advanced Analytics to Answer Strategic Questions

4.1.1.2 Customer Feedback and Online Marketing

4.1.1.3 Need for Faster Response Systems

4.1.1.4 Safe Delivery of Products to Clients

4.1.1.5 Opportunity to Open New Channel Programs

4.1.1.6 Internet of Things and Machine to Machine (M2M) to Help Digital Manufacturing and Digital Services

4.1.1.7 Supply Chain Visibility Improvement

4.1.2 Market Restraints

4.1.2.1 Data Growth Not Being Matched by Hardware and Storage Capabilities

4.1.2.2 Concern for Strong Security Features in Big Data Systems

4.1.2.3 Complex Framework Leads to Performance Issues

4.1.3 Market Opportunities

4.1.3.1 Availability of Funding on a Wider Scale

4.1.3.2 Partnerships between Vendors and Clients

4.2 Top Supply Chain Companies Analysis 4.3 Porter’s Analysis

4.3.1 Threat from New Entrants

4.3.2 Threat from Substitutes

4.3.3 Bargaining Power of Suppliers

4.3.4 Bargaining Power of Customers

4.3.5 Degree of Competition

5 Case studies of big data usage by supply chain companies –

(solutions and benefits) 5.1 Amazon

5.1.1 Amazon Fulfilment Centers Program

5.2 IBM 5.2.1 IBM and Barnes & Noble

5.2.1.1 Overview and SCM Problems

5.2.1.2 Solution and Benefits 5.2.2 IBM and Kramm Groep

5.2.2.1 Overview and SCM Problems 5.2.2.2 Solution and Benefits

5.2.3 IBM and Andrews Distributing 5.2.3.1 Overview and SCM Problem 5.2.3.2 Solution and Benefits

5.2.4 IBM and Sudzucker 5.2.4.1 Overview and SCM Problem

5.2.4.2 Solution and Benefits 5.2.5 IBM and FedeFarma

5.2.5.1 Overview and SCM Problem 5.2.5.2 Solution and Benefits

5.2.6 IBM and Cheesecake factory

5.3 Telogis 5.3.1 Telogis and Pro’s Ranch Market


5.3.1.2 Solution and Benefits

5.3.2 Telogis and ITL



5.3.3 Telogis and Supershuttle



5.4 LeanLogistics 5.4.1 LeanLogistics and Dannon



5.4.2 LeanLogistics and Ace Hardware



5.4.3 LeanLogistics and MTD Products



5.5 Teradata 5.5.1 Teradata Aster and Supervalu



5.5.2 Teradata and Norfolk Southern Railway Company



5.6 SAP 5.6.1 SAP HANA and Suning

5.6.2 SAP HANA and eBay

5.6.3 SAP HANA and Home Shopping Europe

6 Global Market Landscape Analysis of Big Data Providers

6.1 IBM 6.2 HP 6.3 Teradata 6.4 Oracle 6.5 SAP 6.6 EMC 6.7 Amazon 6.8 Microsoft 6.9 Google 6.10 VMware 6.11 Cloudera 6.12 Splunk 6.13 Hortonworks 6.14 MongoDB 6.15 MapR

7 Big Data in SCM – Market Analysis

7.1 Big Data market analysis by industries 7.2 Big Data in SCM market - analysis by industries 7.3 Suppliers of Big Data Solutions 7.4 Big Data in SCM - Solutions Offered

