Predictive Maintenance Market

The Global

Predictive Maintenance market was valued at USD 4 Billion in 2021

. It is projected to reach a value of

USD 19.3 Billion by 2028

at a CAGR of

29.80%

over the forecast period.

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Predictive Maintenance software is used to keep an eye on the functionality and state of any piece of machinery or equipment while it is in use. Utilizing cutting-edge techniques for equipment observation, the program enables maintenance to be planned for failure. Software for Predictive Maintenance has applications in many industries, including detecting harmonic distortion-induced three-phase power imbalances, motor amperage spikes, overheating from worn bearings, preventing insulation breakdowns, and identifying potential overloads or degradation in electrical panels.

As more businesses adopt this technology in the upcoming years, Predictive Maintenance is anticipated to increase. The growing need for big data and the Internet of Things are two key development drivers for the Predictive Maintenance sector. Additionally, organizations are becoming more concerned with lowering the expenses associated with managing and upkeep their assets. By identifying failure patterns and minor irregularities in the processes, adopting technologies like Predictive Maintenance aids organizations in preventing downtime and lowering operations and maintenance expenses. This is accomplished by precisely forecasting asset breakdowns to ensure a productive supply chain. To provide an effective system for Predictive Maintenance solutions and meet the needs of varied organizations, businesses are now integrating sensor technology into maintenance activities. The study of remote and electronic maintenance involves assisting and supporting maintenance operations in remote and hazardous areas. To increase the effectiveness and affordability of the Predictive Maintenance market, businesses are developing technology-based ERP software solutions.

Furthermore, most international suppliers are planning Predictive Maintenance programs, driving the need for a highly educated workforce. Companies must develop competence in fields including networking, apps, and cybersecurity. Additionally, they aim to use IoT data to provide advanced analytics expertise, which includes AI and ML. This will enable them to anticipate outcomes, prevent errors, optimize operations, develop original products, and provide these services. To deploy AI-based IoT technologies and skill sets, trained personnel must operate the most recent software systems. Therefore, it is necessary to train current employees on how to use updated and upgraded technologies.

Additionally, industries are quickly embracing new technologies but need help finding personnel with the necessary skills. Additionally, when businesses integrate AI into the IoT, there will be a rising need for data analyst teams focused on operational intelligence. This is to manage the enormous amounts of data produced by IoT devices.

Moreover, the high costs associated with R&D capabilities, limited infrastructure, and lesser sensitivity of certain liquid biopsies are projected to stymie market expansion. In addition, a lack of favorable reimbursement scenarios and technology penetration in developing economies, the need for large capital investments to set up production facilities, the low sensitivity and specificity limitations of Predictive Maintenance, and a lack of suitable infrastructure in low- and middle-income countries are expected to hamper the market growth during the forecast period.

Top Market Trends

1.

To increase the energy industrys asset efficiency.

: Because of the necessity to cut downtime and the increased knowledge among managers in the industrial sector, the Predictive Maintenance sector has been expanding steadily. Managers in manufacturing firms are constantly trying to improve how their plants' machinery is maintained. They are developing strategies to lessen operations errors and boost the processes' advantages.

2. Rising awareness about Predictive Maintenance:

Because of the necessity to cut downtime and the increased knowledge among managers in the industrial sector, the Predictive Maintenance sector has been expanding steadily. Managers in manufacturing firms are constantly trying to improve how their plants' machinery is maintained. They are developing strategies to reduce errors in operations and boost the efficiency of processes.

3. A growing demand to increase asset uptime:

Insightful technologies, such as big data analytics, the Internet of Things, and cloud data storage, make it possible for industrial equipment and sensors to submit condition-based data to a centralized server, improving the practicality and directness of defect detection. Increased uptime, lower maintenance costs, and a reduction in part inventories have contributed to the markets simultaneous growth and expansion. Additionally, the growth of the Predictive Maintenance market depends on reducing repair and overhaul times.

Market Segmentation

The Predictive Maintenance Market is segmented into

Components,

Deployment Modes, Organization Sizes,

Vertical

, and

Region

. Based on Components, the market is segmented into Solutions and Services. Furthermore, based on Deployment Modes, the market is segmented into On-Premises and Cloud. Moreover, based on Organization Sizes, the market is divided into Large Enterprises, Small & Medium-sized Enterprises (SMEs). In addition, based on Verticals, the market is further categorized into Government & Defense, Manufacturing, Energy & Utilities, Transportation & Logistics, Healthcare & Life Sciences. In addition, based on Region, the market is segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

Based on Components

Solutions are Major Revenue Contributor in the Market as It plays an important role in the potential for future device failure.

