Artificial Intelligence
Global Predictive Maintenance for Manufacturing Industry Market to Reach US$3.9 Billion by the Year 2026
New York, Jan. 18, 2022 (GLOBE NEWSWIRE) — Reportlinker.com announces the release of the report “Global Predictive Maintenance for Manufacturing industry” – https://www.reportlinker.com/p05896244/?utm_source=GNW
As competition in the manufacturing industry intensifies and the challenges for successful survival increase in magnitude, companies are focusing on improving their financial performance by scrutinizing closely the manufacturing reliability of their operations. Effective asset performance management is this regard represents an absolute necessity. Also strengthening the emphasis on asset management is the legislation of stringent workplace safety regulations. Occupational safety norms create the need for routine inspection of the condition of plant and manufacturing assets. As a subset of asset management, predictive maintenance (PdM) is forecast to benefit from the growing manufacturer investments in asset management systems and corporate wide implementation of asset management regimes. Maintenance is getting a notable makeover due to ongoing digital transformation, with the use of advanced data capturing and analytics tools leading to emergence of predictive maintenance. End-to-end integration of PdM with the entire lifecycle of the industrial plant is a key trend in vogue to enable the creation of more efficient workflows, elicit higher productivity and ensure better correlation of data among various sources.
Amid the COVID-19 crisis, the global market for Predictive Maintenance for Manufacturing Industry estimated at US$1.2 Billion in the year 2020, is projected to reach a revised size of US$3.9 Billion by 2026, growing at a CAGR of 21.4% over the analysis period. Software, one of the segments analyzed in the report, is projected to grow at a 19.6% CAGR to reach US$2.5 Billion by the end of the analysis period. After a thorough analysis of the business implications of the pandemic and its induced economic crisis, growth in the Services segment is readjusted to a revised 23.6% CAGR for the next 7-year period. This segment currently accounts for a 41.8% share of the global Predictive Maintenance for Manufacturing Industry market. Predictive maintenance software accesses the plant`s big data to gain additional insights about the operating environment in the plant and other extraneous factors that influence machine operation. Maintenance and repair services are vital for the proper functioning of enterprise assets while being key to the continuity and effectiveness of business operations. The proliferating deployment of sensing systems and advanced digital technologies such as IoT, AI and Big Data will spur the momentum for predictive maintenance.
The U.S. Market is Estimated at $409.6 Million in 2021, While China is Forecast to Reach $634.8 Million by 2026
The Predictive Maintenance for Manufacturing Industry market in the U.S. is estimated at US$409.6 Million in the year 2021. The country currently accounts for a 29.2% share in the global market. China, the world`s second largest economy, is forecast to reach an estimated market size of US$634.8 Million in the year 2026 trailing a CAGR of 26.4% through the analysis period. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 17.9% and 17.5% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 18.2% CAGR while Rest of European market (as defined in the study) will reach US$417.6 Million by the end of the analysis period.
Select Competitors (Total 115 Featured)
- eMaint Enterprises, LLC
- General Electric Company
- IBM Corporation
- PTC, Inc.
- Robert Bosch GmbH
- Rockwell Automation, Inc.
- SAS Institute, Inc.
- Schneider Electric SA
- Software AG
Read the full report: https://www.reportlinker.com/p05896244/?utm_source=GNW
I. METHODOLOGY
II. EXECUTIVE SUMMARY
1. MARKET OVERVIEW
Plant Asset Maintenance, the Cornerstone for Achieving
Excellence in Manufacturing Productivity
Why Migrate to Predictive Maintenance? What?s In It for Companies?
EXHIBIT 1: With Manufacturing Competitiveness Increasing by the
Day, PdM is the Right Step Forward Towards to Higher Value,
Smart Manufacturing: Global Manufacturing Competitiveness
Index (10-100 Index Score) by Country for the Year 2021
The Race Between the Virus & Vaccines Intensifies. Amidst this
Chaotic Battle, Where is the World Economy Heading in 2021 &
Beyond?
These are Times When Questions Abound & Answers Are Few
So How Fast Or Slow Are We Moving?
EXHIBIT 2: How & When Will the World Be Vaccinated? Global
Number of Annual COVID-19 Vaccine Doses (In Million) for Years
2020 through 2025 by Geographic Region/Country
Split Scenarios Unfold: The Great Vaccine Divide Emerges
EXHIBIT 3: Vaccine Imbalances to Stretch the Pandemic Further
into 2022: Global Percentage (%) of Population Administered
With Vaccines by Region as of October 2021
EXHIBIT 4: Time is of Essence! What We Know So Far – ?Vaccine
Efficiency Against New Strains is Decreasing?
Progress on Vaccinations, Why Should Businesses Care?
With IMF?s Upward Revision of Global GDP Forecasts for 2021,
Most Companies Are Bullish About an Economic Comeback Despite
a Continuing Pandemic
EXHIBIT 5: A Strong Yet Exceedingly Patchy & Uncertain Recovery
Shaped by New Variants Comes Into Play: World Economic Growth
Projections (Real GDP, Annual % Change) for 2020 through 2022
EXHIBIT 6: Easing Unemployment Levels in 2021 Although Moderate
Will Infuse Hope for Industries Reliant on Consumer
Discretionary Incomes: Global Number of Unemployed People (In
Million) for Years 2019, 2020, 2021, and 2022
How the Manufacturing Industry Was Impacted by the Pandemic &
What?s the New Normal?