7.4.1 Retail

7.4.2 Transportation

7.5 Competitive Situation and Trends 7.5.1 Funding and Investments

7.5.2 Agreements, Partnerships, Joint Ventures and Collaborations

7.5.3 Mergers and Acquisitions

8 Global Big Data in SCM – Geographic Analysis

8.1 Global Big Data market – Geographic Analysis 8.2 Global Big Data in SCM market– Geographic Analysis

9 Key Company Market Snapshots 9.1 Cloudera

9.1.1 Company Products & Services

9.1.2 Strategic Initiatives

9.1.3 IndustryARC Analysis

9.2 Karmasphere 9.2.1 Company Products


9.3 Pentaho Corporation 9.3.1 Company Products & Services 9.3.2 Strategic Initiatives 9.3.3 IndustryARC Analysis

9.4 Zettaset 9.4.1 Company Products & Services



9.5 Datastax 9.5.1 Company Products & Services



9.6 Talend 9.6.1 Company Products & Services



9.7 Amazon 9.7.1 Company Products & Services



9.8 IBM 9.8.1 Company Products & Services



9.9 Data Direct Networks 9.9.1 Company Products & Services



9.10 MapR Technologies 9.10.1 Company Products & Services



9.11 DELL, INC 9.11.1 Company Products & Services



9.12 DataSift

10 Appendix

10.1 Sources

10.2 Acronyms

LIST OF TABLES

TABLE 1 NUMBER OF TRANSACTIONAL SYSTEMS USED IN COMPANIES ON A

GLOBAL FOOTPRINT

TABLE 2 GLOBAL TOP SUPPLY CHAIN COMPANIES, 2013

TABLE 3 APAC AND EUROPE - TOP SUPPLY CHAIN COMPANIES, 2013

TABLE 4 GLOBAL BIG DATA PROVIDERS AND THEIR SOFTWARE SOLUTIONS

TABLE 5 GLOBAL BIG DATA MARKET REVENUES, BY INDUSTRY SEGMENTS ($BILLIONS), 2012 – 2018

TABLE 6 GLOBAL BIG DATA IN SCM MARKET REVENUES, BY INDUSTRY SEGMENTS ($BILLIONS), 2012 – 2018

TABLE 7 GLOBAL SUPPLIERS OF BIG DATA SOLUTIONS BY SERVICES OFFERED

TABLE 8 GLOBAL BIG DATA MARKET REVENUES, BY GEOGRAPHIC REGIONS ($ BILLIONS), 2012 – 2018

TABLE 9 GLOBAL BIG DATA MARKET REVENUES, BY GEOGRAPHIC REGIONS ($ BILLIONS), 2012 – 2018

LIST OF FIGURES

FIGURE 1 THE 3 V’S OF BIG DATA SYSTEMS FIGURE 2 EVOLUTION OF BIG DATA FIGURE 3 NUMBER OF TRANSACTIONAL SYSTEMS USED IN COMPANIES ON A

GLOBAL FOOTPRINT FIGURE 4 TOP 5 SUPPLY CHAIN PAIN POINTS FIGURE 5 POTENTIAL IT SYSTEMS TO BENEFIT FROM BIG DATA FIGURE 6 TOP 5 SUPPLY CHAIN TRENDS FOR SCM EXCELLENCE BY 2020 FIGURE 7 SIZE OF THE LARGEST ERP IN COMPANY FIGURE 8 NUMBER OF UNIQUE IT SYSTEMS IN A COMPANY FIGURE 9 GLOBAL BIG DATA MARKET SHARE, BY INDUSTRY SEGMENTS (%), 2012 FIGURE 10 GLOBAL BIG DATA MARKET REVENUES, BY INDUSTRY SEGMENTS