Solutions accounted for the largest share of the market in 2021. In recent years, the market has grown significantly. The solutions market is anticipated to expand strongly over the forecast period since it is crucial for forecasting equipment failure in the future. The design of solutions facilitates determining the root cause of equipment failure. The market is anticipated to experience growth over the projected period as more industries, including the banking and financial sector, industrial sector, health care sector, etc., embrace productive maintenance solutions.

Based on the Deployment Modes

Cloud-based Holds the Largest Share of the Market and helps enterprises achieve cost benefits.

In 2021, the Cloud-based category dominated the market with the highest revenue share. Organizations can profit financially from using the cloud-based deployment strategy. As all the data is saved in the cloud and very little maintenance is required at the location where the software is utilized, cloud-based segments are very cost-effective. Cloud-based solutions save the expense of hiring specialized specialists for maintenance. For the on-premises segment, having knowledgeable specialists is increasingly necessary.

Based on Organization Sizes

Large Enterprises Accounted for The Majority Of Revenue Share Due To Cost-Cutting Feature

The Large Enterprises category dominated the global Predictive Maintenance market in 2021-22, and it is anticipated that it will continue to hold this position throughout the forecast period. The use of Predictive Maintenance solutions in large organizations becomes necessary to avert significant losses for the company. This is because, in large enterprises, disruption of any equipment could have a significant impact. The adoption of Predictive Maintenance solutions in large businesses also offers a cost-saving benefit because it can lower additional costs associated with maintenance if equipment malfunctions. Predictive Maintenance solutions are becoming more and more in demand in small and medium-sized businesses. Throughout the forecast period, it is anticipated that the adoption of these solutions will increase in the small and medium-sized business sectors.

Based on Verticals

Manufacturing Dominates the Market Owing to the Increased Used of increasing need for industrial manufacturing.

The market was dominated by Manufacturing, with the highest revenue share in 2021. Due to the growing demand for maintenance of manufacturing machinery, elevators, industrial robots, and pumps to reduce overall downtimes, the manufacturing category held the greatest share of the worldwide Predictive Maintenance market. In addition, it is anticipated that the development of Industry 4.0 will increase demand for Predictive Maintenance over the next few years.

Based on Region

North America is the Largest Revenue Contributor Owing to the Presence of Major Players

In 2021, North America had the highest revenue share at 42.6%. During the forecasting period, North America is anticipated to grow the market

. Due to the increasing adoption of Predictive Maintenance solutions that make use of cutting-edge technologies like IoT, cloud computing, machine learning, and artificial intelligence, North America will dominate the worldwide Predictive Maintenance market during the forecast period (AI). To identify operational performance aspects and enhance maintenance practices and reliability, businesses in the region are embracing Predictive Maintenance solutions. Due to major competitors operating in the Predictive Maintenance market, the US currently holds the biggest market share in North America.

Competitive Landscape

The global Predictive Maintenance market is dominated by companies such as Microsoft, Google, and SAP because of their unique products, financial stability, strategic advances, and global reach. The participants are focusing their efforts on promoting R&D. Additionally; they support strategic expansion activities, including product launches, joint ventures, and partnerships to expand their client base and boost their market position. Some of the key players in the Global Predictive Maintenance Market include- Microsoft(US), to note a few.

Recent Market Developments

● In May 2021, the introduction of Lumada Inspection Insights was announced by Hitachi Ltd. Lumada Inspection Insights, developed by Hitachi Energy and Hitachi Vantara, enables businesses to automate asset inspection and advance sustainability objectives. The proposed approach employs AI and machine learning to evaluate resources, hazards, and a wide range of image types to address multiple reasons for failure.

● In July 2021, the industrys first dual safety and cybersecurity-certified bypass and alarm management software application, EcoStruxureTM TriconexTM Safety View, was introduced by Schneider Electric. The system allows operators to see both the bypass status and the level of risk reduction. It also provides the critical alarms necessary to operate the plant safely when risks are high.

Segmentation of the Global Predictive Maintenance Market