Pandemic Catalyzes Manufacturing Automation
EXHIBIT 7: After Biting the Dust in the Year 2020, the
Manufacturing Industry Makes a Gradual Comeback to Normalcy:
Global Manufacturing PMI Index Points for the Years 2018,
2019, 2020 and 2021 (By Quarter)
Predictive Maintenance (PdM): Definition, Scope, Importance,
Benefits & Applications
Recent Market Activity
Innovations
2. FOCUS ON SELECT PLAYERS
3. MARKET TRENDS & DRIVERS
The Age of Analytics Made More Important by COVID-19 Provides
the Cornerstone for the Disruptive Growth of Predictive
Maintenance
Here?s How the Rise of ?Predictive Analytics? Will Bring the
Concept of Predictive Maintenance to Fruition
EXHIBIT 8: Applications of Predictive Analytics Expand from
Fraud Detection, Personalized Marketing to Asset Management:
Global Investments in Predictive Analytics (In US$ Billion)
for the Years 2020, 2022, 2024 & 2026
Recovery of the Global Manufacturing Industry from the 2020
Slump Bodes Well for Medium to Long-Term Growth of the
Predictive Maintenance Market
Importance of Industrial Digitization in Post Covid World to
Spur Transition from Preventive to Predictive Maintenance
Industry 4.0 Pushes Up the Effectiveness of Predictive Maintenance
What Does IIoT Mean for Predictive Maintenance?
EXHIBIT 9: Market to Witness Unconventional Growth as
Effectiveness of Predictive Analytics in Predicting Failure &
Maintenance Event Improves on the Back of IIoT: Global
Breakdown of Investments in Manufacturing IoT (in US$
Billion) for the Years 2020, 2022, 2024, and 2026
Special Focus on Predictive Maintenance of Bearings
Sensors Remain a Critical Component of Predictive Maintenance
Data Communication Systems, the Backbone for Successful
Predictive Maintenance
5G to Turbocharge Predictive Maintenance Capabilities
Here?s How Artificial Intelligence (AI) Will Revolutionize
Predictive Maintenance
AI Embedded Sensors Vital for Real-Time Fault Detection &
Outsmarting Failure
EXHIBIT 10: Growing Investments in AI by Manufacturing
Companies Will Promote Innovative Uses of AI in Machine
Condition Monitoring: Global AI Investments in Manufacturing
(In US$ Billion) for the Years 2019, 2022 and 2025
Harnessing the Power of Cloud Computing Remains Crucial for
Effective Predictive Maintenance
Moving to the Cloud is a Matter of Survival for Companies in
the 21st Century
Here?s How Cloud Can Make Predictive Maintenance More Effective &
Cheaper
What Has Blockchain to Offer for Predictive Maintenance?
Blockchain Rises like a Phoenix from the Ashes of Bitcoin
Here?s How Blockchain Can Advance Predictive Maintenance & Make
it More Accessible
EXHIBIT 11: As Blockchain Becomes a Mainstream Technology,
Increasing Investments Will be Sunk into the Technology for
Enabling Predictive Plant Maintenance: Global Opportunity for
Blockchain Technology (In US$ Billion) for Years 2021, 2023,
2025 and 2027
Edge Computing Emerges to Enhance Performance of & Value
Created by Predictive Maintenance
The Rise of Edge Computing: A Review
EXHIBIT 12: Edge Computing Becomes Indispensable as Effective
Predictive Maintenance Needs Distributed Intelligence: Global
Opportunity for Edge Computing (In US$ Million) for Years
2021, 2023, 2025 and 2027
Here?s How Edge Computing Helps Unlock the Benefits of
Predictive Maintenance
Digital Twins & Predictive Maintenance: Made for Each Other?