($BILLIONS), 2012 – 2018 FIGURE 11 GLOBAL BIG DATA IN SCM MARKET SHARE, BY INDUSTRY SEGMENTS

(%), 2012 FIGURE 12 GLOBAL BIG DATA IN SCM MARKET REVENUES, BY INDUSTRY

SEGMENTS ($BILLIONS), 2012 – 2018 FIGURE 13 GLOBAL BIG DATA MARKET IN RETAIL INDUSTRY ($ BILLIONS), 2012 –

2018 FIGURE 14 GLOBAL BIG DATA MARKET REVENUE, MARKET SHARE BY

GEOGRAPHY (%), 2012 FIGURE 15 GLOBAL BIG DATA MARKET REVENUES, BY GEOGRAPHIC REGIONS ($

BILLIONS), 2012 – 2018 FIGURE 16 GLOBAL BIG DATA IN SCM MARKET REVENUE, MARKET SHARE BY

GEOGRAPHY (%), 2012 FIGURE 17 GLOBAL BIG DATA MARKET REVENUES, BY GEOGRAPHIC REGIONS ($

BILLIONS), 2012 – 2018

REPORT SCOPE & STAKEHOLDERS SCOPE - MARKETS COVERED Management software, Application software & Performance monitoring or Cluster management software are covered as part of the software part. Services like Consulting Services, Integration & Deployment, Training & Outsourcing and Middleware & Support are included in the scope. The following end use verticals are considered as part of the study: BFSI, Government & Public Utilities, Manufacturing, Retail, Web, Media & Entertainment, IT & Security, and Telecommunication, Healthcare & Life- sciences, Bio-Informatics, Oil & Gas, Education, Transportation, Gaming and others. The following geographies are part of the scope of the Big data market covered: North America, Latin America, Europe, Middle East & Africa, Asia-Pacific. STAKEHOLDERS With respect to the solution providers pure play Big data vendors, Data storage, EDW, tool providers, analytical apps providers, Integration vendors and data visualization service providers are the stakeholders. With respect to the end users companies which are part of the following end use verticals are the key stakeholders: BFSI, Government & Public Utilities, Manufacturing, Retail, Web, Media & Entertainment, IT & Security, and Telecommunication, Healthcare & Life- sciences, Bio-Informatics, Oil & Gas, Education, Transportation, Gaming and others.

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REPORT DESCRIPTION Big data can be best defined as the capture, curation, storage, search and analysis of large and complex data sets which are generally difficult to be processed or handled by traditional data processing systems. These systems are currently being implemented on a limited scale in many supply chain companies for varied purposes. Most supply chain companies on an average use more than two systems for management purposes. Some have two instances of Enterprise Resource Planning (ERP) software installed for different parts of the supply chain and logistics purposes. Different use cases for the systems are order management; demand planning, warehouse management, price management, production planning, tactical supply planning, transportation planning, product lifecycle management and Manufacturing Execution Systems (MES). This is one major reason for the utilization of Big data in companies. Other in-depth reasons for the need for Big data in SCM have been covered in the report. Companies for example need to anticipate problems or understand growth through the usage of advanced analytics. Traditional business analytics can answer the questions that leaders know to ask. But the questions that are important but companies do not know to ask are more crucial to build risk mitigation strategies. An important question for example can be about the ways to learn about product and service failures in the market which can be asked and answered through use of Big data predictive analysis. Text mining and rules-based ontologies are some of the techniques which can be used to build listening capabilities to learn early and mitigate issues quickly. This report discusses the “Global Big Data Market with Focus on Supply Chain Management”key players in the Big data market by their types of software and solution offerings. The overall Big data market has been segmented into key industry verticals and by the geographic regions on a global scale. The need for Big data in supply chain management has been discussed in detail with the key market drivers, market restraints and opportunities presented in this context. The investment scenario, collaborations and joint ventures of Big data companies has been covered in in-depth analysis to give an insight into the rising interest in Big data players from across the private and government entities.

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SAMPLE TABLE 1: GLOBAL BIG DATA MARKET REVENUES, BY INDUSTRY SEGMENTS ($BILLIONS), 2012 – 2018

Industry 2012 2013 2014 2015 2016 2017 2018 CAGR (2013-2018)

BFSI 2.50 XX XX XX XX XX 8.24 19.0% Govt. & Public Utilities

XX XX XX XX XX XX XX XX

Services XX XX XX XX XX 3.98 XX XX Telecommunications XX XX XX XX XX XX XX XX Life -Sciences XX XX XX XX XX XX XX XX Manufacturing XX XX XX XX XX XX XX XX Retail XX XX XX 1.62 XX XX XX XX Health care XX XX XX XX XX XX XX XX Energy and utilities XX XX XX XX XX XX XX XX Education XX XX XX XX XX XX XX 22.8% Others XX XX XX XX XX XX XX XX Total XX XX XX XX XX XX XX XX Source: Expert Interviews, Company Annual Reports, ARC Estimates The key players in the Supply chain management market were identified through secondary research and their market opinions were also gathered in a similar way through telephonic interviews and also questionnaires. We have also studied the annual reports of these top market players and interviews with key opinion leaders such as directors, managers, marketing personnel was used extensively in understanding the need and emergence of Big Data in Supply chain management.

