Water resources, and the assets that treat and transport water and sewage, are coming under increased strain from factors such as increasing population, urbanisation and climate change. In common with all asset-intensive industries, the water industry is facing challenges of reliability and affordability, along with performance and regulatory requirements. Controlling costs and ensuring resilience and business continuity is necessary to meet customer expectations.
The in-service costs, from when an asset enters service through to its retirement or disposal, can be up to 75% of the life-cycle cost of a medium to long life asset in the water industry. Research is currently being undertaken at the University of Bath in collaboration with Wessex Water to define and test a methodology for the effective estimation of in-service costs and performance parameters for high value water industry assets. The research aims to provide tools and methods that can be used to select and direct the operation and maintenance schedule to manage assets at a strategic and tactical level. The model is being developed to allow evaluation of alternative maintenance scenarios, and trade-off analysis between cost and performance.
The SABRE scenario modelling tool is being used to evaluate typical water industry asset scenarios, and this has been demonstrated through the development of a case study.
A group of bio-gas generators were chosen for the case study, being typical of the high-value medium-life assets employed at sludge treatment centres: they are an essential site utility, providing hot water for heating and electricity; they operate continuously; they are supported by a rigorous preventive maintenance regime; and the asset management planning objective for the generators is to maximise availability and efficiency.
The SABRE scenario modeller provides a structured process to define and assess asset management scenarios. For a typical asset management activity, such as providing the optimum number of assets in order to balance required process throughput against cost, the first stage is to describe the scenario of interest, which is to ascertain the effect of increasing the number of assets within a process on cost and performance. The second stage is to identify the necessary action to affect the scenario – in this case to purchase, install and maintain additional assets. The next stage is to estimate the benefits and risks of the action, as identified through the cost and performance model. The model developed extends existing quantitative parametric life-cycle cost estimating techniques to include performance in addition to cost. The model calculates the overall cost and cost per unit of output using parameters input by the user (such as the number of assets, asset utilisation rate, time-scale for modelling, asset run hours to date).
Scenario modelling process:
SCENARIO - Describe the scenario of interest
ACTION - Describe the operation and maintenance strategy
BENEFIT - Describe performance and cost benefits expected
RISK - Identify risks to cost and performance
EVALUATION - Evaluate risks and benefits
Example output from the model illustrates the variation in estimated cumulative cost for two simple scenarios providing equivalent process throughput. The components of cost considered in this scenario are depreciation (%age of purchase and installation cost), consumable cost, major service cost (scheduled according to hours run), preventive maintenance cost (according to manufacturer’s recommendations) and corrective maintenance cost (following failures).
The final stage of the SABRE process is to compare the output for all possible combinations within the specified scenario, and evaluate the overall cost and performance to arrive at strategic or tactical decisions.
The use of modelling approaches has been shown to offer benefits for the management of assets, providing decision making support at tactical and strategic levels, thus enabling informed decision-making for the management of assets. Wessex Water have identified potential applications for modelling, such as setting budgets, benchmarking, activity planning, planning for new investment, and comparison of alternative technologies.
Further model development is continuing to provide a robust and comprehensive means of addressing these aims.