As per the report published by Fior Markets, the global predictive maintenance market is expected to grow from USD 7.9 billion in 2020 and reach USD 21.45 billion by 2028, growing at a CAGR of 33% during the forecast period 2021-2028.
The predictive maintenance market has grown significantly in recent years. The rising usage of new and emerging technologies to acquire helpful insight into decision-making has helped expand the sector. Various vertical end-users are progressively seeking cost savings and downtime, which has fueled the market expansion.
Predictive maintenance is a plan for monitoring equipment performance and condition that decreases the chance of failure under standard operating settings. The goal is to predict a loss and then strive to avert it through corrective maintenance. Traditional systems rely on historical data regarding equipment performance and previous breakdowns. They construct periodic maintenance plans regardless of whether they are necessary to foresee the need for maintenance. On the other hand, modern predictive maintenance solutions constantly monitor equipment behavior to collect data in real-time and utilize advanced neural networks and artificial algorithms to decide and raise an alert when an equipment breakdown is likely to occur. Predictive maintenance systems assist in collecting information about equipment, processing it, and finally predicting its breakdown period. This aids in the prevention of equipment failure and related incidents and thus provides asset management. Some of these benefits serve as an additional impetus for the implementation of predictive maintenance solutions. The predictive maintenance sector requires the use of big data, the internet of things, and analytics. The critical concerns of end-use sectors such as automotive, manufacturing, oil and gas, and the rest reduce maintenance costs and asset operation. The use of predictive maintenance solutions using IoT technologies assists enterprises in reducing downtime as well as operational and maintenance costs.
Because of the requirement to increase asset uptime while minimizing maintenance costs, the global predictive maintenance market is likely to grow significantly. Furthermore, the market is driven by the growing demand for predictive maintenance and the ever-increasing desire to reduce maintenance costs and downtime. Implementation challenges, data security concerns, and a skilled labor shortage are all impeding market expansion. To implement AI-based IoT technologies and skill sets, qualified individuals must work with the most current software systems. Real-time condition monitoring will enable faster reaction and better asset management, resulting in market growth opportunities.
The prominent players of the global preventive maintenance market are Oracle Corporation, Microsoft Corporation, XMPro, IBM Corporation, Axiomtek Co. Ltd, RapidMiner, SAP SE, Hitachi, Ltd, and Comtrade. To expand their market offers, predictive maintenance solutions and service suppliers have employed various organic and inorganic growth tactics, such as new product launches, product upgrades, partnerships and agreements, business expansions, and mergers and acquisitions.
- In March 2019, IBM launched a new IIOT (industrial internet of things) solution for predictive maintenance, employing advanced analytics and artificial intelligence technology. The answer will lower the risk of physical asset failure in manufacturing robots, automobiles, turbines, electrical transformers, elevators, and mining equipment.
- In March 2019, Oracle announced the availability of Oracle IoT Asset Monitoring Cloud Service Release 19.1.5. This version features a digital twin simulator capable of simulating asset sensor simulations. The simulator can be used to prepare and test data patterns for sensors linked to an asset.
- In October 2018, Hitachi, Ltd. announced an AI-Assisted Predictive Maintenance Service for Petrochemical Plants to detect real-time operating concerns. This helps petrochemical plants improve operational efficiency and maintenance tasks.
- In February 2018, SAP announced the availability of the SAP Asset Strategy and Performance Management Solution. This solution extends the capabilities of SAP’s Leonardo IoT technology. SAP Asset Strategy & Performance Management is the most recent addition to SAP’s cloud asset management products, including SAP Asset Intelligence Network, SAP Predictive Engineering Insights, and SAP Predictive Maintenace and Service.
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Solution segment dominated the market and held the largest market share of 54.11% in the year 2020
Based on type, the global predictive maintenance market is segmented into solution and service. The solution segment dominated the market and held the largest market share of 54.11% in 2020. This growth can be due to organizations’ growing concern about saving costs and improving equipment uptime, the increased requirement for customized solutions, and the popularity and awareness of these solutions, which led to application-specific solutions from various industrial sectors.
The manufacturing segment dominated the market and held the largest market share of 23.35% in the year 2020
Based on vertical, the global predictive maintenance market is segmented into Manufacturing, Energy & Utilities, Healthcare, Automotive, Aerospace and Defense, Transportation. The manufacturing segment dominated the market and held the largest market share of 23.35% in 2020. Because of increased automation in the manufacturing sector, there is a greater need for predictive maintenance and manufacturing equipment such as industrial robots, machinery, elevators, and pumps.