SON (Self-Organizing Network) technology minimizes the lifecycle cost of running a mobile network by eliminating manual configuration of network elements at the time of deployment right through to dynamic optimization and troubleshooting during operation. Besides improving network performance and customer experience, SON can significantly reduce the cost of mobile operator services, improving the OpEx-to-revenue ratio and deferring avoidable CapEx.

Early adopters of SON have already witnessed a multitude of benefits in the form of accelerated 5G NR and LTE RAN (Radio Access Network) rollout times, simplified network upgrades, fewer dropped calls, improved call setup success rates, higher end user throughput, alleviation of congestion during special events, increased subscriber satisfaction and loyalty, operational efficiencies such as energy and cost savings, and freeing up radio engineers from repetitive manual tasks.

Although SON was originally developed as an operational approach to streamline and automate cellular RAN deployment and optimization, mobile operators and vendors are increasingly focusing on integrating new capabilities such as self-protection against digital security threats and self-learning through AI (Artificial Intelligence) techniques, as well as extending the scope of SON beyond the RAN to include both mobile core and transport network segments – which will be critical to address 5G requirements such as end-to-end network slicing.

In addition, with the cellular industry’s ongoing shift towards open interfaces, virtualization and software-driven networking, the SON ecosystem is progressively transitioning from the traditional D-SON (Distributed SON) and C-SON (Centralized SON) approach to open standards-based components supporting RAN programmability for advanced automation and intelligent control.

The surging popularity of innovative Open RAN and vRAN (Virtualized RAN) architectures has reignited the traditionally niche and proprietary product-driven SON market with a host of open standards-compliant RIC (RAN Intelligent Controller), xApp and rApp offerings, which are capable of supporting both near real-time D-SON and non real-time C-SON capabilities for RAN automation and optimization needs.

This report estimates that global spending on RIC platforms, xApps and rApps will reach $120 Million in 2023 as initial implementations move from field trials to production-grade deployments. With commercial maturity, the submarket is further expected to quintuple to nearly $600 Million by the end of 2025. Annual investments in the wider SON market – which includes licensing of embedded D-SON features, third party C-SON functions and associated OSS platforms, in-house SON capabilities internally developed by mobile operators, and SON-related professional services across the RAN, mobile core and transport domains – are expected to grow at a CAGR of approximately 7% during the same period.

The “SON (Self-Organizing Networks) in the 5G & Open RAN Era: 2022 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents a detailed assessment of the SON market, including the value chain, market drivers, barriers to uptake, enabling technologies, functional areas, use cases, key trends, future roadmap, standardization, case studies, ecosystem player profiles and strategies. The report also provides global and regional market size forecasts for both SON and conventional mobile network optimization from 2022 till 2030, including submarket projections for three network segments, six SON architecture categories, four access technologies and five regional submarkets.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

Topics Covered

The report covers the following topics:
– Introduction to SON
– Value chain and ecosystem structure
– Market drivers and challenges
– SON technology, architecture and functional areas
– D-SON (Distributed SON), C-SON (Centralized SON), H-SON (Hybrid SON), RIC (RAN Intelligent Controller), xApps and rApps
– Review of over 40 SON use cases across the RAN, core and transport domains, ranging from ANR (Automatic Neighbor Relations) and rapid equipment configuration to advanced traffic steering, QoE-based optimization and automated anomaly detection
– Key trends in next-generation 5G SON implementations, including Open RAN and vRAN (Virtualized RAN) architectures, dynamic spectrum management, network slicing, edge computing, Big Data, advanced analytics, AI (Artificial Intelligence)/ML (Machine Learning) and zero-touch automation
– Case studies of 20 commercial-scale SON deployments and examination of ongoing projects covering both traditional D-SON/C-SON and RIC-x/rApp approaches
– Future roadmap for the SON market
– Standardization, regulatory and collaborative initiatives
– Profiles and strategies of more than 230 ecosystem players
– Strategic recommendations for SON solution providers and mobile operators
– Market analysis and forecasts from 2022 till 2030

