34 resultados para Analysis Model

em Aston University Research Archive


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The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

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Integer-valued data envelopment analysis (DEA) with alternative returns to scale technology has been introduced and developed recently by Kuosmanen and Kazemi Matin. The proportionality assumption of their introduced "natural augmentability" axiom in constant and nondecreasing returns to scale technologies makes it possible to achieve feasible decision-making units (DMUs) of arbitrary large size. In many real world applications it is not possible to achieve such production plans since some of the input and output variables are bounded above. In this paper, we extend the axiomatic foundation of integer-valuedDEAmodels for including bounded output variables. Some model variants are achieved by introducing a new axiom of "boundedness" over the selected output variables. A mixed integer linear programming (MILP) formulation is also introduced for computing efficiency scores in the associated production set. © 2011 The Authors. International Transactions in Operational Research © 2011 International Federation of Operational Research Societies.

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In a Data Envelopment Analysis model, some of the weights used to compute the efficiency of a unit can have zero or negligible value despite of the importance of the corresponding input or output. This paper offers an approach to preventing inputs and outputs from being ignored in the DEA assessment under the multiple input and output VRS environment, building on an approach introduced in Allen and Thanassoulis (2004) for single input multiple output CRS cases. The proposed method is based on the idea of introducing unobserved DMUs created by adjusting input and output levels of certain observed relatively efficient DMUs, in a manner which reflects a combination of technical information and the decision maker's value judgements. In contrast to many alternative techniques used to constrain weights and/or improve envelopment in DEA, this approach allows one to impose local information on production trade-offs, which are in line with the general VRS technology. The suggested procedure is illustrated using real data. © 2011 Elsevier B.V. All rights reserved.

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Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1) Sliding window where the current sentiment-topic word distributions are dependent on the previous sentiment-topic-specific word distributions in the last S epochs; (2) skip model where history sentiment topic word distributions are considered by skipping some epochs in between; and (3) multiscale model where previous long- and shorttimescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011. © 2013 ACM 2157-6904/2013/12-ART5 $ 15.00.

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The proliferation of visual display terminals (VDTs) in offices is an international phenomenon. Numerous studies have investigated the health implications which can be categorised into visual problems, symptoms of musculo-skelctal discomfort, or psychosocial effects. The psychosocial effects are broader and there is mixed evidence in this area. The inconsistent results from the studies of VDT work so far undertaken may reflect several methodological shortcomings. In an attempt to overcome these deficiencies and to broaden the model of inter-relationships a model was developed to investigate their interactions and Ihc outputs of job satisfaction, stress and ill health. The study was a two-stage, long-term investigation with measures taken before the VDTs were introduced and the same measures taken 12 months after the 'go-live' date. The research was conducted in four offices of the Department of Social Security. The data were analysed for each individual site and in addition the total data were used in a path analysis model. Significant positive relationships were found at the pre-implementation stage between the musculo-skeletal discomfort, psychosomatic ailments, visual complaints and stress. Job satisfaction was negatively related to visual complaints and musculo-skeletal discomfort. Direct paths were found for age and job level with variety found in the job and age with job satisfaction and a negative relationship with the office environment. The only job characteristic which had a direct path to stress was 'dealing with others'. Similar inter-relationships were found in the post-implementation data. However, in addition attributes of the computer system, such as screen brightness and glare, were related positively with stress and negatively with job satisfaction. The comparison of the data at the two stages found that there had been no significant changes in the users' perceptions of their job characteristics and job satisfaction but there was a small and significant reduction in the stress measure.

