27.4% CAGR for Predictive Maintenance Market Size Worth $26.58 Billion, Globally, by 2028 with Cloud Segment Driving Growth During 2022-2028 | The Insight Partners
The global predictive maintenance market is set to record incremental growth of $20,375.64 million during 2022-2028, driven by increasing need to boost asset uptime and minimize maintenance costs coupled with rise in investments in predictive maintenance owing to IoT adoption.
Pune, India, July 13, 2023 (GLOBE NEWSWIRE) — According to Recently Published Report by The Insight Partners, “Predictive Maintenance Market Forecast to 2028 – COVID-19 Impact and Global Analysis – by Component, Deployment Type, Technique, And Industry, and Geography,” the predictive maintenance market is projected to reach $26.58 billion by 2028 from $5.31 billion in 2021. it is expected to grow at a CAGR of 27.4% during 2022–2028. The key players, holding majority shares, in predictive maintenance market includes IBM Corporation, General Electric Co, Schneider Electric SE, Microsoft Corporation and Hitachi Ltd.
Download Sample Pages: https://www.theinsightpartners.com/sample/TIPRE00007686
Global Predictive Maintenance Market Analysis: Competitive Landscape and Key Developments
General Electric Co., Hitachi Ltd., IBM Corporation, Microsoft Corporation, PTC, Inc., SAS Institute Inc., Schneider Electric SE, Software AG, Syncron AB, and TOSL Engineering Ltd. are a few of the key companies operating in the predictive maintenance market. The market leaders focus on new product launches, expansion and diversification, and acquisition strategies, which allow them to access prevailing business opportunities.
In 2022, Siemens, a Germany-based technology company focused on transport, healthcare, industry, and infrastructure, acquired Senseye for an undisclosed amount. With this acquisition, Senseye became a subsidiary of Siemens and is expected to strengthen its position in the digital services portfolio.
In June 2020, PTC has improved its ThingWorx Industrial IoT platform to speed up deployments of industrial IoT across the company value chain. ThingWorx 9.0 will have new and enhanced features to assist industrial organizations in developing, implementing, customizing, and scaling their solutions.
In, 2021, Schneider’s Internet-of-Things and Cloud-Based solutions will enable the Nestlé Al Maha factory in the United Arab Emirates to minimize unplanned downtime, increase asset performance, and reduce energy consumption.
Development of AI-powered Predictive Maintenance Platforms Provides Lucrative Opportunities for Global Predictive Maintenance Market:
The development of AI-powered predictive maintenance platforms presents a significant opportunity for the market as it offers advanced capabilities to organizations seeking to optimize their maintenance processes. These platforms leverage artificial intelligence and machine learning algorithms to analyze data collected from sensors, equipment logs, and historical records. By continuously learning from this data, AI-powered platforms can identify patterns, detect anomalies, and accurately predict equipment health and maintenance requirements.
Browse key market insights spread across 221 pages with 116 list of tables & 96 list of figures from the report, “Predictive Maintenance Market Forecast to 2028 – COVID-19 Impact and Global Analysis By Component (Solutions and Services), Deployment Type (Cloud and On-Premise), Technique (Vibration Monitoring, Electrical Testing, Oil Analysis, Ultrasonic Leak Detectors, Shock Pulse, Infrared and Others), and Industry (Manufacturing, Energy & Utilities, Aerospace & Defense, Transportation & Logistics, Oil & Gas, and Others)” in detail along with the table of contents: https://www.theinsightpartners.com/reports/predictive-maintenance-market
The opportunity lies in the ability of these platforms to provide organizations with comprehensive, intelligent solutions that enhance their maintenance operations. AI-powered predictive maintenance platforms offer several benefits. They enable proactive maintenance by identifying early warning signs of equipment degradation or failure, allowing organizations to take preventive measures to avoid costly breakdowns and unplanned downtime. This proactive approach improves equipment reliability and reduces the risk of production disruptions. The AI-powered platforms can optimize maintenance schedules and resource allocation. These platforms can generate maintenance plans that prioritize critical assets and allocate resources efficiently by analyzing historical data and considering factors such as equipment usage patterns and environmental conditions. This leads to better utilization of maintenance personnel, reduced costs, and improved overall operational efficiency. Furthermore, AI-powered predictive maintenance platforms offer advanced analytics capabilities, enabling organizations to gain deeper insights into equipment performance and health. These platforms can identify underlying root causes of failures, pinpoint areas of inefficiency, and provide recommendations for improvement. By leveraging these insights, organizations can make data-driven decisions to optimize asset performance, extend equipment lifespan, and reduce maintenance costs. The market opportunity also lies in the integration and scalability of these platforms. AI-powered predictive maintenance platforms can integrate with existing enterprise systems, such as enterprise resource planning (ERP) and computerized maintenance management systems (CMMS), creating a unified ecosystem for managing maintenance operations. Additionally, these platforms can scale to accommodate large and complex datasets, making them suitable for organizations with diverse equipment portfolios and significant data volumes.