EXHIBIT 13: Growing Popularity of Digital Twins Encourages
Convergence of the Technology With Predictive Maintenance:
Global Digital Twin Opportunity (In US$ Million) for Years
2021, 2023, 2025 and 2027
Going Beyond Predictive: Developments in Prescriptive Analytics
Brings Prescriptive Maintenance Into the Spotlight as the
Future of Asset Management
4. GLOBAL MARKET PERSPECTIVE
Table 1: World Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Geographic Region –
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World Markets – Independent Analysis of Annual Revenues in US$
Thousand for Years 2020 through 2027 and % CAGR
Table 2: World 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Geographic Region – Percentage
Breakdown of Value Revenues for USA, Canada, Japan, China,
Europe, Asia-Pacific and Rest of World Markets for Years 2021 &
2027
Table 3: World Current & Future Analysis for Software by
Geographic Region – USA, Canada, Japan, China, Europe,
Asia-Pacific and Rest of World Markets – Independent Analysis
of Annual Revenues in US$ Thousand for Years 2020 through 2027
and % CAGR
Table 4: World 7-Year Perspective for Software by Geographic
Region – Percentage Breakdown of Value Revenues for USA,
Canada, Japan, China, Europe, Asia-Pacific and Rest of World
for Years 2021 & 2027
Table 5: World Current & Future Analysis for Services by
Geographic Region – USA, Canada, Japan, China, Europe,
Asia-Pacific and Rest of World Markets – Independent Analysis
of Annual Revenues in US$ Thousand for Years 2020 through 2027
and % CAGR
Table 6: World 7-Year Perspective for Services by Geographic
Region – Percentage Breakdown of Value Revenues for USA,
Canada, Japan, China, Europe, Asia-Pacific and Rest of World
for Years 2021 & 2027
Table 7: World Current & Future Analysis for On-Premise by
Geographic Region – USA, Canada, Japan, China, Europe,
Asia-Pacific and Rest of World Markets – Independent Analysis
of Annual Revenues in US$ Thousand for Years 2020 through 2027
and % CAGR
Table 8: World 7-Year Perspective for On-Premise by Geographic
Region – Percentage Breakdown of Value Revenues for USA,
Canada, Japan, China, Europe, Asia-Pacific and Rest of World
for Years 2021 & 2027
Table 9: World Current & Future Analysis for Cloud-Based by
Geographic Region – USA, Canada, Japan, China, Europe,
Asia-Pacific and Rest of World Markets – Independent Analysis
of Annual Revenues in US$ Thousand for Years 2020 through 2027
and % CAGR
Table 10: World 7-Year Perspective for Cloud-Based by
Geographic Region – Percentage Breakdown of Value Revenues for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World for Years 2021 & 2027
Table 11: World Current & Future Analysis for Machine Learning
by Geographic Region – USA, Canada, Japan, China, Europe,
Asia-Pacific and Rest of World Markets – Independent Analysis
of Annual Revenues in US$ Thousand for Years 2020 through 2027
and % CAGR
Table 12: World 7-Year Perspective for Machine Learning by
Geographic Region – Percentage Breakdown of Value Revenues for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World for Years 2021 & 2027
Table 13: World Current & Future Analysis for Deep Learning by
Geographic Region – USA, Canada, Japan, China, Europe,
Asia-Pacific and Rest of World Markets – Independent Analysis
of Annual Revenues in US$ Thousand for Years 2020 through 2027
and % CAGR
Table 14: World 7-Year Perspective for Deep Learning by
Geographic Region – Percentage Breakdown of Value Revenues for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World for Years 2021 & 2027
Table 15: World Current & Future Analysis for Big Data &
Analytics by Geographic Region – USA, Canada, Japan, China,
Europe, Asia-Pacific and Rest of World Markets – Independent
Analysis of Annual Revenues in US$ Thousand for Years 2020
through 2027 and % CAGR
Table 16: World 7-Year Perspective for Big Data & Analytics by
Geographic Region – Percentage Breakdown of Value Revenues for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World for Years 2021 & 2027
III. MARKET ANALYSIS
UNITED STATES
Table 17: USA Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 18: USA 7-Year Perspective for Predictive Maintenance for
Manufacturing Industry by Component – Percentage Breakdown of
Value Revenues for Software and Services for the Years 2021 &
2027
Table 19: USA Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 20: USA 7-Year Perspective for Predictive Maintenance for
Manufacturing Industry by Deployment – Percentage Breakdown of
Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 21: USA Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 22: USA 7-Year Perspective for Predictive Maintenance for
Manufacturing Industry by Technology – Percentage Breakdown of
Value Revenues for Machine Learning, Deep Learning and Big Data &
Analytics for the Years 2021 & 2027
CANADA
Table 23: Canada Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 24: Canada 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Component – Percentage Breakdown
of Value Revenues for Software and Services for the Years 2021 &
2027
Table 25: Canada Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 26: Canada 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Deployment – Percentage Breakdown
of Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 27: Canada Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 28: Canada 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Technology – Percentage Breakdown
of Value Revenues for Machine Learning, Deep Learning and Big
Data & Analytics for the Years 2021 & 2027
JAPAN
Table 29: Japan Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 30: Japan 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Component – Percentage Breakdown
of Value Revenues for Software and Services for the Years 2021 &
2027
Table 31: Japan Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 32: Japan 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Deployment – Percentage Breakdown
of Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 33: Japan Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 34: Japan 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Technology – Percentage Breakdown
of Value Revenues for Machine Learning, Deep Learning and Big
Data & Analytics for the Years 2021 & 2027
CHINA
Table 35: China Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 36: China 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Component – Percentage Breakdown
of Value Revenues for Software and Services for the Years 2021 &
2027
Table 37: China Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 38: China 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Deployment – Percentage Breakdown
of Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 39: China Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 40: China 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Technology – Percentage Breakdown
of Value Revenues for Machine Learning, Deep Learning and Big
Data & Analytics for the Years 2021 & 2027
EUROPE
Table 41: Europe Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Geographic Region –
France, Germany, Italy, UK and Rest of Europe Markets –
Independent Analysis of Annual Revenues in US$ Thousand for
Years 2020 through 2027 and % CAGR
Table 42: Europe 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Geographic Region – Percentage
Breakdown of Value Revenues for France, Germany, Italy, UK and
Rest of Europe Markets for Years 2021 & 2027
Table 43: Europe Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 44: Europe 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Component – Percentage Breakdown
of Value Revenues for Software and Services for the Years 2021 &
2027
Table 45: Europe Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 46: Europe 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Deployment – Percentage Breakdown
of Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 47: Europe Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 48: Europe 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Technology – Percentage Breakdown
