Global Predictive Maintenance Market (2021 to 2026) - Real-Time Condition Monitoring to Assist in Taking Prompt Actions Presents Opportunities

DUBLIN, March 21, 2022 /PRNewswire/ -- The "Global Predictive Maintenance Market with COVID-19 Impact Analysis By Component (Solutions, Services), Deployment Mode (On-premises, Cloud), Organization Size (Large Enterprises, SME), Vertical and Region - Forecast to 2026" report has been added to ResearchAndMarkets.com's offering.

The predictive maintenance market size to grow from USD 4.2 billion in 2021 to USD 15.9 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 30.6% during the forecast period.

Various factors such as increasing spending on marketing and advertising activities by enterprises, changing landscape of customer intelligence to drive the market, and proliferation of customer channels are expected to drive the adoption of predictive maintenance technologies and services.

The cloud segment to have the highest CAGR during the forecast period

By deployment mode, the predictive maintenance market has been segmented into on-premises and cloud. The CAGR of the cloud deployment mode is estimated to be the largest during the forecast period. Cloud-based services are provided directly through the cloud-deployed network connection. Cloud-based platforms are beneficial for organizations that have strict budgets for security investments. The cloud deployment mode is growing, as cloud-based predictive maintenance solutions are easy to maintain and upgrade.

The SMEs segment to hold higher CAGR during the forecast period

The predictive maintenance market has been segmented by organization size into large enterprises and SMEs. The market for SMEs is expected to register a higher CAGR during the forecast period. These enterprises are early adopters of predictive maintenance solutions. They are faced with the troublesome task of effectively managing security because of the diverse nature of IT infrastructure, which is complex in nature.

Among regions, APAC to hold highest CAGR during the forecast period

The predictive maintenance market has been segmented into five major regions: North America, Europe, APAC, Latin America, and MEA. APAC is expected to grow at a good pace during the forecast period. The region will be booming, as it is experiencing a lot of new entrepreneur setups, which would be looking forward to acquiring new customers and gaining customer trust by involving new paradigms of maintenance technologies to have a competitive advantage over the established players. Predictive maintenance vendors in this region focus on innovations related to their product line. China, Japan, India, and Bangladesh have displayed ample growth opportunities in the predictive maintenance market.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights
4.1 Attractive Market Opportunities in the Predictive Maintenance Market
4.2 Market: Top Three Verticals
4.3 Market: by Region
4.4 North America: Market, by Component and Deployment Mode

5 Market Dynamics
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers
5.2.1.1 Increasing Use of Emerging Technologies to Gain Valuable Insights
5.2.1.2 Advent of Ml and Ai
5.2.1.3 Growing Need to Reduce Maintenance Cost, Equipment Failure, and Downtime
5.2.2 Restraints
5.2.2.1 Lack of Skilled Workforce
5.2.2.2 Data Security Concerns
5.2.3 Opportunities
5.2.3.1 Rising Internet Proliferation and Growing Usage of Connected and Integrated Technologies
5.2.3.2 Real-Time Condition Monitoring to Assist in Taking Prompt Actions
5.2.3.3 COVID-19 Pandemic Increasing the Need for Remote Monitoring and Management of Assets and Business Processes
5.2.4 Challenges
5.2.4.1 Frequent Maintenance and Upgradation Requirement to Keep the Systems Updated
5.2.4.2 Ownership and Privacy of Collected Data
5.3 Predictive Maintenance Market: COVID-19 Impact
5.4 Predictive Maintenance: Evolution
5.5 Predictive Maintenance: Ecosystem
5.6 Case Study Analysis
5.6.1 Energy and Utilities
5.6.1.1 Case Study: Implementation of Predictive Maintenance Strategy for the Generating Unit at Tauron Wytwarzanie
5.6.2 Transportation and Logistics
5.6.2.1 Case Study: Implementation of the Pdm Rsims Solution for a Warehouse Stacker Crane in a High Storage Warehouse at M-Logistic
5.6.3 Oil and Gas
5.6.3.1 Case Study: Shell Deploying Microsoft Azure for Detecting Risks and Eliminating Potential Problems
5.6.4 Manufacturing and Mining
5.6.4.1 Case Study: Uptake Creates USD 28 Million in Value for One of the World's Largest Copper Producers
5.6.5 Telecom
5.6.5.1 Case Study: Leading Telecom Operator Deploying Avanseus' Full-Stack Predictive Maintenance Solution to Become 5G-Ready
5.6.6 Manufacturing and Mining
5.6.6.1 Case Study: Leading Global Miner Saves USD 55 Million Through the Implementation of the Dingo Asset Wellness Program
5.6.7 Bfsi
5.6.7.1 Case Study: Leveraging Medical Big Data to Innovate a Personalized Life Insurance Business
5.6.8 Transportation and Logistics
5.6.8.1 Case Study: United Road Capitalizes on Predictive Maintenance with 4X Roi
5.7 Patent Analysis
5.7.1 Methodology
5.7.2 Document Type
5.7.3 Innovation and Patent Applications
5.7.3.1 Top Applicants
5.8 Supply/Value Chain Analysis
5.9 Pricing Model Analysis
5.10 Porter's Five Forces Analysis
5.11 Technology Analysis
5.12 Regulatory Implications