Forecast Segmentation

Market forecasts are provided for each of the following submarkets and their subcategories:

SON & Mobile Network Optimization
– SON
– Conventional Mobile Network Planning & Optimization

SON Network Segment Submarkets
– RAN (Radio Access Network)
– Mobile Core
– Transport (Fronthaul, Midhaul & Backhaul)

RAN Segment SON Architecture Submarkets
– Traditional D-SON & C-SON
○ Embedded D-SON (Distributed SON) Features
○ Third Party C-SON (Centralized SON) & OSS Platforms
– Open RAN RIC, xApps & rApps
○ RIC (RAN Intelligent Controller) Platforms
○ Near Real-Time xApps
○ Non Real-Time rApps
– Mobile Operators’ In-House SON Tools & Systems

SON Access Network Technology Submarkets
– 2G & 3G
– LTE
– 5G NR
– Wi-Fi & Others

Regional Markets
– North America
– Asia Pacific
– Europe
– Middle East & Africa
– Latin & Central America

Key Questions Answered

The report provides answers to the following key questions:
– How big is the SON opportunity?
– What trends, drivers and challenges are influencing its growth?
– What will the market size be in 2025, and at what rate will it grow?
– Which submarkets and regions will see the highest percentage of growth?
– How do SON investments compare with spending on conventional mobile network optimization?
– What are the practical, quantifiable benefits of SON – based on live, commercial deployments?
– How can mobile operators capitalize on SON to ensure optimal network performance, improve customer experience, reduce costs, and drive revenue growth?
– What is the status of D-SON and C-SON adoption worldwide?
– When will open standards-based RIC platforms, xApps and rApps replace the traditional SON approach?
– What are the prospects of AI/ML-driven automation in the SON market?
– What opportunities exist for SON capabilities in the mobile core and transport network domains?
– How can SON ease the deployment of private 4G/5G networks for enterprises and vertical industries?
– In what way will SON facilitate network slicing and other advanced 5G capabilities?
– How does SON impact mobile network optimization engineers?
– Who are the key ecosystem players, and what are their strategies?
– What strategies should SON solution providers and mobile operators adopt to remain competitive?

Key Findings

The report has the following key findings:
– The surging popularity of innovative Open RAN and vRAN (Virtualized RAN) architectures has reignited the traditionally niche and proprietary product-driven SON market with a host of open standards-compliant RIC (RAN Intelligent Controller), xApp and rApp offerings, which are capable of supporting both near real-time D-SON and non real-time C-SON capabilities for RAN automation and optimization needs.
– The report estimates that global spending on RIC platforms, xApps and rApps will reach $120 Million in 2023 as initial implementations move from field trials to production-grade deployments. With commercial maturity, the submarket is further expected to quintuple to nearly $600 Million by the end of 2025.
– Annual investments in the wider SON market – which includes licensing of embedded D-SON features, third party C-SON functions and associated OSS platforms, in-house SON capabilities internally developed by mobile operators, and SON-related professional services across the RAN, mobile core and transport domains – are expected to grow at a CAGR of approximately 7% during the same period.
– The third party SON vendor ecosystem is exhibiting signs of consolidation, with several prominent M&A deals such as Qualcomm’s recent acquisition of C-SON specialist Cellwize – in a bid to strengthen its 5G RAN infrastructure offerings, Elisa Automate’s merger with Polystar to form Elisa Polystar, and HCL’s acquisition of Cisco’s SON technology business.
– However, on the other hand, newer suppliers are also beginning to emerge – extending from VMware, Juniper Networks and other RIC platform providers to x/rApp specialists such as Cohere Technologies, DeepSig, Groundhog Technologies, Subex, B-Yond, Net AI and RIMEDO Labs.
– SON capabilities are playing a pivotal role in the ongoing proliferation of private 4G/5G networks, as evident from a growing number of cross-sector partnerships. For example, private wireless service provider Betacom is collaborating with Qualcomm to accelerate enterprise adoption of private 5G networks by  combining the former’s 5GaaS (5G-as-a-Service) offering with the latter’s enablement ecosystem, including the Cellwize RAN automation and management platform. Similarly, Germany-based systems integrator Opticoms has entered into a partnership with SON specialist Innovile to automate and optimize Open RAN standards-compliant private 5G networks.
– Over the last two years, with the steep rise of mobile data consumption in residential areas during the COVID-19 pandemic-imposed lockdowns, mobile operators – despite coping relatively well – have recognized the importance of a more dynamic and automated approach to the optimization of network assets in order to provide a consistent and seamless user experience.
– The 2020-2022 period saw large-scale C-SON deployments by several operators, including but not limited to Verizon, EE (BT Group), Orange, Telefónica, Turkcell, beCloud (Belarusian Cloud Technologies), VEON, Ooredoo, Zain, BTC (Botswana Telecommunications Corporation), LTT (Libya Telecom & Technology), Telstra, Singtel, Telkomsel, Globe Telecom, Smart Communications (PLDT), and Telecom Argentina.