of Value Revenues for Machine Learning, Deep Learning and Big
Data & Analytics for the Years 2021 & 2027
FRANCE
Table 49: France Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 50: France 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Component – Percentage Breakdown
of Value Revenues for Software and Services for the Years 2021 &
2027
Table 51: France Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 52: France 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Deployment – Percentage Breakdown
of Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 53: France Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 54: France 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Technology – Percentage Breakdown
of Value Revenues for Machine Learning, Deep Learning and Big
Data & Analytics for the Years 2021 & 2027
GERMANY
Table 55: Germany Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 56: Germany 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Component – Percentage Breakdown
of Value Revenues for Software and Services for the Years 2021 &
2027
Table 57: Germany Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 58: Germany 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Deployment – Percentage Breakdown
of Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 59: Germany Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 60: Germany 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Technology – Percentage Breakdown
of Value Revenues for Machine Learning, Deep Learning and Big
Data & Analytics for the Years 2021 & 2027
ITALY
Table 61: Italy Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 62: Italy 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Component – Percentage Breakdown
of Value Revenues for Software and Services for the Years 2021 &
2027
Table 63: Italy Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 64: Italy 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Deployment – Percentage Breakdown
of Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 65: Italy Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 66: Italy 7-Year Perspective for Predictive Maintenance
for Manufacturing Industry by Technology – Percentage Breakdown
of Value Revenues for Machine Learning, Deep Learning and Big
Data & Analytics for the Years 2021 & 2027
UNITED KINGDOM
Table 67: UK Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 68: UK 7-Year Perspective for Predictive Maintenance for
Manufacturing Industry by Component – Percentage Breakdown of
Value Revenues for Software and Services for the Years 2021 &
2027
Table 69: UK Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 70: UK 7-Year Perspective for Predictive Maintenance for
Manufacturing Industry by Deployment – Percentage Breakdown of
Value Revenues for On-Premise and Cloud-Based for the Years
2021 & 2027
Table 71: UK Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 72: UK 7-Year Perspective for Predictive Maintenance for
Manufacturing Industry by Technology – Percentage Breakdown of
Value Revenues for Machine Learning, Deep Learning and Big Data &
Analytics for the Years 2021 & 2027
REST OF EUROPE
Table 73: Rest of Europe Current & Future Analysis for
Predictive Maintenance for Manufacturing Industry by Component –
Software and Services – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 74: Rest of Europe 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Component –
Percentage Breakdown of Value Revenues for Software and
Services for the Years 2021 & 2027
Table 75: Rest of Europe Current & Future Analysis for
Predictive Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 76: Rest of Europe 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Deployment –
Percentage Breakdown of Value Revenues for On-Premise and
Cloud-Based for the Years 2021 & 2027
Table 77: Rest of Europe Current & Future Analysis for
Predictive Maintenance for Manufacturing Industry by Technology –
Machine Learning, Deep Learning and Big Data & Analytics –
Independent Analysis of Annual Revenues in US$ Thousand for the
Years 2020 through 2027 and % CAGR
Table 78: Rest of Europe 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Technology –
Percentage Breakdown of Value Revenues for Machine Learning,
Deep Learning and Big Data & Analytics for the Years 2021 &
2027
ASIA-PACIFIC
Table 79: Asia-Pacific Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Component – Software
and Services – Independent Analysis of Annual Revenues in US$
Thousand for the Years 2020 through 2027 and % CAGR
Table 80: Asia-Pacific 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Component –
Percentage Breakdown of Value Revenues for Software and
Services for the Years 2021 & 2027
Table 81: Asia-Pacific Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 82: Asia-Pacific 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Deployment –
Percentage Breakdown of Value Revenues for On-Premise and
Cloud-Based for the Years 2021 & 2027
Table 83: Asia-Pacific Current & Future Analysis for Predictive
Maintenance for Manufacturing Industry by Technology – Machine
Learning, Deep Learning and Big Data & Analytics – Independent
Analysis of Annual Revenues in US$ Thousand for the Years 2020
through 2027 and % CAGR
Table 84: Asia-Pacific 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Technology –
Percentage Breakdown of Value Revenues for Machine Learning,
Deep Learning and Big Data & Analytics for the Years 2021 &
2027
REST OF WORLD
Table 85: Rest of World Current & Future Analysis for
Predictive Maintenance for Manufacturing Industry by Component –
Software and Services – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 86: Rest of World 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Component –
Percentage Breakdown of Value Revenues for Software and
Services for the Years 2021 & 2027
Table 87: Rest of World Current & Future Analysis for
Predictive Maintenance for Manufacturing Industry by Deployment –
On-Premise and Cloud-Based – Independent Analysis of Annual
Revenues in US$ Thousand for the Years 2020 through 2027 and %
CAGR
Table 88: Rest of World 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Deployment –
Percentage Breakdown of Value Revenues for On-Premise and
Cloud-Based for the Years 2021 & 2027
Table 89: Rest of World Current & Future Analysis for
Predictive Maintenance for Manufacturing Industry by Technology –
Machine Learning, Deep Learning and Big Data & Analytics –
Independent Analysis of Annual Revenues in US$ Thousand for the
Years 2020 through 2027 and % CAGR
Table 90: Rest of World 7-Year Perspective for Predictive
Maintenance for Manufacturing Industry by Technology –
Percentage Breakdown of Value Revenues for Machine Learning,
Deep Learning and Big Data & Analytics for the Years 2021 &
2027
IV. COMPETITION
Total Companies Profiled: 115
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Artificial Intelligence
INSPIRED BY A FIFTIES AUTOMOTIVE LEGEND: INTRODUCING AUTOMOBILI PININFARINA BATTISTA CINQUANTACINQUE
Battista Cinquantacinque bears the iconic Blu Savoia Gloss bodywork and contrasting Bianco Sestriere Gloss roof which is inspired by the original 1955 Lancia Florida show carCinquantacinque name, derived from the Italian word for 55, pays tribute to the iconic Pininfarina-designed classic carElegant Lancia Florida was a favourite of the man who penned the classic model and who gave his name to the fastest and most powerful Italian road car – Battista ‘Pinin’ FarinaOne-of-a-kind Battista Cinquantacinque pictured alongside one of only three Lancia Florida sedansAutomobili Pininfarina Design Team was inspired by the 1955 Lancia Florida when also creating the PURA Vision design concept, which sets the template for all future Automobili Pininfarina modelsAccompanying assets available to download hereCAMBIANO, Italy, May 8, 2024 /PRNewswire/ — Automobili Pininfarina has curated a unique Battista commission as a fitting tribute to the 1955 Lancia Florida – a vehicle designed and loved by Battista ‘Pinin’ Farina.
