At one manufacturing plant, spare parts were stacked beside a machine so technicians could repair it quickly when it failed. While the arrangement looked efficient, it revealed something else: breakdown had become part of the operating model.
Industrial asset management begins with a more fundamental question: What strategy will keep each asset performing its intended job, for its expected life, at the lowest practical cost? Technology can strengthen that strategy. But first, organizations must understand the assets themselves.
In this blog, we’ll take a deeper dive with some of the asset management questions we hear most often.
Let’s get past the dashboards and sensors: What does industrial asset management actually mean?
John Myers: Asset management is a series of asset- and system-level operational and maintenance strategies working together to provide a cohesive lifecycle management plan that’s easy to execute. Those strategies ensure an asset does the job it was purchased to do and performs as expected throughout its intended service life, at the lowest reasonable cost.
The appropriate strategy depends on the asset. Allowing a light bulb to fail before replacing it may be perfectly sensible. The same may be true of a remote pump with redundant equipment ready to take over. But a large, capital-intensive asset requires a very different approach.
You have to prioritize based on which assets have the greatest business impact, which assets are likely to fail, and what the consequences will be. That’s where asset management starts.
Why does reliability come first?
John: You can’t run an efficient plant if the equipment is unreliable. Every unplanned breakdown adds costs to what should be anticipated and scheduled.
Good maintenance means knowing what work an asset will require, when it should happen based on the unique conditions in your operations, which parts must be available, and who is qualified to perform it. You don’t send an electrician to do a plumber’s job, and you can’t assume the right person, part, or contractor will suddenly become available when something fails.
Condition monitoring and predictive analytics can improve that planning. But the basic maintenance strategies have to exist first.
Callie Reis: Planned downtime also improves an organization’s operational resilience, and that can translate directly into business value. When maintenance needs can be anticipated, teams can prepare for supply constraints, resource availability, and operational disruptions instead of responding under pressure. Your customers will often be willing to pay a premium if you can commit to consistently deliver on time and at cost, as it helps them to secure their own operational strategy.
How should organizations start to standardize their approach to asset management?
Callie: Even as the industry begins to step up their baseline digital maturity, in most cases you still need a seasoned expert to walk the shop floor and understand what the equipment actually does, its dependencies, its criticality in the process, and how it is currently maintained.
That knowledge is especially important with legacy or customized equipment, where years of practical experience may live inside the heads of a few employees. Organizations need to capture and codify that knowledge before they can apply consistent practices across a plant or scale them across multiple facilities.
This can be phased. The goal is to establish enough consistency so that people, systems, and data can work together effectively.
John: The operating context also matters. Two similar mine trucks can require different strategies because one continuously carries ore while another occasionally sprays water on a haul road. The equipment may be the same, but its criticality and application are different.
Standard strategies provide a starting point. They still need to be adapted to the asset’s real operating conditions.
How do connected systems improve asset performance?
Callie: The value comes from improved coordination. Asset data, process data, condition monitoring, maintenance systems, and enterprise systems each perform a different role in the orchestration.
When those systems are aligned, organizations can make faster decisions, coordinate work more effectively, plan the workforce, and integrate proactive maintenance into the operating plan. The value lies in the ability to make decisions based on a consistent set of contextualized real-time data.
John: And that last step—execution—is critical. Monitoring may tell you that a temperature or vibration threshold has been reached. Predictive analytics may tell you that a component is likely to fail in three weeks. But neither result improves reliability until it leads to properly planned work.
The maintenance system must translate that insight into a work order, check the schedule, identify the parts and resources, and trigger action. A dashboard can’t turn a wrench.
Where does AI genuinely fit in asset management?
Callie: Before an organization can advance their AI initiatives, it should strengthen those fundamentals we just discussed. The mistake would be to leapfrog by investing only in the newest solutions while overlooking gaps in the data layer made up of physical assets, maintenance processes, and workforce knowledge.
Once that’s in place, AI in asset management has massive potential, though we are just starting to scale use cases clearly tied to real business value. One early example is using agentic AI to generate timelines and risk-prioritized actions to be scheduled alongside the maintenance plan.
The result will also depend on the quality and context of the data. AI can assist the strategy, but it can’t supply decades of missing asset knowledge or maintenance discipline.
John: Exactly. AI can help optimize a response by looking across maintenance schedules, parts availability, workforce certifications, production requirements, and other constraints. It can help determine whether to move planned work forward or combine it with an existing shutdown.
But not every asset needs AI enablement. The operating environment is a good indicator of where value can or cannot be found. A highly repeatable production line is considerably more predictable than a mine where crushers, smelters, and fleets of heavy equipment operate under constantly changing conditions and might benefit from more advanced solutions.
Where should an industrial organization begin?
John: Start by identifying the critical assets. Understand what they do, how they are being used, what their failure modes are, and what strategies currently exist to maintain them.
Then assess the maintenance practices around those assets, from reactive and preventive maintenance through condition monitoring, lifecycle management, and predictive analytics. That tells you where the gaps are and where investment will make a measurable difference.
Callie: From there, organizations can build a phased roadmap connecting asset priorities with data, systems, people, governance, and business objectives. The roadmap should also account for change management. New processes deliver very little when people lack the time, skills, or incentive to adopt them.
The objective is practical progress to secure business and customer value, such as:
- More reliable assets
- More predictable operations
- Better use of resources
- Stronger performance over the complete asset lifecycle
To explore a structured approach to assessing asset-management capabilities and building a practical transformation roadmap, read the Schneider Electric™ Digital Transformation Advisory e-guide, From Downtime to Uptime: Transforming Industrial Asset Management.
About the author
John Myers – Asset Management & Reliability Consultant at Schneider Electric
John Myers is a recognized expert in the field of asset management and performance, specializing in digital transformation and maintenance reliability. With a proven background in life cycle costing and mineral, oil, and gas processing, John excels in MES layer solutions and IT/OT convergence. His extensive experience includes oil and gas program management, PMO management, and business process re-engineering. As a seasoned consultant, John has a proven track record in project management, business analysis, and ERP implementation. He is also a CMMS specialist, particularly in the mining and utilities sectors.
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