Use Case
AI-Empowered Asset Lifecycle Management
Continued advancements in traditional AI and Generative AI are bringing advanced intelligence to asset lifecycle management. Asset lifecycle management (ALM) is the process of managing the health, productivity, and cost of assets across the five stages of the asset lifecycle :
AI-empowered ALM solution leverages traditional AI in combination with GenAI
Companies that rely on high performance across all five asset lifecycle stages will realize significant benefit from deployment of
AI and Generative AI (GenAI) products and solutions.
Current Challenge
Common Problems Faced by Enterprises
Despite advancement in traditional asset performance management solutions, most companies still struggle to fully optimize the management of their assets throughout their entire lifecycle. Most asset-intensive operations are still limited by shallow visibility, static analytics, and non-impactful insights. Problems include:
01
Managing disparate systems and data
Inability to attain an holistic view of asset lifecycle status due to data sources that are scattered and disconnected.
02
Multiple enterprise teams and role
Widely dispersed operational functions managing various aspects of the asset lifecycle (procurement vs finance vs maintenance, etc.) creates miscommunication and inefficiencies.
03
Managing diverse equipment types and locations
Difficultly in managing a wide array of assets that are connected, not-connected, mobile, stationary, geographically close or distant.
04
Maintaining financial contact
Inability to achieve and maintain a near real-time view of critical financial information (depreciation, ROI, TCO (total cost of ownership), etc.
05
Integration friction
Attempts to integrate additional support systems are stymied by lack of expertise and/or inflexibility of existing systems.
The Solution
AI Empowered Asset Lifecycle Management
- Demand forecasting: AI models can predict asset demand based on historical data, seasonal trends, and other market variables.
- Cost optimization: GenAI helps optimize costs during the asset procurement phase by analyzing price trends and recommending the best purchase times or suppliers.
- Risk assessment: AI analyzes past data and external factors to assess and mitigate risks related to asset acquisition, such as supplier reliability, market fluctuations, or geopolitical events.
- Installation: GenAI-enabled workflows improve the installation processes, ensuring precision, repeatability, and reducing human error.
- Resource allocation: AI algorithms help in planning optimal resource allocation (e.g., workforce, equipment) for deploying assets efficiently.
- Predictive maintenance: AI-enabled predictive models analyze real-time operational data to forecast when maintenance will be needed, minimizing downtime, and avoiding costly breakdowns.
- Performance monitoring: Machine learning algorithms continuously monitor asset performance, identifying inefficiencies and providing insights on optimization.
- Asset retirement: Predictive analytics provide insights on when an asset should be decommissioned by analyzing maintenance costs, performance, and return on investment over time.
- Sustainability: AI models optimize asset disposal or recycling processes, ensuring compliance with environmental regulations, and minimizing waste.
The Impact
The Operational Impact
- Improved accuracy in asset demand forecasting
- GenAI-enabled insights into pricing trends and supplier status
- Reduced risk in capital asset acquisition
- More efficient, and cost-effective, commissioning of new assets
- AI-enabled observability and insights into asset performance and health
- Increased asset uptime and productivity via predictive maintenance
- Increased longevity of expensive, mission-critical, assets
- Optimized timing and execution of asset decommission
- Improved return on investment and total cost of ownership