Optimized Decision Modeling For Organizational Asset Management

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1 TANGIBLE CAPITAL ASSETS PHOTO: GARY J. WOOD Optimized Decision Modeling For Organizational Asset Management BY GRACE MCLENAGHAN, REGION OF PEEL Infrastructure state of good repair and availability of sustainable funding are the issues at the forefront of municipal asset management. Organizational-wide prioritization of asset needs across divergent asset groups and services is a unique challenge to municipalities. The task requires a transparent, consistent, and straightforward approach to aid complex decision-making. The Region of Peel s approach to enterprise asset management was to utilize a risk-based prioritization methodology in the Corporate Asset Management Strategy.

2 I. BACKGROUND The Region of Peel is an upper tier municipality with a portfolio of infrastructure that includes water and wastewater treatment, distribution and collection, arterial roads and solid waste management. Peel s asset portfolio also includes the facilities to support its soft services such as health, long-term care, child care, shelters and heritage. The Corporate Asset Management (CAM) Section at the Region of Peel was created to establish an enterprise asset management strategy for all Regional assets. CAM s focus is to develop practices and tools to support organizational decision-making toward determining sufficient asset investments to meet immediate service needs and to forecast long-term funding requirements. Its goal is to ensure the assets and services remain sustainable. A consistent corporate-wide approach to asset management is essential to set effective and transparent organizational priorities and state of good repair capital plans and to ensure the efficient use of the citizens tax dollars. The challenge in doing so is to find an approach that is simple yet effective in assessing the diverse asset and service needs equally. Peel selected risk as the common prioritization criterion for all asset classes. A risk-based approach to prioritizing asset needs removes subjective perceptions and quantifies the relative importance and effects of the assets with respect to the services they support. II. THE CAM FRAMEWORK Peel s risk centric asset management strategy incorporates levels of service requirements, lifecycle strategies, and social, financial, and environmental factors to assess and prioritize asset needs. A CAM framework, illustrated in Figure 1, was created to provide a road map to the implementation of Peel s asset management strategy, and to identify the key areas that needed to be addressed in order to support risk centric optimized decision modeling. LEVELS OF SERVICE A level of service is a defined output for a particular activity or service area against which performance may be measured. Service levels relate to quality, health, safety, quantity, reliability, responsiveness, environmental acceptability, and investment. As part of Peel s Levels of Service Strategy, the assets classes (types) were mapped to the services they support and links were established between the levels of service provided to the customer and the technical levels of service (TLOS) required of the assets to meet customer service expectations. Condition and performance TLOS provide the basis for measuring the state of the assets for strategic planning purposes. The establishment of TLOS, which are measurable and financially quantifiable, is essential to risk based optimized decision modeling. The TLOS are defined at the minimum condition and performance thresholds at which assets should be maintained to cost effectively manage service risks and meet customer service expectations. The TLOS drive capital planning, as they determine to what extent the assets need to be upgraded to meet minimum service performance requirements. RISK MANAGEMENT In order to achieve a risk based decision model for Corporate Asset Management, the establishment of a Risk Management Strategy was imperative to link the effects of changes to TLOS with changes to asset risk. THE CAM ROAD MAP INPUTS TECHNOLOGY OUTPUTS INFRASTRUCTURE INVESTMENT REPORT State of Reserves RISK MANAGEMENT LEVEL OF SERVICE LIFECYCLE MANAGEMENT ODM (OPTIMAL DECISION MAKING) INFRASTRUCTURE INVESTMENT State of Infrastructure FIGURE 1: CAM METHODOLOGY Strategic Asset Plan

