Generative AI in Asset Management is the Real Frontier for Industrial OEMs

A few years back a leading industrial OEM specializing in advanced pumping systems faced a crisis. Their largest client—a petrochemical plant—relied on the OEMs pump for critical operations. As these pumps were designed to operate under critical pressure and were used to ensure smooth transportation of volatile chemicals across the plant, the failure of the pump was a critical loss for the plant. Everything was fine until one afternoon a sudden failure occurred in one of the pumps and the plant’s operations were halted. Results? Millions of dollars went directly into the drain.

After investigations, it was revealed there were unusual vibrations and undetected crack which led to the machine failure. This just not disrupted the operation of the plant but also posed significant safety risks and lost relationship between the OEM and its customer. What else could be concluded from this, except, this was a WAKE-UP CALL!

If the OEM had to safeguard their customer relations, just protecting the assets isn’t enough, they need to enable a system that could predict a failure and also maximize the asset value. Within weeks of realising the importance of the situation, the OEM adopted an AI-powered APM solution, deploying it across all critical assets at the customer sites. The APM solution detected unusual vibrations, heat spikes, and other issues in different pumps and provided alerts for the maintenance team to address the possible issues way before they turned catastrophic. Assuaging the guilt of what they could have done previously, they made up for the lost dollars with an AI-powered APM in place.

Over the next few years, the APM solution enabled timely repairs and reduced the unplanned downtime by almost 75%. For customers, the OEM was now not just an equipment seller but a business that focused on selling services.

Similar to the above instance, majority of industrial OEMs are under constant pressure to deliver the highest quality assets to their customers while maximizing production and profitability. But issues like equipment downtime, lack of real-time asset visibility into asset health, and inefficient processes can reduce productivity as well as increase the cost expenditure. That’s where they need an Asset Performance Management solution backed by AI.

Traditional APM Solutions Are Not Enough

For decades, businesses have relied on Asset Performance Management solutions relying on scheduled maintenance and reactive approaches. While this strategy has been fruitful and delivered value, it is inherently limited because of human intervention.

Businesses handling data sets manually and relying on outdated predictive models have faced:

• ‍Escalated Downtime Costs: With global downtime costs reaching $50 billion annually, traditional APM methods are no longer sufficient for them.‍
• Resource Inefficiencies: Businesses often maintain non-critical assets while neglecting those at higher risk of failure. ‍
• Siloed Data: A lack of integration across systems creates blind spots that limit strategic decision-making.

As a result, what OEMs are left with is a patchwork solution that leaves them as laggards not leaders.

What The Market Leaders Say

AI Is The Next Big Thing In Transforming APM

With the rising competition and the uniquely posed customer expectations, Industrial OEMs have no option but to harness the power of AI in asset management to optimize operations, extend asset lifecycle, and maximise the complete potential of their assets. The only way to capitalize on their physical assets is using the capabilities like AI powered remote monitoring, computer vision, and predictive maintenance. Yet, most of them are still grappling with integrating the latest technologies.

Here’s why:

1. The Power of Predictive Analytics
   From shifting to the idea of “Oh my assets have failed, and I need to fix them” to forecasting when thy might fail, it is a step worth considering. This allows businesses to:
   
   • Reduce Costs: By eliminating unnecessary maintenance tasks.
   • Minimize Downtime: Through precise failure predictions and timely interventions.
   • Enhance Safety: By identifying risks before the assets fail.
2. Actionable Insights at Scale
   Traditional APM solutions often struggle with larger datasets. But this is resolved with the use of AI in Asset Management Solutions. From identifying data patterns to gaining real-time insights, OEMs can manage assets globally.
3. Autonomous Operations
   AI enables assets to self-optimize and, in some cases, self-heal. This means fewer human interventions and more consistent performance, especially in harsh environments like offshore drilling or mining.

Building A Future Proof APM Strategy With AI

After years of research, OEMs have finally understood it's not just about maintaining the assets but unlocking the full potential—from visibility to ensuring long-term operational resilience. With AI leading the charge, they finally have a fail-proof strategy to develop APM Solutions. But isn’t it just theoretical unless they know the “How?”

