930 resultados para Design management


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This paper reports on the design, implementation and outcomes of a mentoring program involving 18 employees in the IT Division of WorkCover Queensland. The paper provides some background information to the development of the program and the design and implementation phases including recruitment and matching of participants, orientation and training, and the mentoring process including transition and/or termination. The paper also outlines the quantitative and qualitative evaluation processes that occurred and the outcomes of that evaluation. Results indicated a wealth of positive individual, mentoring, and organisational outcomes. The organisation and semi-structured processes provided in the program are considered as major contributing factors to the successful outcomes of the program. These outcomes are likely to have long-term benefits for the individuals involved, the IT Division, and the broader organisation

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This paper represents a new theorization of the role of location-based games (LBGs) as potentially playing specific roles in peoples’ access to the culture of cities [22]. A LBG is a game that employs mobile technologies as tools for game play in real world environments. We argue that as a new genre in the field of mobile entertainment, research in this area tends to be preoccupied with the newness of the technology and its commercial possibilities. However, this overlooks its potential to contribute to cultural production. We argue that the potential to contribute to cultural production lies in the capacity of these experiences to enhance relationships between specific groups and new urban spaces. Given that developers can design LBGs to be played with everyday devices in everyday environments, what new creative opportunities are available to everyday people?

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This paper suggests ways for educators and designers to understand and merge priorities in order to inform the development of mobile learning (m-learning) applications that maximise user experiences and hence learning opportunities. It outlines a User Experience Design (UXD) theory and development process that requires designers to conduct a thorough initial contextual inquiry into a particular domain in order to set project priorities and development guidelines. A matrix that identifies the key contextual considerations namely the social, cultural, spatial, technical and temporal constructs of any domain is presented as a vital tool for achieving successful UXD. The frame of reference provided by this matrix ensures that decisions made throughout the design process are attributable to a desired user experience. To illustrate how the proposed UXD theory and development process supports the creation of effective m-learning applications, this paper documents the development process of MILK (Mobile Informal Learning Kit). MILK is a support tool that allows teachers and students to develop event paths that consist of a series SMS question and answer messages that lead players through a series of checkpoints between point A and point B. These event paths can be designed to suit desired learning scenarios and can be used to explore a particular place or subject. They can also be designed to facilitate formal or informal learning experiences.

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In this paper we explore what is required of a User Interface (UI) design in order to encourage participation around playing and creating Location-Based Games (LBGs). To base our research in practice, we present Cipher Cities, a web based system. Through the design of this system, we investigate how UI design can provide tools for complex content creation to compliment and encourage the use of mobile phones for designing, distributing, and playing LBGs. Furthermore we discuss how UI design can promote and support socialisation around LBGs through the design of functional interface components and services such as groups, user profiles, and player status listings.

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Urban infrastructure development in Korea has recently shifted from an old paradigm of conventional infrastructure planning to a new paradigm of intelligent infrastructure provision. This new paradigm, so called ubiquitous infrastructure, is based on a combination of urban infrastructure, information and communication technologies and digital networks. Ubiquitous infrastructure basically refers to an urban infrastructure where any citizen could access any infrastructure and services via any electronic device regardless of time and location. This paper introduces this new paradigm of intellectual infrastructure planning and its design schemes. The paper also examines the ubiquitous infrastructure development in Korea and discusses the positive effects of ubiquitous infrastructure on sustainable urban development.

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Purpose – Financial information about costs and return on investments are of key importance to strategic decision-making but also in the context of process improvement or business engineering. In this paper we propose a value-oriented approach to business process modeling based on key concepts and metrics from operations and financial management, to aid decision making in process re-design projects on the basis of process models. Design/methodology/approach – We suggest a theoretically founded extension to current process modeling approaches, and delineate a framework as well as methodical support to incorporate financial information into process re-design. We use two case studies to evaluate the suggested approach. Findings – Based on two case studies, we show that the value-oriented process modeling approach facilitates and improves managerial decision-making in the context of process re-design. Research limitations / implications – We present design work and two case studies. More research is needed to more thoroughly evaluate the presented approach in a variety of real-life process modeling settings. Practical implications – We show how our approach enables decision makers to make investment decisions in process re-design projects, and also how other decisions, for instance in the context of enterprise architecture design, can be facilitated. Originality/value – This study reports on an attempt to integrate financial considerations into the act of process modeling, in order to provide more comprehensive decision making support in process re-design projects.

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The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

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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.

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The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.