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.
Table Of Contents
Big Data Market with Focus on Supply Chain Management - Key Trends, Competitive Landscape, Geographic and End-User Segment Analysis (2015-2020) 1. Introduction 1.1. Key Takeaways 1.2. Report Description 1.3. Scope and 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 126.96.36.199. Improving Customer and Operations Insights 188.8.131.52. Predictive Maintenance Optimization 184.108.40.206. 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 220.127.116.11. Usage of Advanced Analytics to Answer Strategic Questions 18.104.22.168. Customer Feedback and Online Marketing 22.214.171.124. Need for Faster Response Systems 126.96.36.199. Safe Delivery of Products to Clients 188.8.131.52. Opportunity to Open New Channel Programs 184.108.40.206. Internet of Things and Machine to Machine (M2M) to Help Digital Manufacturing and Digital Services 220.127.116.11. Supply Chain Visibility Improvement 4.1.2. Market Restraints 18.104.22.168. Data Growth Not Being Matched by Hardware and Storage Capabilities 22.214.171.124. Concern for Strong Security Features in Big Data Systems 126.96.36.199. Complex Framework Leads to Performance Issues 4.1.3. Market Opportunities 188.8.131.52. Availability of Funding on a Wider Scale 184.108.40.206. 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 and Noble 220.127.116.11. Overview and SCM Problems 18.104.22.168. Solution and Benefits 5.2.2. IBM andKramm Groep 22.214.171.124. Overview and SCM Problems 126.96.36.199. Solution and Benefits 5.2.3. IBM and Andrews Distributing 188.8.131.52. Overview and SCM Problem 184.108.40.206. Solution and Benefits 5.2.4. IBM and Sudzucker 220.127.116.11. Overview and SCM Problem 18.104.22.168. Solution and Benefits 5.2.5. IBM and FedeFarma 22.214.171.124. Overview and SCM Problem 126.96.36.199. Solution and Benefits 5.2.6. IBM and Cheesecake factory 5.3. Telogis 5.3.1. Telogis and Pro's Ranch Market 188.8.131.52. Overview and SCM Problems 184.108.40.206. Solution and Benefits 5.3.2. Telogis and ITL 220.127.116.11. Overview and SCM Problems 18.104.22.168. Solution and Benefits 5.3.3. Telogis and Supershuttle 22.214.171.124. Overview and SCM Problems 126.96.36.199. Solution and Benefits 5.4. LeanLogistics 5.4.1. LeanLogistics and Dannon 188.8.131.52. Overview and SCM Problems 184.108.40.206. Solution and Benefits 5.4.2. LeanLogistics and Ace Hardware 220.127.116.11. Overview and SCM Problems 18.104.22.168. Solution and Benefits 5.4.3. LeanLogistics and MTD Products 22.214.171.124. Overview and SCM Problems 126.96.36.199. Solution and Benefits 5.5. Teradata 5.5.1. Teradata Aster and Supervalu 188.8.131.52. Overview and SCM Problems 184.108.40.206. Solution and Benefits 5.5.2. Teradata and Norfolk Southern Railway Company 220.127.116.11. Overview and SCM Problems 18.104.22.168. 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 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 and 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 and Services 9.3.2. Strategic Initiatives 9.3.3. IndustryARCAnalysis 9.4. Zettaset 9.4.1. Company Products and Services 9.4.2. Strategic Initiatives 9.4.3. IndustryARC Analysis 9.5. Datastax 9.5.1. Company Products and Services 9.5.2. Strategic Initiatives 9.5.3. IndustryARC Analysis 9.6. Talend 9.6.1. Company Products andServices 9.6.2. Strategic Initiatives 9.6.3. IndustryARC Analysis 9.7. Amazon 9.7.1. Company Products and Services 9.7.2. Strategic Initiatives 9.7.3. IndustryARC Analysis 9.8. IBM 9.8.1. Company Products and Services 9.8.2. Strategic Initiatives 9.8.3. IndustryARC Analysis 9.9. Data Direct Networks 9.9.1. Company Products and Services 9.9.2. Strategic Initiatives 9.9.3. IndustryARC Analysis 9.10. MapR Technologies 9.10.1. Company Products and Services 9.10.2. Strategic Initiatives 9.10.3. IndustryARC Analysis 9.11. DELL, INC 9.11.1. Company Products and 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