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