Predictive Maintenance Market with COVID-19 Impact Analysis by Component (Solutions, Services), Deployment Mode (On-premises, Cloud), Organization Size (Large Enterprises, SME), Vertical Region and forecast till 2027.

Predictive Maintenance Market with COVID-19 Impact Analysis by Component (Solutions, Services), Deployment Mode (On-premises, Cloud), Organization Size (Large Enterprises, SME), Vertical Region and forecast till 2027.

Predictive Maintenance Market

The predictive maintenance market is expected to grow at a compound annual growth rate (CAGR) of 30.6 percent during the forecast period, from USD 4.2 billion in 2021 to USD 15.9 billion by 2026. Increasing enterprise marketing and advertising spending, a changing landscape of customer intelligence to drive the market, and the proliferation of customer channels are all expected to drive the adoption of predictive maintenance technologies and services. Predictive maintenance techniques are intended to assist in determining the condition of in-service equipment and estimating when maintenance should be performed. Because tasks are performed only when warranted, this approach promises cost savings over routine or time-based preventive maintenance.

Drivers: Utilization of emerging technologies to gain valuable insights is increasing.

Continuous advancements in big data, machine-to-machine (M2M) communication, and cloud technology have opened up new avenues for investigating data derived from industrial assets. Sensors, cameras, and other connected devices generate a massive amount of data for IoT devices. The data, on the other hand, has no value unless it is converted into actionable, contextual information. Through batch processing and offline analysis, big data and data visualisation techniques enable users to gain new insights. Real-time data analysis and decision-making are frequently performed manually; however, to make it scalable, it is preferable to be performed automatically. The primary function of AI technology is to investigate massive amounts of data generated by various components of the IoT ecosystem and transform it.

Restraints: Inadequate skilled labour

To deploy AI-based IoT technologies and skillsets, trained workers are required to handle the most recent software systems. As a result, existing employees must be trained on how to operate new and upgraded systems. Furthermore, industries are quick to adopt new technologies; however, they face a shortage of highly skilled and proficient workers. As the majority of global vendors organise predictive maintenance projects, the demand for a highly skilled workforce grows. Companies must develop expertise in areas such as cybersecurity, networking, and application development. Furthermore, they want to use IoT data to predict outcomes, prevent failures, optimise operations, develop new products, and provide advanced analytics competency, which includes AI and ML. These technologies would be critical in lowering overall emissions.

Impact of COVID-19

COVID–19 has altered the global dynamics of business operations. Though the COVID–19 outbreak exposed flaws in business models across industries, it also provided several opportunities for companies to digitalize and expand their operations across borders as adoption and integration of technologies such as cloud, AI, analytics, IoT, and blockchain increased during the lockdown period. During the first and second quarters of 2020, the retail and manufacturing sectors experienced significant declines in business performance. However, with the availability of vaccines and significant control of the pandemic achieved, these sectors are expected to see increased investment throughout the forecast period as predictive maintenance solutions gain prominence across various business functions.

Segmentation By Component:

By Component the market is segmented into Solution & Services. The services are likely to show a rapid growth during the market forecast period.

Segmentation By Deployment Mode:

Based On the deployment mode the market is segmented into On-Premises and Cloud. The on-premise deployment type dominated the overall predictive maintenance market size in 2019 and is expected to maintain its dominance throughout the forecast period. This is due to the modularity of its sensors and the ease with which it can be deployed in pre-existing equipment. Cloud-based predictive maintenance solutions, on the other hand, are expected to grow at the fastest rate during the forecast period due to direct IT control, remote accessibility, internal data delivery and handling, faster data processing using advanced predictive analytics, efficient resource utilisation, and cost-effectiveness.

Segmentation By Organization sizes:

Based on the organization sizes the market is segmented into small & medium enterprises and large-scale enterprises.

Segmentation By Vertical:

Based on the vertical segmentation the market is segmented into Government and Defense Manufacturing, Energy and Utilities, Transportation and Logistics, Healthcare and Life sciences and Other Verticals. In 2019, the manufacturing sector dominated the global predictive maintenance market share. Manufacturing equipment maintenance, such as machinery, pumps, elevators, and industrial robots, faces a number of challenges, which is why predictive maintenance has grown in popularity in the industry. The healthcare sector, on the other hand, is expected to grow at the fastest rate in the future, owing to the fact that predictive maintenance of healthcare equipment such as X-ray, MR, tomography, and mammography is one of the most important considerations for hospitals looking to improve decision-making capabilities and operational efficiencies.

Segmentation by Region

Based on region, the NGS services market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. During the forecast period, North America will have the largest market size.

In the predictive maintenance market, North America is expected to have the largest market share. The increasing technological advancements in the region are key factors favouring the growth of the predictive maintenance market in North America. The increasing number of predictive maintenance players across regions is expected to drive market growth even further.

Competitive Landscape:

The key players covered in the market include The major vendors in the global predictive maintenance market include Microsoft(US), IBM(US), SAP(Germany), SAS Institute (US), Software AG (Germany) .

Industrial Development:

Google Cloud announced BigQuery Omni powered by Anthos for Multi-Cloud Analytics in July 2020. BigQuery Omni gives enterprises the flexibility they need to break down silos and generate actionable business insights without having to pay expensive egress fees when migrating data from other cloud providers to Google Cloud.

Schneider Electric launched EcoStruxureTM TriconexTM Safety View in July 2021, the industry's first dual safety- and cybersecurity-certified bypass and alarm management software application, which allows operators to see both the bypass status, which affects the level of risk reduction in place, and the critical alarms required to operate the plant safely when risks are high.

SAS Institute launched its SAS Viya platform in May 2021 to strengthen the foundation for data and analytic success by incorporating new data management solutions into its powerful, cloud native SASViya platform.

Market taxonomy

By Component:

• Solutions

Integrated

Standalone

• Service

Managed Services

Professional Services

System Integration

Support and Maintenance

Consulting

By Deployment Mode:

• On-premises
• Cloud

Public Cloud

Private Cloud

Hybrid Cloud

By Organization Size:

• Large Enterprises
• Small and Medium-sized Enterprises (SMEs)

By Vertical:

• Government and Defense
• Manufacturing
• Energy and Utilities
• Transportation and Logistics
• Healthcare and Life Sciences

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa