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Global Market Study on Operational Predictive Maintenance: Manufacturing End User Segment Projected to Retain Its Dominance Through 2024

Report Description

Persistence Market Research’s newly published report focuses on the evolution of operational predictive maintenance. Four important topics have been discussed while studying the evolution of operational predictive maintenance – traditional monitoring, event correlation, performance analytics and predictive analytics. Persistence Market Research analysts have also analyzed the operational predictive maintenance life cycle by collecting and integrating data, generating predictive and statistical models, attaining analytical insights, displaying alerts and providing strategic recommendations to market players based on key market insights. The new publication titled “Operational Predictive Maintenance Market: Global Industry Analysis and Forecast, 2016 - 2024,” forecasts the global operational predictive maintenance market for a period of eight years and provides a detailed analysis of the market along with pertinent insights into the various factors driving the popularity of these solutions and services. This comprehensive market study provides an in-depth assessment of key stakeholder strategies and imperatives for succeeding in the business. The report segregates the global operational predictive maintenance market based on operational predictive maintenance solutions across different regions. The report on the global operational predictive maintenance market has been drafted with a view to providing a detailed market assessment for the forecasted period to help companies operating in the global operational predictive maintenance market devise effective business strategies. 

Report structure 

This report consists of an overview of the global operational predictive maintenance market in terms of value. In addition, it also includes an analysis of key trends, drivers and restraints from the supply, demand and economy side, which are influencing the global operational predictive maintenance market. Impact analysis of key growth drivers and restraints, based on the weighted average model is included in this report to facilitate clients with useful decision-making insights. The report provides detailed analysis of the global operational predictive maintenance market across various regions. It provides a market outlook for 2016–2024 and sets the forecast within the context of the global operational predictive maintenance market including latest technological developments as well as service offerings in the market. This study discusses key trends within regions contributing to the development of the global market, as well as analyzes the degree to which drivers are influencing this market in each region. Insights from this extensive market study are provided with an objective to enable market players to plan their differentiating strategies capable of evolving with the changing market landscape. 

A detailed analysis has been provided for every segment in terms of market size analysis for operational predictive maintenance across the different regions. The report provides a detailed analysis covering absolute dollar opportunity, incremental opportunity and BPS analysis. The report concludes with industry recommendations to companies planning to foray into the global operational predictive maintenance market.

Market segmentation 

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

 Research methodology

This report evaluates the present scenario and future growth prospects of the global operational predictive maintenance market across various regions for the projected period. The analysts have considered 2015 as the base year and provided data for the trailing 12 months.

To calculate the global operational predictive maintenance market size, the analysts have considered country wise adoption rates of predictive analytics across different verticals. Further, they have also analyzed the revenue contribution from predictive analytics software and services players operating in the global operational maintenance application field.  The forecast presented here assesses the total revenue by value across the market. In order to offer an accurate forecast, the report starts by sizing the current market, which forms the basis of how the global operational predictive maintenance market will develop in the future. Given the characteristics of the market, Persistence Market Research analysts have triangulated the outcome of different types of analyses based on adoption trends. In addition, it is imperative to note that in an ever-fluctuating global economy, the report not only conducts forecasts in terms of CAGR but also analyzes the market on the basis of key parameters such as year-on-year (Y-o-Y) growth to understand the predictability of the market and to identify the right opportunities across the market.

As previously highlighted, the global operational predictive maintenance market is split into a number of segments. All segments in terms of type, deployment model, end user and based on different regions are analyzed in terms of basis point share to understand individual segments’ relative contribution to market growth. This detailed level of information is important for identifying various key trends of the global operational predictive maintenance market. Also, another important feature of this report is the analysis of all key segments in terms of absolute dollar opportunity, critical in assessing the level of opportunity that a provider can look to achieve, as well as to identify potential resources from a sales and delivery perspective in the global operational predictive maintenance market.

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