The Lancia Florida was penned by Battista ‘Pinin’ Farina in the early fifties. It was renowned as a rolling sculpture which inspired new design perspectives. Pinin’s personal Florida, which can be admired as part of the Pininfarina SpA Collection in Cambiano (Torino), was the daily driver of Carrozzeria Pininfarina’s founder throughout his career. The stunning Battista Cinquantacinque hyper GT pays homage to this unique heritage.
Its exterior is finished in a flawless Blu Savoia Gloss paint, contrasted by the Bianco Sestriere Gloss roof and completed by the elegant Brushed Anodised jewellery pack. Inside, it is upholstered in a bespoke Mahagoni (Poltrona Frau Heritage Leather). The livery and name Cinquantacinque, translating to ’55’ in Italian, elegantly honours the iconic, classic 1955 Lancia Florida, styled by Carrozzeria Pininfarina.
The Cinquantacinque model features a number of unique inscriptions that point to its heritage and provenance. The passenger door plate as well as the underside of the active rear wing both feature the ‘Cinquantacinque 55’ signature.
Each Battista is propelled by four independent electric motors and a powerful 120 kWh lithium-ion battery, delivering 1,900hp and 2,340Nm of torque. This advanced powertrain ensures thrilling performances, such as accelerating 0-100 kph in 1.86 seconds and 0-200 kph in 4.75 seconds, as well as a comfortable driving range of up to 476 km.
The Battista Cinquantacinque will make its public debut in Tokyo, Japan, when it is introduced to clients in the region, alongside retail partner, SKY GROUP, as part of Automobili Pininfarina’s continued global expansion.
Clients in the region will get to see Automobili Pininfarina’s promise of ‘Dream Cars. Made Real.’ first-hand, providing a personalised client experience and ensuring every hand-crafted vehicle to leave the Atelier facility in Cambiano destined for Japan is a unique expression of each client’s personality.
Dave Amantea, Chief Design Officer at Automobili Pininfarina, said: “This was a truly special and unique opportunity to design a car that plays homage to Pininfarina SpA’s heritage. The colour combination of the Battista Cinquantacinque is incredible and shows the vision Battista Farina had when he designed the Lancia Florida back in the early fifties. Not only that, but that very same model helped guide me when creating the PURA Vision design concept, a truly unique vehicle that sets the template for future models from Automobili Pininfarina.”
The Lancia Florida sedan not only inspired the Cinquantacinque Battista, but also some of the key design elements of the PURA Vision design concept, a vehicle which sets the template for all future Automobili Pininfarina models.
The 50s sedan has rear-opening doors and no B pillar, which directly translated to the Lounge Doors on the PURA Vision. They hinge dramatically upwards and, in combination with the pillarless opening and rear-hinged back doors, provide unrestricted access to the design concept’s spacious 2+2 seating.
Automobili Pininfarina stands at the vanguard of pure Italian luxury experience, with its bespoke curated approach allowing for precise tailoring to the specific preferences of individual clients. Each vehicle is a unique, hand-crafted masterpiece and a reflection of the client’s personality with inspiration from Automobili Pininfarina’s artisans.
FOR MORE INFORMATION, VISIT: automobili-pininfarina.com/media-hub
EDITOR’S NOTES
ABOUT AUTOMOBILI PININFARINA
Automobili Pininfarina is based in operational headquarters in Cambiano, Italy, with a commercial office in Munich, Germany, and resourced with a team of experienced automotive executives from luxury and premium car brands. Designed, engineered and produced by hand in Italy, with a focus on designing experiences for the world’s foremost taste makers, all of Automobili Pininfarina’s vehicles embody the PURA design philosophy. This philosophy will also permeate all future production cars, seamlessly blending classic inspiration with cutting-edge technology.
