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Predictive Maintenance (PDM) Market to grow by USD 33.76 Billion from 2024-2028, driven by AI and cloud adoption in SMEs – Technavio

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NEW YORK, Nov. 27, 2024 /PRNewswire/ — Report with the AI impact on market trends – The global predictive maintenance (PDM) market  size is estimated to grow by USD 33.76 billion from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of  39%  during the forecast period. Increased adoption of advanced analytics by SMES owing to rise in cloud computing is driving market growth, with a trend towards proliferation of advanced technologies, AI, and IoT. However, lack of expertise and technical knowledge  poses a challenge.Key market players include Augury Inc., Avnet Inc., C3.ai Inc, Dell Technologies Inc., Deutsche Telekom AG, Fortive Corp., General Electric Co., Hitachi Ltd., Honeywell International Inc., International Business Machines Corp., PTC Inc., RapidMiner Inc., Reliability Solutions sp. Z o.o., Robert Bosch GmbH, Rockwell Automation Inc., SAP SE, SAS Institute Inc., Schneider Electric SE, Siemens AG, and Warwick Analytics Services Ltd..

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Forecast period

2024-2028

Base Year

2023

Historic Data

2017 – 2021

Segment Covered

Component (Solutions and Service), Deployment (On-premises and Cloud), and Geography (North America, Europe, APAC, South America, and Middle East and Africa)

Region Covered

North America, Europe, APAC, South America, and Middle East and Africa

Key companies profiled

Augury Inc., Avnet Inc., C3.ai Inc, Dell Technologies Inc., Deutsche Telekom AG, Fortive Corp., General Electric Co., Hitachi Ltd., Honeywell International Inc., International Business Machines Corp., PTC Inc., RapidMiner Inc., Reliability Solutions sp. Z o.o., Robert Bosch GmbH, Rockwell Automation Inc., SAP SE, SAS Institute Inc., Schneider Electric SE, Siemens AG, and Warwick Analytics Services Ltd.

Key Market Trends Fueling Growth

Predictive Maintenance (PDM) is a cutting-edge business trend revolutionizing equipment maintenance. It uses condition-based strategies to predict and prevent equipment failure, moving beyond time- and reactive-based methods. PDM leverages various technologies like electromagnetic radio fields, NFC chips, and sensor devices to gather real-time data. Devices such as vibration meters and acoustic analysis tools help identify potential issues. Machine learning algorithms analyze sensor data to predict faults, enabling action before human error or pocket dials cause problems. NFC technology facilitates transactions for maintenance work, while smart posters and maintenance software like CMMS, FTMaintenance, and mobile CMMS features streamline work orders and communication between maintenance staff, machine operators, and technicians. Predictive maintenance saves costs by minimizing downtime and extending asset life. It’s being adopted in diverse industries, from coal preparation plants to fleet maintenance and building management. Predictive maintenance is the future, combining advanced technologies like machine learning, computer-based modeling, and analytics tools with wireless internet connections to provide actionable insights. Meteorologists and Doppler radars, even satellites, contribute to predictive maintenance by providing weather data and environmental conditions. Predictive maintenance is transforming maintenance work, making it more efficient, effective, and proactive. 

Predictive maintenance (PdM) is a proactive approach to equipment maintenance that uses data analysis and machine learning algorithms to predict potential failures before they occur. By analyzing historical data and current performance indicators, PdM solutions can identify patterns and trends that may indicate an impending issue. The acceptance of advanced technologies like AI, machine learning, blockchain, cloud computing, and big data is driving the adoption of PdM in various industries. These technologies enable real-time monitoring, predictive analytics, and automated maintenance, leading to increased efficiency, cost savings, and improved asset performance. Billions of dollars are being invested in research and development to further enhance the capabilities of these technologies, making PdM an essential component of modern maintenance strategies. 