6 Predictive Maintenance Market, by Component
6.1 Introduction
6.1.1 Components: COVID-19 Impact
6.2 Solutions
6.2.1 Real-Time Asset Monitoring Enables Organizations to Implement Proactive Maintenance Strategies
6.2.2 Solutions: Market Drivers
6.2.3 Integrated
6.2.4 Standalone
6.3 Services
6.3.1 Adoption of Predictive Maintenance Services to Reduce Risk and Failure of Machine
6.3.2 Services: Predictive Maintenance Market Drivers
6.3.3 Professional Services
6.3.3.1 System Integration
6.3.3.2 Support and Maintenance
6.3.3.3 Consulting
6.3.4 Managed Services
6.3.4.1 Increasing Need of It Infrastructure Support to Drive the Growth of Managed Services

7 Predictive Maintenance Market, by Deployment Mode
7.1 Introduction
7.1.1 Deployment Mode: COVID-19 Impact
7.2 Cloud
7.2.1 Reduced Operational Cost and Higher Scalability to Enable Growth in Cloud-Based Deployments
7.2.2 Cloud: Market Drivers
7.2.3 Public Cloud
7.2.4 Private Cloud
7.2.5 Hybrid Cloud
7.3 On-Premises
7.3.1 Rising Cost of Support and Maintenance Impacting the Growth of On-Premises Solution
7.3.2 On-Premises: Predictive Maintenance Market Drivers

8 Predictive Maintenance Market, by Organization Size
8.1 Introduction
8.1.1 Organization Size: COVID-19 Impact
8.2 Large Enterprises
8.2.1 Large Enterprises: Market Drivers
8.3 Small and Medium-Sized Enterprises
8.3.1 Small and Medium-Sized Enterprises: Market Drivers

9 Predictive Maintenance Market, by Vertical
9.1 Introduction
9.1.1 Verticals: COVID-19 Impact
9.2 Government and Defense
9.2.1 Initiatives Taken by the Government to Improve the Lifestyle of Citizens
9.2.2 Government and Defense: Market Drivers
9.3 Manufacturing
9.3.1 IoT to Change the Ways of Product Development, Manufacturing, Transportation, and Selling
9.3.2 Manufacturing: Predictive Maintenance Market Drivers
9.4 Energy and Utilities
9.4.1 Need for New Data Sources, New Programs, and Efficient Management of Resources
9.4.2 Energy and Utilities: Market Drivers
9.5 Transportation and Logistics
9.5.1 Initatives Taken to Improve the Core Functions, Optimize Marketing, and Enhance Risk Management Functions
9.5.2 Transportation and Logistics: Market Drivers
9.6 Healthcare and Life Sciences
9.6.1 Introducing New Diagnostic Equipment, Patient Monitoring Tools, and Operational Technologies for Improved Patient Care
9.6.2 Healthcare and Life Sciences: Predictive Maintenance Market Drivers
9.7 Other Verticals

10 Predictive Maintenance Market, by Region

11 Competitive Landscape
11.1 Overview
11.2 Key Player Strategies
11.3 Revenue Analysis
11.4 Market Share Analysis
11.5 Company Evaluation Quadrant
11.5.1 Stars
11.5.2 Emerging Leaders
11.5.3 Pervasive Players
11.5.4 Participants
11.6 Competitive Benchmarking
11.7 Startup/Sme Evaluation Quadrant
11.7.1 Progressive Companies
11.7.2 Responsive Companies
11.7.3 Dynamic Companies
11.7.4 Starting Blocks
11.8 Competitive Scenario
11.8.1 Product Launches
11.8.2 Deals

12 Company Profiles
12.1 Introduction
12.2 Key Players
12.2.1 Microsoft
12.2.2 Ibm
12.2.3 Sap
12.2.4 Sas Institute
12.2.5 Software Ag
12.2.6 Tibco Software
12.2.7 Hpe
12.2.8 Altair
12.2.9 Splunk
12.2.10 Oracle
12.2.11 Google
12.2.12 Aws
12.2.13 Ge
12.2.14 Schneider Electric
12.2.15 Hitachi
12.2.16 Ptc
12.2.17 Rapidminer
12.2.18 Opex Group
12.2.19 Dingo
12.2.20 Factory5

13 Appendix

For more information about this report visit https://www.researchandmarkets.com/r/hznm7

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