THE AUTOMOBILI PININFARINA BATTISTA (LINK TO PRESS KIT)
The Battista is the most powerful car ever designed and built in Italy and it delivers a level of performance that is unachievable today in any road-legal sports car featuring internal combustion engine technology. Faster than a current Formula 1 race car in its 0-100 km/h sub-two second sprint, and with 1,900 hp and 2,340 Nm torque on tap, the Battista will combine extreme engineering and technology in a zero emissions package. The Battista’s 120 kWh battery provides power to four electric motors – one at each wheel – with a combined WLTP range of up to 476 km (U.S. combined EPA: 300 miles) on a single charge. No more than 150 examples of Battista will be individually hand-crafted at the Pininfarina SpA atelier in Cambiano, Italy.
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View original content:https://www.prnewswire.co.uk/news-releases/inspired-by-a-fifties-automotive-legend-introducing-automobili-pininfarina-battista-cinquantacinque-302138362.html
Artificial Intelligence
Exploring the Surge in Digital Infrastructure Spending Driven by Generative AI Innovations
USA News Group Commentary Issued on behalf of Avant Technologies Inc.
VANCOUVER, BC, May 7, 2024 /PRNewswire/ — USA News Group – The rise of generative AI is currently adding considerable potential for accelerating economic growth across a wide array of sectors. In order to fully realize this potential, analysts at ING are highlighting how crucial investment in data and digital infrastructure will be moving forward. Citing estimates from Dell’Oro and Bank of America, ING has pointed out that investments from big tech companies in digital infrastructure is expected to grow between 15-22% this year alone. Another report from analysts at SNS Insider are projecting the AI Infrastructure Market to hit US$222.42 billion by 2030, growing at an explosive CAGR of 25.5%. Behind the scenes, developments in AI infrastructure are being made and provided by a variety of tech companies that include Avant Technologies Inc. (OTCQB: AVAI), Vertiv Holdings Co (NYSE: VRT), Equinix, Inc. (NASDAQ: EQIX), Innodata Inc. (NASDAQ: INOD), and Salesforce, Inc. (NYSE: CRM).
Earlier this year, Avant Technologies Inc. (OTCQB: AVAI) took a significant step in expanding the capabilities of its flagship AvantAI platform, reinforcing its position as one of the first companies to market generative AI. So far, this enhancement has focused on advancing its high-performance data center infrastructure to accommodate the latest developments in AI technology, including the launch of its state-of-the-art supercomputing network and comprehensive licensable data set. As per the launch, Avant’s platform works in collaboration with tech partners Wired4Health, which the company would later acquire and name its former CFO William Hisey as its new CEO.
“Avant’s supercomputing network and our expansive licensable dataset will facilitate significant advancements in AIdriven solutions,” said Danny Rittman, Chief Information Officer of Avant of the launch. “By providing robust computational resources and a rich dataset, Avant is set to eliminate many of the technical and financial barriers that have traditionally hampered AI development. This initiative aims to empower developers with the tools necessary to create more sophisticated and efficient AI models, driving progress and innovation in innumerable fields.”
Providing even more security for its prospective clients, Avant Technologies has also announced its plans to implement AI-empowered, Zero Trust Architecture (ZTA) across its data center operations. In AI-empowered data centers, Zero Trust Architecture ensures that all users and devices are continuously authenticated and verified before accessing network resources.
“By integrating AI with Zero Trust Architecture, we are creating a robust and future-proof security framework for our data centers,” said William Hisey, Avant’s newly-appointed CEO. “This combined approach ensures the highest level of security for our customers’ data while optimizing data center operations for efficiency and cost-effectiveness. Avant is committed to providing innovative technology to help businesses optimize data center operations, improve resource utilization, and enhance security.”
Recently, Avant Technologies also expanded its AvantAI™ platform to include smart, proactive monitoring and management for data centers. AvantAI™ boosts data center efficiency through real-time monitoring and proactive management, helping to ensure systems operate continuously and perform optimally. It also uses extensive data analysis to predict and prevent potential issues and dynamically adjusts resources to enhance data traffic efficiency and responsiveness.
Working to further bolster the AI revolution, Vertiv Holdings Co (NYSE: VRT) saw 60% growth in organic orders of its critical digital infrastructure and continuity solutions in Q1 2024 compared to the same time period the year prior, along with 8% higher net sales.
As well, Vertiv saw a 42% increase in its adjusted operating profit to $249 million, compared to Q1 2024, as the company pointed towards continued acceleration in the data center market, including the deployment of high-performance compute, and increased velocity in its opportunity pipeline to orders.
“We are seeing order patterns with longer lead times based on customer build schedules, largely in 2025 and beyond, suggesting AI is starting to scale,” said Giordano Albertazzi, CEO of Vertiv. “We are continuing to advance our portfolio to enable high-density and GPU based deployments. With our global capacity, the most complete portfolio of critical digital infrastructure solutions across the entire thermal and power technology spectrum, vast global service network and alignment with key technology partners, Vertiv is uniquely positioned and ready to bring scale and support development of the entire AI ecosystem.”
Digital infrastructure company Equinix, Inc. (NASDAQ: EQIX) recently announced a joint venture with PGIM Real Estate for a $600 million project to develop and operate the first xScale data center in the US, located in California’s Silicon Valley. Together, Equinix and PGIM had already successfully opened the first xScale data center in Australia in 2022, after a similar US$575 million JV was announced in 2021.
