Asset Performance Management Market Driven by Need for Predictive Maintenance Solutions | Reaching USD 7.52 Bn by 2027

Asset Performance Management Market Value

Asset Performance Management Market Value

Increasing focus on optimizing asset utilization & minimizing downtime and adoption of Industrial IoT are driving asset performance management market growth.

PORTLAND, PORTLAND, OR, UNITED STATES, May 22, 2023 /EINPresswire.com/ — The size of the world market for asset performance management, which was estimated to be worth $3,463 million in 2019, is expected to increase to $7,529 million by 2027, rising at a CAGR of 10.3% from 2020 to 2027.

The market for asset performance management is expanding due to a variety of causes, including the growing demand for asset maintenance based on risk and the increased requirement to create the highest possible economic return on assets. Additionally, factors like the growing popularity of cloud-based APM software among end users contribute to the market’s expansion globally.

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The asset performance management (APM) market is experiencing significant growth and is driven by several key factors and trends. Firstly, the increasing focus on optimizing asset utilization and minimizing downtime is a major driver. Organizations across various industries, such as manufacturing, energy, and utilities, are seeking ways to enhance the performance and reliability of their assets. APM solutions offer predictive maintenance capabilities, real-time monitoring, and advanced analytics to identify potential failures and optimize maintenance schedules, resulting in improved asset performance and reduced downtime.

The adoption of Industrial Internet of Things (IIoT) and connected devices is driving the growth of the APM market. The proliferation of sensors and data-generating devices embedded in industrial assets enables real-time monitoring and collection of performance data. APM solutions leverage this data to provide actionable insights and enable proactive maintenance strategies. The integration of IIoT with APM facilitates condition-based maintenance, remote monitoring, and predictive analytics, leading to improved asset reliability and performance.

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The increasing demand for cost optimization and operational efficiency is a significant driver for APM adoption. Organizations are under pressure to streamline their operations, reduce maintenance costs, and optimize asset lifecycle management. APM solutions help in achieving these objectives by providing visibility into asset health, optimizing maintenance processes, and enabling data-driven decision-making. By maximizing asset performance and minimizing unplanned downtime, APM solutions contribute to cost savings and improved operational efficiency.

The advancements in analytics and artificial intelligence (AI) are transforming the APM market. AI-powered analytics algorithms enable organizations to process and analyze large volumes of data collected from assets. These advanced analytics capabilities enable predictive and prescriptive maintenance, anomaly detection, and performance optimization. The integration of AI and machine learning algorithms with APM solutions allows for more accurate asset performance predictions and proactive maintenance actions, leading to improved asset reliability and reduced maintenance costs.

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In summary, the APM market is driven by the need for optimizing asset utilization and minimizing downtime, the adoption of IIoT and connected devices, the demand for cost optimization and operational efficiency, and the advancements in analytics and AI. These drivers and trends are shaping the APM market as organizations strive to improve asset performance, reduce maintenance costs, and achieve operational excellence.

Some of the key asset performance management industry players profiled in the report include ABB Group, Aveva Inc., Bentley System, GE digital, IBM Corporation, Infor Inc., Oracle Corporation, SAP SE, SAS Institute Inc. and Siemens AG. This study includes market trends, asset performance management market analysis, and future estimations to determine the imminent investment pockets.

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David Correa
Allied Analytics LLP
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