313 resultados para Asset Maintenance
Resumo:
The concept of a substantive integrator is introduced as a method for integrated resource and environmental management as a means to assimilate different resource values at the operational or field level. A substantive integrator is a strategic management tool for integrating multiple uses into coprorate management regimes that traditionally manage for single values. Wildlife habitat management is presented as a substantive integrator for managing vegetation on electric utility power line corridors. A case study from northern British Columbia provides an example of wildlife habitat management as a means to integrate other resource values such as aesthetics, access and subsistence along British Columbia Hydro and Power Authority's transmission rights-of-way.
Resumo:
The firm is faced with a decision concerning the nature of intra-organizational exchange relationships with internal human resources and the nature or inter-organizational exchange relationships with market firms. In both situations, the firm can develop an exchange that ranges from a discrete exchange to a relational exchange. Transaction Cost Economics (TCE) and the Resource Dependency View (RDV) represent alternative efficiency-based explanations fo the nature of the exchange relationship. The aim of the paper is to test these two theories in respect of air conditioning maintenance in retail centres. Multiple sources of information are genereated from case studies of Australian retail centres to test these theories in respoect of internalized operations management (concerning strategic aspects of air conditioning maintenance) and externalized planned routine air conditioning maintenance. The analysis of the data centres on pattern matching. It is concluded that the data supports TCE - on the basis of a development in TCE's contractual schema. Further research is suggested towards taking a pluralistic stance and developing a combined efficiency and power hypothesis - upon which Williamson has speculated. For practice, the conclusions also offer a timely cautionary note concerning the adoption of one approach in all exchange relationships.
Resumo:
It is questionable whether activities like construction, including maintenance and repair, can be considered a single entity or industry - on the basis that different sectors of construction/maintenance use fundamentally distinct resource and skill bases. This creates a number of issues including the development of competition and reform policy. de Valance deployed the Structure-Conduct-Performance model (SCP) to delineate sectors of new/installation construction activity and, in doing so, proposes that there exists multiple market structures in a given project. The purpose of this paper is to apply the SCP model to a different sector of construction activity, that is air conditioning maintenance and test de Valance's proposition concerning the existence of multiple market structures in a supply chain but this time to a built facility. The research method combines secondary data concerning the "Structure" component of the SCP model and primary data with regard to the "Conduct" and "Performance" parts of the SCP model. The results provide further support (beyond de Valance's analysis of new/installation activity) that a sector system approach using the SCP model is a more effective way to analyse market structures in construction activity. This paper also supports de Valance's proposition concerning the existence of multiple market structures in a supply chain to a project/facility.
Resumo:
Sales growth and employment growth are the two most widely used growth indicators for new ventures; yet, sales growth and employment growth are not interchangeable measures of new venture growth. Rather, they are related, but somewhat independent constructs that respond differently to a variety of criteria. Most of the literature treats this as a methodological technicality. However, sales growth with or without accompanying employment growth has very different implications for managers and policy makers. A better understanding of what drives these different growth metrics has the potential to lead to better decision making. To improve that understanding we apply transaction cost economics reasoning to predict when sales growth will be or will not be accompanied by employment growth. Our results indicate that our predictions are borne out consistently in resource-constrained contexts but not in resource-munificent contexts.
Resumo:
Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.
Using Agents for Mining Maintenance Data while interacting in 3D Objectoriented Virtual Environments
Resumo:
This report demonstrates the development of: (a) object-oriented representation to provide 3D interactive environment using data provided by Woods Bagot; (b) establishing basis of agent technology for mining building maintenance data, and (C) 3D interaction in virtual environments using object-oriented representation. Applying data mining over industry maintenance database has been demonstrated in the previous report.
Resumo:
This report demonstrates the development of: • Development of software agents for data mining • Link data mining to building model in virtual environments • Link knowledge development with building model in virtual environments • Demonstration of software agents for data mining • Populate with maintenance data
Resumo:
This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.
Resumo:
Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.
