Why this matters now

A strong reliability program has long been tied directly to improved production, safety, and operational performance. Today’s sensing technologies deliver unprecedented visibility into asset health, enabling teams to keep critical equipment operating 24x7x365.

Yet as sensing technologies have advanced, the underlying delivery model for reliability software has often remained unchanged. Many teams are still expected to deploy, manage, and maintain on-premises servers—introducing complexity, cost, and skills requirements that fall outside core reliability expertise.

In a recent article in Smart Industry magazine, Drew Mackley draws on his decades of experience with reliability solutions to explain why the traditional approach is increasingly unsustainable. The traditional model works, he explains,

“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.”

Takeaway: Traditional, infrastructure-heavy delivery models have become a barrier to fully realizing predictive maintenance value.

TL;DR

• Traditional predictive maintenance systems add IT complexity and cost.
• Lean reliability teams lack time and skills to manage infrastructure.
• SaaS models shift responsibility away from internal IT resources.
• Subscription pricing improves scalability and financial flexibility.
• Cloud delivery makes advanced reliability accessible to more organizations.

Enter the cloud

Cloud technology has fundamentally changed how predictive maintenance solutions can be delivered and consumed. Rather than owning and managing infrastructure, organizations can now rely on cloud platforms operated and maintained by the automation solutions provider.

“Modern cloud applications provide a fundamental shift in how reliability solutions are delivered and consumed, empowering manufacturers to shift away from IT tasks, instead focusing on the operational technology (OT) workflows that drive their productivity.”

Organizations can choose between private cloud hosting or full software-as-a-service (SaaS) delivery, depending on their requirements:

“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.”

In practice, Emerson experts deploy and maintain applications such as AMS Machine Works, AMS Device Manager, and AMS Optics, so reliability teams can stay focused on asset performance rather than infrastructure.

Takeaway: SaaS delivery removes infrastructure burden and lets reliability teams focus on outcomes.

How far can I go?

For organizations seeking a near hands-off reliability model—or those with limited access to expert analysts—SaaS can extend beyond software delivery to include analytics and expertise.

“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.”

Takeaway: SaaS reliability can bundle software, analysis, and expertise into a single operating model.

Reliability solutions, no matter the size

Historically, advanced reliability programs were limited to the largest organizations with deep benches of expert personnel. SaaS predictive maintenance is changing that dynamic.

“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. 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 predictive maintenance program—not just sites with deep pockets and benches of expert analysts.”

By capturing expertise in cloud systems, SaaS reliability solutions also help organizations retain institutional knowledge even as experienced personnel retire or transition roles.

Takeaway: SaaS reliability democratizes predictive maintenance while preserving expertise.