Global Predictive Maintenance Market (2021 to 2026)

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 (Enterprise, SMB), Industry and Region – Forecast to 2026” Report has been added Offer.

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The predictive maintenance market size to grow from $4.2 billion in 2021 to $15.9 billion to 2026 at a compound annual growth rate (CAGR) of 30.6% during the forecast period.

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

The cloud segment with the highest CAGR over the forecast period

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

The SME segment will hold a higher CAGR during the forecast period

The predictive maintenance market has been segmented into large enterprise and SME based on organization size. The SME market is expected to register a higher CAGR during the forecast period. These companies are early adopters of predictive maintenance solutions. Due to the diverse nature of IT infrastructure, which is inherently complex, they face the difficult task of effectively managing security.

Among the regions, APAC will exhibit the highest CAGR during the forecast period

The predictive maintenance market has been segmented into five major regions: North America, EuropeAPAC, Latin America, and MEA. APAC is expected to grow at a good pace during the forecast period. The region will boom as it sees many new business startups that would look forward to attracting new customers and earning customer trust by incorporating new paradigms of maintenance technologies to have a competitive edge over the established players. Predictive maintenance providers in this region are focused on innovation related to their product line. China, Japan, Indiaand Bangladesh have highlighted ample opportunities for growth in the predictive maintenance market.

Main topics covered:

1 Introduction

2 research methodology

3 Summary

4 premium insights
4.1 Attractive market opportunities in the Predictive Maintenance Market
4.2 Market: Top three industries
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 Increasing use of new technologies to gain valuable insights Advent of M1 and Ai Growing need to reduce maintenance costs, equipment failures and downtime
5.2.2 Restrictions Lack of qualified workers Concerns about data security
5.2.3 Opportunities Increasing penetration of the Internet and increasing use of networked and integrated technologies Real-time condition monitoring to help take immediate action COVID-19 pandemic increases the need for remote monitoring and management of assets and business processes
5.2.4 Challenges Common maintenance and update requirements to keep systems current Ownership and Privacy of Collected Data
5.3 Predictive Maintenance Market: Impact of COVID-19
5.4 Predictive Maintenance: Evolution
5.5 Predictive Maintenance: Ecosystem
5.6 Case Study Analysis
5.6.1 Energy and Utilities Case study: Implementation of a predictive maintenance strategy for the generation unit at Tauron Wytwarzanie
5.6.2 Transportation and Logistics Case study: Implementation of the Pdm Rsims solution for a stacker crane in a high-bay warehouse at M-Logistic
5.6.3 Oil and Gas Case Study: Shell uses Microsoft Azure to identify risks and eliminate potential problems
5.6.4 Manufacturing and Mining Case study: Recording creates $28 million worth for one of the world’s largest copper producers
5.6.5 Telecom Case Study: Leading telecom operator deploys Avanseus’ full-stack predictive maintenance solution to become 5G ready
5.6.6 Manufacturing and Mining Case Study: Leading Global Miners Save $55 million By implementing the Dingo Asset Wellness program
5.6.7 Bfsi Case Study: Using Medical Big Data to Innovate a Personalized Life Insurance Business
5.6.8 Transportation and Logistics Case Study: United Road uses 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 Top Contenders
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: Impact of COVID-19
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 Independent
6.3 Services
6.3.1 Introduce predictive maintenance services to reduce risk and machine downtime
6.3.2 Services: Predictive Maintenance Market Drivers
6.3.3 Professional Services System integration Support and Maintenance Advice
6.3.4 Managed Services Increasing need for IT infrastructure support to drive managed services growth

7 Predictive Maintenance Market by Deployment Mode
7.1 Introduction
7.1.1 Delivery Mode: Impact of COVID-19
7.2 Cloud
7.2.1 Reduced operational costs and increased scalability to accommodate growth in cloud-based deployments
7.2.2 Cloud: Market Drivers
7.2.3 Public Cloud
7.2.4 Private Clouds
7.2.5 Hybrid Cloud
7.3 Local
7.3.1 Rising support and maintenance costs impact the growth of on-premises solutions
7.3.2 On-Premises: Predictive Maintenance Market Drivers

8 Predictive Maintenance Market by Organization Size
8.1 Introduction
8.1.1 Company Size: Impact of COVID-19
8.2 Large Companies
8.2.1 Large companies: market drivers
8.3 Small and Medium Businesses
8.3.1 Small and Medium Enterprises: Market Drivers

9 Predictive Maintenance Market by Industry
9.1 Introduction
9.1.1 Industries: Impact of COVID-19
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 way products are designed, manufactured, transported and sold
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 resource management
9.4.2 Energy and Utilities: Market Drivers
9.5 Transportation and Logistics
9.5.1 Initiatives to improve core functions, optimize marketing and improve risk management functions
9.5.2 Transport and logistics: market drivers
9.6 Healthcare and Life Sciences
9.6.1 Introduction of new diagnostic devices, patient monitoring tools and operational technologies for improved patient care
9.6.2 Healthcare and Life Sciences: Predictive Maintenance Market Drivers
9.7 Other Industries

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 Business Valuation Quadrant
11.5.1 Stars
11.5.2 Emerging Leaders
11.5.3 Omnipresent 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 Enterprises
11.7.4 Starting Blocks
11.8 Competitive Scenario
11.8.1 Product Launches
11.8.2 Shops

12 company profiles
12.1 Introduction
12.2 Key Players
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 Oh
12.2.13 Give
12.2.14 Schneider Electric
12.2.15 hitachi
12.2.16 pt
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, see

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