Resumo:
Executive Summary The objective of this report was to use the Sydney Opera House as a case study of the application of Building Information Modelling (BIM). The Sydney opera House is a complex, large building with very irregular building configuration, that makes it a challenging test. A number of key concerns are evident at SOH: • the building structure is complex, and building service systems - already the major cost of ongoing maintenance - are undergoing technology change, with new computer based services becoming increasingly important. • the current “documentation” of the facility is comprised of several independent systems, some overlapping and is inadequate to service current and future services required • the building has reached a milestone age in terms of the condition and maintainability of key public areas and service systems, functionality of spaces and longer term strategic management. • many business functions such as space or event management require up-to-date information of the facility that are currently inadequately delivered, expensive and time consuming to update and deliver to customers. • major building upgrades are being planned that will put considerable strain on existing Facilities Portfolio services, and their capacity to manage them effectively While some of these concerns are unique to the House, many will be common to larger commercial and institutional portfolios. The work described here supported a complementary task which sought to identify if a building information model – an integrated building database – could be created, that would support asset & facility management functions (see Sydney Opera House – FM Exemplar Project, Report Number: 2005-001-C-4 Building Information Modelling for FM at Sydney Opera House), a business strategy that has been well demonstrated. The development of the BIMSS - Open Specification for BIM has been surprisingly straightforward. The lack of technical difficulties in converting the House’s existing conventions and standards to the new model based environment can be related to three key factors: • SOH Facilities Portfolio – the internal group responsible for asset and facility management - have already well established building and documentation policies in place. The setting and adherence to well thought out operational standards has been based on the need to create an environment that is understood by all users and that addresses the major business needs of the House. • The second factor is the nature of the IFC Model Specification used to define the BIM protocol. The IFC standard is based on building practice and nomenclature, widely used in the construction industries across the globe. For example the nomenclature of building parts – eg ifcWall, corresponds to our normal terminology, but extends the traditional drawing environment currently used for design and documentation. This demonstrates that the international IFC model accurately represents local practice for building data representation and management. • a BIM environment sets up opportunities for innovative processes that can exploit the rich data in the model and improve services and functions for the House: for example several high-level processes have been identified that could benefit from standardized Building Information Models such as maintenance processes using engineering data, business processes using scheduling, venue access, security data and benchmarking processes using building performance data. The new technology matches business needs for current and new services. The adoption of IFC compliant applications opens the way forward for shared building model collaboration and new processes, a significant new focus of the BIM standards. In summary, SOH current building standards have been successfully drafted for a BIM environment and are confidently expected to be fully developed when BIM is adopted operationally by SOH. These BIM standards and their application to the Opera House are intended as a template for other organisations to adopt for the own procurement and facility management activities. Appendices provide an overview of the IFC Integrated Object Model and an understanding IFC Model Data.
