Predictive maintenance is the process of keeping equipment in good working order by using condition-monitoring devices and systems. The Internet of Things, integrated systems, and artificial intelligence (AI), are included in order to connect various assets and systems, share data, and analyze it. Additionally, it includes industrial controls, sensors for preventive maintenance, and business systems like enterprise asset management (EAM) and enterprise resource planning (ERP) software. It contributes to increased equipment longevity, lower maintenance costs, and higher levels of production. Additionally, demand for predictive maintenance is rising globally as it offers safety compliance and proactive corrective actions.
In 2021, the market for predictive maintenance was USD 4.32 billion, and by 2030 it will reach USD 45.75 billion, growing at a 29.98% CAGR during the forecast period.
The demand for enterprises to optimize their maintenance procedures, technology advancements, and rising industrial automation adoption are driving the market for predictive maintenance, which is expanding quickly. Predictive maintenance predicts when machinery or equipment will need repair using data analytics, machine learning, and artificial intelligence, enabling businesses to do maintenance proactively rather than reactively.
The primary reason for using predictive maintenance systems is to increase machine efficiency and, secondly, to ensure lower maintenance costs. The data analyzed by encryption software and electronics indicate the need for timely maintenance to prevent the machinery from breaking down. Breakdowns result in longer and more expensive repairs and a halt in production. According to one study, US manufacturing units spend more than USD 50 billion annually on maintenance and repair. Another study discovered that using predictive maintenance can reduce maintenance costs by about 20% while increasing production capacity by about 10%. Furthermore, businesses that used big data and data analytics in their operations saw an average 8% increase in profits. With an astounding 97% of companies in North America alone investing in AI and big data, we will see an increase in the use of artificial intelligence (AI), machine learning (ML), and analytics in predictive maintenance leading to its exceptional growth.
The expansion of the Predictive Maintenance market will be constrained due to low user knowledge of the benefits of Predictive Maintenance and the high operating and maintenance costs of these Predictive Maintenance systems.
Real-time condition tracking to aid in prompt action. In almost every sector, improved asset administration is becoming increasingly necessary. As IoT produces significant data from connected devices, solution providers equipped with AI and ML can gather and transform the vast amount of customer-related data into important insights. Without human intervention, AI can be non-segregated with IoT devices to enhance several service delivery aspects, including predictive maintenance and quality evaluation. Real-time inputs from sensors, actuators, and other control parameters would help businesses monitor in real time, respond quickly, and anticipate early asset failures.
The on-premises segment ruled the market with 68% of the revenue share. Due to the increased adoption of cloud-based technologies across sectors, the cloud deployment method is expected to expand rapidly during the forecast period. In 2017, the burden in cloud-based data centers was 86%, increasing to 94% by 2021.
The solution segment led the entire market with significant revenue share. The solutions market will expand rapidly because it is crucial for forecasting equipment failure in the future. The design of solutions facilitates determining the root cause of device failure. Various industries, such as banking, manufacturing, and healthcare, are expected to adopt productive maintenance solutions, leading to growth in the market.
By Organization Size
The large enterprise segment was the largest market contributor and will expand at the highest CAGR. In large enterprises, it is crucial to use predictive maintenance solutions to prevent significant losses for the company. Any disruption in equipment can have a substantial impact, making it necessary to take preventative measures. Predictive maintenance options are increasingly in demand in small and medium-sized businesses. Throughout the forecast period, the use of these products will increase in the small and medium-sized business sectors.
Predictive maintenance is most popular in North America due to the presence of key market players in the region. The market will expand due to the region’s increasing technological advancements or developments. In the North American region, more industry participants in predictive maintenance exist.
Like developed countries, developing countries look for technological innovations and advancements to maximize production while keeping their assets or machinery. The Asia Pacific regional market will expand rapidly due to the rising demand for maintenance solutions. Predictive maintenance solutions are expected to experience a surge in demand in this region, particularly in developing countries like China and Japan, as small- and medium-sized manufacturing sectors continue to expand.
- Asystom, C3.ai, Inc.,
- Axiomtek Co. Ltd
- AVEVA Group plc
- C3 IoT
- Expert Microsystems, Inc.,
- Engineering Consultants Group, Inc.
- Fiix Inc.
- Hitachi Ltd
- General Electric
- Operational Excellence (OPEX) Group Ltd
- IBM Corporation
- Microsoft Corporation
- Oracle Corporation
- SAP SE
- PTC Inc.
- SAS Institute
- Software AG
- Schneider Electric
- Sigma Industrial Precision
- Spark Cognition
- Uptake Technologies Inc
- TIBCO Software Inc
In 2021, the market for predictive maintenance was USD 4.32 billion, and by 2030 it will reach USD 45.75 billion, growing at a 29.98% CAGR during the forecast period. Predictive maintenances solutions and services are in high demand due to factors like declining maintenance costs with decreased downtime, increasing need for improved monitoring and predictive technologies in developed countries, and broad adoption of the Internet of Things (IoT).