Leveraging Simple LLMs for Asset Management and Risk Analysis Opportunities

When a critical system goes down because of a failed component, it exposes gaps in how we manage and understand our assets. Recently, during a DDS call, a technician revealed that a failed part was not normally stocked and had a life expectancy of 3 to 5 years. This information was not readily available but might be buried in the system manual. This situation highlights a common challenge: how can teams better capture and analyze asset data to anticipate risks before failures happen?

One practical approach is to use simple large language models (LLMs) to analyze asset lists, manuals, and documentation. Even without a full organizational AI system, these tools can help identify potential risks and improve maintenance planning. This post explores how teams can listen for opportunities like this, use LLMs effectively, and build a culture of continuous improvement in asset management.

Why Asset Data Often Falls Short

Many organizations track assets with basic spreadsheets or databases listing serial numbers, purchase dates, and locations. However, critical details like component life expectancy, maintenance history, or manufacturer warnings often remain in manuals or separate documents. This disconnect leads to:

• Unplanned downtime when parts fail unexpectedly

• Stock shortages because teams don’t know which parts to keep on hand

• Inefficient maintenance schedules that don’t reflect actual wear and tear

  
  

For example, the technician’s insight about the 3 to 5-year life expectancy was not in the main asset list but could be found in the manual. Without linking these sources, teams miss valuable information that could prevent failures.

How Simple LLMs Can Help Analyze Asset Information

Large language models like GPT can read and understand unstructured text, such as manuals, emails, or notes. By feeding an LLM with asset lists and related documents, teams can:

• Extract key data points like manufacture dates, warranty periods, and expected lifespans

• Identify risks based on component age or known failure modes

• Generate summaries or alerts for upcoming maintenance needs

• Cross-reference multiple documents to fill gaps in knowledge

  
  

For instance, uploading a list of assets with manufacture dates alongside scanned manuals allows the LLM to highlight parts approaching or exceeding their expected life. This insight supports proactive replacement and stocking decisions.

Steps to Implement LLM-Based Asset Risk Analysis

1. Gather Asset Data

Collect existing asset lists, including manufacture dates, serial numbers, and locations. Ensure data is as complete and accurate as possible.

2. Collect Documentation

Assemble manuals, maintenance logs, warranty papers, and any other relevant documents related to the assets.

3. Prepare Data for LLM Input

Convert documents to text format if needed. Organize asset lists and documents in a way that the LLM can process together.

4. Run Analysis with LLM

Use an LLM to extract relevant information from manuals and compare it with asset data. Ask the model to identify components nearing end-of-life or with known failure risks.

5. Review and Act on Insights

Validate the LLM’s findings with technical experts. Update maintenance schedules, stock lists, and risk registers accordingly.

6. Train Team Members

Teach staff how to maintain asset lists with manufacture dates and how to use LLM tools for ongoing analysis.

Listening for Opportunities to Improve Asset Management

The example from the DDS call shows how valuable insights often come from frontline technicians or unexpected conversations. To build a culture that captures these opportunities:

• Encourage open communication where team members share observations and concerns

• Set up regular reviews of asset performance and failures

• Use simple tools like LLMs to analyze data without waiting for complex AI systems

• Document lessons learned and update asset records continuously

  
  

By staying alert to these moments, organizations can improve reliability and reduce costly downtime.

Practical Benefits of Using Simple LLMs Today

Even without a full AI infrastructure, simple LLMs offer tangible advantages:

• Cost-effective: No need for expensive custom AI solutions

• Flexible: Can analyze diverse documents and formats

• Scalable: Works with small teams or large organizations

• Accessible: Many LLM tools are user-friendly and require minimal technical skills

  
  

For example, a maintenance team can upload a batch of manuals and asset lists monthly to get updated risk reports. This process helps prioritize inspections and parts ordering.

Challenges and Considerations

While promising, using LLMs for asset risk analysis requires attention to:

• Data quality: Incomplete or inaccurate data limits usefulness

• Model limitations: LLMs may misinterpret technical details without proper prompts

• Security: Sensitive asset information must be protected when using cloud-based LLMs

• Human oversight: Experts should always review AI-generated insights before action

  
  

Addressing these challenges ensures the approach adds value without introducing new risks.

Building a Continuous Improvement Cycle

To maximize benefits, integrate LLM analysis into a continuous improvement cycle:

• Update asset lists and documentation regularly

• Run LLM analysis on a set schedule or after incidents

• Share findings with maintenance and procurement teams

• Adjust stocking and maintenance plans based on insights

• Collect feedback to refine data collection and analysis methods

  
  

This cycle helps teams stay ahead of failures and manage assets more effectively.