Predictive Maintenance Market - Global Forecast to 2024: Opportunities in Real-Time Condition Monitoring to Assist in Taking Prompt Actions
DUBLIN, June 27, 2019 /PRNewswire/ -- The "Predictive Maintenance Market by Component (Solutions and Services), Deployment Mode, Organization Size, Vertical (Government and Defense, Manufacturing, Energy and Utilities, Transportation and Logistics), and Region - Global Forecast to 2024" report has been added to ResearchAndMarkets.com's offering.
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The global predictive maintenance market size is forecasted to grow from USD 3.0 billion in 2019 to USD 10.7 billion by 2024
The major growth drivers of the predictive maintenance market include the increasing use of emerging technologies to gain valuable insights. A lack of a skilled workforce may restrain the growth of the predictive maintenance market.
The services segment to grow at a higher CAGR during the forecast period
Predictive maintenance is an approach used by the enterprises to predict the future failure point, as well as monitor the condition of an asset in real time. Besides passive monitoring, the predictive maintenance technique leverages ML algorithms that take critical historical data, such as temperature, pressure, and vibration, as an input, thus providing prediction related to the condition of an asset in real time. This, in turn, enables enterprises to significantly reduce unplanned machine downtime and decide whether any particular asset needs maintenance. Predictive maintenance ensures the machine is taken for maintenance before it fails due to which there are minimal loses in production.
The traditional maintenance software currently cannot manage these expectations as these maintenance solutions are reactive and periodic, which might affect the productivity of an enterprise due to unexpected downtime of the asset. Predictive maintenance solutions leverage technologies, such as Artificial Intelligence (AI), Internet of Things (IoT), and big data, to gather meaningful insights from all the data received from the machines, thus helping in taking necessary actions before the breakdown of the asset.
The major factors driving the growth of the predictive maintenance market are the increasing demand for effective management of data generated from IoT devices to gain valuable insights and reducing maintenance cost and downtime. Furthermore, condition monitoring in real time to take prompt actions is expected to provide an opportunity for the growth of organizations from various industry verticals in the predictive maintenance market. Moreover, companies are leveraging AI and ML to achieve significant advantages over traditional business intelligence tools to analyze the IoT data.
With the advent of predictive maintenance solutions, enterprises can make operational predictions much faster and with greater accuracy than traditional maintenance solutions. The widespread use of IoT devices with industrial equipment provides a plethora of data. With AI algorithms applied to the gathered data, business owners can detect potential issues and fix these issues in advance. Data security and privacy issues, maintenance and update issues, and lack of skilled workforce are some factors that are acting as challenges for the predictive maintenance market and slowing the adoption of predictive maintenance solutions.
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 3 Verticals
4.3 Market By Region
4.4 Market in North America, By Component and Deployment Mode
5 Market Overview and Industry Trends
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 Growing Need to Reduce Maintenance Cost and Downtime
5.2.2 Restraints
5.2.2.1 Lack of Skilled Workforce
5.2.3 Opportunities
5.2.3.1 Real-Time Condition Monitoring to Assist in Taking Prompt Actions
5.2.4 Challenges
5.2.4.1 Companies' Concern Over Data Security and Privacy Issues
5.2.4.2 Frequent Maintenance and Upgradation Requirement to Keep Systems Updated
5.3 Use Cases
5.3.1 Introduction
