How Edge Infrastructure Unlocks Over $100M in Predictive Maintenance Savings

The Challenge of Scaling Predictive Maintenance

Industrial companies often find themselves trapped in successful pilots of predictive maintenance that fail to expand across multiple assets or plants. While initial implementations on critical equipment like compressors or turbines demonstrate up to 30% reductions in unplanned downtime, scaling these solutions encounters obstacles such as diverse hardware, inconsistent connectivity, and integration difficulties. These challenges stem from treating predictive maintenance primarily as a software problem, ignoring the complexities of varying equipment vintages and network architectures across facilities.

Managing Massive Data at the Edge

Industrial sensors generate huge volumes of data—sometimes several gigabytes per asset daily. Transmitting all this data to centralized cloud platforms results in latency and bandwidth issues, making real-time analytics impractical for time-sensitive operations. For instance, in oil and gas, a 20–30 minute warning for compressor failure can prevent catastrophic events, but cloud latency undermines this capability. Edge computing offers a solution by processing data locally, filtering what needs to be sent to the cloud, and enabling consistent, real-time analysis across diverse environments.

Integration Across Enterprise Systems

For predictive maintenance to deliver true value, the insights must integrate seamlessly with enterprise systems such as maintenance management, ERP, and production planning. This integration enables automated workflows that generate work orders, order parts, and schedule maintenance proactively. However, integration is complicated by differing legacy systems and protocols across plants. Successful companies develop a consistent integration layer, accommodating each facility's unique requirements and enabling scalable, automated maintenance processes.

Economic Benefits of Scaling with Edge Infrastructure

Initial predictive maintenance projects involve significant investments in hardware, connectivity, model development, and integration. Without a standardized edge infrastructure, these costs multiply with each new asset or location. Companies that build repeatable deployment models reduce incremental costs, transforming predictive maintenance from isolated projects into enterprise-wide capabilities. For example, savings of $300,000 on a single asset can scale to over $52 million across multiple plants.

Staying Competitive in the Industrial Landscape

The gap between companies adopting scalable predictive maintenance and those stuck in pilot phases is widening. With downtime costs escalating beyond millions per hour, leveraging edge infrastructure is becoming critical. Organizations recognizing edge computing as foundational for scalable industrial intelligence position themselves advantageously as maintenance evolves from predictive to prescriptive models. Implementing standardized edge infrastructure now ensures readiness for the next generation of industrial innovation.