Here’s an actionable roadmap to building a future-proof APM strategy backed by AI.

Step 1: Begin with understanding what you have currently

Before your start implementing AI, it is crucial to assess where your organization stands today. The very first step is to map your existing processes to identify your current asset management workflows.

• Highlight gaps or inefficiencies, such as reactive maintenance practices or data silos, that may hinder optimization.
• Evaluate your data readiness by auditing the quality and availability of your operational data.
• Ensure that IoT-enabled assets or other mechanisms for data collection are in place to support AI-driven insights.
• Finally, benchmark your current Asset Performance Management (APM) practices against industry standards.

Step 2: Have a defined goal in mind

Without a clear objective, AI implementation can lead to missed opportunities and wasted resources. Therefore, it is essential to have a well-defined roadmap for where your APM strategy is headed. Having these objectives in place ensures that your AI-driven APM initiatives remain focused and deliver measurable results.

• Start by setting operational goals, such as reducing downtime and extending asset life.
• Establish financial goals, including decreasing maintenance costs and improving the return on investment (ROI) for high-value assets.
• Additionally, align your efforts with strategic goals, such as enhancing customer satisfaction through improved reliability and achieving sustainability targets by optimizing energy usage.

Step 3: Choose the AI platform you need

You can’t have the right AI-driven APM strategy without the right platform in place. An Application Enablement Platform like Flex83 is the ideal choice for OEMs as it offers the unique features tailored to their different needs. Flex83 is cloud-agnostic, enabling deployment across any cloud, edge, or hybrid environment. Its portability allows seamless movement of applications between platforms as required. The platform ensures full control over your intellectual property(IP) and offers extensive customization to meet specific operational requirements. With a modular design, Flex83 ensures scalability, growing with your business and adapting to new challenges and opportunities. Unlike rigid, one-size-fits-all solutions, Flex83 provides unparalleled flexibility, scalability, and seamless integration with existing systems to support your transformation journey.

Step 4: Begin with a pilot

To minimize risks and ensure success, start with a controlled pilot program. Begin by selecting high-impact assets, focusing on critical or high-value equipment with measurable performance data. Define clear success metrics, such as mean time between failures (MTBF), maintenance cost reduction, and uptime improvement, to evaluate the pilot’s effectiveness. Use this phase to test and learn, monitoring results and refining your approach before scaling the solution across the organization. This iterative process allows you to address any challenges early and will be the foundation for the next steps you will be making in the AI-driven APM strategy.

Step 5: Scale and optimize

Once the pilot program proves successful, the next step is to scale and optimize the solution. Rollout the AI-driven APM strategy across all relevant physical assets and geographies, ensuring consistent implementation. Continuously improve the system by leveraging feedback loops from your customers to refine AI algorithms and operational practices. Add more value with AI features such as prescriptive/predictive maintenance and self-healing systems to unlock next-level optimization. This approach ensures that your APM strategy remains dynamic and evolves with your business needs.

Step 6: You can’t do it without your team

Technology alone is not enough to achieve success, a prepared and skilled team is equally important.

• Begin by training your workforce to interpret AI insights and take appropriate actions based on them.
• Promote cross-functional collaboration by breaking down silos between maintenance, operations, and IT teams, fostering a culture of shared goals and teamwork.
• Encourage experimentation and innovation within your organization, creating a mindset of continuous improvement.

By equipping your team with the right skills and fostering collaboration, you can maximize the potential of your AI-driven APM strategy and drive sustainable success.

Practices For AI-Driven APM Implementation

As assets become more complex than ever, AI-powered asset management for businesses are no longer a luxury but a necessity for businesses. With AI, you gain the capabilities of capturing data, integration, analytics, and visualization, all tied together to have the maximum reliability and availability of your assets.