Resumo:
“SOH see significant benefit in digitising its drawings and operation and maintenance manuals. Since SOH do not currently have digital models of the Opera House structure or other components, there is an opportunity for this national case study to promote the application of Digital Facility Modelling using standardized Building Information Models (BIM)”. The digital modelling element of this project examined the potential of building information models for Facility Management focusing on the following areas: • The re-usability of building information for FM purposes • BIM as an Integrated information model for facility management • Extendibility of the BIM to cope with business specific requirements • Commercial facility management software using standardised building information models • The ability to add (organisation specific) intelligence to the model • A roadmap for SOH to adopt BIM for FM The project has established that BIM – building information modelling - is an appropriate and potentially beneficial technology for the storage of integrated building, maintenance and management data for SOH. Based on the attributes of a BIM, several advantages can be envisioned: consistency in the data, intelligence in the model, multiple representations, source of information for intelligent programs and intelligent queries. The IFC – open building exchange standard – specification provides comprehensive support for asset and facility management functions, and offers new management, collaboration and procurement relationships based on sharing of intelligent building data. The major advantages of using an open standard are: information can be read and manipulated by any compliant software, reduced user “lock in” to proprietary solutions, third party software can be the “best of breed” to suit the process and scope at hand, standardised BIM solutions consider the wider implications of information exchange outside the scope of any particular vendor, information can be archived as ASCII files for archival purposes, and data quality can be enhanced as the now single source of users’ information has improved accuracy, correctness, currency, completeness and relevance. SOH current building standards have been successfully drafted for a BIM environment and are confidently expected to be fully developed when BIM is adopted operationally by SOH. There have been remarkably few technical difficulties in converting the House’s existing conventions and standards to the new model based environment. This demonstrates that the IFC model represents world practice for building data representation and management (see Sydney Opera House – FM Exemplar Project Report Number 2005-001-C-3, Open Specification for BIM: Sydney Opera House Case Study). Availability of FM applications based on BIM is in its infancy but focussed systems are already in operation internationally and show excellent prospects for implementation systems at SOH. In addition to the generic benefits of standardised BIM described above, the following FM specific advantages can be expected from this new integrated facilities management environment: faster and more effective processes, controlled whole life costs and environmental data, better customer service, common operational picture for current and strategic planning, visual decision-making and a total ownership cost model. Tests with partial BIM data – provided by several of SOH’s current consultants – show that the creation of a SOH complete model is realistic, but subject to resolution of compliance and detailed functional support by participating software applications. The showcase has demonstrated successfully that IFC based exchange is possible with several common BIM based applications through the creation of a new partial model of the building. Data exchanged has been geometrically accurate (the SOH building structure represents some of the most complex building elements) and supports rich information describing the types of objects, with their properties and relationships.
Resumo:
This document provides a review of international and national practices in investment decision support tools in road asset management. Efforts were concentrated on identifying analytic frameworks, evaluation methodologies and criteria adopted by current tools. Emphasis was also given to how current approaches support Triple Bottom Line decision-making. Benefit Cost Analysis and Multiple Criteria Analysis are principle methodologies in supporting decision-making in Road Asset Management. The complexity of the applications shows significant differences in international practices. There is continuing discussion amongst practitioners and researchers regarding to which one is more appropriate in supporting decision-making. It is suggested that the two approaches should be regarded as complementary instead of competitive means. Multiple Criteria Analysis may be particularly helpful in early stages of project development, say strategic planning. Benefit Cost Analysis is used most widely for project prioritisation and selecting the final project from amongst a set of alternatives. Benefit Cost Analysis approach is useful tool for investment decision-making from an economic perspective. An extension of the approach, which includes social and environmental externalities, is currently used in supporting Triple Bottom Line decision-making in the road sector. However, efforts should be given to several issues in the applications. First of all, there is a need to reach a degree of commonality on considering social and environmental externalities, which may be achieved by aggregating the best practices. At different decision-making level, the detail of consideration of the externalities should be different. It is intended to develop a generic framework to coordinate the range of existing practices. The standard framework will also be helpful in reducing double counting, which appears in some current practices. Cautions should also be given to the methods of determining the value of social and environmental externalities. A number of methods, such as market price, resource costs and Willingness to Pay, are found in the review. The use of unreasonable monetisation methods in some cases has discredited Benefit Cost Analysis in the eyes of decision makers and the public. Some social externalities, such as employment and regional economic impacts, are generally omitted in current practices. This is due to the lack of information and credible models. It may be appropriate to consider these externalities in qualitative forms in a