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Market Challenges

Predictive Maintenance (PDM) is a cutting-edge technology that uses machine learning and sensor data to predict equipment failures before they occur. However, implementing PDM comes with challenges. Electromagnetic radio fields from sensors can interfere with NFC chips in devices, leading to transaction errors. Human error, such as pocket dialing maintenance work orders, can also cause delays. Distance and battery life are concerns for wireless sensor devices. PDM relies on condition-based maintenance using sensor devices and real-time data. Time-based maintenance and reactive maintenance are outdated methods. Maintenance software like CMMS, FTMaintenance, and mobile CMMS features play a crucial role in managing work orders and dispatching maintenance staff. Vibration analysis, acoustic analysis, and infrared analysis are common condition-monitoring techniques. Baselines and work orders help maintenance technicians identify potential issues. Machine operators should be trained to use condition-monitoring devices like vibration meters. Predictive algorithms use data from sensors, computer-based modeling, and analytics tools to predict faults. Predictive maintenance is essential for fleet maintenance and building maintenance. Doppler radars, satellites, and meteorologists provide additional data for predictive maintenance in extreme environments. Challenges include ensuring accurate sensor data and a reliable wireless internet connection. Maintenance staff should be trained to use predictive maintenance software and understand the importance of preventive maintenance. Collaboration between maintenance technicians, machine operators, and data analysts is crucial for successful implementation of predictive maintenance.Predictive maintenance (PdM) is a crucial business strategy that helps enterprises prevent equipment failure through corrective or scheduled maintenance. However, the implementation of PdM comes with challenges. The lack of skilled labor and specialized knowledge in condition monitoring and predictive analytics is a significant hurdle. This complex process requires extensive domain expertise for micro-segmentation deployment. As historical data grows and PdM use cases expand, the complexity of the models increases, leading to management overhead and inefficiencies. To overcome these challenges, extensive training and specialized resources are necessary for successful PdM adoption.

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Segment Overview 

This predictive maintenance (pdm) market report extensively covers market segmentation by

Component 1.1 Solutions1.2 ServiceDeployment 2.1 On-premises2.2 CloudGeography 3.1 North America3.2 Europe3.3 APAC3.4 South America3.5 Middle East and Africa

1.1 Solutions-  Predictive maintenance (PdM) solutions are integrated with new or existing machinery infrastructure to monitor machine health and identify early signs of deterioration. This integration ensures a good return on investment (ROI) and helps organizations meet sustainability goals by enabling remote machine monitoring worldwide. By keeping assets in optimal working condition and available at all times, PdM solutions increase asset life expectancy and reduce high maintenance costs. The energy and utilities, manufacturing, healthcare, aerospace and defense, and automotive sectors are among those driving the growth of the global PdM market due to their increasing adoption of PdM solutions. These industries use sensors and equipment to generate data for analysis, which is then transferred to the cloud for analysis and monitoring via gateways. The cloud provides computing, data storage, and analytics reporting, while management software serves as an interface for users to handle equipment conditions from anywhere. The use of PdM solutions is expected to increase significantly, leading to market growth during the forecast period. These solutions help improve product quality and process efficiency by analyzing data generated from equipment and sensors. Gateways serve as data transporters and translators, while cloud services offer shared software resources for computing, data storage, and analytics reporting. Management software acts as an interface for users to monitor equipment conditions in real-time.

Download complimentary Sample Report to gain insights into AI’s impact on market dynamics, emerging trends, and future opportunities- including forecast (2024-2028) and historic data (2017 – 2021) 

Research Analysis

Predictive maintenance (PDM) is a proactive approach to equipment maintenance using real-time data analysis and various sensor devices. Electromagnetic radio fields and NFC chips are among the technologies utilized in PDM. NFC transactions enable data exchange between devices, providing distance information for condition-based maintenance. Human error can be minimized through smart posters and NFC technology, triggering action when maintenance is required. PDM employs NFC technology to monitor assets, collecting data for analysis in real-time. This information helps identify potential equipment failure before it occurs, moving away from time-based and reactive maintenance. Maintenance software, such as CMMS, uses baselines and work orders to manage maintenance tasks, with machine operators and maintenance staff receiving notifications for necessary actions. Vibration analysis, acoustic analysis, and infrared analysis are common methods used in PDM. A centrifugal pump motor in a coal preparation plant, for instance, can be monitored using a vibration meter to detect anomalies and prevent costly downtime. By leveraging these advanced technologies and techniques, predictive maintenance significantly improves equipment reliability and reduces maintenance costs.

