Big Data Market with Focus on Supply Chain Management Key Trends & Competitive Landscape 2020 | IndustryARC
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Big data market
Big Data Market with Focus on Supply Chain Management - Key Trends, Competitive Landscape, Geographic & End-User Segment Analysis (2015-2020)
Report Code : ITR 0013
Published: 14 October, 2013   No. of pages: 110

  • Report Description
  • Table of Contents
  • Tables And Figures
  • Customization Options
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 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. 
1. Introduction
   1.1. Key Takeaways
   1.2. Report Description
   1.3. Scope & Markets Covered
   1.4. Stakeholders
2. Executive Summary
3. Big Data Market Overview
   3.1. Introduction
   3.2. Definition
      3.2.1. Big Data
      3.2.2. Supply Chain Management (SCM)
   3.3. Global Big Data 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. Big Data 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 Fulfillment 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 andKramm 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
            5.3.1.1. Overview and SCM Problems
            5.3.1.2. Solution and Benefits
      5.3.2. Telogis and ITL
            5.3.2.1. Overview and SCM Problems
            5.3.2.2. Solution and Benefits
      5.3.3. Telogis and Supershuttle
            5.3.3.1. Overview and SCM Problems
            5.3.3.2. Solution and Benefits
   5.4. LeanLogistics
      5.4.1. LeanLogistics and Dannon
            5.4.1.1. Overview and SCM Problems
            5.4.1.2. Solution and Benefits
      5.4.2. LeanLogistics and Ace Hardware
            5.4.2.1. Overview and SCM Problems
            5.4.2.2. Solution and Benefits
      5.4.3. LeanLogistics and MTD Products
            5.4.3.1. Overview and SCM Problems
            5.4.3.2. Solution and Benefits
   5.5. Teradata
      5.5.1. Teradata Aster and Supervalu
            5.5.1.1. Overview and SCM Problems
            5.5.1.2. Solution and Benefits
      5.5.2. Teradata and Norfolk Southern Railway Company
            5.5.2.1. Overview and SCM Problems
            5.5.2.2. Solution and Benefits
   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 Big Data 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
      9.2.2. IndustryARC Analysis
   9.3. Pentaho Corporation
      9.3.1. Company Products & Services
      9.3.2. Strategic Initiatives
      9.3.3. IndustryARCAnalysis
   9.4. Zettaset
      9.4.1. Company Products & Services
      9.4.2. Strategic Initiatives
      9.4.3. IndustryARC Analysis
   9.5. Datastax
      9.5.1. Company Products & Services
      9.5.2. Strategic Initiatives
      9.5.3. IndustryARC Analysis
   9.6. Talend
      9.6.1. Company Products &Services
      9.6.2. Strategic Initiatives
      9.6.3. IndustryARC Analysis
   9.7. Amazon
      9.7.1. Company Products & Services
      9.7.2. Strategic Initiatives
      9.7.3. IndustryARC Analysis
   9.8. IBM
      9.8.1. Company Products & Services
      9.8.2. Strategic Initiatives
      9.8.3. IndustryARC Analysis
   9.9. Data Direct Networks
      9.9.1. Company Products & Services
      9.9.2. Strategic Initiatives
      9.9.3. IndustryARC Analysis
   9.10. MapR Technologies
      9.10.1. Company Products & Services
      9.10.2. Strategic Initiatives
      9.10.3. IndustryARC Analysis
   9.11. DELL, INC
      9.11.1. Company Products & Services
      9.11.2. Strategic Initiatives
      9.11.3. IndustryARC Analysis
   9.12. DataSift
*More than 40 Companies are profiled in this Research Report, Complete List available on Request*
"*Financials would be provided on a best efforts basis for private companies"
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 
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