Welcome!

Microsoft Cloud Authors: Pat Romanski, Liz McMillan, Lori MacVittie, Elizabeth White, Yeshim Deniz

News Feed Item

Global Big Data Market with Focus on Supply Chain Management - Key Trends, Competitive Landscape, Geographic & End-User Segment Analysis (2012-2018)

NEW YORK, April 1, 2014 /PRNewswire/ -- Reportlinker.com announces that a new market research report is available in its catalogue:

Global Big Data Market with Focus on Supply Chain Management - Key Trends, Competitive Landscape, Geographic & End-User Segment Analysis (2012-2018)
http://www.reportlinker.com/p02057076/Global-Big-Data-Market-with-Focus-...

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 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
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
10 Appendix
10.1 Sources
10.2 Acronyms



To order this report: Global Big Data Market with Focus on Supply Chain Management - Key Trends, Competitive Landscape, Geographic & End-User Segment Analysis (2012-2018)
http://www.reportlinker.com/p02057076/Global-Big-Data-Market-with-Focus-...



__________________________
Contact Clare: [email protected]
US: (339)-368-6001
Intl: +1 339-368-6001

 

SOURCE Reportlinker

More Stories By PR Newswire

Copyright © 2007 PR Newswire. All rights reserved. Republication or redistribution of PRNewswire content is expressly prohibited without the prior written consent of PRNewswire. PRNewswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

IoT & Smart Cities Stories
Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., discussed how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determin...
The hierarchical architecture that distributes "compute" within the network specially at the edge can enable new services by harnessing emerging technologies. But Edge-Compute comes at increased cost that needs to be managed and potentially augmented by creative architecture solutions as there will always a catching-up with the capacity demands. Processing power in smartphones has enhanced YoY and there is increasingly spare compute capacity that can be potentially pooled. Uber has successfully ...
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
We are seeing a major migration of enterprises applications to the cloud. As cloud and business use of real time applications accelerate, legacy networks are no longer able to architecturally support cloud adoption and deliver the performance and security required by highly distributed enterprises. These outdated solutions have become more costly and complicated to implement, install, manage, and maintain.SD-WAN offers unlimited capabilities for accessing the benefits of the cloud and Internet. ...
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
With 10 simultaneous tracks, keynotes, general sessions and targeted breakout classes, @CloudEXPO and DXWorldEXPO are two of the most important technology events of the year. Since its launch over eight years ago, @CloudEXPO and DXWorldEXPO have presented a rock star faculty as well as showcased hundreds of sponsors and exhibitors! In this blog post, we provide 7 tips on how, as part of our world-class faculty, you can deliver one of the most popular sessions at our events. But before reading...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.