3 Risk is the chance of an event that will positively or negatively impact the achievement of objectives. Levels of service to the community are key objectives for the municipality that can be impacted by events for which the risks must be considered and managed. The Australian and New Zealand risk management frameworks (AS/NZS 436) provided the start for Peel s Risk Management Strategy. Impact and likelihood measurement tools were customized from the AS/NZS 436 framework to meet the asset evaluation needs in the Peel environment. While The AS/NZS 436 framework was selected by CAM for the asset risk assessments, other risk frameworks and measurement tools can be applied to Peel s Risk Management Strategy. The risk measurement tools were used to assess the asset risk in three (3) categories for the risk profile: 1. INHERENT RISK: The level of risk that the asset presents to the service before undertaking any mitigation measures. Inherent Risk can also indicate the most critical assets to the quality of service delivery. 2. RESIDUAL RISK: The remaining risk after desired mitigation measures are put into effect. Mitigation measures include achieving the levels of service at which the assets need to perform, undertaking proactive maintenance practices, adhering to regulatory requirements or enacting service improvement objectives. Residual Risk is often the organization s risk objective. The ability to meet that objective through available funding, time, and resources defines the organization s risk tolerance in relationship to the risk objectives. 3. CURRENT RISK: The level of risk the assets currently present to service delivery. The Current Risk is calculated based on how well the organization is meeting its levels of service, regulatory requirements, maintenance practices and service improvement objectives today. Unit Breaks (breaks/km) Age (Years) FIGURE 2 - LIFECYCLE CURVES FOR DISTRIBUTION MAINS The risk profiles provide the main parameters for determining the optimized allocation of capital funds to areas where infrastructure investments will yield the greatest risk reduction. Further details on Peel s asset Risk Management Strategy can be found in the article CI DI PVC TLOS Connecting Risk and Levels of Service at the Region of Peel by Leanne Brannigan, published in the August 21 issue of Public Sector Digest. LIFECYCLE STRATEGIES Lifecycle strategies are unique to different asset classes and provide the basis for predicting changes to TLOS and asset risks as the assets age over time. Using watermains as an example illustrates the concept of lifecycle modeling for strategic planning purposes. Figure 2 shows a family of lifecycle curves for local distribution mains, differentiated by material type. The horizontal red line represents a condition TLOS value of 5 breaks per km of watermain; the point at which watermains have deteriorated to a level where repair or replacement should be considered in order to meet customer expectations of a reliable water system. When the assets no longer meet the condition TLOS requirements for the asset class (in this case, 5 breaks per km of watermain), the risk score will begin to rise above the desired level of risk (residual risk) and will continue to increase as the average number of breaks in the system increases. As funding is committed to repair the watermains and the number of breaks in the system decreases, the risk score will reduce toward desired levels. Should the number of watermain breaks be reduced to the point they fall below TLOS requirements, the corresponding risk score would improve to be better than the risk level objective; thereby, indicating that some funds can be shifted to other asset classes in need of additional capital funding. A lifecycle curve similar to Figure 2 cannot be constructed for all TLOS. Curves work best for asset classes made of similar assets, for which a distinct parameter can be used to define asset failure. Where the nature of the asset class consists of more complex structures, (buildings, bridges, pumping stations) alternate methods of lifecycle modeling must be considered. As mentioned previously, both condition and performance TLOS are used to assess the state of the assets and they are combined to provide the overall asset risk value. Performance-based TLOS are not readily represented through a lifecycle strategy; however, they are selected to account for those desired operational parameters that affect the functionality of the assets and their ability to meet program service requirements. Performance parameters are more difficult to forecast than condition-based parameters, as they may not apply to all assets in the same way (e.g., replacement of assets to fix a low water service pressure area may affect assets only within a geographic location) or may be triggered by unforeseen changes such as changes in legislation, service improvements or intended use of the assets (e.g., reconfiguring homeless shelters to meet accessibility regulations, changes to long-term care facilities to provide public day programs). Performance parameters are input into the model as capital requirements, and the risk scores are adjusted as funds and works are committed. III. THE OPTIMIZED DECISION MODEL The purpose of optimized decision modeling (ODM) is to arrive at the best balance between managing risk to services at desired levels and the financial costs to do so. For each asset class, the ODM combines existing levels of service parameters, the range in risk values between residual and inherent risk and the lifecycle

4 information with the intents of: evaluating current condition and performance of the asset class; calculating the current risk score and the deviation from the desired risk score; forecasting the level of service and risk parameters for future years based on predictive lifecycle information; producing capital plan recommendations, based on the current and forecasted ability of the asset class to meet the required levels of service; and FIGURE 3 CONSTRAINED BUDGET ODM OUTPUT - DISTRIBUTION MAINS forecasting risk levels to prioritize investments across asset classes to meet budgetary constraints. Three asset classes were selected as part of a pilot project to test the procedures associated with ODM and show its applicability across a diverse group of assets. Water distribution mains, road pavement, and social housing facilities were chosen as they make up a significant portion of the Region s infrastructure and a good amount of condition and performance data was readily available for these assets. Asset data related to basic asset parameters (age, location) and specific to the TLOS (e.g., number of watermain breaks) was sourced from existing asset management systems, and used for input into the ODM model. The first run of the ODM set budget constraints for the upgrades to each asset class. Figure 3 illustrates the output for a scenario of $3 million per year investment in watermain replacement. It then calculates the equivalent network condition (the percentage of assets meeting TLOS), the total length of watermain which can be replaced given that investment, and the change in risk score for the asset class as a result of the work, accounting for the aging of the existing infrastructure as time progresses. Replacement Cost ($M) Year Social Housing Roads Local Distribution Mains FIGURE 4 - STATE OF GOOD REPAIR NEEDS - UNLIMITED FUNDS SCENARIO An alternative modeling scenario applied unlimited funding and resources in order that 1 percent of the assets achieve the established TLOS requirements. As illustrated in Figure 4, distribution mains would require significant investment in the short term to fully meet the desired TLOS, which goes beyond the scope of the current capital works plans (currently about $3 million per year). Once the older inventory is assumed to be replaced, the model shows diminishing capital needs for this asset class. Road pavement and social housing assets, on the other hand, are shown as needing a smaller investment in the shorter term, but the investment needs escalate in future years as more assets fail to meet the desired TLOS. A different picture arises when examining the most cost effective approach to addressing asset needs using risk centric modeling. This is when a risk centric approach to asset management can provide the clarity to make better decisions on asset investment. The ODM model calculates the risk reduction per capital investment dollar (benefit cost) and is then able to prioritize asset classes in order of highest risk mitigation potential from an organizational standpoint. Risk Score Reduced per $M Years Social Housing Roads Local Distribution Mains FIGURE 5 - RISK SCORE REDUCED PER MILLION DOLLARS SPENT

