The “Global Operational Predictive Maintenance Market, by Type (Software, and Services (Implementation and Integration, Training & Support, and Consulting)), by Deployment Model (On-premise, and Cloud-based), by End User (Public Sector, Automotive, Manufacturing , Healthcare, Energy & Utility, Transportation, and Others) and by Geography (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa) - Global Forecast to 2027”, was valued at US$ 1,138.4 million in 2019, witnessing a CAGR of 23.2% during the forecast period (2019-2027), as highlighted in a report published by Coherent Market Insights.

Overview

Predictive maintenance techniques aids to determine the condition of the in-service equipment, in order to predict when maintenance should be performed. Predictive maintenance practices ensure cost-savings over time or routine-based preventive maintenance, as these tasks are performed only when required. Moreover, operational predictive maintenance system can identify failure patterns and minor issues, in order to determine which assets and operational practices are that are at risk of failure. Operational predictive maintenance services consist of integration and implementation, consulting services, and training & support. These services optimize operational processes by supporting operational departments with the help of advanced analytics software in daily business process.

The global operational predictive maintenance market was estimated to account for US$ 1,138.4 Mn in terms of value by the end of 2019

Market Driver

Entry of emerging key players in the market is expected to propel growth of the global operational predictive maintenance market during the forecast period

Due to absence of entry barriers, several new key players are entering the global operational predictive maintenance market providing high growth potential. These organizations are offering cost-effective solutions, which has compelled established players to provide solutions at affordable cost or offer add-on services with the solutions. This, in turn, is expected to accelerate the adoption operational predictive maintenance solution thereby driving the global operational predictive maintenance market growth in the near future.

Market Opportunity

Providing real-time condition monitoring is expected to pose lucrative business opportunity for market players

Although operational predictive maintenance provides vital insight to enterprises, addition of real-time monitoring offers immediate data required for maintenance. Adoption of real-time monitoring can offer crucial information regarding potential failures and breakdowns in a manufacturing unit, thereby saving cost and unnecessary time period for maintenance. Hence, providing real-time condition monitoring is expected to present significant growth opportunity for market players during the forecast period. 

Market Restraint

Lack of skilled resources is expected to restrain growth of the global operational predictive maintenance market during the forecast period  

Performing operational predictive maintenance practices require well-trained and skilled resources. Such skilled workers are scarcely found. Moreover, new vendors have to incur high initial costs and deal with complexity of software, hardware, and systems integration, which in turn, is expected to restrain growth of the global operational predictive maintenance market during the forecast period.

Market Trends

  1. Increasing demand for segregation of IoT and big data is a major ongoing trend in the market

Currently, a vast volume of date is collected in real-time regarding performance of equipment. Internet of Things (IoT) facilitates stream analytics, in order to obtain data simultaneously from multiple data points and aggregate for analysis in real-time. For instance, enterprises can visualize and analyze equipment performance date minimizing downtime and conduct several maintenance operations. This is owing to enterprise software supporting smart devices and IoT. This trend is expected to continue during the forecast period.

  1. Mergers and acquisitions among major market players is another trend in the market

Major players in the global operational predictive maintenance market are involved in merger and acquisition activities, in order to gain competitive advantage in the market. For instance, in August 2014, PTC Inc. acquired Axeda Corporation, which is a company that offers solutions to connect machines and sensors on cloud, in order to improve its IoT-based offerings and enhance consumer experience.

Competitive Landscape

Competitive Section

Key companies in the global operational predictive maintenance market are General Electric Company, IBM Corporation, eMaint Enterprises LLC, Software AG, Schneider Electric SE, SAS Institute Inc., Rockwell Automation Inc., PTC, Inc., and Robert Bosch GmbH.

Key Developments

  1. Key players in the market are involved partnerships and collaborations, in order to gain competitive edge in the global market. For instance, in September 2019, Schneider Electric SE, a France-based provider of electrical equipment, partnered with BASF, a Germany-based chemicals company, for implementation of EcoStruxue Asset Advisor at its new electrical substation plant in Texas, U.S.
  2. Major companies in the market are focused on collaborations and partnerships, in order to enhance the global market presence. For instance, in April 2019, Siemens AG partnered with SAS Institute Inc. to deliver embedded IoT analytics for edge and cloud. The combination of SAS Analytics and Siemens’ MindSphere can increase productivity and reduce operational risk through predictive maintenance.

Segmentation

Market Taxonomy:

  1. By Type
  • Software
  • Services
  • Implementation and Integration
  • Training & Support
  • Consulting
  1. Deployment Model
  • On-premise
  • Cloud-based
  1. By End User
  • Public Sector
  • Automotive
  • Manufacturing
  • Healthcare
  • Energy & Utility
  • Transportation
  • Others
  1. By Region
  • North America
  • Latin America
  • Asia Pacific
  • Europe
  • Middle East & Africa
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