Real-time analysis and decision-making will be made easier by
developments in AI and IoT integration, which will drive the
Predictive Maintenance Market in the future. Innovation will be
fueled by prognostics, industry-specific solutions, and
interoperability, while Predictive Analytics as a Service (PAaaS)
models will make advanced predictive maintenance skills more
accessible to all.
CHICAGO, March 29,
2024 /PRNewswire/ -- The Predictive
Maintenance Market is estimated to grow from USD 10.6 billion in 2024 to USD 47.8 billion in 2029, at a CAGR of 35.1%
during the forecast period, according to a new report by
MarketsandMarkets™. The driving factors for the Predictive
Maintenance Market include the widespread adoption of emerging
technologies like IoT sensors and data analytics, enabling
real-time monitoring of equipment health. The integration of
machine learning (ML) and artificial intelligence (AI) algorithms
allows for predictive analysis of potential failures, reducing
downtime and optimizing asset performance. Organizations are
increasingly focused on cost savings and operational efficiency,
driving the need for proactive maintenance strategies to minimize
unplanned downtime and maximize productivity. Additionally,
regulatory requirements and the shift towards predictive
analytics-driven decision-making further contribute to the growth
of the Predictive Maintenance Market.
Browse in-depth TOC on "Predictive Maintenance
Market"
280 - Tables
60 - Figures
300 - Pages
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Scope of the Report
Report
Metrics
|
Details
|
Market size available
for years
|
2019–2029
|
Base year
considered
|
2023
|
Forecast
period
|
2024–2019
|
Forecast
units
|
USD Billion
|
Segments
Covered
|
Component (Hardware,
Solution [Solution by Deployment mode {Cloud (Public, Private,
Hybrid) & On-premises}] & Services), Technology (Analytics
& Data Management, Artificial Intelligence, IoT Platform,
Sensors & Other Devices), Technique (Vibration Analysis,
Infrared Thermography, Acoustic Monitoring, Oil Analysis, Motor
Circuit Analysis, Other Techniques) Organization size (Large
Enterprises, SMEs), Vertical (Energy & Utilities,
Manufacturing, Automotive & Transportation, Aerospace &
Defense, Construction & Mining, Healthcare, Telecommunications)
and Region.
|
Geographies
covered
|
North America, Europe,
Asia Pacific, Middle East & Africa, Latin America
|
Companies
covered
|
IBM (US), ABB
(Switzerland), Schneider Electric (France), AWS (US), Google (US),
Microsoft (US), Hitachi (Japan), SAP (Germany), SAS Institute (US),
Software AG (Germany), TIBCO Software (US), Altair (US), Oracle
(US), Splunk (US), C3.ai (US), Emerson (US), GE (US), Honeywell
(US), Siemens (Germany), PTC (US), Dingo (Australia), Uptake (US),
Samotics (Netherlands), WaveScan (Singapore), Quadrical Ai
(Canada), UpKeep (US), Limble (US), SenseGrow (US), Presage
Insights (India), Falcon Labs (India).
|
By component, the services segment to account for higher CAGR
during the forecast period.
The services segment plays a crucial role in the Predictive
Maintenance Market, serving as a core component essential for the
efficient operation of software solutions. Many companies are
turning to intelligent devices, robust AI systems, and Industrial
Internet of Things (IIoT) solutions to monitor the health and
productivity of critical equipment, aiming to minimize costly
production shutdowns. Remote monitoring of machinery and equipment
has become a significant priority for organizations grappling with
challenges in detecting machinery failures. The adoption of
predictive maintenance services, including IoT, has become
imperative to mitigate the risks and failures of machines across
various industries. Within the services segment, managed and
professional services are considered vital for enhancing overall
process efficiency.
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By Technique, Vibration Analysis is expected to hold the
largest market size for the year 2024.
Vibration analysis is a crucial technique employed primarily for
high-speed rotating equipment in predictive maintenance strategies.
It enables technicians to monitor the vibrations of machines using
handheld analyzers or real-time sensors integrated into the
equipment itself. Machines operating optimally exhibit specific
vibration patterns, which can be compared against known standards.
However, as components like bearings and shafts wear down or
develop faults, they generate distinct vibration patterns,
signaling potential issues. By continuously monitoring equipment
vibrations, trained technicians can identify deviations from normal
patterns and diagnose problems early on. The range of issues
detectable through vibration analysis is extensive and includes
misalignment, bent shafts, unbalanced components, loose mechanical
parts, and motor irregularities.
By Vertical, Automotive & Transportation is projected to
grow at the highest CAGR during the forecast period.
As automotive technology progresses rapidly, traditional fault
detection methods are inadequate for ensuring vehicle smoothness.
However, modern automobiles are equipped with various sensors,
instruments, and cameras that generate diverse data. Leveraging
this data, past service records, and employing AI and ML,
predictive maintenance in the automotive & transportation
sector emerges as a powerful solution to enhance vehicle
performance and minimize downtime. The surge in intelligent
technologies has spurred predictive maintenance investments in
transportation, particularly accelerated by the Covid-19 crisis,
where consumer preferences shifted towards individual mobility due
to health and safety concerns, leading to an increased demand for
cars. This demand surge, coupled with slowed new vehicle
production, is driving the resurgence of the used car market.
Predictive maintenance plays a crucial role in reducing the
lifespan of used cars and preventing unexpected downtimes.
Solutions like IBM's monitoring for connected vehicles and
collaborations between automakers and tech companies like Ford,
CARUSO, and HIGH MOBILITY showcase the industry's commitment to
leveraging predictive maintenance for improved operations and
customer services.