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Manufacturing firms are driven by competitive pressures to continually improve the effectiveness and efficiency of their organisations. For this reason, manufacturing engineers often implement changes to existing processes, or design new production facilities, with the expectation of making further gains in manufacturing system performance. This thesis relates to how the likely outcome of this type of decision should be predicted prior to its implementation. The thesis argues that since manufacturing systems must also interact with many other parts of an organisation, the expected performance improvements can often be significantly hampered by constraints that arise elsewhere in the business. As a result, decision-makers should attempt to predict just how well a proposed design will perform when these other factors, or 'support departments', are taken into consideration. However, the thesis also demonstrates that, in practice, where quantitative analysis is used to evaluate design decisions, the analysis model invariably ignores the potential impact of support functions on a system's overall performance. A more comprehensive modelling approach is therefore required. A study of how various business functions interact establishes that to properly represent the kind of delays that give rise to support department constraints, a model should actually portray the dynamic and stochastic behaviour of entities in both the manufacturing and non-manufacturing aspects of a business. This implies that computer simulation be used to model design decisions but current simulation software does not provide a sufficient range of functionality to enable the behaviour of all of these entities to be represented in this way. The main objective of the research has therefore been the development of a new simulator that will overcome limitations of existing software and so enable decision-makers to conduct a more holistic evaluation of design decisions. It is argued that the application of object-oriented techniques offers a potentially better way of fulfilling both the functional and ease-of-use issues relating to development of the new simulator. An object-oriented analysis and design of the system, called WBS/Office, are therefore presented that extends to modelling a firm's administrative and other support activities in the context of the manufacturing system design process. A particularly novel feature of the design is the ability for decision-makers to model how a firm's specific information and document processing requirements might hamper shop-floor performance. The simulator is primarily intended for modelling make-to-order batch manufacturing systems and the thesis presents example models created using a working version of WBS/Office that demonstrate the feasibility of using the system to analyse manufacturing system designs in this way.

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We propose a novel electroencephalographic application of a recently developed cerebral source extraction method (Functional Source Separation, FSS), which starts from extracranial signals and adds a functional constraint to the cost function of a basic independent component analysis model without requiring solutions to be independent. Five ad-hoc functional constraints were used to extract the activity reflecting the temporal sequence of sensory information processing along the somatosensory pathway in response to the separate left and right median nerve galvanic stimulation. Constraints required only the maximization of the responsiveness at specific latencies following sensory stimulation, without taking into account that any frequency or spatial information. After source extraction, the reliability of identified FS was assessed based on the position of single dipoles fitted on its retroprojected signals and on a discrepancy measure. The FS positions were consistent with previously reported data (two early subcortical sources localized in the brain stem and thalamus, the three later sources in cortical areas), leaving negligible residual activity at the corresponding latencies. The high-frequency component of the oscillatory activity (HFO) of the extracted component was analyzed. The integrity of the low amplitude HFOs was preserved for each FS. On the basis of our data, we suggest that FSS can be an effective tool to investigate the HFO behavior of the different neuronal pools, recruited at successive times after median nerve galvanic stimulation. As FSs are reconstructed along the entire experimental session, directional and dynamic HFO synchronization phenomena can be studied.

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This paper describes how dimensional variation management could be integrated throughout design, manufacture and verification, to improve quality while reducing cycle times and manufacturing cost in the Digital Factory environment. Initially variation analysis is used to optimize tolerances during product and tooling design and also results in the creation of a simplified representation of product key characteristics. This simplified representation can then be used to carry out measurability analysis and process simulation. The link established between the variation analysis model and measurement processes can subsequently be used throughout the production process to automatically update the variation analysis model in real time with measurement data. This ‘live’ simulation of variation during manufacture will allow early detection of quality issues and facilitate autonomous measurement assisted processes such as predictive shimming. A study is described showing how these principles can be demonstrated using commercially available software combined with a number of prototype applications operating as discrete modules. The commercially available modules include Catia/Delmia for product and process design, 3DCS for variation analysis and Spatial Analyzer for measurement simulation. Prototype modules are used to carry out measurability analysis and instrument selection. Realizing the full potential of Metrology in the Digital Factory will require that these modules are integrated and software architecture to facilitate this is described. Crucially this integration must facilitate the use of realtime metrology data describing the emerging assembly to update the digital model.

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This paper introduces a new technique in the investigation of limited-dependent variable models. This paper illustrates that variable precision rough set theory (VPRS), allied with the use of a modern method of classification, or discretisation of data, can out-perform the more standard approaches that are employed in economics, such as a probit model. These approaches and certain inductive decision tree methods are compared (through a Monte Carlo simulation approach) in the analysis of the decisions reached by the UK Monopolies and Mergers Committee. We show that, particularly in small samples, the VPRS model can improve on more traditional models, both in-sample, and particularly in out-of-sample prediction. A similar improvement in out-of-sample prediction over the decision tree methods is also shown.

