Global Predictive Maintenance Market - Insights
Global predictive maintenance market is estimated to be valued at US$ 2,123.8 Mn in 2018, up from US$ 1,639.2 Mn in 2017. By 2026, the market is projected to reach US$ 17,700.8 Mn, exhibiting a CAGR of 30.3% over the forecast period (2018–2026). Increasing adoption of predictive maintenance in automotive aftermarket, growing industrial automation, increasing demand for periodic maintenance (for operations, assets, and production) are some major factors contributing in growth of the global predictive maintenance market.
On the basis of component, the global predictive maintenance market is segmented into solutions and services. On the basis of deployment, the market is segmented into on-premise and cloud. On the basis of application, the market is segmented into asset maintenance, operations maintenance, and health & performance optimization. On the basis of end-use industry, the market is segmented into oil & gas, aerospace & defense, automotive, power generation, hospitals/clinics/diagnostic laboratories, biotechnology/pharmaceutical manufacturers, medical device manufacturers, and shipping.
Manufacturing industries are in constant need for improving performance, reliability, and service management. Data acquisition, monitoring, and analytics aids in achieving better reliability and service management. Data-based predictive models help in boosting performance of manufacturing process and offers improved manufacturing quality. Therefore, such benefits of predictive maintenance increasing its adoption in manufacturing industries. For instance, a global aircraft design and manufacturing company, implemented IoT WoRKS by HCL for predictive maintenance.
Automotive companies use predictive maintenance to collect and monitor real-time data. The collected real-time data is used for gaining insights of the vehicle and engine parts. For instance, in 2017, Mercedes Benz implemented predictive maintenance for real-time data gathering, which allowed the company to acquire data from vehicle when it is being used by the customer. It aids the company to predict any incident or failure regarding the vehicle.
The solution segment held dominant position in the market in 2018
Solution segment held dominant position in the market in 2018 and it is expected to retain its dominance over the forecast period. Emerging economies such as India and China in Asia Pacific are expected to witness significant growth in the solution segment in the predictive maintenance market. This is owing to growing manufacturing sector and increasing industrial automation across various industry verticals in the region such as automotive and aerospace & defense. For instance, according to India Brand Equity Foundation, 2018, the manufacturing sector in India witnessed a CAGR of 4.34% during FY12 and FY18 (as per the second advance estimates of annual national income published by the Government of India).
A basic predictive maintenance solution has a well -designed process model, which includes connected equipment, feature engineering, machine learning, implementation, and prediction. A smart maintenance predictive solution deals with capturing data and offers automated feature generation and optimized predictive model with real-time estimates and outcomes. Moreover, growth of smart maintenance predictive solutions segment is attributed to increasing number of connected assets throughout industrial applications. For instance, according to World Economic Forum, in 2018, less than 20% of assets were connected worldwide and the number is rapidly surging due to increasing automation across various industry verticals.
Figure. Global Predictive Maintenance Market, by Component, 2017 & 2026 (US$ Mn)
Source: CMI, 2018
North America held dominant position in the predictive maintenance market in 2017
North America held dominant position in the market in 2017 and is expected to retain its dominance throughout the forecast period. This is owing to high presence of manufacturing companies and increasing demand for lean manufacturing across different industry verticals in the region. New startup companies deploy and adopt predictive maintenance to optimize the overall production process and make the operations cost-effective.
Figure. Global Predictive maintenance Market Share (%), by Region, 2017 & 2026
Source: CMI, 2018
The U.S. accounted for the largest share in North America and the global predictive maintenance market in 2017, and is projected to retain its dominance during the forecast period. This is owing to increasing investment by major companies to increase profitability and to achieve cost optimization.
In North America predictive maintenance market, the manufacturing segment held dominant position, followed by aerospace & defense, and energy & utilities sector. Key players operating in the North America predictive maintenance market include, Robert Bosch, General Electric, and Rockwell Automation.
Global Predictive Maintenance Market: Key Players
Major players operating in the global predictive maintenance industry include, Amiral Technologies, Embitel, General Electric, Honeywell International, Inc., IBM Corporation, Microsoft Corporation, Rockwell Automation, Inc., Senseye Ltd., Softweb Solutions, Inc., Warwick Analytics, SAP SE, Robert Bosch GmbH, Software AG, and Siemens AG.
Predictive maintenance techniques are aimed to help users in determining the condition of in-service equipment to evaluate accurate time for maintenance. The adoption of predictive maintenance is mainly driven by cost-effective results achieved by their use. Predictive maintenance allows convenient scheduling of corrective maintenance, which prevents equipment failure.
Market Drivers: Benefits of Predictive Maintenance in Automotive Industry
Automation and predictive maintenance finds application in automotive industry. Implementation of predictive maintenance aids in production process and after sale maintenance. Several automobile companies are implementing predictive maintenance in post-sale maintenance to offer better and cost effective service to the customers. For examples, Nissan’s Predictive Forward Collision Warning feature, which analyses the speed and distance to the vehicle travelling ahead of the Nissan vehicle as well as that of the next preceding vehicle, by utilizing sensors.
An automobile contains large number of sensors that collect data about speed, emissions, fuel consumption, usage data for resources, security, and about car and engine parts. All the collected data can be utilized to find patterns and resolve quality issues either before time or prevent issues from arising. This reduces cost of quality management and increases customer satisfaction. Therefore, increasing industrial automation that includes data acquisition and processing is expected to boost growth of the market.
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