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Product overview
AI uses slacks of data and advance algorithm from the source and provide the machine the ability to calculate, think, and react to the provided information just like a human brain. Artificial intelligence provides real-time insights into the industrial application, thereby meeting the demand for the industry by increasing the efficiency of the system. Electricity trade, smart grids, or the electricity, heat, and transport sector connection are typical application areas. Analyzing and assessing data volumes, AI helps the industry to be more efficient and secure.AI has similar features as an understanding of languages, reasoning, learning, and problem-solving. AI is related to human intelligence. In creating and revising such a technology, market manufacturers face huge underlying intellectual challenges. AI is at the forefront of the market’s next-generation software technology.

Market Highlights
This report anticipates the Artificial intelligence market to surpass USD 900.62 Billion by 2030; this is valued at 37.99 billion in 2020 at a compound annual growth rate of 37.27% from 2020-2030. The growth is expected to be driven by the demand for improved operating efficiency, rising energy efficiency issues. Decentralized power producers are increasingly concerned about rising energy demand in the power distribution supply chain. Investments in artificial intelligence technology have grown due to the rise in the number of successful start-ups and technological development. Also, the need for large quantities of data processing and interpretation is increasing the need for solutions from the artificial intelligence sector. The growth potential in the AI market is also strongly influenced by the development of more stable cloud infrastructures and advancements in artificial adaptive intelligence solutions.

Recent Highlights in the artificial intelligence market

November 2019, Baker Hughes, C3.ai, and Microsoft announced their partnership to allow customers to implement Microsoft Azure-based, scalable AI solutions.
GE Power is working on incorporating AI to enhance its energy supply chain, which generates 30 percent of the world’s electricity. Through the aid of AI and machine learning (ML), GE aims to boost its business operations.
In January 2019, a cloud-based AI platform with real-time analytics for oil & gas industrial applications was launched byKellton Tech Solutions Ltd.
Artificial intelligence market: Segments

Global Artificial intelligence market has been segmented based on technology, product offering, and application. It has been further segmented based on region into North America, Europe, Asia-Pacific, Mid East, and Africa.

Based on Technology (in %), GlobalArtificial intelligence market,2019

In 2019, Computer Vision dominated the market and accounted for XX% of the global market

Global Artificial intelligence market is segmented by technology into Context-Aware Computing, Machine Learning, Natural Language Processing, and Computer Vision. The ability of machine learning to retrieve and handle data analytics, their implementations, and autonomous robots in real-time speech translation and facial analysis augments the growth of these data. Increasing the use of computer vision in independent or semiautonomous-sustaining applications in different industries, like the manufacturing and automotive industries, is driving this technology to increase growth in the AI market.

By Solution Type (in %), GlobalArtificial intelligence market, 2019

In 2019the software, solution accounted for more than 60% of the total artificial intelligence market

Global Artificial intelligence market is segmented by solution type: Software, hardware, and AI- as-a-service. Due to technological advances in the provision of customized application-specific cloud platforms and enhanced operating systems, this AI segment is expected to show its highest growth, which is expected to further contribute to strengthening end-user data storage architecture.

