The predictive maintenance market in SAM is expected to grow from US$ 274.62 million in 2019 to US$ 736.89 million by 2027; it is estimated to grow at a CAGR of 13.3% from 2020 to 2027.
Improved asset management for every vertical is increasingly needed. Solution providers equipped with machine learning (ML) and artificial intelligence (AI) can collect and make meaningful insights into the vast amount of customer-related data as internet of things (IoT) generates a massive amount of data from connected devices. Moreover, to optimize various aspects of service delivery, such as quality assessment and predictive maintenance, AI can also be integrated with IoT devices without any human intervention. Inputs from actuators, sensors, and other control parameters in real-time would predict embryonic asset failures and help companies monitor and take prompt action in real-time, further driving the demand for predictive maintenance. The inputs from various control parameters help predict asset failures. This is among the other factors expected to positively influence the demand for predictive maintenance.
The technology industry is one of the victims of COVID-19, and since the start of 2020, this industry has been reflecting the declining trend. With the imposition of lockdown across the SAM region, the trades have been witnessing shattering experience due to the unavailability of retailers, suppliers, online and authorized sales representatives in the market. The region is projected to register a swift decline in their supply of predictive maintenance components and have halted their manufacturing activities and subsequently disrupting the importing of parts and equipment.
Based on industry, the manufacturing segment led the SAM predictive maintenance market in 2019. Traditionally, maintenance professionals used to combine both quantitative and qualitative techniques, to predict impending failures and mitigate downtime in their manufacturing plants. Predictive maintenance provides them with the potential to optimize maintenance tasks in real-time, maximizing their equipment's useful life while avoiding operational disruption. For organizations producing products on a mass scale, predictive maintenance is an impressive way to reduce defects in products and eliminate waste. For those who provide parts and machinery, predictive maintenance is commonly used to set the technology for monitoring and inspecting the condition of the moving apparatus and motors. Productivity, power, state of health, and wear within are all effectively monitored. The growing need to maintain production equipment, such as industrial robots, machinery, elevators, and pumps, to reduce overall downtimes drives the adoption of predictive maintenance solutions and services in the manufacturing industry. The growing automation in the manufacturing sector, coupled with Industry 4.0, is expected to boost the demand for predictive maintenance to protect the high-end equipment from damage, which ultimately drives the SAM predictive maintenance market.
The overall SAM predictive maintenance market size has been derived using both primary and secondary sources. To begin the research process, exhaustive secondary research has been conducted using internal and external sources to obtain qualitative and quantitative information related to the market. The process also serves the purpose of obtaining overview and forecast for the SAM predictive maintenance market with respect to all the segments pertaining to the region. Also, multiple primary interviews have been conducted with industry participants and commentators to validate the data, as well as to gain more analytical insights into the topic. The participants who typically take part in such a process include industry experts, such as VPs, business development managers, market intelligence managers, and national sales managers, along with external consultants, such as valuation experts, research analysts, and key opinion leaders specializing in the SAM predictive maintenance market. Key players operating in the SAM predictive maintenance market include Hitachi, Ltd.; IBM Corporation; Microsoft Corporation; PTC Inc.; Schneider Electric SE; SAS Institute Inc.; and General Electric Company.