Predictive maintenance – techniques designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed – promises to help organizations reduce downtime costs by accurately predicting asset failures. Driving the forecasted growth, says the firm, is growing demand for Internet of Things (IoT) and big data along with rising concerns in the organizations regarding asset maintenance and operational costs.
“Evolving technologies such as IoT, cloud storage, and big data analytics are enabling more industrial equipment and assembly robots to provide condition-based data, making fault detection easier and practical,” says the firm. “Information collected from this equipment can be turned into actionable and meaningful insights by using these solutions.”
The deployment of predictive maintenance solutions is not limited to the manufacturing sector, says the firm, and these solutions are now increasingly being deployed in energy and utilities, IT and telecommunication, and automotive and transportation, among others. Growing adoption of these solutions in various sectors is anticipated to support the market growth over the forecast period.
“Predictive maintenance can be applicable to all industry verticals where machines produce significant amounts of data and require maintenance,” says the firm. “Industries namely automotive, aerospace, healthcare, manufacturing, process industries like chemicals, food and beverage, oil and gas can be transformed with the help of these solutions. Additionally, apart from the advantages such as reducing downtime, eliminating the causes of failure, and controlling repair costs, these solutions also employs non-intrusive testing techniques for evaluating and computing asset performance trends.”
Companies operating in the predictive maintenance market are also integrating sensor-based technologies with the predictive maintenance solutions to deliver an efficient solution to users. Additionally, the venders are also investing in R&D of remote maintenance solutions, which are anticipated to support maintenance activities in remote locations and also unsafe working conditions.
The report segments the global predictive maintenance market on the basis of solution (integrated vs standalone), service, deployment (cloud vs on premise), enterprise size, end use, and region. Key takeaways from the report include the following:
A rise in deployment of customized predictive maintenance solutions is the key trend driving the growth of the integrated segment
Training and consulting services segment is expected to reach $1,991.8 million by 2025 owing to the growing need for skilled workforce for operating predictive maintenance solutions
Demand for cloud-based predictive maintenance solutions is likely to witness a rise owing to cost-effectiveness and easy maintenance of produced data through these solutions
Small and medium enterprise segment is projected to witness remarkable growth over the forecast period owing to reduce the operational costs associated with downtimes
Growth of the aerospace and defense segment is being driven by the growing demand for effective flight operation and avoidance of mishaps or accidents due to failure of any component within the airplane
Asia Pacific predictive maintenance market is anticipated to witness the highest growth owing to rising adoption of deep learning and artificial intelligence technologies in the region
Key players operating in the market include IBM, Microsoft, SAP ERP, General Electric, Siemens, Schneider Electric, Software AG, Accenture, Honeywell International, and Cisco Systems.
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