GlobalArtificial intelligence market: Market Dynamics

Drivers

Collaborative Policies adopted by many companies
The competitive landscape in the market for AI shows a tendency towards companies that adopt strategies such as partnerships, cooperation, and joint ventures as well as launching and developing their products. With the growing global market growth, companies in this sector are being forced to develop collaborative policies to sustain themselves in a highly competitive market.

Efficient energy storage
AI in the industrial sector can prove to be beneficial to optimize energy storage efficiency. The production of energy is frequent and sometimes chaotic, and the storage of renewable energy is quite problematic. The combination of renewable energy with AI storage can make energy storage management much simpler, enhance business value, and reduce the loss of electricity.

Restraints

Insufficient Knowledge
One reason for the slow adoption of AI in the sector is a lack of necessary knowledge about AI technology among decision-makers. Many companies simply don’t have sufficient technical background to understand how their business can benefit from AI adoption. Conservative stakeholders prefer to stick with time-proven methods and tools rather than risk trying something new.

The fewer number of trained professionals
AI is still a new technology, and skilled professionals are few. There are many experts with thorough theoretical insight into the topic. Yet it is incredibly difficult to find professionals who can develop reliable, functional AI applications.

Artificial intelligence market: Key Players
IBM Corporation
Company Overview
Business Strategy
Key Product Offerings
Financial Performance
Key Performance Indicators
Risk Analysis
Recent Development
Regional Presence
SWOT Analysis
SAP SE
Microsoft Corporation
Huawei Technologies
HCL Technologies
Accenture Plc
Schneider Electric
Honeywell International
Rockwell Automation
Schlumberger Limited
Global Artificial intelligence Market: Regions

The largest share of the market in 2019 was held by America which accounting for over XX% share in the world.

Asia Pacific region despite having the lowest market share is anticipated to grow at the highest CAGR during the forecast period.

The expansion of the marketing industries has led to the development of increasing e-commerce, online streaming, and the spread of the internet. Marketers now seek more advanced and cutting-edge marketing solutions for a wide range and ever-growing customer bases. This led to the implementation of AI-compatible marketing solutions. Insecurity, organizations, and governments focus on comprehensive defense infrastructure with rising occurrences of cyber-attacks and widespread cyberwar in the region. APAC is considered the largest market for industrial robots, particularly China, Japan, and South Korea.

Asia Pacific: China, Japan, India, and Rest of Asia Pacific
Europe: Germany, the UK, France, and Rest of Europe
North America: The US, Mexico, and Canada
Latin America: Brazil and Rest of Latin America
Middle East & Africa: GCC Countries and Rest of Middle East & Africa

Global Artificial intelligence market is further segmented by region into:

North America Market Size, Share, Trends, Opportunities, Y-o-Y
Growth, CAGR – the United States and Canada

Latin America Market Size, Share, Trends, Opportunities, Y-o-Y
Growth, CAGR – Mexico, Argentina, Brazil and Rest of Latin America

Europe Market Size, Share, Trends, Opportunities, Y-o-Y Growth,
CAGR – United Kingdom, France, Germany, Italy, Spain, Belgium,

Hungary, Luxembourg, Netherlands, Poland, NORDIC, Russia,

Turkey and Rest of Europe

APAC Market Size, Share, Trends, Opportunities, Y-o-Y Growth,
CAGR – India, China, South Korea, Japan, Malaysia, Indonesia, New

Zealand, Australia, and Rest of APAC

MENA Market Size, Share, Trends, Opportunities, Y-o-Y Growth,
CAGR – North Africa, Israel, GCC, South Africa

Global Artificial intelligence Market: Impact of COVID-19
In response to a disease outbreak of COVID-19 and to continue expanding problems, artificial intelligence (AI), virtual reality (VR) and augmented reality (AR) technologies are expected to contribute significantly. The present situation due to the epidemic will motivate pharmaceutical providers and health centers, as the key technologies for different initiatives, to strengthen their R&D investments in AI. Cost-efficiency pressures are anticipated in the insurance sector. In the course of the renewal process, the use of AI can drastically reduce operating costs and, at the same time, can improve customer satisfaction. VR / AR can support Digital-learning, which will be increasingly demanded by the shutdown of many schools and institutions. VR / AR can also provide offline assistance as they help minimize potential journeys and can prove to be a worthwhile solution.

