New York, NY -- (SBWIRE) -- 09/21/2017 -- 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.

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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 Content

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 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 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

……Continued

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