Predictive Maintenance (PDM) Market to grow by USD 33.76 Billion from 2024-2028, driven by AI and cloud adoption in SMEs - Technavio

NEW YORK, Nov. 27, 2024 /PRNewswire/ -- Report with the AI impact on market trends - The global predictive maintenance (PDM) market  size is estimated to grow by USD 33.76 billion from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of  39%  during the forecast period. Increased adoption of advanced analytics by SMES owing to rise in cloud computing is driving market growth, with a trend towards proliferation of advanced technologies, AI, and IoT. However, lack of expertise and technical knowledge  poses a challenge.Key market players include Augury Inc., Avnet Inc., C3.ai Inc, Dell Technologies Inc., Deutsche Telekom AG, Fortive Corp., General Electric Co., Hitachi Ltd., Honeywell International Inc., International Business Machines Corp., PTC Inc., RapidMiner Inc., Reliability Solutions sp. Z o.o., Robert Bosch GmbH, Rockwell Automation Inc., SAP SE, SAS Institute Inc., Schneider Electric SE, Siemens AG, and Warwick Analytics Services Ltd..

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Key Market Trends Fueling Growth

Predictive Maintenance (PDM) is a cutting-edge business trend revolutionizing equipment maintenance. It uses condition-based strategies to predict and prevent equipment failure, moving beyond time- and reactive-based methods. PDM leverages various technologies like electromagnetic radio fields, NFC chips, and sensor devices to gather real-time data. Devices such as vibration meters and acoustic analysis tools help identify potential issues. Machine learning algorithms analyze sensor data to predict faults, enabling action before human error or pocket dials cause problems. NFC technology facilitates transactions for maintenance work, while smart posters and maintenance software like CMMS, FTMaintenance, and mobile CMMS features streamline work orders and communication between maintenance staff, machine operators, and technicians. Predictive maintenance saves costs by minimizing downtime and extending asset life. It's being adopted in diverse industries, from coal preparation plants to fleet maintenance and building management. Predictive maintenance is the future, combining advanced technologies like machine learning, computer-based modeling, and analytics tools with wireless internet connections to provide actionable insights. Meteorologists and Doppler radars, even satellites, contribute to predictive maintenance by providing weather data and environmental conditions. Predictive maintenance is transforming maintenance work, making it more efficient, effective, and proactive. 

Predictive maintenance (PdM) is a proactive approach to equipment maintenance that uses data analysis and machine learning algorithms to predict potential failures before they occur. By analyzing historical data and current performance indicators, PdM solutions can identify patterns and trends that may indicate an impending issue. The acceptance of advanced technologies like AI, machine learning, blockchain, cloud computing, and big data is driving the adoption of PdM in various industries. These technologies enable real-time monitoring, predictive analytics, and automated maintenance, leading to increased efficiency, cost savings, and improved asset performance. Billions of dollars are being invested in research and development to further enhance the capabilities of these technologies, making PdM an essential component of modern maintenance strategies. 

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Market Challenges

• Predictive Maintenance (PDM) is a cutting-edge technology that uses machine learning and sensor data to predict equipment failures before they occur. However, implementing PDM comes with challenges. Electromagnetic radio fields from sensors can interfere with NFC chips in devices, leading to transaction errors. Human error, such as pocket dialing maintenance work orders, can also cause delays. Distance and battery life are concerns for wireless sensor devices. PDM relies on condition-based maintenance using sensor devices and real-time data. Time-based maintenance and reactive maintenance are outdated methods. Maintenance software like CMMS, FTMaintenance, and mobile CMMS features play a crucial role in managing work orders and dispatching maintenance staff. Vibration analysis, acoustic analysis, and infrared analysis are common condition-monitoring techniques. Baselines and work orders help maintenance technicians identify potential issues. Machine operators should be trained to use condition-monitoring devices like vibration meters. Predictive algorithms use data from sensors, computer-based modeling, and analytics tools to predict faults. Predictive maintenance is essential for fleet maintenance and building maintenance. Doppler radars, satellites, and meteorologists provide additional data for predictive maintenance in extreme environments. Challenges include ensuring accurate sensor data and a reliable wireless internet connection. Maintenance staff should be trained to use predictive maintenance software and understand the importance of preventive maintenance. Collaboration between maintenance technicians, machine operators, and data analysts is crucial for successful implementation of predictive maintenance.
• Predictive maintenance (PdM) is a crucial business strategy that helps enterprises prevent equipment failure through corrective or scheduled maintenance. However, the implementation of PdM comes with challenges. The lack of skilled labor and specialized knowledge in condition monitoring and predictive analytics is a significant hurdle. This complex process requires extensive domain expertise for micro-segmentation deployment. As historical data grows and PdM use cases expand, the complexity of the models increases, leading to management overhead and inefficiencies. To overcome these challenges, extensive training and specialized resources are necessary for successful PdM adoption.

