138 resultados para Wynn, Elliott J.


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With the development of enterprise informatisation, Product Lifecycle Management (PLM) systems have been widely deployed and applied in enterprises. This paper analyzes the requirement that conducting version operations on business objects as specified in process models should be compliant with the versioning policies imposed by product lifecycles. This leads to the introduction of the concept of versioning compliance, and the approach of compliance checking that we proposed in our earlier work, which comprises both syntactical compatibility and behavioural compatibility checking. The paper then focuses on the tool implementation for providing automated support to the versioning compliance checking. An empirical evaluation of the tool was also performed with industrial partners using the well-known questionnaire-based method. The evaluation and feedback from practitioners further evidence the practical significance of this research question in the PLM field and demonstrate that the proposed solution with its automated tool support possesses a high application potential.

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Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution.

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Organisations are constantly seeking efficiency improvements for their business processes in terms of time and cost. Management accounting enables reporting of detailed cost of operations for decision making purpose, although significant effort is required to gather accurate operational data. Business process management is concerned with systematically documenting, managing, automating, and optimising processes. Process mining gives valuable insight into processes through analysis of events recorded by an IT system in the form of an event log with the focus on efficient utilisation of time and resources, although its primary focus is not on cost implications. In this paper, we propose a framework to support management accounting decisions on cost control by automatically incorporating cost data with historical data from event logs for monitoring, predicting and reporting process-related costs. We also illustrate how accurate, relevant and timely management accounting style cost reports can be produced on demand by extending open-source process mining framework ProM.

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On a balmy summer morning a small group of us gathered for breakfast at the China National Convention Centre in Beijing during the 2010 ISME World Conference. As the cafe started to fill up with delegates from around the globe, we sat intently discussing our grand plan. Quite simply, we wanted to develop a network of community music practitioners and scholars in the Asia Pacific region. Inspired by the Community Music Activity (CMA) Commission we had just attended in Hangzhou the week before, we felt a pressing need to keep the seminar’s momentum going. During the seminar we had heard many stories and examples of musical practices, community contexts, pedagogical approaches and research ethics; however, set against the backdrop of this Chinese context, these familiar topics seemed to take on a new meaning (see Bartleet 2011). As we experienced the local culture and interacted with some of our Chinese colleagues, we were reminded that the concept of community is always contextual, contingent and contested(see Elliott et al. 2008: 3). We were also reminded that notions of what community music is and its social and educational functions are always fluid and varied depending on where you are in the world. After the seminar, our sense was that there are new voices and ideas relating to community music coming from this region that need to be heard. We hoped that this network would serve as a vehicle for activating relationships between these practitioners and scholars, as well as a channel for developing cross cultural partnerships, and disseminating research about community music in this region. On the surface this goal seemed quite straightforward...

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Speeding represents a major contributor to road trauma, increasing crash frequency and severity. Antispeeding campaigns represent a key strategy aimed at discouraging individuals from speeding. This paper investigated salient beliefs underpinning male and female drivers’ travel speed behaviour, with the view to use such insight to, ultimately, inform the content of targeted anti-speeding messages. A survey of N = 751 (579 males, 16-79 years) drivers assessed what they regarded as speeding in 60km/hr and 100km/hr zones and their beliefs about how they would respond to receiving a speeding infringement. Participants responded to scales which extended up to 20km/hr above each respective speed limit, the lowest speed that they considered was speeding and the speed at which they would be willing to drive and still feel in control. For analyses, to enable greater scrutiny of potential gender differences regarding the speeds identified, participants’ responses to these items were categorised into 5km/hr increments and chi-square analyses conducted. For their responses to (beliefs about) the possibility of being caught speeding, drivers were asked how applicable various beliefs were to them (e.g., feeling unlucky). These beliefs were analysed via MANOVA. The results revealed that there was considerable variability in the speeds identified, thus supporting the value of categorising speeds. Within the 100km/hr zone, based on the categories, a significant difference was found regarding the speed that males would be willing to drive (and still feel in control) relative to females. Specifically, the greatest proportion of males (30.4%) identified speeds within the 106-110km/hr category whereas the greatest proportion of females (38.1%) identified a lower speed, within the 101-105km/hr category, as the speed they would be willing to drive. No other significant differences emerged, however, either in relation to the definition of speeding reported for 100km/hr zones (i.e., males and females tended to identify a similar speed as indicative of speeding) nor for these same items as assessed in relation to the 60km/hr zones. For their responses to the possibility of being caught, males were significantly more likely than females to report that, if caught, a likely response they would have would be to think that they had still been driving safely. In contrast, females were significantly more likely than males to report thinking that their speeding had been unsafe and that they should not have been speeding. Females were also significantly more likely to report feeling embarrassed to tell important others about having received a speeding infringement than males. The findings are discussed in terms of their implications for developing well-targeted advertising messages aimed at discouraging drivers’ from speeding.

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Having a reliable understanding about the behaviours, problems, and performance of existing processes is important in enabling a targeted process improvement initiative. Recently, there has been an increase in the application of innovative process mining techniques to facilitate evidence-based understanding about organizations' business processes. Nevertheless, the application of these techniques in the domain of finance in Australia is, at best, scarce. This paper details a 6-month case study on the application of process mining in one of the largest insurance companies in Australia. In particular, the challenges encountered, the lessons learned, and the results obtained from this case study are detailed. Through this case study, we not only validated existing `lessons learned' from other similar case studies, but also added new insights that can be beneficial to other practitioners in applying process mining in their respective fields.

