Asset renewal decision modelling with application to the water utility industry

Reliable infrastructure assets impact significantly on quality of life and provide a stable foundation for economic growth and competitiveness. Decisions about the way assets are managed are of utmost importance in achieving this. Timely renewal of infrastructure assets supports reliability and maximum utilisation of infrastructure and enables business and community to grow and prosper. This research initially examined a framework for asset management decisions and then focused on asset renewal optimisation and renewal engineering optimisation in depth. This study had four primary objectives. The first was to develop a new Asset Management Decision Framework (AMDF) for identifying and classifying asset management decisions. The AMDF was developed by applying multi-criteria decision theory, classical management theory and life cycle management. The AMDF is an original and innovative contribution to asset management in that: · it is the first framework to provide guidance for developing asset management decision criteria based on fundamental business objectives; · it is the first framework to provide a decision context identification and analysis process for asset management decisions; and · it is the only comprehensive listing of asset management decision types developed from first principles. The second objective of this research was to develop a novel multi-attribute Asset Renewal Decision Model (ARDM) that takes account of financial, customer service, health and safety, environmental and socio-economic objectives. The unique feature of this ARDM is that it is the only model to optimise timing of asset renewal with respect to fundamental business objectives. The third objective of this research was to develop a novel Renewal Engineering Decision Model (REDM) that uses multiple criteria to determine the optimal timing for renewal engineering. The unique features of this model are that: · it is a novel extension to existing real options valuation models in that it uses overall utility rather than present value of cash flows to model engineering value; and · it is the only REDM that optimises timing of renewal engineering with respect to fundamental business objectives; The final objective was to develop and validate an Asset Renewal Engineering Philosophy (AREP) consisting of three principles of asset renewal engineering. The principles were validated using a novel application of real options theory. The AREP is the only renewal engineering philosophy in existence. The original contributions of this research are expected to enrich the body of knowledge in asset management through effectively addressing the need for an asset management decision framework, asset renewal and renewal engineering optimisation based on fundamental business objectives and a novel renewal engineering philosophy.

Using data mining on road asset management data in analysing road crashes

Road safety is a major concern worldwide. Road safety will improve as road conditions and their effects on crashes are continually investigated. This paper proposes to use the capability of data mining to include the greater set of road variables for all available crashes with skid resistance values across the Queensland state main road network in order to understand the relationships among crash, traffic and road variables. This paper presents a data mining based methodology for the road asset management data to find out the various road properties that contribute unduly to crashes. The models demonstrate high levels of accuracy in predicting crashes in roads when various road properties are included. This paper presents the findings of these models to show the relationships among skid resistance, crashes, crash characteristics and other road characteristics such as seal type, seal age, road type, texture depth, lane count, pavement width, rutting, speed limit, traffic rates intersections, traffic signage and road design and so on.

Capability for infrastructure asset capacity management

Infrastructure capacity management is the process of ensuring optimal provision of infrastructure assets to support business operations. Effectiveness in this process will enable infrastructure asset owners and its stakeholders to receive full value on their investment. Management research has shown that an organisation can only achieve business value when it has the right capabilities. This paradigm can also be applied to infrastructure capacity management. With competing needs for limited organisation resources, the challenge for infrastructure organisations is to identify and invest their limited resources to develop the right capabilities in the management of their infrastructure capacity. Using a multiple case study approach, the challenges faced in the management of infrastructure asset capacity and the approaches that can be adopted to overcome these challenges were explored. Conceptualising the approaches adopted by the case participants, the findings suggest that infrastructure organisations must strengthen their stakeholder connectivity capability in order to effectively manage the capacity of their infrastructure assets.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Infrastructure asset : developing maintenance management capability

