Predictive Maintenance Market Research Report and Outlook by 2028

The predictive maintenance sector is anticipated to grow significantly, reaching a valuation of US$ 26,588.00 million by 2028, up from US$ 5,316.99 million in 2021, with a compound annual growth rate (CAGR) of 27.4% projected from 2022 to 2028.

Businesses are increasingly implementing advanced predictive maintenance solutions and services to enhance their competitive position and reduce unforeseen production interruptions. Various macroeconomic factors have contributed to a challenging landscape for companies worldwide. The COVID-19 pandemic, for example, caused major disruptions and led to considerable losses across multiple industries. Additionally, political instability in regions such as Europe, the Middle East, and South America has adversely affected numerous sectors. Economic downturns in several countries have also resulted in a decline in consumer spending on non-essential items.

In response to these challenges, enterprises are adopting strategies to maintain competitiveness amid economic and political uncertainties that have rendered business conditions less favorable. One effective strategy is the integration of technologies that minimize production downtime and facilitate continuous manufacturing processes. Predictive maintenance stands out as a technology that helps organizations cut costs while remaining competitive. Research indicates that offshore oil and gas firms can leverage predictive maintenance solutions to decrease downtime by 20%, which can lead to an increase in production exceeding 500,000 barrels of oil annually. This potential for efficiency is expected to drive growth in the predictive maintenance market throughout the forecast period.

Effects of the COVID-19 Pandemic on the Predictive Maintenance Market

The COVID-19 pandemic prompted governments to impose various restrictions to curb its spread, which negatively impacted the daily operations of many businesses. Companies encountered supply chain disruptions, travel limitations, and temporary closures, resulting in revenue losses and a reduction in technology investments. Consequently, many multinational corporations had to adapt their manufacturing processes to operate with fewer employees. This abrupt transition led to a heightened demand for predictive maintenance market solutions globally, as businesses increasingly embraced digital transformation to navigate the evolving business landscape. As a result, both large corporations and small to medium-sized enterprises (SMEs) are expected to allocate more resources towards identifying intelligent management solutions for their machinery and equipment. Moreover, the trend of remote work is likely to persist, creating a positive outlook for the growth of the predictive maintenance market in the post-COVID-19 era.

In the Asia Pacific region, countries like China, Japan, and India are leading the predictive maintenance market. China, in particular, is experiencing rapid growth and is among the fastest-growing markets globally. Chinese enterprises are keen adopters of technology within the Asia Pacific, striving to achieve a competitive advantage through digitalization and innovation. The rising demand for predictive maintenance solutions across sectors such as oil & gas, energy & utilities, transportation, and logistics presents substantial opportunities for market participants. For instance, in February 2022, Shentong Bombardier (Shanghai) Rail Transit Vehicle Maintenance Co., Ltd., a joint venture of Alstom in China, secured a contract valued at US$ 44.85 million from Shanghai Shentong Metro Group Co., Ltd. (Shanghai Metro) to deliver lifecycle maintenance services for 204 Movia metro cars and 34 trains, further stimulating the growth of the predictive maintenance market in China.
The solutions segment has been the primary revenue driver in the predictive maintenance market. These solutions are designed to meet the growing demand from various industries for energy-efficient, cost-effective operations, and data-driven decision-making supported by analytics tools. Companies in the predictive maintenance sector, such as Embitel and Cariad Group Company, offer solutions that integrate seamlessly with production systems and industrial assets.

Large organizations are increasingly seeking predictive maintenance solutions tailored to specific applications to manage and gather data for uninterrupted production through effective maintenance scheduling. The growth of this segment is further fueled by the ongoing efforts of solution providers to enhance fleet management, HVAC controls, and remote diagnostics for vehicles, among other applications. For example, Continental AG has launched ContiConnect 2.0, a predictive maintenance solution that offers both standard and premium packages for tire pressure and temperature management, along with insights into tread depth and tire condition.

The use of drones equipped with sensors and cameras to identify corrosion in grid insulators and provide predictive maintenance solutions across various industries is propelling the growth of the solutions segment and the overall predictive maintenance market. These drones utilize AI and 5G technology to relay insights, leading to increased demand for such innovations.

Key players in the predictive maintenance market include General Electric Co., Hitachi Ltd., IBM Corporation, Microsoft Corporation, PTC, Inc., SAS Institute Inc., Schneider Electric SE, Software AG, Syncron AB, and TOSL Engineering Ltd.

The size of the predictive maintenance market has been determined through a combination of primary and secondary research methods. The research process commenced with extensive secondary research to gather qualitative and quantitative data related to the market from both internal and external sources. This approach aimed to provide a comprehensive overview and forecast of the predictive maintenance market size across all segments. Additionally, industry participants and experts were consulted through multiple primary interviews to validate the data and gain deeper analytical insights. Participants included key personnel such as VPs, business development managers, market intelligence managers, national sales managers, external consultants like valuation experts, research analysts, and leading opinion leaders in the predictive maintenance field.