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Segment Overview 

This predictive maintenance (pdm) market report extensively covers market segmentation by

1. Component 
• 1.1 Solutions
• 1.2 Service
2. Deployment 
• 2.1 On-premises
• 2.2 Cloud
3. Geography 
• 3.1 North America
• 3.2 Europe
• 3.3 APAC
• 3.4 South America
• 3.5 Middle East and Africa

1.1 Solutions-  Predictive maintenance (PdM) solutions are integrated with new or existing machinery infrastructure to monitor machine health and identify early signs of deterioration. This integration ensures a good return on investment (ROI) and helps organizations meet sustainability goals by enabling remote machine monitoring worldwide. By keeping assets in optimal working condition and available at all times, PdM solutions increase asset life expectancy and reduce high maintenance costs. The energy and utilities, manufacturing, healthcare, aerospace and defense, and automotive sectors are among those driving the growth of the global PdM market due to their increasing adoption of PdM solutions. These industries use sensors and equipment to generate data for analysis, which is then transferred to the cloud for analysis and monitoring via gateways. The cloud provides computing, data storage, and analytics reporting, while management software serves as an interface for users to handle equipment conditions from anywhere. The use of PdM solutions is expected to increase significantly, leading to market growth during the forecast period. These solutions help improve product quality and process efficiency by analyzing data generated from equipment and sensors. Gateways serve as data transporters and translators, while cloud services offer shared software resources for computing, data storage, and analytics reporting. Management software acts as an interface for users to monitor equipment conditions in real-time.

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Research Analysis

Predictive maintenance (PDM) is a proactive approach to equipment maintenance using real-time data analysis and various sensor devices. Electromagnetic radio fields and NFC chips are among the technologies utilized in PDM. NFC transactions enable data exchange between devices, providing distance information for condition-based maintenance. Human error can be minimized through smart posters and NFC technology, triggering action when maintenance is required. PDM employs NFC technology to monitor assets, collecting data for analysis in real-time. This information helps identify potential equipment failure before it occurs, moving away from time-based and reactive maintenance. Maintenance software, such as CMMS, uses baselines and work orders to manage maintenance tasks, with machine operators and maintenance staff receiving notifications for necessary actions. Vibration analysis, acoustic analysis, and infrared analysis are common methods used in PDM. A centrifugal pump motor in a coal preparation plant, for instance, can be monitored using a vibration meter to detect anomalies and prevent costly downtime. By leveraging these advanced technologies and techniques, predictive maintenance significantly improves equipment reliability and reduces maintenance costs.

Market Research Overview

Predictive Maintenance (PDM) is a cutting-edge technology that utilizes various sensors, condition-monitoring devices, and advanced analytics tools to predict equipment failures before they occur. This proactive approach to maintenance reduces downtime, lowers maintenance costs, and increases asset productivity. Electromagnetic radio fields, NFC chips, and sensor devices collect real-time data on machine performance, temperature, vibration, and other key indicators. Machine learning algorithms analyze this data to identify patterns and anomalies, predicting potential failures and suggesting preventive actions. NFC technology enables wireless transactions for maintenance work orders, while machine operators and maintenance staff receive notifications for required actions. Distance learning and smart posters provide training and instructions for maintenance technicians. Predictive maintenance applications range from centrifugal pump motors in coal preparation plants to fleet maintenance and building systems. Vibration analysis, acoustic analysis, infrared analysis, and computer-based modeling are essential tools for predictive maintenance. Predictive algorithms, wireless internet connection, and CMMS software facilitate efficient and effective maintenance work. Meteorologists, Doppler radars, and satellites provide external data for predicting weather-related maintenance needs.

Table of Contents:

1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation

• Component
• Solutions
• Service
• Deployment
• On-premises
• Cloud
• Geography
• North America
• Europe
• APAC
• South America
• Middle East And Africa

7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix

About Technavio

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SOURCE Technavio