Market Research Overview

Predictive Maintenance (PDM) is a cutting-edge technology that utilizes various sensors, condition-monitoring devices, and advanced analytics tools to predict equipment failures before they occur. This proactive approach to maintenance reduces downtime, lowers maintenance costs, and increases asset productivity. Electromagnetic radio fields, NFC chips, and sensor devices collect real-time data on machine performance, temperature, vibration, and other key indicators. Machine learning algorithms analyze this data to identify patterns and anomalies, predicting potential failures and suggesting preventive actions. NFC technology enables wireless transactions for maintenance work orders, while machine operators and maintenance staff receive notifications for required actions. Distance learning and smart posters provide training and instructions for maintenance technicians. Predictive maintenance applications range from centrifugal pump motors in coal preparation plants to fleet maintenance and building systems. Vibration analysis, acoustic analysis, infrared analysis, and computer-based modeling are essential tools for predictive maintenance. Predictive algorithms, wireless internet connection, and CMMS software facilitate efficient and effective maintenance work. Meteorologists, Doppler radars, and satellites provide external data for predicting weather-related maintenance needs.

Table of Contents:

1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation

ComponentSolutionsServiceDeploymentOn-premisesCloudGeographyNorth AmericaEuropeAPACSouth AmericaMiddle East And Africa

7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio’s report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio’s comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contacts

Technavio Research
Jesse Maida
Media & Marketing Executive
US: +1 844 364 1100
UK: +44 203 893 3200
Email: media@technavio.com
Website: www.technavio.com/

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SOURCE Technavio

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AIXI Investors Have Opportunity to Lead Xiao-I Corporation Securities Fraud Lawsuit

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November 27, 2024

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BENSALEM, Pa., Nov. 27, 2024 /PRNewswire/ –Law Offices of Howard G. Smith announces that investors with substantial losses have opportunity to lead the securities fraud class action lawsuit against Xiao-I Corporation (“Xiao-I” or the “Company”) (NASDAQ: AIXI).

Class Period: March 9, 2023 – July 12, 2024

Lead Plaintiff Deadline: December 16, 2024

Investors suffering losses on their Xiao-I investments are encouraged to contact the Law Offices of Howard G. Smith to discuss their legal rights in this class action at 215-638-4847 or by email to howardsmith@howardsmithlaw.com.

The complaint filed alleges that, throughout the Class Period, Defendants failed to disclose to investors that: (1) Defendants had downplayed the true scope and severity of risks that Xiao-I faced due to certain of its Chinese shareholders’ non-compliance with Circular 37 Registration, including the Company’s inability to use Offering proceeds for intended business purposes; (2) Xiao-I failed to comply with GAAP in preparing its financial statements; (3) Defendants overstated Xiao-I’s efforts to remediate material weaknesses in the Company’s financial controls; (4) Xiao-I was forced to incur significant R&D expenses to effectively compete in the AI industry; (5) Xiao-I downplayed the significant negative impact that such expenses would have on the Company’s business and financial results; (6) accordingly, Xiao-I overstated its AI capabilities, R&D resources, and overall ability to compete in the AI market; (7) as a result of all the foregoing, there was a substantial likelihood that Xiao-I would fail to comply with the NASDAQ’s Minimum Bid Price Requirement; and (8) as a result, Defendants’ positive statements about the Company’s business, operations, and prospects were materially misleading and/or lacked a reasonable basis at all relevant times.

To be a member of the class action you need not take any action at this time; you may retain counsel of your choice or take no action and remain an absent member of the class action. If you wish to learn more about this class action, or if you have any questions concerning this announcement or your rights or interests with respect to the pending class action lawsuit, please contact Howard G. Smith, Esquire, of Law Offices of Howard G. Smith, 3070 Bristol Pike, Suite 112, Bensalem, Pennsylvania 19020, by telephone at (215) 638-4847 or by email to howardsmith@howardsmithlaw.com, or visit our website at www.howardsmithlaw.com.