Global Artificial intelligence market report also contains analysis on:
Artificial intelligence market Segments:
By Technology:
Context-Aware Computing
Machine Learning
Natural Language Processing
Computer Vision
solution Type:
Software
Hardware
AI-as-a-solution
Artificial intelligence market Dynamics
Artificial intelligence market Size
Supply & Demand
Current Trends/Issues/Challenges
Competition & Companies Involved in the Market
Value Chain of the Market
Market Drivers and Restraints

The post Global Artificial intelligence Market Analysis and Forecast Report 2030 first appeared on CRI Report.

The post Artificial Intelligence in Indian Manufacturing Industry 2020 first appeared on CRI Report.

Market insights:
Artificial Intelligence for speech recognition market in India is anticipated to expand at a compound annual growth rate (CAGR) of ~65.17% during the forecast period (2019-2024), and is expected to reach a value of INR 14.61 Bn by 2024, from INR 0.67 Bn in 2018. This market growth can be attributed to the growing use of speech recognition technology for voice commands in call intelligence, voice-powered devices, voice software systems, and third-party voice apps. Moreover, industries such as ICT, healthcare, BFSI, and automobile are increasingly using AI-based speech recognition technology to enhance efficiency, gain insights, and identify pertinent information from large databases.
Dependency on AI and machine learning has increased with the launch of new voice-interactive devices like smartphones, smartwatches, and smart home appliances. These voice-enabled devices, compatible with Alexa and Google Assistant, are gaining immense popularity among Indian consumers. Currently, more than 50% of the population use at least one wearable device that is embedded with AI-based speech recognition technology.

Competitive insights:
India is among the top five countries which has a large base of AI-driven start-ups. These start-ups provide solutions related to natural language processing (NLP), machine learning, and other technologies, and attract huge funding from investors who are keen on huge returns in the short run. Key investors in the AI-based speech recognition market include March Capital Partners, Sistema Asia, Accel Partners, and Ideaspring Capital.
The market is currently dominated by voice-based assistants like Siri, Cortana, Alexa, and Google Assistant. With a total of 22 scheduled languages and several hundred recognized dialects, companies are updating their databases with languages and dialects to remain competitive.
Although large parts of the market are held by major players like Microsoft, Apple, Google, and Amazon, several start-ups (Uniphore Software Systems, Manthan Software Services and Liv.ai) are also venturing into the voice assistants’ market. The success behind these AI-based speech recognition start-ups depends mainly on the personalization and uniqueness of the products and services provided by these companies.
Companies covered
• Liv.ai Pvt. Ltd.
• Manthan Software Services Pvt. Ltd.
• Mihup Communications Pvt. Ltd.
• Uniphore Software Systems Pvt. Ltd.
• Voxta Communications Pvt. Ltd.
• Amazon India Ltd.
• Apple India Pvt. Ltd.
• Google India Pvt. Ltd.
• Microsoft India Pvt. Ltd.
• Nuance India Pvt. Ltd.

The post Artificial Intelligence for Speech Recognition Market in India 2019 first appeared on CRI Report.

Further, state of the art customizations are increasingly replacing traditional automotive platforms – a trend that is strengthening in the automotive industry. This is fueling the development of a large number of intelligent platforms covering in-vehicle experiences, supply chains, automotive sales and marketing, insurance tools, vehicle ecommerce and other applications – that are detailed in the report.

Organizations are adopting the merger and acquisition (M&A) route as a key strategy for acquiring AI skills, technologies, and relevant portfolios for leveraging organic and inorganic growth opportunities in the mobility market. Such initiatives are poised to create high potential for these acquirers, by helping them launch new AI services and increase their customer base.