1. Prioritize Data Readiness
   High-quality data is the full-course meal for AI to perform at its best. Without a robust data foundation, even the most advanced AI models will fall short. Follow these steps to prepare your data:
   a. Break Down Data Silos
   
   • Integrate data from IoT sensors, ERP systems, and SCADA platforms.
   • Ensure seamless data flow across departments.
   b. Standardize Data Formats
   
   • Use taxonomy and ontology frameworks to ensure consistency.
   • Eliminate duplicate or redundant data entries.
   c. Embrace Real-Time Analytics
   • Invest in tools that enable real-time data processing and insights.
   ‍
2. Startwith High-Impact Assets
   Not all assets are created equal. Begin your AI-driven APM journey by focusing on the assets that:
   
   • Are Critical to Operations: High-value machinery or processes that directly impact output.
   • Have a History of Failures: Assets with frequent breakdowns or costly maintenance requirements.
   • Provide Rich Data: Equipment equipped with IoT sensors or detailed maintenance logs.
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3. Align Stakeholders Early
   The success of AI-driven APM depends on cross-functional collaboration. Engage key stakeholders early to:
   a. Build Consensus
   
   • Highlight the benefits of AI-driven APM to leadership and operations teams.
   • Use data to illustrate potential ROI.
   b. Train Your Teams
   
   • Provide hands-on training to help employees understand AI-generated insights.
   • Address any resistance by showing how AI enhances—not replaces—their expertise.
   c. Foster Cross-Functional Collaboration some text
   • Create workflows that integrate maintenance, operations, and IT teams.
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4. Leverage Predictive and Prescriptive Capabilities
   AI-driven APM excels at both predicting potential failures and recommending optimal solutions. Maximize these capabilities by:
   a. Monitoring Continuously
   
   • Use AI algorithms to analyze real-time performance data and detect anomalies.
   • Flag potential issues before they escalate into failures.
   b. Automating Responses
   
   • Setup workflows to automatically adjust operating parameters or schedule maintenance tasks.
   • Prioritize prescriptive insights that suggest actionable next steps.
   ‍
5. Measure, Optimize, Repeat
   AI-driven APM isn’t a one-time project—it’s an ongoing process of refinement and improvement. Ensure continuous optimization by:
   a. Defining Key Metrics
   • Track KPIs such as Mean Time Between Failures (MTBF), downtime reduction, and cost savings.
   • Use these metrics to assess the effectiveness of your AI-driven APM strategy.
   b. Gathering Feedback
   • Regularly collect input from operators and maintenance teams.
   • Use their insights to improve AI models and processes.
   c. Updating AI Models
   • Incorporate new data to refine predictive and prescriptive algorithms.
   • Stay agile by adapting to evolving asset conditions and business needs.

Designing An APM Architecture That Scales To Million Devices

Once you know you have a defined APM strategy in place and you have managed to create your own APM solution, this is where you need to think of scalability.

Reports suggest most of the APM projects fail at pilot because scalability is a major challenge. Building an architecture that provides the flexibility to scale your APM is crucial. Here are the steps you can follow:

1. Assess Current Capabilities
   
   • Evaluate your existing infrastructure and identify bottlenecks.
   • Use Flex83 to benchmark performance and scalability.
2. Plan for Modular Expansion
   
   • Design your APM platform with modular components that can scale independently.
   • Prioritize features like edge computing and cloud-agnostic deployment.
3. Implement Edge-to-Cloud Integration
   
   • Deploy edge nodes for local processing of critical data.
   • Ensure seamless integration with centralized cloud systems for global insights.
4. Optimize AI Workflows
   
   • Automate data processing and decision-making.
   • Continuously train models with new data to improve accuracy.
5. Ensure Security at Scale
   
   • Implement end-to-end encryption and access controls.
   • Regularly audit systems to maintain compliance with industry standards.

Making your APM solution scale-ready for millions of devices is no longer a smaller feat but having the right architecture is something that needs to be achieved. By leveraging Flex83’s cloud-agnostic, AI-powered, and modular design, OEMs can build an APM solution that grows with their business needs.

Decomplexify Your Asset Performance Journey With AI

AI isn’t just another tool in the APM arsenal—it’s a transformative enabler of operational excellence. Organizations that embrace AI today will not only out perform their peers but also future proof their operations against the challenges of tomorrow. The question isn’t whether to adopt AI-driven APM; it’s whether you can afford not to.

The clock is ticking.

Your competitors are already leveraging AI to reduce costs, improve efficiency and deliver unparalleled value. Don’t get left behind.