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In this paper we propose a data envelopment analysis (DEA) based method for assessing the comparative efficiencies of units operating production processes where input-output levels are inter-temporally dependent. One cause of inter-temporal dependence between input and output levels is capital stock which influences output levels over many production periods. Such units cannot be assessed by traditional or 'static' DEA which assumes input-output correspondences are contemporaneous in the sense that the output levels observed in a time period are the product solely of the input levels observed during that same period. The method developed in the paper overcomes the problem of inter-temporal input-output dependence by using input-output 'paths' mapped out by operating units over time as the basis of assessing them. As an application we compare the results of the dynamic and static model for a set of UK universities. The paper is suggested that dynamic model capture the efficiency better than static model. © 2003 Elsevier Inc. All rights reserved.

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There is an alternative model of the 1-way ANOVA called the 'random effects' model or ‘nested’ design in which the objective is not to test specific effects but to estimate the degree of variation of a particular measurement and to compare different sources of variation that influence the measurement in space and/or time. The most important statistics from a random effects model are the components of variance which estimate the variance associated with each of the sources of variation influencing a measurement. The nested design is particularly useful in preliminary experiments designed to estimate different sources of variation and in the planning of appropriate sampling strategies.

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The topic of my research is consumer brand equity (CBE). My thesis is that the success or otherwise of a brand is better viewed from the consumers’ perspective. I specifically focus on consumers as a unique group of stakeholders whose involvement with brands is crucial to the overall success of branding strategy. To this end, this research examines the constellation of ideas on brand equity that have hitherto been offered by various scholars. Through a systematic integration of the concepts and practices identified but these scholars (concepts and practices such as: competitiveness, consumer searching, consumer behaviour, brand image, brand relevance, consumer perceived value, etc.), this research identifies CBE as a construct that is shaped, directed and made valuable by the beliefs, attitudes and the subjective preferences of consumers. This is done by examining the criteria on the basis of which the consumers evaluate brands and make brand purchase decisions. Understanding the criteria by which consumers evaluate brands is crucial for several reasons. First, as the basis upon which consumers select brands changes with consumption norms and technology, understanding the consumer choice process will help in formulating branding strategy. Secondly, an understanding of these criteria will help in formulating a creative and innovative agenda for ‘new brand’ propositions. Thirdly, it will also influence firms’ ability to simulate and mould the plasticity of demand for existing brands. In examining these three issues, this thesis presents a comprehensive account of CBE. This is because the first issue raised in the preceding paragraph deals with the content of CBE. The second issue addresses the problem of how to develop a reliable and valid measuring instrument for CBE. The third issue examines the structural and statistical relationships between the factors of CBE and the consequences of CBE on consumer perceived value (CPV). Using LISREL-SIMPLIS 8.30, the study finds direct and significant influential links between consumer brand equity and consumer value perception.

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This dissertation studies the process of operations systems design within the context of the manufacturing organization. Using the DRAMA (Design Routine for Adopting Modular Assembly) model as developed by a team from the IDOM Research Unit at Aston University as a starting point, the research employed empirically based fieldwork and a survey to investigate the process of production systems design and implementation within four UK manufacturing industries: electronics assembly, electrical engineering, mechanical engineering and carpet manufacturing. The intention was to validate the basic DRAMA model as a framework for research enquiry within the electronics industry, where the initial IDOM work was conducted, and then to test its generic applicability, further developing the model where appropriate, within the other industries selected. The thesis contains a review of production systems design theory and practice prior to presenting thirteen industrial case studies of production systems design from the four industry sectors. The results and analysis of the postal survey into production systems design are then presented. The strategic decisions of manufacturing and their relationship to production systems design, and the detailed process of production systems design and operation are then discussed. These analyses are used to develop the generic model of production systems design entitled DRAMA II (Decision Rules for Analysing Manufacturing Activities). The model contains three main constituent parts: the basic DRAMA model, the extended DRAMA II model showing the imperatives and relationships within the design process, and a benchmark generic approach for the design and analysis of each component in the design process. DRAMA II is primarily intended for use by researchers as an analytical framework of enquiry, but is also seen as having application for manufacturing practitioners.