Manufacturing operations are becoming more connected, automated, and data-intensive. Machines generate information continuously, production systems exchange data across departments, and maintenance teams have access to more operational history than ever before.

Yet more data does not automatically lead to better asset management. Manufacturers still need practical strategies for deciding which equipment deserves attention, when maintenance should occur, and how asset performance affects production goals.

Effective asset management brings these considerations together. It connects maintenance activities with equipment condition, operational priorities, resource availability, and long-term business objectives.

For modern manufacturers, the focus is increasingly shifting from simply maintaining equipment to making smarter decisions throughout the asset lifecycle.

1. Build a Stronger Foundation for Data-Driven Asset Management

A modern asset management strategy starts with reliable information.

Maintenance teams may have data stored across computerized maintenance management systems, enterprise resource planning platforms, machine sensors, spreadsheets, inspection reports, and operator logs. When these sources remain disconnected, it becomes difficult to develop a complete picture of asset performance.

Integrating relevant information creates a stronger foundation for decision-making.

For organizations already working with IBM Maximo environments, IBM Maximo AI can be considered as part of a broader effort to use asset and maintenance information more effectively. The important point is not simply adding AI to an existing system, but determining where intelligent analysis can help maintenance professionals make more informed decisions.

Establish a Reliable Asset Information Structure

Before introducing advanced analytics, manufacturers should establish consistent asset records.

Important information includes:

• Asset identification and hierarchy
• Equipment specifications
• Maintenance history
• Failure records
• Inspection results
• Operating conditions
• Spare-parts information
• Work-order history
• Asset criticality

Without consistent data, even sophisticated analytical tools can produce incomplete or misleading insights.

2. Prioritize Assets According to Operational Risk

Not every machine deserves the same level of monitoring or maintenance attention.

A low-cost auxiliary motor and a production bottleneck may both be important, but their failure consequences can be very different.

Asset criticality analysis helps organizations determine where limited maintenance resources should be focused.

Evaluate More Than Replacement Cost

A useful asset-criticality assessment can consider:

1. Impact on production
2. Safety consequences
3. Environmental risks
4. Repair and replacement costs
5. Availability of spare parts
6. Frequency of failure
7. Availability of backup equipment
8. Effect on product quality

This approach helps prevent maintenance teams from treating every asset equally.

For example, a machine that causes an entire production line to stop may justify condition monitoring and predictive analysis, while a noncritical piece of equipment may be better managed through preventive or run-to-failure maintenance.

3. Choose the Right Maintenance Strategy for Each Asset

There is no single maintenance strategy that works for every machine.

Modern manufacturing operations typically use a combination of reactive, preventive, predictive, and reliability-centered approaches.

Match Strategy to Asset Behavior

A simple framework can help:

Run-to-failure: Appropriate for assets that are inexpensive, noncritical, and easy to replace.

Preventive maintenance: Useful when components have predictable service intervals or manufacturers provide established maintenance schedules.

Condition-based maintenance: Appropriate when equipment condition can be monitored effectively.

Predictive maintenance: Valuable when historical and real-time data can reveal degradation patterns before failure.

Reliability-centered maintenance: Useful when multiple maintenance strategies need to be evaluated against safety, operational, and cost requirements.

The objective is not to make every asset predictive. It is to use the most appropriate strategy for each asset.

4. Use Condition Monitoring to Detect Changes Earlier

Condition monitoring provides a way to observe equipment behavior continuously or at regular intervals.

Depending on the equipment, teams may monitor vibration, temperature, pressure, current, flow, lubrication conditions, energy consumption, or other operating variables.

Focus on Trends Rather Than Isolated Readings

A single abnormal reading may not indicate a serious problem.

A trend can be more informative.

For instance, a motor may show a gradual increase in vibration over several weeks. When that trend is considered alongside operating load and maintenance history, it may indicate developing mechanical degradation.

This allows technicians to investigate before the problem becomes a major production event.

The value comes from connecting measurements with decisions. Data should help answer what changed, why it may have changed, and what action should follow.

5. Improve Work Planning With Better Asset Information

Good asset management does not stop at identifying potential problems.

Maintenance teams also need to plan the work.

An early warning can provide valuable time to verify the issue, source parts, assign technicians, and coordinate the intervention with production.

Connect Detection With Execution

Consider a critical pump showing signs of declining performance.

A practical response could include:

• Reviewing recent condition data
• Checking previous repair records
• Inspecting the equipment
• Confirming the suspected failure mode
• Checking spare-parts availability
• Estimating labor requirements
• Coordinating a maintenance window
• Recording the completed work

This process reduces the gap between identifying an issue and actually resolving it.

A maintenance organization becomes more effective when information flows naturally from monitoring into planning and execution.

6. Use Historical Maintenance Data to Find Recurring Problems

Maintenance history can reveal patterns that are difficult to identify when work orders are viewed individually.

Repeated component replacements, recurring failure codes, frequent emergency work, and unusually high maintenance costs may indicate underlying reliability problems.

Look for Root Causes

Suppose several machines repeatedly experience similar bearing failures.

Instead of treating every failure as an independent maintenance event, the organization can investigate whether the failures are related to:

• Misalignment
• Excessive loads
• Lubrication practices
• Installation methods
• Operating conditions
• Component quality
• Maintenance procedures

Finding the underlying cause can reduce repeated corrective work.

Historical data can therefore support both day-to-day maintenance decisions and broader reliability improvements.

