A Comprehensive Approach to Asset Lifecycle Management for OEMs
Nishant Puri
Business Transformation Voice for Industrial OEMs | Enabling Equipment as a Service Revenue | Accelerating OEMs' Journey to Smart Products | Impacting 1 Trillion $$ Market | Security | CCIE | Ex-Cisco
For Original Equipment Manufacturers (OEMs), the digital revolution presents opportunities to transform asset management across every phase of the lifecycle. Yet, achieving this transformation requires more than data collection; it demands structured data handling and actionable insights. By leveraging a platform that follows Ontology and taxonomy, OEMs can establish a robust framework for managing assets from inception to reclamation while extracting valuable insights through advanced Knowledge Graphs. The OEM Asset Lifecycle Journey
The journey begins with asset inception and extends through logistics, commissioning, performance monitoring, analytics, and post-sales support, culminating in end-of-life reclamation. Let’s explore each stage in detail:
1. Asset Inception (Birth): This initial stage involves manufacturing and defining the asset’s essential attributes. It includes quality testing, compliance verification, and the creation of a digital identity for each asset. This is where a robust ontology is crucial for defining assets accurately, setting the groundwork for subsequent data handling.
Key Activities:
o Asset manufacturing and testing.
o Asset identification and registration within the system.
o ERP and CRM integration for material and inventory management.
2. Asset Logistics (Track & Trace): Effective logistics management ensures the asset reaches its intended location in the right condition. Tracking data must be captured and stored for transparency and traceability, aiding in efficient resource management.
Key Activities:
o Shipment planning and execution.
o Real-time tracking and monitoring.
o Handling exceptions or deviations during transportation.
3. Asset Commissioning (Operational Readiness): At this stage, the asset is prepared for active use. It undergoes multi-asset handling checks to confirm operational readiness, including setting up connectivity and assigning responsibility within the APM framework.
Key Activities:
o Multi-asset handler applications for commissioning.
o Data integration for operational visibility.
o Setting up baseline performance parameters.
4. Asset Performance & Remote Management (APM): This core stage focuses on monitoring and optimizing asset performance in real-time. Leveraging a mix of performance monitoring, predictive analytics, and contextual insights, APM aims to enhance efficiency and extend the asset’s operational lifespan.
Key Activities:
o Continuous asset health monitoring.
o Real-time performance analytics.
o Automated alerts and predictive maintenance.
5. Asset Analytics & Health Monitoring: In this phase, analytics frameworks and advanced knowledge graphs are employed to contextualize asset data. This helps identify patterns and correlations that facilitate strategic decision-making.
Key Activities:
o Implementing health metrics and thresholds.
o Correlation analysis to detect failure points.
o Predictive models for maintenance planning.
6. Asset Post Sales Support After sales, the asset enters the post-support phase, where OEMs can provide value-added services to their customers. This stage involves efficient field operations, customer support, and periodic performance checks.
Key Activities:
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o Customer service applications for real-time assistance.
o Field operations and asset issue resolution.
o Asset upgrades and warranty management.
7. Asset Reclamation (End-of-Life Support): The final stage addresses the sustainable disposal or reclamation of assets. This involves managing recalls, recycling, or reusing valuable components to close the loop in the asset lifecycle.
Key Activities:
o Asset reclamation planning and execution.
o Environmental and regulatory compliance.
o Reverse logistics and asset disposal.
A Data-Driven Approach to Asset Lifecycle Management
We introduce three critical components to streamline and optimize these stages: Ontology, Taxonomy, and Knowledge Graphs. Let’s explore how these elements drive efficiencies throughout the asset journey.
1. Ontology: Defining Assets Precisely from Birth
The foundation of an effective IIoT strategy is a precise and comprehensive definition of each asset. Ontology creates a structured vocabulary for defining assets, their properties, and relationships. It enables OEMs to standardize the representation of assets, enhancing their visibility and manageability from inception.
Example: During the Asset Inception and Commissioning phases, the ontology would include definitions for each component, its properties, and dependencies. For an OEM, this could mean specifying the machine IDs, types, capabilities, and operational roles within the broader system.
2. Taxonomy: Organizing and Contextualizing Data for Effective Management
Once assets are defined, the next step is contextualizing and organizing their data through taxonomy. Taxonomy provides a hierarchical structure that organizes information based on contextual relevance, allowing data to be classified and accessed efficiently.
Example: In the Asset Logistics phase, data related to shipment locations, times, and environmental conditions can be categorized and linked to the asset, enabling effective tracking and resource allocation. This contextual organization allows stakeholders to monitor and manage assets effectively in real-time.
3. Knowledge Graphs: Establishing Advanced Relationships for Insights
Knowledge Graphs connect data points to identify complex relationships and patterns, supporting advanced analytics and decision-making. By linking ontology-based asset definitions and taxonomy-organized data, knowledge graphs enable OEMs to uncover deeper insights into asset performance, reliability, and potential risks.
Example: During the Asset Performance and Remote Management phase, a knowledge graph could link sensor readings with historical maintenance data to predict potential failures, ensuring proactive interventions. It gives a more comprehensive understanding of how different factors interact, driving predictive and prescriptive maintenance strategies.
Flex83 Approach to Asset Lifecycle Management
Flex83’s platform leverages the combined power of Ontology, Taxonomy, and Knowledge Graphs to create a unified data model that supports all stages of the asset lifecycle. Here’s how it addresses each phase:
Conclusion
For OEMs, asset lifecycle management is not just about optimizing operations; it’s about creating new value propositions and ensuring sustainable practices. By focusing on APM and ARM, combined with a solid data framework, OEMs can enhance asset longevity, improve customer experiences, and contribute to global sustainability goals.