Predictive Maintenance Goes Mainstream
Paving the way for Industrial Analytics and Integrated Smart Factory
Predictive maintenance, the monitoring of machines to prevent breakdowns and extend their life spans, is not a new concept and it has been in use long before the IoT settled in. However, the complexity and cost of the technology kept it at a premium for many manufacturers, hardly making the case for mass-scale implementation.
The Industrial IoT transformation has made things more straightforward and dramatically lowered the barriers for the predictive maintenance implementation. Rather than focusing on single-minded measures, IIoT ties up the predictive maintenance practices to overall factory automation and cloud enablement. In this way, there is a compound effect achieved by Industrial IoT deployments, which also include asset management, customer usage analytics, industrial analytics, and other use cases.
With that strategic approach, smart electric grid, and smaller, cheaper sensors for vibration, thermal, acoustic and other mechanical analysis, predictive maintenance value is set to soar to over $500 billion by 2025, according to McKinsey Global Institute. Most importantly, the manufacturing industry is likely to be totally reshaped into a digital powerhouse that brings new, better jobs, cuts down emissions, and drives up the GDP all across the globe.
Predictive Maintenance Improves Outcomes in a Wide Variety of Industries
In May 2015, Hadoop published a case study on predictive maintenance for the oil and gas industry. The report showed that use of predictive maintenance led to an increase in efficiency and savings. In addition, since the industry involves the use of dangerous, remote and difficult-to-see equipment, the study revealed that preventive maintenance had helped companies avoid safety concerns. A more targeted yet similar report on Santos Ltd., a leading oil and gas producer in Australia and Asia, indicated that identifying the most pressing issues with predictive maintenance helped the company to avoid preventable failures.
Predictive maintenance has also proved useful in remote monitoring of other types of equipment, like elevators and vehicles in a fleet. Using this practice has helped companies reduce downtime for repair, improve resource planning, predict costs and create a well-managed maintenance schedule. Predictive maintenance gives companies the opportunity to improve internal management and realize benefits for their customers. It also allows companies to improve relations with contractors who engage in repairs and maintain equipment.
Predictive maintenance has the potential to benefit the global economy in many ways. For example, in many industries like the oil and gas industry, the economic slowdown led to a reticence to fix aging equipment. Now that the economy has picked up, companies are eager to create more effective maintenance programs. Predictive maintenance allows them to do that at a lower cost than adhering to traditional repair schedules.
From Predictive Maintenance to Industrial Analytics
Predictive maintenance goes hand in hand with Industrial Analytics, a methodology that collects, reviews and quantifies data about business operations. In order for predictive maintenance to work correctly, the software program must retrieve the right data. It must then relate that data to current concerns, such as “What might happen?” and “What action should be taken?” to a piece of equipment. This is one of the challenges related to the IIoT.
There is currently a growing number of Industrial Analytics solutions but the biggest trouble for manufacturers is to apply them for some practical use cases that bring good ROI. While IIoT technology is already there, businesses are not sure how to start with it. In this regard, predictive maintenance has proven to be one of the best candidates - it offers calculable ROI and requires relatively modest technological effort.
The study by the Digital Analytics Association e.V. Germany (DAAG), conducted among 151 analytics professionals in industrial companies in 2016/2017, suggests that predictive maintenance is the most important application for Industrial Analytics for upcoming years. One third of the respondents also claimed increased revenue as the biggest benefit from the Industrial Analytics for their companies.
Industrial IoT Cloud for Integrated Smart Factory
Predictive maintenance is the Industrial IoT staple but, on a larger scale, it is just one link in the chain of evolution towards an integrated smart factory, the holy grail of the Industry 4.0. To achieve this goal, handling integration between new IoT solutions is as critical for manufacturing businesses as creating smart, snappy IoT applications and sensors themselves.
The modern Industrial IoT stack is taking necessary advancements to bring into order sporadic use cases and task-specific solutions so that a smart factory concept steers clear from being an intimidating technology amalgam. An Industrial IoT cloud provides the benefit of centralized solution orchestration, data and user management, and straightforward IoT innovation. Moreover, manufacturers have a unique capability to grow into strong IoT technology companies by growing their own IoT cloud ecosystems and standing out from the rest with modern cloud services for customers and business partners.
Predictive maintenance is already on the factory floor but it’s success as that of an IIoT pioneer is also a good role model for greater convergence of sensors, machines, robots, and clouds. The best is yet to come.