Predictive Maintenance Market

Predictive Maintenance Market is segmented By Component (Solutions and Services), By Deployment (Cloud and On-Premise), By Organization Size (Large Enterprises and SMEs), By End Use Industry (Manufacturing, Transportation and Logistics, Energy and Utilities, Healthcare, Aerospace and Defense, Government, and Others) and Region (United States, Canada, Mexico, France, Germany, Italy, Spain, United Kingdom, Russia, China, India, Philippines, Malaysia, Australia, Austria, South Korea, Middle East, Japan, Africa, Rest of World)

  • Report ID : MD2873
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  • Pages : 225
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  • Tables : 60
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  • Formats :

Predictive Maintenance Market size was estimated at $4.14 billion in 2021 and is expected to reach $22.3 billion by 2031, growing at a CAGR of 23.4% during the forecast period of 2022 to 2031.

Predictive Maintenance Market
Predictive maintenance is a technique designed to assist in determining the condition of equipment in operations in order to estimate when maintenance is to be done. The predictive maintenance technique was data analysis to monitor and detect abnormality in operations and feasible flaws in apparatus and processes so that maintenance is done before their failures. This technique is considered as cost-effective over routine maintenance practice as tasks are performed when called off.

Predictive Maintenance Market Growth and Trend
The significant growth in the global predictive maintenance market can be attributed to the growing need to curtail maintenance costs and downtimes. The companies operating in the market are using artificial intelligence and machine learning technology for achieving precise and accurate business intelligence told to analyze data. Moreover, the growing focus of organizations toward scheduling a maintenance process before misfires is the key factor driving the growth of the market. In addition, the predictive maintenance technique helps in effective and efficient asset management. The real-time condition monitoring assists the enterprises to take prompt action. Further, an increase in penetration of IoT-based technology and the emergence of 5G network may result in various opportunities for the growth of the predictive maintenance market during the forecast timeframe. However, data security and privacy are the main threats faced by the companies. The security concerns include poor authentication and authorization, unknown network services, lack of encryption, and poor network and physical security. Additionally, the lack of skilled labor to proceed with the predictive maintenance process may hinder the market growth over the forecast timeframe.

Predictive Maintenance Market Segmentation
The predictive maintenance market is segmented on the basis of component, deployment, organization size, and end-use industry. Based on components, the market is classified into solutions and services. The solution segment is expected to witness robust growth in the coming years. By deployment, the market is divided into cloud-based and on-premises. The cloud-based segment is anticipated to expand at a rapid pace over the forecast period due to low implementation costs. Based on organizational size, the market is segmented into large enterprises and small and medium enterprises. The large enterprises segment holds the largest share in the global predictive maintenance market due to the large infrastructure and the constant need to monitor the equipment in operations. By end-user industry, the market is segregated into manufacturing, transportation, and logistics, energy, and utilities, healthcare, aerospace and defense, government, and others. The manufacturing segment holds the largest share in the market. However, the aerospace and defense segment is expected to expand rapidly during the forecast period.
Predictive Maintenance Market Country Analysis
North America is anticipated to hold a significant share in the global predictive maintenance market due to increased investment in IoT-based technology and artificial intelligence by large enterprises in the region. However, Asia Pacific is expected to witness a notable rise in the market over the forecast period due to ongoing technological advancements in the region.
Predictive Maintenance Market Share and Competition
The key companies operating in the global predictive maintenance market are Microsoft Corporation, IBM Corporation, SAP, Hitachi, Schneider Electric, SAS, General Electric, TIBCO Software Inc., Softweb Solutions, and PTC.
Report Highlights
• Historical data available (as per request)
• Estimation/projections/forecast for revenue and unit sales (2022 – 2031)
• Data breakdown for every market segment (2022 – 2031)
• Gross margin and profitability analysis of companies
• Price analysis of each product type
• Business trend and expansion analysis
• Import and export analysis
• Competition analysis/market share
• Supply chain analysis
• Client list and case studies
• Market entry strategy

Industry Segmentation and Revenue Breakdown
Component Analysis (Revenue, USD Million, 2022 - 2031)
• Solution
• Service
Deployment Analysis (Revenue, USD Million, 2022 - 2031)
• Cloud-Based
• On-Premises
Organization Size Analysis (Revenue, USD Million, 2022 - 2031)
• Large Enterprises
• Small and Medium Enterprise
End-User Industry Analysis (Revenue, USD Million, 2022 - 2031)
• Manufacturing
• Transportation and Logistics
• Energy and Utilities
• Healthcare
• Aerospace and Defense
• Government
• Others
Region Analysis (Revenue, USD Million, 2022 – 2031)
• United States
• Canada
• Mexico
• France
• Germany
• Italy
• Spain
• United Kingdom
• Russia
• China
• India
• Philippines
• Malaysia
• Australia
• Austria
• South Korea
• Middle East
• Japan
• Africa
• Rest of World


Predictive Maintenance Market Companies
• Microsoft Corporation
• IBM Corporation
• SAP
• Hitachi
• Schneider Electric
• SAS
• General Electric
• TIBCO Software Inc.
• Softweb Solutions
• PTC

Available Versions:-
• United States Predictive Maintenance Industry Research Report
• Europe Predictive Maintenance Industry Research Report
• Asia Pacific Predictive Maintenance Industry Research Report

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