Under the terms of the agreement, PGIM Real Estate will control an 80% equity interest in the JV, while Equinix will own the remaining 20% equity interest. Equinix xScale data centers allow hyperscale companies to expand their core deployments within Equinix IBX data centers. This setup supports growth across more than 70 global metros on a unified platform that facilitates direct interconnections with over 10,000 customers.
The new joint venture, alongside Equinix’s existing hyperscale collaborations in Europe, Asia-Pacific, and the Americas, will enhance the global xScale data center portfolio. Once fully constructed, this expansion will exceed $8 billion across more than 35 facilities, providing over 725 megawatts of power capacity.
Building upon the confidence of one of its existing “Magnificent Seven” Big Tech customers, Innodata Inc. (NASDAQ: INOD) recently announced it has been awarded three new large language model (LLM) development programs, totaling approximately $20 million of additional annualized run rate revenue. Before these new awards, the annualized run rate revenue from the customer was about $23 million. Innodata anticipates signing an amendment to its agreement with this customer that will reflect these awards within the next few weeks. Work on the newly awarded programs has already started by Innodata.
“We are very excited to announce this significant expansion,” saidJack Abuhoff, CEO of Innodata. “Moreover, we anticipate potential opportunities for further expansion in 2024. The customer continues to express how our high-quality, large-scale, custom data results in superior fine-tuning of their LLMs and that their AI engineering teams greatly value the Innodata partnership.”
Innodata provides large technology companies with scaled data services for refining LLMs and offers LLM evaluation services. By the end of 2023, Innodata had secured five of the top seven major tech firms as clients for its LLM services.
With its own array of big tech allies, Salesforce, Inc. (NYSE: CRM) recently unveiled its Zero Copy Partner Network, a global ecosystem of technology and solution providers building secure, bidirectional zero copy integrations with Salesforce Data Cloud which integrates both structured and unstructured customer data into a comprehensive 360-degree view accessible within Salesforce itself. This integration enables teams to make informed decisions directly within their workflow.
“In today’s digital landscape, companies struggle with islands of data spread across various systems,” said Brian Millham, President and Chief Operating Officer at Salesforce. “With this global ecosystem of partners, companies can access all of their data, no matter where it resides, and unlock the power of all of that data within Salesforce — creating more personalized customer interactions and establishing a foundation for trusted AI, in less time and at lower cost.”
The network will feature a list of initial partners that will include such big names as Amazon Web Services (AWS), Databricks, Google Cloud, and Snowflake, while also adding Microsoft. Together, the network partners are all committed to zero copy integrations with Salesforce that give customers a secure and cost-effective way to connect and take action on all of their data.
Source: https://usanewsgroup.com/2023/10/26/unlocking-the-trillion-dollar-ai-market-what-investors-need-to-know/
CONTACT:USA NEWS [email protected](604) 265-2873
DISCLAIMER: Nothing in this publication should be considered as personalized financial advice. We are not licensed under securities laws to address your particular financial situation. No communication by our employees to you should be deemed as personalized financial advice. Please consult a licensed financial advisor before making any investment decision. This is a paid advertisement and is neither an offer nor recommendation to buy or sell any security. We hold no investment licenses and are thus neither licensed nor qualified to provide investment advice. The content in this report or email is not provided to any individual with a view toward their individual circumstances. USA News Group is a wholly-owned subsidiary of Market IQ Media Group, Inc. (“MIQ”). MIQ has been paid a fee for Avant Technologies Inc. advertising and digital media from the company directly. There may be 3rd parties who may have shares Avant Technologies Inc., and may liquidate their shares which could have a negative effect on the price of the stock. This compensation constitutes a conflict of interest as to our ability to remain objective in our communication regarding the profiled company. Because of this conflict, individuals are strongly encouraged to not use this publication as the basis for any investment decision. The owner/operator of MIQ own shares of Avant Technologies Inc. which were purchased as a part of a private placement. MIQ reserves the right to buy and sell, and will buy and sell shares of Avant Technologies Inc. at any time thereafter without any further notice. We also expect further compensation as an ongoing digital media effort to increase visibility for the company, no further notice will be given, but let this disclaimer serve as notice that all material disseminated by MIQ has been approved by the above mentioned company; this is a paid advertisement, and we own shares of the mentioned company that we will sell, and we also reserve the right to buy shares of the company in the open market, or through further private placements and/or investment vehicles. While all information is believed to be reliable, it is not guaranteed by us to be accurate. Individuals should assume that all information contained in our newsletter is not trustworthy unless verified by their own independent research. Also, because events and circumstances frequently do not occur as expected, there will likely be differences between any predictions and actual results. Always consult a licensed investment professional before making any investment decision. Be extremely careful, investing in securities carries a high degree of risk; you may likely lose some or all of the investment.