Multiple Criteria Analysis. Consensus has been reached in considering noise and air pollution in international practices. However, Australia practices generally omitted these externalities. Equity is an important consideration in Road Asset Management. The considerations are either between regions, or social groups, such as income, age, gender, disable, etc. In current practice, there is not a well developed quantitative measure for equity issues. More research is needed to target this issue. Although Multiple Criteria Analysis has been used for decades, there is not a generally accepted framework in the choice of modelling methods and various externalities. The result is that different analysts are unlikely to reach consistent conclusions about a policy measure. In current practices, some favour using methods which are able to prioritise alternatives, such as Goal Programming, Goal Achievement Matrix, Analytic Hierarchy Process. The others just present various impacts to decision-makers to characterise the projects. Weighting and scoring system are critical in most Multiple Criteria Analysis. However, the processes of assessing weights and scores were criticised as highly arbitrary and subjective. It is essential that the process should be as transparent as possible. Obtaining weights and scores by consulting local communities is a common practice, but is likely to result in bias towards local interests. Interactive approach has the advantage in helping decision-makers elaborating their preferences. However, computation burden may result in lose of interests of decision-makers during the solution process of a large-scale problem, say a large state road network. Current practices tend to use cardinal or ordinal scales in measure in non-monetised externalities. Distorted valuations can occur where variables measured in physical units, are converted to scales. For example, decibels of noise converts to a scale of -4 to +4 with a linear transformation, the difference between 3 and 4 represents a far greater increase in discomfort to people than the increase from 0 to 1. It is suggested to assign different weights to individual score. Due to overlapped goals, the problem of double counting also appears in some of Multiple Criteria Analysis. The situation can be improved by carefully selecting and defining investment goals and criteria. Other issues, such as the treatment of time effect, incorporating risk and uncertainty, have been given scant attention in current practices. This report suggested establishing a common analytic framework to deal with these issues.
Resumo:
The aim of this project is to develop a systematic investment decision-making framework for infrastructure asset management by incorporation economic justification, social and environmental consideration in the decision-making process. This project assesses the factors that are expected to provide significant impacts on the variability of expenditures. A procedure for assessing risk and reliability for project investment appraisals will be developed. The project investigates public perception, social and environmental impacts on road infrastructure investment. This research will contribute to the debate about how important social and environmental issues should be incorporated into the investment decision-making process for infrastructure asset management.
Resumo:
This document provides the findings of an international review of investment decision-making practices in road asset management. Efforts were concentrated on identifying the strategic objectives of agencies in road asset management, establishing and understanding criteria different organisations adopted and ascertaining the exact methodologies used by different countries and international organisations. Road assets are powerful drivers of economic development and social equity. They also have significant impacts on the natural and man-made environment. The traditional definition of asset management is “A systematic process of maintaining, upgrading and operating physical assets cost effectively. It combines engineering principles with sound business practices and economic theory and it provides tools to facilitate a more organised, logical approach to decision-making” (US Dept. of Transportation, 1999). In recent years, the concept has been broadened to cover the complexity of decision making, based on a wider variety of policy considerations as well as social and environmental issues rather than is covered by Benefit-Cost analysis and pure technical considerations. Current international practices are summarised in table 2. It was evident that Engineering-economic analysis methods are well advanced to support decision-making. A range of tools available supports performance predicting of road assets and associated cost/benefit in technical context. The need for considering triple plus one bottom line of social, environmental and economic as well as political factors in decision-making is well understood by road agencies around the world. The techniques used to incorporate these however, are limited. Most countries adopt a scoring method, a goal achievement matrix or information collected from surveys. The greater uncertainty associated with these non-quantitative factors has generally not been taken into consideration. There is a gap between the capacities of the decision-making support systems and the requirements from decision-makers to make more rational and transparent decisions. The challenges faced in developing an integrated decision making framework are both procedural and conceptual. In operational terms, the framework should be easy to be understood and employed. In philosophical terms, the framework should be able to deal with challenging issues, such as uncertainty, time frame, network effects, model changes, while integrating cost and non-cost values into the evaluation. The choice of evaluation techniques depends on the feature of the problem at hand, on the aims of the analysis, and on the underlying information base At different management levels, the complexity in considering social, environmental, economic and political factor in decision-making is different. At higher the strategic planning level, more non-cost factors are involved. The complexity also varies based on the scope of the investment proposals. Road agencies traditionally place less emphasis on evaluation of maintenance works. In some cases, social equity, safety, environmental issues have been used in maintenance project selection. However, there is not a common base for the applications.