This press release may be considered Attorney Advertising in some jurisdictions under the applicable law and ethical rules.

Contacts

Law Offices of Howard G. Smith
Howard G. Smith, Esquire
215-638-4847
howardsmith@howardsmithlaw.com
www.howardsmithlaw.com

View original content:https://www.prnewswire.com/news-releases/aixi-investors-have-opportunity-to-lead-xiao-i-corporation-securities-fraud-lawsuit-302317731.html

SOURCE Law Offices of Howard G. Smith

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WorkFar Robotics Mass Produces Humanoid Robots without Venture Capital

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17 minutes ago

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November 27, 2024

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As robotics investing climbs out of its 2023 slump, humanoid robotics pioneer WorkFar — which has not received any funding from venture capital — is ready to produce at the level of competitors already receiving billions of investment dollars.

SANTA CLARA, Calif., Nov. 27, 2024 /PRNewswire/ — There’s nothing quite like the tenacity of a new company with a unique value proposition that directly addresses the needs of its target customer base. WorkFar Robotics, a business specializing in commercial humanoid service robots for industrial applications, has yet to get on the radar of today’s venture capitalists — but that hasn’t stopped them from reaching the mass-producing stage.

Many companies, particularly those in the robotics industry, are reliant on venture capital, and they can go for years — or even a decade — without turning a profit. Building a cash-flowing robotics company with no investment aside from hard work, creativity, and business acumen is a feat rarely accomplished. Yet WorkFar has managed to achieve the same level of progress as competitors receiving $100 million to over $1 billion in investment funding.

WorkFar’s Business Model: An Autonomous, Remote-operatable Robot for $0 down

WorkFar’s offering is unique in the world of industrial robotics. The industry’s most common business model is to sell an expensive product to a manufacturer and possibly provide some integration services. For companies unable to afford the high price tag, certain robotics manufacturers offer a subscription-based “Robot-as-a-Service.” WorkFar takes this a step further by allowing clients to lease both a robot and a trained, remote operator on a monthly basis without a down payment.

The combination of sophisticated humanoid robot, AI-enhanced programming, and an optional human operator constitutes a turnkey solution for warehouses and manufacturers dealing with aggravating challenges like long-lasting labor shortages, concerns around worker safety and burnout, and issues with efficiency and consistency. Since the optional teleoperator is remote-based, WorkFar can leverage the global workforce to support its customers.

The WorkFar “Syntro” robot uses virtual reality eye tracking and AI algorithms to target and grasp objects at the operator’s direction, and the operator gets feedback on object pick-up through haptic gloves. The robot’s “core logic” is human intelligence, which — despite rapid advances in AI — still can’t be beat.

WorkFar’s Manufacturing Expertise goes back Decades

Although the ‘Syntro’ robot is brand-new, WorkFar’s US based manufacturing facility has over 40 years of experience producing plastic and metal parts for industrial machinery and consumer products. This expertise is now being leveraged to mass produce humanoid robot in-house — an arrangement that cuts out the middleman and leads to more efficient operations. With supply chain issues wreaking havoc on robotics companies’ operations for the past several years, this is a major advantage.

Robotics Investing dipped in 2023, but it’s Coming Back strong with AI and Humanoid Technology

Investment in the robotics industry hit a five-year low last year, particularly in the area of autonomous vehicles (AVs). This was partially a result of a widespread market correction within venture capital investing, but the legislative concerns and negative press surrounding AVs didn’t help. The slump was temporary, however, and robotics venture capital is starting to rise again rapidly, with vertical-specific robotics companies focusing on logistics, security, and medical applications leading the way.

One thing that’s making robotics investing much more appealing is the awe-inspiring takeoff in artificial intelligence capabilities. AI models give robots the capacity to execute complex tasks like grasping unpredictably shaped objects much more smoothly and accurately. Even better, AI allows the robots to learn from each effort, rapidly increasing their accuracy and efficiency over time. Robot vision will gain clarity with improved object detection and image segmentation — essential tools for interacting “intuitively” with the environment.