Artificial Intelligence In The Automotive Industry – Acquisition Trends

The report includes an analysis of more than 60 deals with a detailed technology overview, deal amount, and the purpose of acquisition. The acquisitions listed in the report capture the intricate requirements of automotive sector to upgrade its value in the marketplace.

Our M&A analysis section offers a comprehensive view of the transactions in the automotive sector, spanning AI, machine learning, and deep learning technologies. The different target technology areas highlighted include intelligent vision systems, data analytics, cloud computing, AI hardware and software, localization, security and other related software stacks.

Some of the prominent deals observed include:

– Intel – Mobileye
– Analog Devices – OtoSense
– Baidu – xPerception
– Continental – Argus Cyber security
– Xilinx – Deephi
– Daimler – Torc
– Zurich Insurance – Brightbox
– Blackberry – Cylance
– DoorDash – IvI5
– WPCS International – DropCar
– Verizon – Telogis

Key Insights:
– Automotive suppliers are leading the AI acquisition trend with a focus on acquiring complementary skills such as computer vision, gesture recognition, and AI-based analytics.
– In terms of technology, computer vision has attracted the largest number of acquires due to its capabilities for object detection, classification, and semantic segmentation.
– AI-based data analytics, conversational AI, intelligent hardware and software solutions, and annotation techniques are the other key technology areas that are prominent in the acquisition trend.
– Acquirers are targeting AI companies for tuck-ins, business line expansion, and vertical and horizontal integration.
– Ford, Intel, and Baidu have made three acquisitions each for gaining AI technologies for the mobility market.
– Uber has made two acquisitions related to data-training capabilities.
– Acquisition trends suggest that automotive retail is undergoing a transformation, with dealers and automotive suppliers focusing on customer-centric solutions.

Key questions addressed in the report:
– What are the key trends driving AI adoption in the automotive industry?
How new business models are driving the need to acquire AI capabilities in the automotive industry?
– What is the span of M&A activity in the automotive value chain?
– How is the acquisition trend changing over the years?
– How are target companies leading the acquisition of different AI technologies in the US, Europe, Asia, and other regions?
– Who are the different acquirers actively involved in the acquisition scenario?
– What are the different AI technology capabilities acquired in the automotive space?
– How are the acquired AI capabilities such as computer vision, AI-based analytics, data training, and other technologies strengthening the portfolio of the acquirers?
– What are the post-acquisition scenarios for the major deals?
– What are the untapped technology areas that can be considered as potential targets in the future?

Global ADAS and Autonomous Driving Component Market Anticipated to Reach $66.23 Billion by 2030

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Market insights:
It is predicted that the applications of artificial intelligence in the healthcare space will be worth INR ~431.97 Bn by 2021, expanding at a rate of ~40%. Based on this growth of AI applications in healthcare, the doctor-patient ratio in India is expected to reach ~6.9:1,000 by 2023, from its 2017 ratio of ~4.8:1000. The capability of AI applications to improve doctors’ efficiency will help in tackling challenges like uneven doctor-patient ratio, by providing rural populations high-quality healthcare, and training doctors and nurses to handle complex medical procedures.
Artificial intelligence is used in six healthcare segments: hospitals, pharmaceuticals, diagnostics, medical equipment and supplies, medical insurance, and telemedicine. Artivatic Data Labs Private Limited, Analytix Private Limited, IBM India Limited, Niramai Health Sigtuple Technologies Private Limited, and Tricog Health Services Private Limited are the major players operating in this sector.

Government initiatives:
o The Information Technology Act, 2000, and the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011, mandate that service providers and patients exchange information constantly by using the latest technologies.
o National eHealth Authority (NeHA) – An authority which is responsible for the expansion of the integrated health information system within India.
o The United States–India Science and Technology Endowment Fund, is aimed at helping teams of innovators and entrepreneurs from both countries, whose products will improve the quality of healthcare, by harnessing the power of artificial intelligence.

