From servers to outcomes: The Software-as-a-Service shift transforming industrial reliability
What you’ll learn:
- Operating under the traditional deployment model for predictive maintenance, many OT teams find themselves deeply immersed in IT-based solutions.
- The rapid expansion and increased affordability of cloud computing have created a new model for delivering predictive maintenance at both the individual site and enterprise level.
- The most advanced predictive maintenance software delivers actionable recommendations, rather than just raw data.
Significant technological leaps have given process manufacturers access to a wide range of modern sensing technologies that provide improved feedback and thorough, detailed analysis. However, even as these technologies have improved dramatically, the deployment method has typically stayed much the same.
Predictive maintenance programs have traditionally relied on users buying hardware and software, sourcing servers, and installing and managing those systems internally.
That model works, but over the years it has introduced complexity and cost. Moreover, as organizations are increasingly forced to accomplish more with leaner teams, few of those teams still have the time or the skillsets to realize full value from their predictive maintenance investments on an ongoing basis.
Today, a new model of delivery is changing that paradigm. Modern cloud applications provide a shift in how reliability solutions are delivered and consumed, empowering manufacturers to shift away from IT tasks, instead focusing on the OT workflows that drive their productivity.
Traditional predictive maintenance is powerful, but time consuming
Operating under the traditional deployment model for predictive maintenance, many OT teams find themselves deeply immersed in IT-based solutions.
The users responsible for PM solutions typically need to understand not only how to deploy and maintain sensor technologies, but often also how to set up, configure, update, and maintain server-based systems or engage in lengthy requests for internal or third-party IT support.
This not only means setting up the systems and applying regular patches, but also managing users, permissions, backups, and troubleshooting.
Modern cloud applications provide a shift in how reliability solutions are delivered and consumed, empowering manufacturers to shift away from IT tasks, instead focusing on the OT workflows that drive their productivity.
Over time, servers age, operating systems change, expertise turns over, and organizations find themselves facing unexpected lifecycle costs to maintain their predictive maintenance systems.
These responsibilities often pull experts away from higher-value tasks, or they result in using outdated and less capable software versions much longer than desired.
Fortunately, a solution is at hand to address these and related issues.
Cloud computing upends traditional PM models
The rapid expansion and increased affordability of cloud computing have created a new model for delivering predictive maintenance at both the individual site and enterprise level.
Organizations that embrace cloud adoption now choose between hosting software in their own private cloud environment, or using third-party, provider-hosted applications as part of a software-as-a-service (SaaS) model.
This transition empowers reliability teams to focus on OT outcomes rather than IT infrastructure. Instead of managing local servers and software, the reliability team offloads system management responsibility to the automation solution provider, with centralized deployment, configuration, and patching.
When predictive maintenance software is hosted by a third-party automation solutions provider, new versions, updates, and patches are deployed automatically, ensuring that the system is evergreen by design.
Concrete, practical benefits of cloud reliability solutions
Instead of deploying predictive maintenance solutions on a local server, cloud systems empower users to access centralized software via a web browser. By design, the system is more portable and scalable, as users can securely access it from anywhere they have an internet connection.
This helps today’s more mobile digital workers stay informed about the health of their assets and plants, no matter where they may be. In addition, simplified availability helps teams easily expand solutions across their enterprise of facilities.
Configuration, database setup, and data modeling are also frequently included as part of a cloud predictive maintenance subscription. This not only simplifies the setup process but also streamlines expansion. Adding users, assets, or even additional sites no longer requires complex IT projects and capital spend.
Holistic SaaS solutions
As part of a SaaS predictive maintenance solution, organizations are increasingly opting to deploy fully end-to-end models, where hardware, software, analysis, and expertise are all bundled as part of a cloud solution. These organizations rely on automation solution providers to deliver automated analysis and human expert analysts as part of their cloud service.
Intelligent sensors, whether installed by the organization or deployed as part of the subscription by the automation solutions provider, send data to the cloud where automated analytics run continuously.
The most advanced predictive maintenance software delivers actionable recommendations, rather than just raw data, to eliminate the dependency for in-house vibration and other types of analysis expertise to gain predictive insights.
The results of those analytics are delivered via the SaaS predictive maintenance software. They can be reviewed by the organization’s reliability team to help prioritize action and deliver actionable results, and, when necessary, can also be reviewed by authorized experts from the automation solutions provider to detect more complex anomalies, alerts, and trends.
Leveling the playing field
One of the primary advantages of SaaS predictive maintenance is that it lowers the barrier to entry and smooths workforce challenges. Smaller sites can now deploy the same predictive technologies as large enterprises.
Moving to a SaaS model can increase an organization’s cybersecurity posture. Most OT teams do not have the expertise or desire to manage cybersecurity solutions.
Moreover, subscription models reduce upfront capital expenditure, and costs shift from CAPEX to OPEX, freeing capital for other priorities. Leveraging the cloud, nearly anyone can implement a PM program—not just sites with deep pockets and benches of expert analysts.
A SaaS predictive maintenance strategy can also smooth the peaks and valleys of staffing turnover. Cloud solutions help teams more easily embed expertise into the organization’s software, where it can be shared across the plant or across the enterprise, ensuring it is not lost due to workforce turnover.
A secure solution
Modern cloud SaaS predictive maintenance solutions are built with secure-by-design architecture. The software uses enterprise-grade security, where monitoring and advanced certifications are standard practice.
In many cases, moving to a SaaS model can increase an organization’s cybersecurity posture. Most OT teams do not have the expertise or desire to manage cybersecurity solutions, but they still need to provide access to critical software for more mobile digital workers.
SaaS solutions empower these teams to provide enterprise-wide access without complex networking projects or the need to manage firewalls, VPNs, and security infrastructure.
Focus on what matters most
The ubiquity of cloud technology has brought significant benefits to reliability teams. Cloud applications remove infrastructure burden from reliability programs, allowing teams to spend less time managing software and more time improving equipment health and overall operations.
SaaS solutions make predictive maintenance more accessible, scalable, and sustainable. Moreover, as reliability organizations modernize, cloud-based delivery models support long-term lifecycle value. A shift to cloud architecture can be the turning point for enabling better reliability outcomes with less complexity.
About the Author
Drew Mackley
Drew Mackley is director of sales enablement at Emerson Automation Solutions. He has over 25 years of experience in predictive maintenance and has worked with customers to establish and grow machinery health management programs in a variety of industries and locations around the world. He works with customers and other industry professionals to incorporate best practices and modern technologies in their asset monitoring digital transformation journey for efficiency, safety, and performance improvements.