With a design meant to evoke their maker, humanoid robots are poised to reap the greatest benefits from this rapid growth in AI. They show promise across multiple industries, ranging from manufacturing to healthcare to personal assistance. Once AI’s transformative capabilities became apparent, projections for the humanoid robot market ten years from now shot up from just $6 billion to almost $200 billion — or in some estimates, well over $24 trillion.

Sheer Business Acumen has propelled WorkFar to the point of Mass Production

Although the robotics investment outlook is getting brighter, the recent dip has prompted investors to be more discerning and focus on areas where robotic solutions can make important strides right now. Venture capitalists have seen plenty of technology demos that turn heads; now it’s time to back these up with solid business plans that show real returns on investment. With its robot-as-a-service offering at $0 down payment, this is WorkFar’s strong suit.

Even with rapid AI advances, this model will always benefit from the authority and decision-making power of human intelligence. This is central to WorkFar’s vision: a human-robot team that will unleash a new era of productivity, bringing collaborative efficiency to factories and facilities worldwide. This innovative solution takes into account what other solutions overlook: the fact that true productivity depends on human decision-making and robotic efficiency being intertwined, not isolated.

This vision is what has enabled WorkFar to grow on its own revenue in an industry that usually requires millions or even billions of dollars in venture capital. No longer a startup, this company has now pushed into a higher corporate level of investment based on business acumen alone. With a market-ready product that can be manufactured in WorkFar’s own factory, the humanoid robotics pioneer is stronger because it does not rely on venture capital. 

To inquire, contact us via www.WorkFar.com now!

Contact: info@workfar.com

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SOURCE WorkFar Inc

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ASML Investors Have Opportunity to Lead ASML Holding N.V. Securities Fraud Lawsuit

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November 27, 2024

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LOS ANGELES, Nov. 27, 2024 /PRNewswire/ — The Law Offices of Frank R. Cruz announces that investors with substantial losses have opportunity to lead the securities fraud class action lawsuit against ASML Holding N.V. (“ASML” or the “Company”) (NASDAQ: ASML).

Class Period: January 24, 2024 – October 15, 2024

Lead Plaintiff Deadline: January 13, 2025

If you are a shareholder who suffered a loss, click here to participate.

The complaint filed alleges that, throughout the Class Period, Defendants failed to disclose to investors that: (1) the issues being faced by suppliers, like ASML, in the semiconductor industry were much more severe than Defendants had indicated to investors; (2) the pace of recovery of sales in the semiconductor industry was much slower than Defendants had publicly acknowledged; (3) Defendants had created the false impression that they possessed reliable information pertaining to customer demand and anticipated growth, while also downplaying risk from macroeconomic and industry fluctuations, as well as stronger regulations restricting the export of semiconductor technology, including the products that ASML sells; and (4) as a result, Defendants’ positive statements about the Company’s business, operations, and prospects were materially misleading and/or lacked a reasonable basis at all relevant times.

Follow us for updates on Twitter: twitter.com/FRC_LAW.

To be a member of the class action you need not take any action at this time; you may retain counsel of your choice or take no action and remain an absent member of the class action.  If you wish to learn more about this class action, or if you have any questions concerning this announcement or your rights or interests with respect to the pending class action lawsuit, please contact Frank R. Cruz, of The Law Offices of Frank R. Cruz, 2121 Avenue of the Stars, Suite 800, Century City, California 90067 at 310-914-5007, by email to info@frankcruzlaw.com, or visit our website at www.frankcruzlaw.com.  If you inquire by email please include your mailing address, telephone number, and number of shares purchased.

This press release may be considered Attorney Advertising in some jurisdictions under the applicable law and ethical rules.

Contacts

The Law Offices of Frank R. Cruz, Los Angeles
Frank R. Cruz, 310-914-5007
fcruz@frankcruzlaw.com
www.frankcruzlaw.com

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SOURCE The Law Offices of Frank R. Cruz, Los Angeles

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