5 The ODM tool makes such considerations and discussions with senior management, Council, and the public possible as they can see the impacts of changing TLOS and what the associated costs will be in terms of dollars and risks to their services. Figure 5 shows that the risk reduction per dollar spent is low for a relatively large, low risk (less critical) asset class like local distribution mains. Since the Regional watermain network is already performing very close to its desired levels of service (achieving 9 percent or better of TLOS requirements) and watermain replacement has a comparatively high cost, investing additional capital in watermain replacement to get 1 percent compliance with the TLOS would be an expensive and less cost effective exercise than tackling some of the more critical, higher risk asset classes. Social Housing, on the other hand, has a much better risk improvement return, as it is presently further from its minimal risk thresholds. It is a more critical asset to service delivery and more can be done with less funding to improve service and the level of risk. Road pavement, while also being a large network of assets, is a faster deteriorating asset, and presents a higher level of risk due to its heavy use and significant economic and safety impact to the community. For this reason, investment in pavement improvements also presents a better financial opportunity for service improvement and risk reduction than the local watermains. This does not mean that work can be deferred indefinitely for lower risk asset classes. Due to the non-linear nature of risk, once the condition and performance for an asset class deteriorates significantly, risk for the asset class will increase exponentially. The ODM is useful when applied to rebalance capital investment while still preserving all asset classes ability to continue to work as closely to the desired TLOS as possible and remain within risk tolerances. The risk centric optimized decision making methodology helps to justify and prioritize capital budgets, and provides the additional benefit of monitoring the effectiveness of asset management strategies. Where the TLOS is difficult to achieve in the shorter term, and risk to the Region is relatively low, such as with distribution mains, a decision may be taken to operate below the selected TLOS on a short term basis, or change the TLOS to less stringent criteria. The ODM tool makes such considerations and discussions with senior management, Council, and the public possible as they can see the impacts of changing TLOS and what the associated costs will be in terms of dollars and risks to their services. IV. LESSONS LEARNED The pilot asset classes used in the testing of the ODM model were selected in keeping with the availability, completeness, and reliability of asset related data. When creating a methodology and procedures for implementing a risk centric ODM, ensuring quality of data is one of the most significant challenges. Where accurate records and asset inventories are not available, a number of assumptions must be made in order to fill in the gaps. Tapping into industry standards and documented research can be useful to formulate assumptions, but the knowledge of program staff is essential in verifying their applicability to local conditions. When dealing with asset management at the corporate level, there is also the tendency to treat an asset class as a single identifiable unit rather than as a grouping of individual assets. For example, define levels of service in terms of number of breaks/1km/year for the entire network of watermains rather than breaks/km at the individual watermain level. Analyzing assets as individual components of a greater grouping, rather than as a whole allows for integration between corporate level asset management and the more detailed asset management undertaken at the program level. A significant challenge inherent to implementing a risk-centric ODM comes in ensuring that all programs and services are equally treated in the setting of the asset management decision parameters and the collection of data. In this way, all programs and services regardless of size have their assets assessed equally in the asset management plans and thereby increase the reliability of the ODM outputs. Implementing a corporate-wide risk centric optimized decision modeling tool is central to CAM s Asset Management Strategy. It fulfills CAM s objectives of improving planning and prioritization of infrastructure needs across the organization, meeting the longterm sustainability of assets and service, improving transparency of the annual budgets and forecasts. While the task of collecting data and forecasting TLOS for each asset class is daunting, the rewards of a uniform, defensible asset prioritization are well worth the effort. GRACE MCLENAGHAN is a Project Manager with the Corporate Asset Management Section at the Region of Peel. Grace is the Corporate Asset Management lead for the Lifecycle Strategy and Optimized Decision Making model, and for the Asset and Reserves Management Strategy. Grace has a Bachelor of Applied Science Degree in Environmental Engineering and is registered as a Professional Engineer in Ontario.

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