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134 Improvements in life expectancy analysis, by implementing the techniques in this guide, undoubtedly will involve some extra investment in data collection, training, staff time, and management attention. Stakeholders making this investment will want to ensure that the investment pays off. Staff members who work to improve their professional capabilities will want to know that this improvement enhances their professional advancement and the quality of service they provide to the public. As a whole, the agency will be successful in extending its implementation of these methods as long as the stakeholders, internal and external, continue to find the effort worthwhile. 7.1 Measuring and Promoting Success Like any new asset management technique, the success of life expectancy analysis will be judged by whether stakeholders think their objectives are being served. There are both quanti- tative and qualitative ways of assessing this, all stemming from the agencyâs original goals and objectives for starting the process. One way of approaching this is to ask a series of questions. Long-term view ⢠Is the agency now confident in publishing life expectancy estimates and using them to evaluate and anchor budgetary requests? ⢠Do senior managers have confidence that they know how much it will cost in the long term to sustain the desired level of service? ⢠Do outside stakeholders agree with management estimates of the long-term cost of sustaining the desired level of service? ⢠Do senior managers and stakeholders know what level of service can be sustained under current or proposed future funding levels? Transparency ⢠Is there a public comparison of forecasted versus actual life expectancies? ⢠Are actions taken in response to life expectancy estimates and findings and do stakeholders know what these actions are? ⢠Are comparisons routinely and publicly made of the agencyâs performance against peer agencies and against itself over time? Levels of service ⢠Can the agency accurately measure, track, and publish the level of service it is currently providing? C h a p t e r 7 Ensure Implementation: How to Improve Life Expectancy Models
ensure Implementation: how to Improve Life expectancy Models 135 ⢠Are life extension and replacement decisions accurately timed to avoid interruptions in service while minimizing costs? ⢠Is the agency reducing the annual number of traffic disruptions resulting from planned and unplanned maintenance, repair, and replacement activity? Efficiency ⢠Is the agency improving in its quantitative performance in relation to the cost of providing the desired levels of service? ⢠Can the agency show, from its actual data, that its more refined timing of life extension and replacement actions is saving money, relative to earlier practice? ⢠Does the agency routinely compute, and effectively communicate, the lifecycle costs of its services? Are these costs showing a clear trend of improvement? Agency competitiveness ⢠Is the agency using its asset management information to secure adequate funding? ⢠Are legislators confident that the agency is doing everything it can to control costs? ⢠Is the agency able to maintain adequate funding levels over time in the face of competing uses of the money? Constructive relationships ⢠Is the agency working actively with outside stakeholders on strategies to maintain and enhance the level of service provided to the public? ⢠Do outside stakeholders understand how their own interests are served by maintaining the agencyâs level-of-service objectives? ⢠Do legislators and funding bodies rely on the agencyâs models of the relationship between level of service and funding? Although these questions may seem vague, agencies have developed very specific tools and methods to conduct these measurements in the context of advancing their asset management maturity level. Gordon (2010) contains a wealth of case studies on these efforts. It is especially important to use asset management tools, such as life expectancy analysis, to build credibility by communicating the agencyâs successes in satisfying published goals. 7.2 Incorporation into Management Systems The kind of proactive decision-making needed of agencies in the more mature stages of asset management requires adoption and consistent use of analysis tools, especially deterioration and lifecycle cost models. Many agencies have responded to this need by adopting pavement and bridge management systems. Many of the techniques described in this guide can be found in those systems. An advantage of using the management system approach is that the tools for data collection, quality assurance, analysis, and reporting are all integrated under one system architecture, helping to ensure their consistency. Agencies not only invest in the creation of these systems, but in the procedures surrounding them, including manuals and training that reinforce the correct use of the systems. Similarly, implementation of these techniques on assets other than pavements and bridges can be solidified by incorporating them in maintenance planning systems or asset management systems. Potential models for this type of system have been documented in several recent reports (Harrison et al. 2004, Cambridge et al. 2005, and Patidar et al. 2007).
136 estimating Life expectancies of highway assets One criticism of this approach is that it is often difficult to develop the data collection and data management tools while developing the analysis methods and management reports. Agencies often prefer to take it a step at a time, first fully implementing inspection and quality assurance, with very simple management reports, before developing analysis tools such as lifecycle cost models. This is partly why many more agencies have implemented the data collection parts of their pavement and bridge management systems than have implemented the management decision support parts (Thompson 2006). Other reasons include ⢠Data collection and management systems are more easily standardized into off-the-shelf software systems. Decision support systems are more often tailored to the needs of specific agencies and are harder to standardize. ⢠The kinds of expertise necessary to develop decision support analysis tools differ from the expertise necessary to develop data capture and database management tools. ⢠Management requirements for analytical reports change relatively frequently. Management turnover, changes in stakeholders, political trends, and continuous learning all cause changes in perspectives and requirements. ⢠Developing management tools that fit evolving agency requirements is more incremental, involving smaller and more frequent updates, than the traditional software development cycle used on the large systems that transportation agencies traditionally procure. Management tools have had an uncomfortable fit with the traditional information technology business model. Because of all these factors, the de facto business model for development and enhancement of analysis tools in asset management has been more like the evolving model of Software as a Service (SaaS). In this model, software systems are kept very modular, each module being small and updated frequently. These systems are loosely joined by standardized interfaces, agency procedural manuals, and database schemas. The most successful SaaS systems have many software authors, each with very specialized capabilities, from within the agency and from various private-sector organizations. There is a high level of interaction between the end-users and the software authors. The development tools are often off-the-shelf end-user tools such as Microsoft Office (Word, Excel, and Access), ArcGIS, and Crystal Reports. Very often some of the agencyâs end-users develop technical knowledge of the development tools and want to take an active role in system development if they are to be willing to accept the final product. It is likely that most of the decision support models developed as a result of this guide will end up being suites of Microsoft Excel spreadsheet models and reports that are added onto existing agency databases and maintenance management systems. For many agencies, this path has proven to be the quickest way to get the tools into management hands and put them to work. It is also the path of least resistance to ensure that the tools are continuously improved.
