6 ways generative AI can optimize asset management
5 February 2024
3 min read
Jen Stevenson IBM

Every asset manager, regardless of the organization’s size, faces similar mandates: streamline maintenance planning, enhance asset or equipment reliability and optimize workflows to improve quality and productivity. In a recent IBM Institute for Business Value study of chief supply chain officers, nearly half of the respondents stated that they have adopted new technologies in response to challenges.

There is even more help on the horizon with the power of generative artificial intelligence (AI) foundation models, combined with traditional AI, to exert greater control over complex asset environments. These foundation models, built on large language models, are trained on vast amounts of unstructured and external data. They can generate responses like text and images, while simultaneously interpreting and manipulating existing data.

Let’s explore 6 ways generative AI can optimize your enterprise asset management operations, including field service, maintenance and compliance. Generative AI can:

1. Generate work instructions

Field service technicians, maintenance planners and field performance supervisors comprise your front-line team. They require job plans and work instructions for asset failures and repairs. Using a hybrid AI or machine learning (ML) model, you can train it on enterprise and published data, including newly acquired assets and sites.

Through interactive dialog, it can generate visual analytics and promptly deliver content to your team. Access to this knowledge can boost field service uptime by 10%–30% and increase first-time fix rates by 20%, resulting in cost savings, improved worker productivity and increased client satisfaction.

2. Increase the efficiency of work order planning

Work orders drive activity, relying on work plans and job plans to authorize and provide resources to handle tasks. The process itself, although straightforward, is time-intensive, making it no surprise that work order planning often experiences delays.

Generative AI empowers foundation models by training them with all the necessary instructions, parts, tools and skills for a specific asset or class, enabling the generation of work plans. This enhances your staff’s capabilities, resulting in a 10%–20% increase in planning proficiency. Also, generative AI can facilitate automation and recommend updates to maintenance standards, potentially leading to a 10%–25% increase in compliance.

3. Support reliability engineering

Reliability is a critical key performance indicator in any asset-driven business. Unfortunately, experienced reliability engineers are leaving many sites, resulting in limited resources for training replacements. By using hybrid AI/ML models, generative AI generates failure and effects analyses from historical data, enabling you to prioritize and reduce serial failures by up to 25%–50% while increasing site reliability by 10%–15%.

4. Analyze and apply maintenance standards

Generative AI foundation models can train on asset class standards, including work history, maintenance plans, job plans and spare parts. They identify and recommend compliance with current standards for existing assets. By enhancing staff skills, generative AI analyses extend asset lifespan by 15%–20% and increase uptime by approximately 5%–10%.

5. Update maintenance quality

When work orders are completed, they often signal the need to move on to the next one. However, intelligent analysis of completed work orders can reveal areas where compliance or maintenance processes need improvement. Generative AI can recommend updates to improve the effectiveness of planned maintenance by 15%–25% and create new job plans based on the completed work plan if they are absent, therefore increasing planning proficiency by 10%–20%.

6. Assist with safety and regulatory compliance

Generative AI provides real-time assistance for industry, site or asset safety and regulatory compliance, reducing fines by 25% and improving compliance by up to 50%. Training models that use published safety guidelines, regulations, regulatory filings, rulings and internal data sources significantly improve the speed, accuracy and success rate of regulatory filings for planners and technicians.

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