Describing the Closed Loop Asset Lifecycle

The closed loop asset lifecycle describes all process steps and activities regarding assets in an organization. This covers everything from deciding which assets to commission all the way to decommissioning assets after a certain amount of time. Because this course focuses on the Intelligent Asset Management solutions, the focus is on activities and actions performed in the time span an asset is in operation.

The closed loop aspect of the asset lifecycle emphasizes the importance of feedback and continuous improvement. It involves gathering data and insights at each stage of the lifecycle and using that information to inform decision-making and optimize asset management practices. This feedback loop helps organizations identify areas for improvement, reduce costs, enhance asset performance, and minimize environmental impact.

• Asset Collaboration: Having a network for collaboration between asset operators, original manufacturers, suppliers, and service providers enables information exchange to improve asset commissioning and streamline work order processing. It also achieves resiliency and transparency across the entire asset lifecycle.

• Asset Risk and Criticality Assessment: During the asset risk and criticality assessment, the risks associated with assets within an organization and their criticality to business operations are identified. This assessment helps prioritize assets based on their importance, potential impact on operations, and vulnerability to risks. The risk and criticality assessment and thus the calculation of a risk score is done using Failure Mode and Effects Analysis (FMEA).

• Implementation of Asset Maintenance Strategy: Decide which maintenance strategy fits best to your asset based on its historical as well as on its upcoming data collected using IoT. Select among three approaches: time-based, performance-based, and risk-based maintenance. In contrast to the rather traditional models of time- and performance-based maintenance, risk-based maintenance allows precise assessments based on asset health data and an alerting system.

• IoT and Inspection Data Management: Using IoT sensors within operating assets, real-time data can be used to define indicators to gain insight on the current state of the respective asset. This data enables the definition of rules for maintenance needs in the process steps ahead. It is also the basis for predictive and condition-based maintenance.

• Maintenance Rules and Alerts Management: In this step, users can define maintenance rules. These are conditions or criteria that trigger specific alerts when met. These rules can be based on factors such as asset health, operating conditions, and performance thresholds. The indicators defined in the previous steps can be used within the defined rules. Alerts are maintenance notifications generated by the system when maintenance rules are triggered.

• Anomaly Detection and Failure Prediction: Anomaly detection and failure prediction involves leveraging advanced analytics and machine learning algorithms to identify abnormal patterns or behaviors in asset data and predict potential failures before they occur. This optimizes maintenance activities, drives continuous improvement in asset reliability, availability, and performance and is the basis for predictive maintenance.

• Create/Manage orders: In this process step, maintenance orders are created and assigned to the responsible maintenance teams. Different analytics features give an overview of maintenance orders and activities within organizations helping with the management of the current maintenance backlog.

• Asset Management Resource Planning: In this step, the available asset maintenance resources are efficiently allocated and utilized. This involves scheduling and planning maintenance activities based on resource availability and workload capacity. Individual maintenance tasks can be assigned to specific maintenance technicians or teams.

• Schedule and Dispatch: Leveraging manual, assisted, and fully automated planning, workforce deployment can be optimized. It includes route optimization for efficient travel, real-time dispatch optimization, and seamless integration of contract labor. Workforce management facilitates call-out management, ensuring that skills and certifications align with assigned tasks. This comprehensive approach ensures that the right work is performed at the right time, with the right resources, enhancing maintenance efficiency and asset reliability.