Purpose Maintenance management is a core process in infrastructure asset management. Infrastructure organisations must constantly strive to ensure the effectiveness of this process in order to obtain the greatest lifetime value from their infrastructure assets. This paper aims to investigate how infrastructure organisations can enhance the effectiveness of their maintenance management process. Approach This study utilised multiple case studies as the research approach. The case organisations were asked to identify the challenges faced in the maintenance process and the approaches they have adopted to overcome these challenges. Analysis of these findings, together with deductive reasoning, leads to the development of the proposed capability needed for effective maintenance management process. Findings The case studies reveal that maintenance management process is a core process in ensuring that infrastructure assets are optimally and functionally available to support business operations. However, the main challenge is the lack of skilled and experienced personnel to understand and anticipate maintenance requirement. A second challenge is the reduced window of time available to carry out inspection and maintenance works. To overcome these challenges, the case organisations have invested in technologies. However, technologies available to facilitate this process are complex and constantly changing. Consequently, there is a need for infrastructure organizations to develop their technological absorptive capability, i.e. the ability to embrace and capitalize on new technologies to enhance their maintenance management process. Originality/Value This paper is original in that it provides empirical evidence to identify technological absorptive capability as core to improving the maintenance management process. The findings are valuable because it sheds light on where infrastructure organisation, regardless of whether they are privately or publicly owned, should channel their scarce resources. The development of the core capability will ensure that the maintenance process can contribute value to their organisation.

A capability model to improve infrastructure asset performance

Infrastructure organizations are operating in an increasingly challenging business environment as a result of globalization, privatization and deregulation. In an external business environment that is constantly changing, extant literature on strategic management advocates the need to focus on factors internal to the organization such as resources and capabilities to sustain their performance. Specifically, they need to develop dynamic capabilities in order to survive and prosper under conditions of change. The aim of this paper is to explore the dynamic capabilities needed in the management of transport infrastructure assets using a multiple case study research strategy. This paper produced a number of findings. First, the empirical evidence showed that the core infrastructure asset management processes are capacity management, options evaluation, procurement & delivery, maintenance management, and asset information management. Second, the study identified five dynamic capabilities namely stakeholder connectivity, cross-functional, relational, technology absorptive and integrated information capability as central to executing the strategic infrastructure asset management processes well. These findings culminate in the development of a capability model to improve the performance of infrastructure assets in an increasingly dynamic business environment.

Analysis of productive performance of crop and animal production systems : an integrated analytical framework

This article presents a two-stage analytical framework that integrates ecological crop (animal) growth and economic frontier production models to analyse the productive efficiency of crop (animal) production systems. The ecological crop (animal) growth model estimates "potential" output levels given the genetic characteristics of crops (animals) and the physical conditions of locations where the crops (animals) are grown (reared). The economic frontier production model estimates "best practice" production levels, taking into account economic, institutional and social factors that cause farm and spatial heterogeneity. In the first stage, both ecological crop growth and economic frontier production models are estimated to calculate three measures of productive efficiency: (1) technical efficiency, as the ratio of actual to "best practice" output levels; (2) agronomic efficiency, as the ratio of actual to "potential" output levels; and (3) agro-economic efficiency, as the ratio of "best practice" to "potential" output levels. Also in the first stage, the economic frontier production model identifies factors that determine technical efficiency. In the second stage, agro-economic efficiency is analysed econometrically in relation to economic, institutional and social factors that cause farm and spatial heterogeneity. The proposed framework has several important advantages in comparison with existing proposals. Firstly, it allows the systematic incorporation of all physical, economic, institutional and social factors that cause farm and spatial heterogeneity in analysing the productive performance of crop and animal production systems. Secondly, the location-specific physical factors are not modelled symmetrically as other economic inputs of production. Thirdly, climate change and technological advancements in crop and animal sciences can be modelled in a "forward-looking" manner. Fourthly, knowledge in agronomy and data from experimental studies can be utilised for socio-economic policy analysis. The proposed framework can be easily applied in empirical studies due to the current availability of ecological crop (animal) growth models, farm or secondary data, and econometric software packages. The article highlights several directions of empirical studies that researchers may pursue in the future.

M-ary trees for combinatorial asset management decision problems

A novel m-ary tree based approach is presented to solve asset management decisions which are combinatorial in nature. The approach introduces a new dynamic constraint based control mechanism which is capable of excluding infeasible solutions from the solution space. The approach also provides a solution to the challenges with ordering of assets decisions.

