Operational predictive maintenance market

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2. OPERATIONAL PREDICTIVE MAINTENANCE MARKET ANALYSIS • 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) - Size, Share, Outlook, and Opportunity Analysis, 2019 - 2027

3. Market Overview • Operational predictive maintenance refers to a practice of determining the risk of failures in an operational process. In this, predicative analytics software and services access multiple data sources in real-time, in order to identify asset failure and quality issues in product development process. Moreover, predicative analytics software evaluates failure patterns and minor variances, in order to determine the risk of failures in operational process in order to reduce or avoid future losses. In other words, this software helps in improving device and equipment uptime. Operational predictive maintenance services are comprised 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 Mnin terms of value by the end of 2019

4. Market Dynamics- Drivers • Growing awareness regarding maintenance operation and reducing asset downtime is expected to drive growth of the global operational predictive maintenance market during the forecast period • In industrial facilities, managers are constantly focused on enhancing maintenance process at manufacturing plants and other operating ecosystems. Facility manager can prevent virtual downtime with predictive maintenance, especially when the equipment is not operating to its maximum potential Moreover, predictive maintenance system can control various types of data including temperature, energy use, output, run time, etc. to enhance decision making and operations at manufacturing plants or manufacturing units. This, in turn, is accelerating the adoption of operational predictive maintenance solutions at manufacturing facilities and thereby driving the market growth in the near future. For instance, according to Schneider Electric’s study, an operational predictive maintenance system can facilitate significant operational benefits, connected devices, helping in manufacturing facilities through its network, and monitoring facilities in order to minimize the downtime of a manufacturing plant. • Growing implementation of new technologies is expected to propel the global operational predicative maintenance market over the forecast period • Technology-based ERP software solutions are developed for enterprises for predictive maintenance and to enhance their efficiency. Numerous energy-based manufacturing plants have implemented operational predictive maintenance solution. Moreover, many public sector and manufacturing companies are rapidly implementing new technologies, in order to predict failures and reduce losses related to it. These factors are expected to boost the market growth in the near future. Furthermore, advancements in technology have made predictive maintenance cost-effective and easily available for manufacturers. Since IoT is connected to every industry including logistics, transportation, utility, automotive, manufacturing, etc. many enterprises are increasingly adopting operational predictive maintenance solutions, in order to minimize downtime and optimize efficiency by allowing key vendors to invest in the global operational predictive maintenance market for innovation in services. • North America region dominated the global operational predictive maintenance market in 2019, accounting for 42.4% share in terms of value, followed by Europe, and Asia Pacific respectively.

5. Market Dynamics- Restraints • High initial cost and complexity related to software coupled with lack of skilled workers are expected to restrain growth of the global operational predictive maintenance market during the forecast period • Advancements in technology have allowed numerous vendors to offer operational predictive maintenance solutions with latest algorithms and technology. However, many of these new vendors entering the market have to sustain high initial cost. Moreover, complexity associated with software, hardware, and systems integration combined with low availability of skilled resources is major factors expected to hinder the global operational predictive maintenance market during the forecast period. • Various challenges related to data integration are expected to hamper the global operational predictive maintenance market over the forecast period • There are various challenges associated with data integration that restrict implementation of operational predictive maintenance solution in various industry verticals such as manufacturing plants, especially in emerging economies such as China, India, and others. These factors are expected to restrain growth of the global operational predictive maintenance market growth in the near future.

6. Market Opportunity • Advancement in cloud-based solution targeting new industries is expected to pose lucrative business opportunity for market players in the near future • Rapid adoption of cloud computing, Internet of Things, artificial intelligence, mobile devices, cognitive systems, and machine learning has provided real-time data processing, in order to provide efficient services in the shortest time possible anytime and anywhere. Moreover, there is significant demand for cloud-based analytical tools with many several key players already offering ready-to-run online services, which do not require installation. These factors are expected to present lucrative business opportunity for market players in the near future. • Growing adoption of operational predictive maintenance solutions by small and mid-sized enterprises (SMEs) • With the development of solutions with updated algorithms, the service providers are offering bundled services with solutions, which is more affordable for customers. Increasing number of key players has led to high availability of such solutions with bundled services. Moreover, SMEs are rapidly deploying such bundled services. Many market players are offering novel bundled solutions, in order to attract SMEs, which in turn, is presenting major business opportunities for market players in the region. • Global operational predictive maintenance market, by end-user segment, public sector, automotive, manufacturing, healthcare, energy & utility, transportation, others. manufacturing sub-segment was accounted for 28.2% market share in 2019 and is expected to grow at a CAGR of 22.4% between 2019 and 2027.

7. Market Trends • Partnerships and mergers and acquisitions among key market players • Key companies in the market are focused on partnerships and mergers and acquisitions, in order to enhance their market presence. For instance, in September 2016, Fluke Corporation, an electronic test tools and software providing company, acquired eMaint Enterprises LLC. In September 2016, Robert Bosch GmbH partnered with SAP SE, in order to expand their product portfolio based on IoT and Industry 4.0, and cloud technologies and software solutions. • New product launches by key market players • Key players in the market are involved in product development and launches, in order to expand their product portfolio and gain competitive advantage in the market. For instance, in April 2016, SAS Institute, Inc. introduced Viya, an analytics and visualization architecture platform for businesses, for government agencies and other organizations for a single, open, and cloud-ready architecture.

8. 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 • Major companies in the market are involved in collaborations and agreements, in order to enhance their market presence. For instance, in April 2017, GE Digital, a subsidiary of General Electric Company, collaborated with Huawei Technologies Co. Ltd. to launch Industrial Cloud-based Predictive Maintenance Solution. • Key players in the market are focused on partnerships and collaborations, in order to gain competitive advantage in the market. For instance, in August 2019, Arrow Electronics partnered with IBM Corporation and National Instruments to offer a predictive maintenance suite that facilitates data-driven operations in oil & gas plants, mines, and factories.

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