Big Data Analytics in Global Condition Monitoring, Forecast to 2023

  • ID: 4296377
  • Report
  • Region: Global
  • 64 Pages
  • Frost & Sullivan
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Rise of New Business Models Through Focus on Software

The application of Big Data analytics in the condition monitoring market is at a nascent stage. Current solutions offered by vendors are only able to analyze condition data such as vibration. The true value of big data will be realized when analytics service providers are able to offer solutions by combining condition and process data (SCADA and PLC data).

The condition monitoring market is gradually changing. In the past, this market was highly hardware driven. The needs of customers are evolving as they look for a more holistic solution that combines hardware, software, and services.

Hardware is becoming increasingly commoditized and product differentiation is diminishing. The main areas of innovation are in software and data analytics, which will represent future opportunities in which companies can invest.

Traditional condition monitoring hardware companies are struggling to develop the right market approach and business model. The transition from a hardware company to a subscription-based services company has been a challenge for most condition monitoring vendors. In the process of growth in condition monitoring, predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. The main goal is to allow convenient scheduling of corrective maintenance and to prevent unexpected equipment failures.

By installing sensors on key assets and analyzing the data, maintenance teams know that equipment needs maintenance, maintenance work can be better planned (spare parts, people, and so on), and what would have been an unscheduled breakdown is transformed to shorter and fewer planned maintenance, thus, increasing plant availability.

Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with a negative impact on the environment, and optimized spare parts handling.

While predictive maintenance is still in its infancy, there is already talk about moving to prescriptive maintenance, where experts can recommend actions based on desired outcomes, taking into account specific scenarios, resources, and knowledge of past and current events.

All this has been possible through the introduction of Big Data analytics to the world of condition monitoring.

Additionally, because of an aging workforce and the lack of skilled personnel, customers are turning to their hardware providers for additional support. Opportunities in design, installation, maintenance, data collection, and diagnostic services have created alternate revenue streams for condition monitoring equipment companies.

Data analytics has the potential to save billions of dollars in annual operating expenses for businesses by analyzing historical and real-time data to predict faults with greater statistical accuracy.

Condition monitoring equipment companies are expected to be more than hardware solution providers, with software and data analytics services being critical requirements for customers.

Key Questions this Study will Answer:

  • Is the global Big Data analytics market for condition monitoring applications growing? How long will it continue to grow and at what rate?
  • What are the key growth drivers and restraints for the market? Are there any potential future challenges that could restrict the growth of this market?
  • Which are the leading participants? What best practices are they following to tap into the market for Big Data analytics?
  • Which regions are expected to provide the highest growth potential for the future? Can North America sustain its position as a key revenue contributor in the face of rising global competition?
  • Are the services offered today meeting customers’ needs or is additional development needed?
  • Which applications and industries will drive the demand for predictive maintenance services? Will traditional end-user industries continue to be the biggest revenue generators?
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1. Executive Summary
  • Key Findings
  • Key Conclusions and Future Outlook
  • Market Engineering Measurements
  • CEO’s Perspective
2. Research Scope and Segmentation
  • Research Scope
  • Segment Definitions
  • End-user Industries Covered
  • Research Methodology
  • Key Questions this Study will Answer
3. Drivers and Restraints-Big Data Analytics in Condition Monitoring Applications Market
  • Market Drivers
  • Drivers Explained
  • Additional Key Enablers for Advanced Analytics in Condition Monitoring Applications Market
  • Market Restraints
  • Restraints Explained
4. Forecasts and Trends-Big Data Analytics in Condition Monitoring Applications Market
  • Market Engineering Measurements
  • Forecast Assumptions
  • Revenue Forecast
  • Revenue Forecast Discussion
  • Revenue Forecast Discussion-Breakdown of Services
  • Current Application of Big Data Analytics in Condition Monitoring
  • Evolution of Big Data in Condition Monitoring Applications
  • The Next Evolution-Prescriptive Analytics
  • Snapshot of Manufacturing Versus Other Sectors
  • Percent Revenue Forecast by Region
  • Revenue Forecast by Region
  • Revenue Forecast by Region Discussion
  • Revenue Forecast by Vertical Market
  • Revenue Forecast by Vertical Market Discussion
5. Market Share and Competitive Analysis-Big Data Analytics in Condition Monitoring Applications Market
  • Competitive Landscape
  • Case Study-BP Using GE’s Predix Platform
  • Case Study-Mtell’s Prescriptive Analytics Platform
  • Case Study-Siemens’ Remote Maintenance Solution
  • Case Study-National Instruments and IBM Partnership
  • Competitive Factors and Assessment
6. Growth Opportunities and Companies to Action
  • Growth Opportunity-Improving Production Efficiency
  • Growth Opportunity-Technology Advancement
  • Strategic Imperatives for Success and Growth
  • TIES Project-5 Major Growth Opportunities for Condition Monitoring
7. Evolving Business Models
  • Taxonomy of Business Models
  • Taxonomy of B2B Business Models
  • Service-based Model-PaaS, Platform as a Service, and DaaS
  • Fee-based Model-Pay Per Use, Renting/Leasing, and Subscription Model (SaaS)
  • Evolving Business Models
  • Case Study-Rolls-Royce
8. The Last Word
  • The Last Word-3 Big Predictions
  • Legal Disclaimer
9. Appendix
  • Market Engineering Methodology
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  • BP
  • GE
  • IBM
  • Mtell
  • Siemens
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