Benefits of AI in the healthcare sector:
o The patient-doctor ratio in India is as low as 1,700:1. Also, ~70% of the healthcare infrastructure is in cities, which cater to ~30% of the country’s population. With the use of artificial intelligence applications, doctors can offer their services to more patients and reduce the existing gap in demand and supply of medical services in the country.
o AI-enabled healthcare services can be delivered at lower costs with increased efficiency and an emphasis on diagnostics. Moreover, artificial intelligence enables hospitals to implement patient-centric plans and eliminate unnecessary hospital procedures, making delivery of healthcare services faster in India.

Key deterrents to the growth of the market:
India lacks standardized guidelines for designing AI applications that can be used in healthcare systems. Lack of clarity deters the use of artificial intelligence in the Indian healthcare industry. Also, most AI companies which aid the healthcare sector, are start-ups. Medical practitioners are not quick to trust start-ups whose products are not nationally or internationally certified. As a result, start-ups’ sales get hampered, resulting in the limited implementation of AI in the Indian healthcare industry.
Companies covered:
o Aindra Systems Private Limited
o Advenio Tecnosys Private Limited
o Artivatic Data Labs Private Limited
o Niramai Health Analytix Private Limited
o Predible Health Private Limited
o Qure.Ai Technologies Private Limited
o Sigtuple Technologies Private Limited
o Tricog Health Services Private Limited
o IBM India Private Limited
o Microsoft Corporation (India) Private Limited

The post Artificial Intelligence (AI) in Healthcare Market in India (2018-2023) first appeared on CRI Report.

The rise in IoT enabled smart devices such as RFID sensor tags and barcodes help in the automation of supply chains. Thus, the adoption of IoT technology has facilitated the generation of significant amounts of data. This comprises unstructured data which helps the enterprises lower the operational and capital expenditures with the help of predictive and optimization models. Furthermore, most of the industrialists and experts recognize that integration of artificial intelligence with supply chain analytics could be an effective growth strategy in predicting the possible outcomes and offering a pro-active action plan in handling inventory management. This integration is expected to act as a competitive advantage for implementing supply chain analytics in reducing cost fluctuations by improving sourcing and logistics activities.
However, lack of data privacy in public cloud deployment and monetary constraints among enterprise are likely to hinder the growth of supply chain analytics market during the forecast period.
The global supply chain analytics market is expected to reach approximately USD 8.89 billion by 2023 registering a 13.7% CAGR during the forecast period 2017–2023.
Key Players
Some of the key players of Supply Chain Analytics market include IBM Corporation (US), MicroStrategy, Inc. (US), Oracle Corporation (US), Tableau Software, Inc. (US), SAP SE (France), Accenture PLC (Ireland), Genpact (US), SAS Institute Inc. (US), Manhattan Associates, (US), and JDA Software Group, Inc. (US).
Key Findings
• The global supply chain analytics market is expected to reach USD 8.89 billion by 2023.
• By component, the solution segment is expected to grow at a significant rate in the global supply chain analytics market during the forecast period.
• By solution, the supply chain planning and procurement segment is expected to hold the largest market share and is presumed to remain the highest market share over the next few years.
• By services, the managed services segment is likely to be the fastest growing segment during the assessment period.
• The cloud sub-segment of supply chain analytics market is expected to dominate the market during the forecast period and is also expected to be the fastest growing sub-segment from 2018 to 2023.
• By vertical, the retail segment is expected to be largest contributor during the forecast period and also projected to be the fastest growing segment during the next few years.
• Geographically, North America has been projected to hold the largest market share whereas Asia-Pacific is likely to be the fastest growing market in the supply chain analytics market.