An object oriented road asset information model

Preservation and enhancement of transportation infrastructure is critical to continuous economic development in Australia. Of particular importance are the road assets infrastructure, due to their high costs of setting up and their social and economic impact on the national economy. Continuous availability of road assets, however, is contingent upon their effective design, condition monitoring, maintenance, and renovation and upgrading. However, in order to achieve this data exchange, integration, and interoperability is required across municipal boundaries. On the other hand, there are no agreed reference frameworks that consistently describe road infrastructure assets. As a consequence, specifications and technical solutions being chosen to manage road assets do not provide adequate detail and quality of information to support asset lifecycle management processes and decisions taken are based on perception not reality. This paper presents a road asset information model, which works as reference framework to, link other kinds of information with asset information; integrate different data suppliers; and provide a foundation for service driven integrated information framework for community infrastructure and asset management.

An exploration of the professional competencies required in engineering asset management

Engineering asset management (EAM) is a rapidly growing and developing field. However, efforts to select and develop engineers in this area are complicated by our lack of understanding of the full range of competencies required to perform. This exploratory study sought to clarify and categorise the professional competencies required of individuals at different hierarchical levels within EAM. Data from 14 interviews and 61 on-line survey participants has informed the development of an initial Professional Competency Framework. The nine competency categories indicate that Engineers working in this field need to be able to collaborate and influence others, complete objectives within organizational guidelines and be able to manage themselves effectively. Limitations and potential uses in practice and research for this framework are discussed.

The exemplification of governance principles within state asset management laws and policies : the case of Indonesia

This chapter reviews aspects of the challenge of reviewing and reforming Indonesian practice within state asset management law and policy specifically related to public housing, public buildings, parklands, and vacant land. A critical issue in beginning this review is how Indonesia currently conceptualizes the notion of asset governance and how this meaning is embodied in recent changes in law and policy and importantly in options for future change. This chapter discusses the potential complexities uniquely Indonesian characteristics such as decentralisation and regional autonomy regime, political history, and bureaucratic culture.

A flexible asset maintenance decision-making process model

Optimal Asset Maintenance decisions are imperative for efficient asset management. Decision Support Systems are often used to help asset managers make maintenance decisions, but high quality decision support must be based on sound decision-making principles. For long-lived assets, a successful Asset Maintenance decision-making process must effectively handle multiple time scales. For example, high-level strategic plans are normally made for periods of years, while daily operational decisions may need to be made within a space of mere minutes. When making strategic decisions, one usually has the luxury of time to explore alternatives, whereas routine operational decisions must often be made with no time for contemplation. In this paper, we present an innovative, flexible decision-making process model which distinguishes meta-level decision making, i.e., deciding how to make decisions, from the information gathering and analysis steps required to make the decisions themselves. The new model can accommodate various decision types. Three industrial case studies are given to demonstrate its applicability.

Planning for transit system reliability using productive performance and risk assessment

Urban transit system performance may be quantified and assessed using transit capacity and productive capacity for planning, design and operational management. Bunker (4) defines important productive performance measures of an individual transit service and transit line. Transit work (p-km) captures transit task performed over distance. Transit productiveness (p-km/h) captures transit work performed over time. This paper applies productive performance with risk assessment to quantify transit system reliability. Theory is developed to monetize transit segment reliability risk on the basis of demonstration Annual Reliability Event rates by transit facility type, segment productiveness, and unit-event severity. A comparative example of peak hour performance of a transit sub-system containing bus-on-street, busway, and rail components in Brisbane, Australia demonstrates through practical application the importance of valuing reliability. Comparison reveals the highest risk segments to be long, highly productive on street bus segments followed by busway (BRT) segments and then rail segments. A transit reliability risk reduction treatment example demonstrates that benefits can be significant and should be incorporated into project evaluation in addition to those of regular travel time savings, reduced emissions and safety improvements. Reliability can be used to identify high risk components of the transit system and draw comparisons between modes both in planning and operations settings, and value improvement scenarios in a project evaluation setting. The methodology can also be applied to inform daily transit system operational management.