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Many researchers have demonstrated the applicability of the Theory of Planned Behaviour (TPB) in predicting both intention to speed and actual speeding behaviour. However, there remain shortcomings in the explanatory power of the TPB, with research suggesting that even when drivers had reported an intention to not speed approximately 25% of drivers report behaviour that does not align with their intentions (i.e., they engaged in speeding, Elliott & Armitage, 2006). This research explores the role of a novel and promising construct, mindfulness, in enhancing the explanatory utility of the TPB for the understanding of drivers’ speeding behaviour in school zones. Mindfulness is a concept which has been widely used in studies of consciousness, but has recently been applied to the understanding of behaviour in other areas, including clinical psychology, physical activity, education and business. It has been suggested that mindfulness can also be applied to road safety, though its application within this context currently remains limited. This study was based on an e-survey of the general driving public (N=240). Overall, the results identified mindfulness as a construct which may aid understanding of the relationship between drivers’ intentions and behaviour. Theoretically, the findings may have implications in terms of identifying mindfulness as an additional explanatory construct within a TPB framework. In road safety practice, the findings suggest that efficacious countermeasures around school zones may be those that function to heighten drivers’ mindfulness, such as flashing lights and physical speed reduction measures.

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We evaluated the Minnesota Multiphasic Personality Inventory-Second Edition (MMPI-2) Response Bias Scale (RBS). Archival data from 83 individuals who were referred for neuropsychological assessment with no formal diagnosis (n = 10), following a known or suspected traumatic brain injury (n = 36), with a psychiatric diagnosis (n = 20), or with a history of both trauma and a psychiatric condition (n = 17) were retrieved. The criteria for malingered neurocognitive dysfunction (MNCD) were applied, and two groups of participants were formed: poor effort (n = 15) and genuine responders (n = 68). Consistent with previous studies, the difference in scores between groups was greatest for the RBS (d = 2.44), followed by two established MMPI-2 validity scales, F (d = 0.25) and K (d = 0.23), and strong significant correlations were found between RBS and F (rs = .48) and RBS and K (r = −.41). When MNCD group membership was predicted using logistic regression, the RBS failed to add incrementally to F. In a separate regression to predict group membership, K added significantly to the RBS. Receiver-operating curve analysis revealed a nonsignificant area under the curve statistic, and at the ideal cutoff in this sample of >12, specificity was moderate (.79), sensitivity was low (.47), and positive and negative predictive power values at a 13% base rate were .25 and .91, respectively. Although the results of this study require replication because of a number of limitations, this study has made an important first attempt to report RBS classification accuracy statistics for predicting poor effort at a range of base rates.

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A pressing cost issue facing construction is the procurement of off-site pre-manufactured assemblies. In order to encourage Australian adoption of off-site manufacture (OSM), a new approach to underlying processes is required. The advent of object oriented digital models for construction design assumes intelligent use of data. However, the construction production system relies on traditional methods and data sources and is expected to benefit from the application of well-established business process management techniques. The integration of the old and new data sources allows for the development of business process models which, by capturing typical construction processes involving OSM, provides insights into such processes. This integrative approach is the foundation of research into the use of OSM to increase construction productivity in Australia. The purpose of this study is to develop business process models capturing the procurement, resources and information flow of construction projects. For each stage of the construction value chain, a number of sub-processes are identified. Business Process Modelling Notation (BPMN), a mainstream business process modelling standard, is used to create base-line generic construction process models. These models identify OSM decision-making points that could provide cost reductions in procurement workflow and management systems. This paper reports on phase one of an on-going research aiming to develop a proto-type workflow application that can provide semi-automated support to construction processes involving OSM and assist in decision-making in the adoption of OSM thus contributing to a sustainable built environment.

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Many construction industry decision-makers believe there is a lack of off-site manufacture (OSM) adoption for non-residential construction in Australia. Identification of construction business process was considered imperative in order to assist decision-makers to increase OSM utilisation. The premise that domain knowledge can be re-used to provide an intervention point in the construction process led a team of researchers to construct simple base-line process models for the complete construction process, segmented into six phases. Sixteen domain knowledge industry experts were asked to review the construction phase base-line models to answer the question “Where in the process illustrated by this base-line model phase is an OSM task?”. Through an iterative and generative process a number of off-site manufacture intervention points were identified and integrated into the process models. The re-use of industry expert domain knowledge provided suggestions for new ways to do basic tasks thus facilitating changes to current practice. It is expected that implementation of the new processes will lead to systemic industry change and thus a growth in productivity due to increased adoption of OSM.

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Norms regulate the behaviour of their subjects and define what is legal and what is illegal. Norms typically describe the conditions under which they are applicable and the normative effects as a results of their applications. On the other hand, process models specify how a business operation or service is to be carried out to achieve a desired outcome. Norms can have significant impact on how business operations are conducted and they can apply to the whole or part of a business process. For example, they may impose conditions on the different aspects of a process (e.g., perform tasks in a specific sequence (control-flow), at a specific time or within a certain time frame (temporal aspect), by specific people (resources)). We propose a framework that provides the formal semantics of the normative requirements for determining whether a business process complies with a normative document (where a normative document can be understood in a very broad sense, ranging from internal policies to best practice policies, to statutory acts). We also present a classification of normal requirements based on the notion of different types of obligations and the effects of violating these obligations.

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Existing compliance management frameworks (CMFs) offer a multitude of compliance management capabilities that makes difficult for enterprises to decide on the suitability of a framework. Making a decision on the suitability requires a deep understanding of the functionalities of a framework. Gaining such an understanding is a difficult task which, in turn, requires specialised tools and methodologies for evaluation. Current compliance research lacks such tools and methodologies for evaluating CMFs. This paper reports a methodological evaluation of existing CMFs based on a pre-defined evaluation criteria. Our evaluation highlights what existing CMFs offer, and what they cannot. Also, it underpins various open questions and discusses the challenges in this direction.

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Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators(PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.