Middle East &
Africa is expected to grow at the
second-highest CAGR during the forecast period.
The Middle East &
Africa (MEA) lacks technological
development as well as primary business growth in many verticals.
Slow economic growth and geopolitical conditions are the major
hurdles to the growth of the Predictive Maintenance Market in the
region. Moreover, it generates the majority of the revenues from
natural resources. The government policy in the United Arab Emirates (UAE) is supportive of
the industry with the vision to be one of the most technologically
advanced nations by 2022. The proliferation of telecom and
IT-enabled industry in the African countries is steering the growth
of AI-based IoT companies in the region. The major reasons that are
said to influence the growth of the Predictive Maintenance Market
in the region are the increasing investments in data center
infrastructures and the growing number of high-growth start-ups.
Only a few countries, such as the UAE, Israel, and Qatar, across the region, are advancing in
this market at an economical pace. The UAE, Israel, and Qatar have demonstrated a strong commitment
toward the development and implementation of AI and IoT
technologies.
Top Key Companies in Predictive Maintenance Market:
The major predictive maintenance hardware, solution and service
providers include IBM (US), ABB (Switzerland), Schneider Electric (France), AWS
(US), Google (US), Microsoft (US), Hitachi (Japan), SAP (Germany), SAS Institute (US), Software AG
(Germany), TIBCO Software (US),
Altair (US), Oracle (US), Splunk (US), C3.ai (US), Emerson (US),
GE (US), Honeywell (US), Siemens
(Germany), PTC (US), Dingo
(Australia), Uptake (US), Samotics
(Netherlands), WaveScan
(Singapore), Quadrical Ai
(Canada), UpKeep (US), Limble
(US), SenseGrow (US), Presage Insights (India), Falcon Labs (India). These companies have used both organic
and inorganic growth strategies such as product launches,
acquisitions, and partnerships to strengthen their position in the
Predictive Maintenance Market.
Recent Developments:
- In January 2024, Siemens and AWS
deepened their collaboration to simplify the development and
scaling of generative artificial intelligence (AI) applications for
businesses across various industries and sizes. This partnership
enables domain experts in fields like engineering, manufacturing,
logistics, insurance, or banking to leverage advanced generative AI
technology to create and enhance applications efficiently.
- In December 2023, ABB enhanced
its ABB Ability Field Information Manager (FIM 3.0) to provide system engineers and
maintenance teams with enhanced connectivity and expanded reach
across the latest communication protocols.
- In June 2023, Qatar Airways and
Google Cloud partnered to create innovative data and artificial
intelligence (AI) solutions tailored for the airline industry. This
collaboration will concentrate on enhancing areas like predictive
maintenance, passenger experience, and cargo operations, aiming to
elevate efficiency and customer satisfaction within the airline
sector.
- In April 2023, TrendMiner
launched an updated version of its predictive maintenance software,
the Digital Twin Manager. This release includes enhanced support
for cloud data sources from AWS and Microsoft, along with
interactive search functionality, enabling users to make
data-driven decisions more efficiently.
- In January 2023, AVEVA, a global
leader in industrial software, finalized its acquisition by
Schneider Electric. AVEVA's strategic objective is to emerge as the
top Software as a Service (SaaS) provider in software and
industrial information, transitioning to a subscription-only
business model.
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Predictive Maintenance Market Advantages:
- Through proactive equipment maintenance based on data-driven
insights, the prevention of expensive unplanned downtime, and the
reduction of repair costs, predictive maintenance assists
organisations in cutting maintenance costs.
- Predictive maintenance increases equipment availability and
uptime by anticipating possible equipment faults before they
happen, guaranteeing continuous operations and maximising
output.
- By locating and resolving underlying problems that may
compromise the dependability and efficiency of equipment,
predictive maintenance extends the life of assets and lowers the
need for untimely replacements.
- Predictive maintenance reduces the likelihood of mishaps,
injuries, and interruptions to operations brought on by
malfunctioning equipment by anticipating possible equipment
breakdowns.
- By scheduling maintenance jobs more effectively, predictive
maintenance helps businesses minimise interruptions to existing
operations and match maintenance duties with production
schedules.
- Using data analytics and machine learning algorithms,
predictive maintenance produces actionable insights that help with
equipment investments, resource allocation, and maintenance
strategy selection.
- Predictive maintenance ensures continuous functioning and
customer satisfaction by proactively addressing equipment concerns,
minimising unplanned downtime and production losses.
- Status-based maintenance
techniques, which optimise maintenance resources and cut down on
pointless inspections and repairs by triggering maintenance actions
based on real-time equipment status data, are made possible by
predictive maintenance.
Report Objectives
- To describe and forecast the Predictive Maintenance Market, in
terms of value,
- by component, technology, technique, organization size, and
vertical
- To describe and forecast the Predictive Maintenance Market, in
terms of value,
- by region—North America, Europe, Asia
Pacific, Middle East &
Africa and Latin America
- To provide detailed information regarding major factors
influencing the market growth (drivers, restraints, opportunities,
and challenges)
- To strategically analyze micromarkets1 with respect to
individual growth trends, prospects, and contribution to the
overall Predictive Maintenance Market
- To profile key players and comprehensively analyze their market
positions in terms of ranking and core competencies2, along with
detailing the competitive landscape for market leaders
- To analyze competitive developments such as joint ventures,
mergers and acquisitions, product developments, and ongoing
research and development (R&D) in the Predictive Maintenance
Market
- To provide the illustrative segmentation, analysis, and
projection of the main regional markets
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