Global Predictive Maintenance Market: Segmental Overview
Based on component, the predictive maintenance market is segmented into solutions and services. The solutions segment held the largest share of the market in 2020 and is anticipated to register the highest CAGR in the market during the forecast period.
Based on deployment type, the predictive maintenance market is bifurcated into cloud and on-premise. The cloud segment held the largest share of the predictive maintenance market in 2020 and is anticipated to register the highest CAGR in the market during the forecast period.
Based on technique, the market is segmented into vibration monitoring, electrical testing, oil analysis, ultrasonic leak detectors, shock pulse, infrared, and others. The vibration monitoring segment held the largest share of the market in 2020, whereas the infrared segment is estimated to register the highest CAGR in the market during the forecast period.
Based on industry, the predictive maintenance market is segmented into manufacturing, energy & utilities, aerospace & defense, transportation & logistics, oil & gas, and others. The manufacturing segment held the largest share of the market in 2020, whereas the energy & utilities segment is estimated to register the highest CAGR in the market during the forecast period.
Directly Purchase this Report at https://www.theinsightpartners.com/buy/TIPRE00007686
FOR SEVERAL REASONS, the US has been a leader in the predictive maintenance market. Firstly, the US has been an early adopter of this technology, with many companies recognizing the benefits of predictive maintenance early on. Predictive maintenance allows companies to identify potential equipment failures before they occur, which can help to prevent unplanned downtime and reduce maintenance costs. US companies have quickly recognized these benefits, which have helped to drive the adoption of predictive maintenance solutions. Secondly, the US has many companies that specialize in developing predictive maintenance solutions. Many of these companies are focused on developing advanced analytics and machine learning algorithms that can be used to predict equipment failures. This has helped drive innovation in the market, with new predictive maintenance solutions being constantly developed.
Finally, the US has a strong focus on research and development, which has led to the creation of advanced predictive maintenance technologies. Many universities and research institutions in the US are focused on developing new predictive maintenance technologies, which has helped drive the market’s growth. These factors have contributed to the US’s leadership in the predictive maintenance market. The US has developed advanced predictive maintenance solutions, which have helped reduce maintenance costs and increase equipment uptime for many companies. As a result, many companies worldwide are now adopting predictive maintenance solutions, driving market growth.
Go through further research published by The Insight Partners: (Purchase with 10% Instant Discount):
Predictive Analytics Market Forecast to 2028 – COVID-19 Impact and Global Analysis
Manufacturing Predictive Analytics Market Forecast to 2028 – Covid-19 Impact and Global Analysis
Advanced Predictive Analytics Software Market Forecast to 2028 – COVID-19 Impact and Global Analysis
Prescriptive and Predictive Analytics Market Forecast to 2028 – COVID-19 Impact and Global Analysis
Healthcare Predictive Analytics Market Forecast to 2028 – COVID-19 Impact and Global Analysis
The Insight Partners is a one stop industry research provider of actionable intelligence. We help our clients in getting solutions to their research requirements through our syndicated and consulting research services. We specialize in industries such as Semiconductor and Electronics, Aerospace and Defense, Automotive and Transportation, Biotechnology, Healthcare IT, Manufacturing and Construction, Medical Device, Technology, Media and Telecommunications, Chemicals and Materials.
If you have any queries about this report or if you would like further information, please contact us:
Contact Person: Ankit Mathur
E-mail: [email protected]
Press Release: https://www.theinsightpartners.com/pr/predictive-maintenance-market
Disclaimer: The above press release comes to you under an arrangement with GlobeNewswire. AfternoonHeadlines.com takes no editorial responsibility for the same.