The operational predictive maintenance global market report 2024 from The Business Research Company provides comprehensive market statistics, including global market size, regional shares, competitor market share, detailed segments, trends, and opportunities. This report offers an in-depth analysis of current and future industry scenarios, delivering a complete perspective for thriving in the industrial automation software market.
Operational Predictive Maintenance Market, 2024 report by The Business Research Company offers comprehensive insights into the current state of the market and highlights future growth opportunities.
Market Size -
The operational predictive maintenance market size has grown exponentially in recent years. It will grow from $5.78 billion in 2023 to $7.31 billion in 2024 at a compound annual growth rate (CAGR) of 26.5%. The growth in the historic period can be attributed to cost savings from reduced downtime and maintenance costs, improved asset reliability and performance, enhanced safety and risk mitigation, regulatory compliance requirements, growing awareness of predictive maintenance benefits.
The operational predictive maintenance market size is expected to see exponential growth in the next few years. It will grow to $18.62 billion in 2028 at a compound annual growth rate (CAGR) of 26.3%. The growth in the forecast period can be attributed to expansion into new industries and applications, demand for proactive maintenance solutions, market penetration in emerging economies, predictive maintenance adoption, increased focus on sustainability and energy efficiency initiatives. Major trends in the forecast period include integration of IoT sensors and data analytics, adoption of machine learning algorithms, expansion of applications across industries, development of cloud-based platforms, integration with enterprise asset management systems.
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Scope Of Operational Predictive Maintenance Market
The Business Research Company's reports encompass a wide range of information, including:
1. Market Size (Historic and Forecast): Analysis of the market's historical performance and projections for future growth.
2. Drivers: Examination of the key factors propelling market growth.
3. Trends: Identification of emerging trends and patterns shaping the market landscape.
4. Key Segments: Breakdown of the market into its primary segments and their respective performance.
5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.
6. Macro Economic Factors: Assessment of broader economic elements impacting the market.
Operational Predictive Maintenance Market Overview
Market Drivers -
The increasing number of IoT (Internet of Things) devices is expected to propel the growth of the operational predictive maintenance market going forward. IoT devices refer to nonstandard computing hardware such as sensors, actuators, or appliances that connect wirelessly to a network and can transmit data. It arises because of the widespread availability of high-speed internet connectivity, increasing industrial automation and supply chain management, and data analytics capabilities. IoT devices play a critical role in operational predictive maintenance by enabling real-time monitoring, data analytics, early issue detection, condition-based maintenance, predictive insights, and continuous improvement, ultimately assisting organizations in optimizing asset performance, lowering costs, and increasing operating efficiency. For instance, in August 2022, according to Akamai Technologies Inc., a US-based internet company, IoT connections are expected to increase from 15.1 billion in 2021 to 23.3 billion IoT connections in 2025. Therefore, the increasing number of IoT devices drives the operational predictive maintenance market.
Market Trends -
Major companies operating in the operational predictive maintenance market are developing innovative AI advancements, such as AI-based predictive maintenance solutions, to improve the accuracy, efficiency, and effectiveness of predictive maintenance. This solution uses AI (artificial intelligence) or ML (machine learning) technology to locally monitor the condition of industrial equipment without requiring an internet-based cloud connection. For instance, in May 2021, QuickLogic Corporation, a US-based developer of endpoint AI solutions, introduced an AI-based predictive maintenance solution utilizing the QuickLogic EOS S3 Platform and SensiML Analytics Toolkit. This solution integrates AI and ML technology to monitor manufacturing equipment, enabling the differentiation between normal and abnormal operations. It is a low-power, multi-core ARM Cortex System-on-Chip designed for mobile markets, including always-on voice applic
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