View original content:https://www.prnewswire.co.uk/news-releases/exploring-the-surge-in-digital-infrastructure-spending-driven-by-generative-ai-innovations-302138670.html
Artificial Intelligence
nodeQ Launches PQtunnel™: The Leading-Edge Cybersecurity Solution for Quantum-Safe Communication
The latest application developed by nodeQ simplifies the migration to Post-Quantum Cryptography for both SMEs and large enterprises
YORK, England, May 7, 2024 /PRNewswire/ — The advent of Quantum Computing poses a significant threat to existing public-key cryptosystems. A large quantum computer can break algorithms like RSA or ECDH, which form the backbone of current internet security. Already today, confidential data is in danger: It can be harvested now to be decrypted later when a quantum computer can run Shor’s factorization quantum algorithm on a sufficiently large number of qubits.
This vulnerability exposes a large number of applications to potential security breaches, threatening our trillion-dollar digital economy. Multiple sectors are under threat: Financial Institutions, government entities, telecommunications companies, cloud providers, the healthcare industry, the defense sector, universities and research centers, and virtually any IT-based enterprise that relies on encrypted data.
Companies must transition IT infrastructures to quantum-resistant solutions, such as Post-Quantum Cryptography (PQC). In the US the transition to PQC is set to start in 2024, as a result of Biden’s 2022 Computing Cybersecurity Preparedness Act and the end of NIST standardization.
nodeQ has developed PQtunnel™, a groundbreaking tool designed to assist businesses – ranging from SMEs to large enterprises – in transitioning their end-to-end (E2E) secure communication to PQC. This innovative software application is available in two variants: PQtunnel™ TLS and PQtunnel™ SSH, meeting diverse business requirements.
PQtunnel™ TLS is an enterprise-oriented tunneling application that leverages the Transport Layer Security (TLS) protocol to secure traffic between a client (or a proxy) and the target application. This solution has been thoroughly tested on various enterprise-grade applications, such as GitLab EE, demonstrating its effectiveness and reliability.
On the other hand, PQtunnel™ SSH offers a comprehensive Quantum-Safe SSH server, alongside a client equipped with a user-friendly interface. This software suite is designed for secure shell operations, ensuring the highest level of security and ease of use.
Both PQtunnel™ variants utilize a wide range of PQC algorithms, including the most recent ones under standardization by NIST, and hybrid algorithms. This approach combines the stability of current cryptographic methods with the quantum-safeness of new cryptographic suites, offering unmatched security against emerging cyber threats.
Developed as cloud-native applications, both variants of PQtunnel™ are designed to facilitate easy distribution and provide flexibility in a wide range of scenarios. Secure Post-Quantum tunneling to your GitLab server, quantum-safe data transfer, secure cloud IT management, and PQC benchmarking are just a few of the use cases where PQtunnel™ can be applied, showcasing its versatility and significance for today’s cybersecurity landscape.
Ignazio Pedone, the cybersecurity lead at nodeQ, emphasized the significance of their latest security product, stating, “This security product represents an important step towards a more effective and robust application of quantum-safe cryptography for enterprises.”
Various early customers have already adopted PQtunnel™ to test their readiness to migrate to quantum-safe cryptography. PQC is going to play an important role for telecom operators.
“At Fibraweb, PQtunnel demonstrated our readiness for quantum-security migration,” says Walid Bounassif, CEO of Fibraweb (an Italian telco). “The software created quantum-resistant connections between external clients and our internal servers, showing that post-quantum encrypted communication can efficiently be implemented for remote IT administration and secure file transfer.”
PQC is essential for medium to large enterprises, including educational institutions like universities, where maintaining data confidentiality at various levels is crucial. At the University of York (UK), PQtunnel™ has been deployed for the quantum-safe transfer of documents. This pioneering demonstration, the first of its kind in the world within the education sector, actively involved staff and students from the Department of Computer Science.
“PQtunnel has effectively established high-speed, quantum-resistant connections both within and beyond the campus perimeter,” says Prof Paul Cairns (Head of the Computer Science Department at the University of York, UK). “This technology has clearly demonstrated its capacity for facilitating quantum-secure data transfers, affirming its readiness to enhance our university’s digital infrastructure.”
For additional information about PQtunnel™, nodeQ, and their range of products, the reader can visit their website at www.nodequantum.com.
Key Features
Crypto-Agility: implements all NIST PQC algorithms to counteract the quantum threat.
Cross-Platform: available for the most adopted operating systems and hardware architectures.
Easy Setup: no need for long or complex installation procedures to get started.
On-the-Fly Benchmarking and Optimization: can benchmark and optimize high-level security protocols against all the available combinations of PQC algorithms for key exchange and digital signature.
Target Customers
Financial Institutions
Telecommunications Companies
Universities and Research Centers
Cloud Providers
Healthcare Industry
Government Entities
Defense Sector
Manufacturing Companies
SMEs handling confidential data
About nodeQ
At nodeQ, we are pioneering the future of computer networks, leveraging our deep expertise in quantum communication, artificial intelligence, and software-defined networking. Our primary objective is to transition computer networks to quantum security, placing a critical emphasis on maintaining speed, performance, and usability at the heart of this transformation.
Contacts
Stefano Pirandola
[email protected]
Request a demo or a brochure at [email protected] or visit www.nodequantum.com.
Photo – https://mma.prnewswire.com/media/2406722/PQtunnel_Graphics.jpg
View original content:https://www.prnewswire.co.uk/news-releases/nodeq-launches-pqtunneltm-the-leading-edge-cybersecurity-solution-for-quantum-safe-communication-302138586.html
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