Asset lifecycle modelling is a structured approach designed to assist asset planners and decision-makers in achieving an optimal balance between risk and investment over various time horizons, ranging from short-term to long-term (1 to 30 years). Lifecycle modelling considers the Operational and Capital costs incurred as an asset age and degrades over time and identifies the most appropriate point to renew or replace an asset such that its risk remains below a defined threshold.
Key Challenges:
The initiative acknowledged several common challenges faced in business environments, including:
- Insufficient Data: A lack of comprehensive datasets can hinder accurate modelling.
- Data Quality Issues: Poor or inconsistent data reduces reliability and confidence in predictions.
- Complex Contexts: Geographic, environmental, and systemic complexities add layers of difficulty to lifecycle assessments.
TeleMARS has been working with Weerran to conduct asset lifecycle modelling for a major Australian rural water supplier since September 2023.
Weerran is an Australian consulting business with expertise in asset management for urban and rural water, solid waste and energy utilities. The directors all have over 20 years’ experience in their respective infrastructure sectors. Its project partners and clients include some of Australia’s largest infrastructure providers. Weerran’s goal is to improve clients’ capability to improve their Asset Management and business processes to achieve their corporate goals of greater productivity, extract maximum value from their assets, and improve services to customers.
To address these challenges, a practical methodology was proposed to:
- Validate potential solutions against industry standards;
- Identify critical data and process gaps and
- Establish a framework for iterative improvement and accuracy enhancement.
Methodology and Outcomes:
TeleMARS team developed and implemented a preliminary model to test and refine the proposed method. This model incorporates several key assumptions to address data limitations and uncertainties related to operational and environmental conditions.
The model achieved the following goals.
- A long-term investment outlook based on principles of lifecycle asset management and risk evaluation.
- Identification of data gaps that require attention for enhancing future modelling accuracy and reliability.
- Validation of the overall approach, providing a foundation for scaling and further refinement.
This POC demonstrated the potential of asset lifecycle modelling to improve decision-making processes, even in the face of data and system challenges. It also highlighted the importance of integrating structured validation methods and iterative learning into asset management practices.
Asset Lifecycle is Complex
We have learned valuable lessons through this POC to build sustainable models for achieving optimal asset planning and management.
- Knowledge Gap: Asset lifecycle modeling requires deep expertise in the domain of asset management. The data science team faced challenges in keeping up with requirement variations and changes due to a lack of subject matter knowledge.
- Transparency: The asset lifecycle model is a complex solution. Subject Matter Experts (SMEs) require a mechanism to understand how the solution is developed, how it functions, and how the results are derived.
- Confidence: A mechanism is needed to ensure alignment between SMEs and data science teams, fostering confidence among SMEs in the solution development process.
- Trust: SMEs need the capability to validate the model at each step and verify each output to ensure the model is built according to business requirements, enabling them to trust the results.