7. Integrate Maintenance With Production Planning

Maintenance decisions rarely happen in isolation.

Production schedules, customer demand, inventory levels, staffing, and planned shutdowns can all influence when equipment should be serviced.

Coordinate Maintenance Windows

Suppose condition data suggests that a component may need replacement within the next several weeks.

Immediately stopping production may not be the best option.

Instead, maintenance and production teams can assess the remaining operating window and determine whether the work can be scheduled during an existing shutdown.

This type of coordination allows organizations to consider equipment condition alongside operational requirements.

The result is a more balanced decision rather than a simple choice between “repair now” and “wait until failure.”

8. Give Maintenance Teams Better Decision Support

Technology should make maintenance work easier to understand, not create another layer of complexity.

If technicians have to move between multiple systems to understand an asset’s condition, maintenance history, work orders, and recommendations, valuable time can be lost.

Present Relevant Information Clearly

A useful maintenance workflow should make important information easy to find.

For a critical asset, technicians may need to see:

• Current condition
• Recent trends
• Open work orders
• Previous failures
• Recent inspections
• Recommended actions
• Relevant maintenance procedures
• Parts availability

The objective is to give people the information they need at the point of decision.

NIST research on maintenance technology emphasizes the importance of combining digital tools with human problem-solving rather than separating technology and maintenance expertise.

9. Keep Human Expertise in the Decision Loop

Advanced analytics can identify patterns, but industrial environments often contain variables that are difficult to capture completely in structured data.

An experienced technician may know that a particular machine behaves differently during seasonal temperature changes or that an unusual reading is associated with a specific production setup.

Combine Technology With Experience

A practical decision process can follow this model:

Data → Analysis → Recommendation → Expert review → Action → Outcome

This allows digital systems to support human expertise rather than replace it.

It also creates an opportunity for continuous learning. When technicians record what happened after an alert or recommendation, that outcome becomes part of the organization’s future knowledge base.

10. Measure Asset Management Performance

A strategy needs measurable outcomes.

Tracking the number of connected assets or completed digital initiatives can show implementation progress, but it does not necessarily demonstrate operational improvement.

Choose Meaningful KPIs

Manufacturers can monitor metrics including:

• Unplanned downtime
• Mean time between failures
• Mean time to repair
• Emergency maintenance percentage
• Preventive maintenance compliance
• Maintenance backlog
• Repeat failure rate
• Equipment availability
• Maintenance cost per asset
• Spare-parts availability

The most useful KPIs depend on the organization’s goals.

If production interruptions are the primary concern, availability and downtime may be more important. If maintenance costs are rising, repeat failures and emergency work may deserve greater attention.

11. Scale Asset Management Gradually

Manufacturers do not need to transform every asset simultaneously.

Large-scale implementations can become difficult to manage when organizations attempt to introduce new technologies, processes, and workflows across an entire facility at once.

Start With a Defined Use Case

A practical starting point could be:

1. Select a critical asset group.
2. Identify the primary maintenance problem.
3. Review available data.
4. Establish a performance baseline.
5. Introduce the appropriate monitoring or analytics capability.
6. Measure the outcome.
7. Refine the process.
8. Expand to additional assets.

This approach creates an opportunity to learn before scaling.

It also helps organizations distinguish between technology that looks promising in theory and technology that actually improves maintenance decisions in their operating environment.

12. Treat Asset Management as a Continuous Improvement Process

Modern asset management should evolve as the organization learns more about its equipment.

New operating conditions, equipment upgrades, production changes, and maintenance outcomes can all influence future decisions.

Review the Strategy Regularly

Teams should periodically ask:

• Which assets generate the most downtime?
• Which maintenance activities prevent recurring failures?
• Are predictive alerts producing useful actions?
• Are technicians receiving the right information?
• Which assets need additional monitoring?
• Are maintenance strategies still appropriate?
• What lessons can be applied to similar equipment?

Regular reviews keep asset management aligned with actual operating conditions rather than allowing maintenance strategies to remain unchanged for years.

Conclusion

Modern manufacturing asset management is increasingly about making better decisions with the information already available.

A strong strategy combines reliable asset data, criticality assessment, appropriate maintenance methods, condition monitoring, effective work planning, historical analysis, and human expertise. Advanced technologies can strengthen these processes, but they work best when connected to clearly defined operational needs.

Manufacturers that focus on the right assets, measure meaningful outcomes, and improve their processes gradually can build a more resilient approach to asset management. The goal is not to automate every maintenance decision. It is to give teams better information and a clearer basis for deciding what should happen next.

Frequently Asked Questions

1. What is the best asset management strategy for modern manufacturing?

There is no single strategy for every asset. Manufacturers should combine preventive, predictive, condition-based, reactive, and reliability-centered approaches according to asset criticality, failure behavior, operational impact, and available data. The right strategy is the one that balances reliability, safety, maintenance resources, and production requirements.

2. How can AI support manufacturing asset management?

AI can analyze large amounts of equipment and maintenance data to identify patterns, unusual conditions, and potential risks. It can support prioritization and decision-making, but maintenance professionals should review important recommendations and consider operational factors that may not be captured in the underlying data.

3. How should manufacturers begin improving asset management?

Start with a specific operational problem and a small group of important assets. Review data quality, establish a baseline, select an appropriate maintenance strategy, and define measurable KPIs. Once the approach demonstrates practical value, organizations can refine the process and gradually expand it to other assets.