Big Data Analytics in Global Condition Monitoring, Forecast to 2023

Rise of New Business Models Through Focus on Software

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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.

Although condition monitoring companies will continue to invest in software development and improve their condition data analysis capability, it is likely that they will partner or acquire a big data analytics company to provide their customers with a holistic solution rather than develop this capability in house.
Big data is expected to play a more comprehensive role to improve predictive and prescriptive maintenance, manufacturing, supply chain, sales , design and R&D
Big Data will help create new growth opportunities and entirely new categories of companies. Traditional condition monitoring companies will be incapable of handling such large volumes of data and may look to partner with Big Data experts such as IBM, HP, and Oracle among others.
Big Data revenue is expected to exponentially rise to a billion-dollar market to $2.9 billion by 2023

 

Table of Contents

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
3. Drivers and Restraints—Big Data Analytics in Condition Monitoring Applications Market
Market Drivers
Additional Key Enablers for Advanced Analytics in Condition Monitoring Applications Market
Market Restraints
4. Forecasts and Trends—Big Data Analytics in Condition Monitoring Applications Market
Market Engineering Measurements
Revenue Forecast
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 Vertical Market
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
3 Big Predictions
Legal Disclaimer
9. Appendix
Market Engineering Methodology
Legal Disclaimer

Infographic



List of Figures & Charts

1. Big Data Analytics in Condition Monitoring Applications Market: Key Conclusions and Future Outlook, Global, 2016 and 2023
2. Big Data Analytics in Condition Monitoring Applications Market: Key Market Drivers, Global, 2017–2023
3. Big Data Analytics in Condition Monitoring Applications Market: Key Market Restraints, Global, 2017–2023
4. Big Data Analytics in Condition Monitoring Applications Market: Market Engineering Measurements, Global, 2016
5. Big Data Analytics in Condition Monitoring Applications Market: Revenue Forecast by Region, Global, 2014–2023


1. Big Data Analytics in Condition Monitoring Applications Market: Market Engineering Measurements, Global, 2016
2. Big Data Analytics in Condition Monitoring Applications Market: Revenue Forecast, Global, 2014–2023
3. Big Data Analytics in Condition Monitoring Applications Market: Revenue Contribution by Service Type, Global, 2016 and 2023
4. Big Data Analytics in Condition Monitoring Applications Market: Current Application of Big Data Analytics in Condition Monitoring, Global, 2016
5. Big Data Analytics in Condition Monitoring Applications Market: Evolution of Big Data in Condition Monitoring Applications, Global, 2000–2020
6. Big Data Analytics in Condition Monitoring Market: Snapshot of Manufacturing Versus Other Sectors, Global, 2016
7. Big Data Analytics in Condition Monitoring Applications Market: Percent Revenue Forecast by Region, Global, 2014–2023
8. Big Data Analytics in Condition Monitoring Applications Market: Revenue Forecast by Vertical Market, Global, 2014–2023
9. Big Data Analytics in Condition Monitoring Market: Taxonomy of B2B Business Models, Global, 2016



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