Segmentation
The supply chain analytics market is segmented by component, deployment, and vertical.
Based on component, the supply chain analytics market is sub-segmented into solution and services. The solution segment is further divided into supply chain planning and procurement, transportation and logistics, sales and operation planning, manufacturing analytics, and visualization and reporting tools. Based on services, the supply chain analytics market is segmented into professional services and managed services.
By deployment, the market is categorized into on-premises and cloud. Based on the industry vertical, the market is categorized into retail, manufacturing, healthcare, automotive and transportation, aerospace and defense, and others.
Regional Analysis
Geographically, the supply chain analytics market is segmented into North America, Asia-Pacific, Europe, and the rest of the world.
North America dominated the supply chain analytics market in 2017 and gained a 39% market share in 2017. The presence of solution providers such as IBM Corporation, Oracle, and Tableau Software, Inc and highly skilled and technical expertise are some of the major factors for the dominance of North America in the market. The US is the leading the market whereas Canada is expected to witness significant growth over the assessment period. However, countries in the Asia-Pacific region such as China, Japan, and Taiwan are hubs electronics and semiconductor manufacturers; this may lead to an increase in the adoption of supply chain analytics software solutions for better inventory management. Due to this, Asia-Pacific is expected to be the fastest growing market during the forecast period
Target Audience
• Retail Vendors
• Local/Government Agencies
• Supply Chain Analytics Services Providers
• Cloud Service Providers
• IT Providers
• Value-added Resellers
• Cloud Infrastructure Agencies
• Research Firms
• Software Vendors

The post Global Supply Chain Analytics Market Research Report Information – Forecast Till 2023 first appeared on CRI Report.

Amid the proliferation of real-time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for over $57 Billion in 2017 alone. These investments are further expected to grow at a CAGR of approximately 10% over the next three years.

The “Big Data Market: 2017 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2017 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

Topics Covered
The report covers the following topics:
– Big Data ecosystem
– Market drivers and barriers
– Big Data technology, standardization and regulatory initiatives
– Big Data industry roadmap and value chain
– Analysis and use cases for 14 vertical markets
– Big Data analytics technology and case studies
– Big Data vendor market share
– Company profiles and strategies of 240 Big Data ecosystem players
– Strategic recommendations for Big Data hardware, software and professional services vendors, and enterprises
– Market analysis and forecasts from 2017 till 2030

Forecast Segmentation
Market forecasts are provided for each of the following submarkets and their subcategories:

Hardware, Software & Professional Services
– Hardware
– Software
– Professional Services

Horizontal Submarkets
– Storage & Compute Infrastructure
– Networking Infrastructure
– Hadoop & Infrastructure Software
– SQL
– NoSQL
– Analytic Platforms & Applications
– Cloud Platforms
– Professional Services

Vertical Submarkets
– Automotive, Aerospace & Transportation
– Banking & Securities
– Defense & Intelligence
– Education
– Healthcare & Pharmaceutical
– Smart Cities & Intelligent Buildings
– Insurance
– Manufacturing & Natural Resources
– Web, Media & Entertainment
– Public Safety & Homeland Security
– Public Services
– Retail, Wholesale & Hospitality
– Telecommunications
– Utilities & Energy
– Others

Regional Markets
– Asia Pacific
– Eastern Europe
– Latin & Central America
– Middle East & Africa
– North America
– Western Europe

Country Markets
– Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA

Key Questions Answered
The report provides answers to the following key questions:
– How big is the Big Data ecosystem?
– How is the ecosystem evolving by segment and region?
– What will the market size be in 2020 and at what rate will it grow?
– What trends, challenges and barriers are influencing its growth?
– Who are the key Big Data software, hardware and services vendors and what are their strategies?
– How much are vertical enterprises investing in Big Data?
– What opportunities exist for Big Data analytics?
– Which countries and verticals will see the highest percentage of Big Data investments?

Key Findings
The report has the following key findings:
– In 2017, Big Data vendors will pocket over $57 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 10% over the next three years, eventually accounting for over $76 Billion by the end of 2020.
– As part of wider plans to revitalize their economies, countries across the world are incorporating legislative initiatives to capitalize on Big Data. For example, the Japanese government is engaged in developing intellectual property protection and dispute resolution frameworks for Big Data assets, in a bid to encourage data sharing and accelerate the development of domestic industries.
– By the end of 2017, SNS Research estimates that as much as 30% of all Big Data workloads will be processed via cloud services as enterprises seek to avoid large-scale infrastructure investments and security issues associated with on-premise implementations.
– The vendor arena is continuing to consolidate with several prominent M&A deals such as computer hardware giant Dell’s $60 Billion merger with data storage specialist EMC.

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