SAMPLE TABLE 2: GLOBAL BIG DATA MARKET REVENUES, BY GEOGRAPHIC REGIONS ($ BILLIONS), 2012 – 2018

Region 2012 2013 2014 2015 2016 2017 2018 CAGR (2013-

2018) North America XX XX XX XX 12.25 XX XX 21.5% Europe 1.91 XX XX XX XX XX XX XX Middle East & Africa

XX XX XX XX XX XX XX XX

APAC XX XX XX XX XX 6.74 XX XX Latin America XX XX XX XX XX XX XX 37.4% Total XX XX XX 22.89 XX XX XX XX Source: Expert Interviews, Company Annual Reports, ARC Estimates

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KEY MARKET TRENDS

The was around $6.93bn in 2012 and this is expected to Global Big Data Market

double to around $12.21bn in 2013. The market is expected to reach $40.4bn by

2018, growing at a CAGR of 27% during 2013 – 2018.

Big data is being used already in various formats and scales in different industry

verticals. While BFSI or the banking and financial sector currently has the highest

market share of around 36%, government and public utilities contribute around 13%

market revenues.

The services industry is in the third position with around 12% market share. The

services industry has the highest market share of around 25% followed by

manufacturing with 24% and retail with 19% currently.

Meanwhile the global big data in supply chain market is expected to increase from

around $0.43bn in 2012 to around $3.7bn in 2018 growing at a CAGR of around

31.4% during 2013 – 2018.

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RESEARCH METHODOLOGY The quantitative and qualitative data collected for the Big Data in Supply Chain Management report is from a combination of secondary and primary sources. Research interviews were conducted with senior executives and/or managers of leading Big Data solution providers. These Key opinion leaders (KOLs) were then provided a questionnaire to gather quantitative and qualitative inputs on their operations, performance, strategies and views on the overall market, including key developments and technology trends. Data from interviews is consolidated, checked for consistency and accuracy, and the final market numbers are again validated by experts. The global market was split by industry and geography based on different factors like primary and secondary sources, understanding of the number of companies operating in each segment and also KOL insights. We have used various secondary sources such as directories, articles, white papers, newsletters, annual reports and paid databases such as OneSource, Hoovers and Factiva to identify and collect information for extensive technical and commercial study of the Big Data market.

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THE ARC ADVANTAGE An analytical model lies at the core of our process, ensuring logical consistency throughout our research. We complement the model with secondary data and interviews with industry experts to reflect the latest trends. With our final expert validation, we provide you with only the most accurate and actionable intelligence.

THE ARC PROCESS

ANALYTICAL MODEL BASE MODEL CONSOLIDATED MODEL ARC MODEL

Analytical Method

Base Method Consolidation Method

Delphi Verification

1. Granular breakdown of drivers into factors 2. Validate all factors in terms of their present impact on the market 3. Assign weights to these factors in terms of their relevance and impact on the market 4. Build the Analytical Model

1. Get a top-down estimate of the market 2. Follow it up with a bottom-up estimate of the market 3. Check forconsistency and new growth factors that are relevant over the next 10 Years 4. Build the Base model

1. Granular breakdown of drivers into factors 2. Validate all factors in terms of their present impact on the market. 3. Assign weights to these factors in terms of their relevance and impact on the market. 4. Build the Consolidated Model

1. Verify the findings of the model with experts from across the value chain 2. Verify the findings with players across small and large enterprises 3. Tweak the model and add new factors 4. Finalize the ARC Model

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Global Big Data Market With Focus on Supply Chain Management

Documents

big data systems

scm problems

growing data

data growth

current scenario of

supply chain management

market analysis

global market overview