5.4 Regulatory Implications
5.4.1 Introduction
5.4.2 General Data Protection Regulation
5.4.3 Health Insurance Portability and Accountability Act
5.4.4 Federal Trade Commission
5.4.5 Federal Communications Commission
5.4.6 Iso/IEC Standards
5.4.6.1 ISO 55000 Standards
5.4.6.2 ISO 13374 on Condition Monitoring and Diagnostics of Machines
5.4.6.3 ISO/IEC JTC 1
5.4.6.4 ISO/IEC JTC 1/SC 42
5.4.6.5 ISO/IEC JTC1/SC3 1
5.4.6.6 ISO/IEC JTC1/SC2 7
5.4.7 Industrial Internet Consortium Reference Architecture
5.4.8 CEN/ISO
5.4.8.1 CEN/Cenelec
5.4.9 National Institute of Standards and Technology
5.4.10 Eprivacy
5.4.11 ANSI Tappi Tip 0305-34:2008
5.4.12 Mimosa
6 Predictive Maintenance Market By Component
6.1 Introduction
6.2 Solutions
6.2.1 Integrated
6.2.1.1 Growing Adoption of the Integrated Solution as It Integrates Multiple Capabilities Within A Single Solution
6.2.2 Standalone
6.2.2.1 Increasing Demand for Advanced and Vertical-Focused Predictive Maintenance Capabilities to Drive the Growth of Standalone Solutions
6.3 Services
6.3.1 System Integration
6.3.1.1 Predictive Maintenance Vendors to Offer System Integration Services to Overcome System-Related Issues Effectively
6.3.2 Support and Maintenance
6.3.2.1 Growing Deployment of Predictive Maintenance Solution to Increase the Demand for Support and Maintenance Services
6.3.3 Consulting
6.3.3.1 Technicalities Involved in Implementing Predictive Maintenance Solution to Boost the Growth of Consulting Services
7 Predictive Maintenance Market By Deployment Mode
7.1 Introduction
7.2 Cloud
7.2.1 Benefits, Such as Scalability and Ease of Implementation, to Boost the Growth of the Cloud Deployment Mode
7.3 On-Premises
7.3.1 Data-Sensitive Organizations to Adopt the On-Premises Deployment Mode for Predictive Maintenance Solutions
8 Predictive Maintenance Market By Organization Size
8.1 Introduction
8.2 Large Enterprises
8.2.1 Large Enterprises to Adopt Predictive Maintenance Solutions to Optimize Operational Maintenance Processes
8.3 Small and Medium-Sized Enterprises
8.3.1 Small and Medium-Sized Enterprises to Adopt Predictive Maintenance Solutions With Rising Technological Advancement
9 Predictive Maintenance Market By Vertical
9.1 Introduction
9.2 Government and Defense
9.2.1 Government and Defense Vertical to Adopt Predictive Maintenance Solutions for Automating the Defense System
9.3 Manufacturing
9.3.1 Growing Need to Track, Diagnose, and Monitor Machines to Fuel the Growth of the Predictive Maintenance Application in the Manufacturing Vertical
9.4 Energy and Utilities
9.4.1 the Growing Demand of Power-Usage Analytics Applications Fuel the Growth of Energy and Utilities Vertical
9.5 Transportation and Logistics
9.5.1 Increasing Need to Improve Asset Tracking and Performance Management for Minimizing Risks Lead to Growth in Transportation and Logistics Vertical
9.6 Healthcare and Life Sciences
9.6.1 Growing Demand for Monitoring Patient Health and Personalized Treatment in Real Time to Fuel the Growth of Healthcare and Life Sciences Vertical
9.7 Others
10 Predictive Maintenance Market By Region
11 Competitive Landscape
11.1 Overview
11.2 Competitive Leadership Mapping
11.2.1 Visionaries
11.2.2 Innovators
11.2.3 Dynamic Differentiators
11.2.4 Emerging Companies
11.3 Strength of Product Portfolio
11.4 Business Strategy Excellence
11.5 Competitive Leadership Mapping (Startups)
11.5.1 Progressive Companies
11.5.2 Responsive Companies
11.5.3 Dynamic Companies
11.5.4 Starting Blocks
11.6 Strength of Product Portfolio (Startups)
11.7 Business Strategy Excellence (Startups)
12 Company Profiles
12.1 Introduction
12.2 IBM
12.3 Microsoft
12.4 SAP
12.5 GE
12.6 Schneider Electric
12.7 Hitachi
12.8 PTC
12.9 Software AG
12.10 SAS
12.11 TIBCO
12.12 C3 IoT
12.13 Uptake
12.14 Softweb Solutions
12.15 Asystom
12.16 Ecolibrium Energy
12.17 Fiix
12.18 OPEX Group
12.19 Dingo
12.20 Sigma Industrial Precision
For more information about this report visit https://www.researchandmarkets.com/r/h0qooy
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