Academic literature on the topic 'Human values dashboard'
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Journal articles on the topic "Human values dashboard"
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van der Heijden, Hans. "Evaluating Dual Performance Measures on Information Dashboards: Effects of Anchoring and Presentation Format." Journal of Information Systems 27, no. 2 (June 1, 2013): 21–34. http://dx.doi.org/10.2308/isys-50556.
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ABSTRACT This study examines aspects of dual performance measures in the context of organizations disclosing operational performance to the general public through information dashboards. Dual performance measures are measures where performance is a function of two values, one value denoting the percentage of a group to which the measure refers and one value denoting the performance level achieved by that particular percentage. Dual measures must be anchored to either target percentage or target performance level before a decision on over- or under-performance can be made. A 2 × 2 experiment (n = 222), involving performance assessment of a fictional emergency room, varies anchor and presentation format, and measures the effects on subjective performance of the emergency room, as well as perceived informativeness and attractiveness of the dashboard. The results indicate, first, that choice of anchor matters, in the sense that anchor choice can mask or accentuate relevant information, thereby influencing subjective performance. Second, a pictorial unit chart combined with a performance-level anchor is perceived to be the most informative and most attractive dashboard display. The study contributes to research on the design of information dashboards by developing theory on the effectiveness of reporting dual performance measures.
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Papacharalampopoulos, Alexios, Christos Giannoulis, Panos Stavropoulos, and Dimitris Mourtzis. "A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT." Applied Sciences 10, no. 7 (March 31, 2020): 2377. http://dx.doi.org/10.3390/app10072377.
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A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out.
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Watkins, Scott Christopher, Christine Hammerschmidt, Geoffrey M. Gray, Angela Green, Anna Varughese, and Luis Ahumada. "How do we measure organisational wellness? Development of a comprehensive patient-centred and employee-centred visual analytical solution." BMJ Open Quality 11, no. 4 (December 2022): e002081. http://dx.doi.org/10.1136/bmjoq-2022-002081.
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BackgroundDashboards are visual information systems frequently employed by healthcare organisations to track key quality improvement and patient safety performance metrics. The typical healthcare dashboard focuses on specific metrics, disease processes or units within a larger healthcare organisation. Here, we describe the development of a visual analytical solution (keystone dashboard) for monitoring an entire healthcare organisation.MethodsThe improvement team reviewed and assessed various data sources across the organisation and selected a group of patient and employee related metrics that afforded a broad overview of the organisation’s well-being. Metrics spanned the organisation and included data from patient safety, quality improvement, human resources, risk management and medical staff affairs. Each metric was assigned a numeric weight that correlated with its impact. A visual model incorporating the various data fields was then constructed.ResultsThe keystone dashboard incorporates a data heatmap and density visualisation to emphasis areas of higher density and/or weighted values. The heatmap is used to indicate the weight/magnitude of each metric within a data range in two dimensions: location and time. The visualisation ‘heats up’ depending on the combination of counts events and their assigned impact for the reporting month. Most data sources update in near real time.SummaryThe keystone dashboard serves as a comprehensive and collaborative integration of data from patient safety, quality improvement, human resources, risk management and medical staff affairs. This visual analytical solution incorporates and analyses metrics into a single view with the intent of providing valuable insight into the health of an entire organisation. This dashboard is unique as it provides a broad overview of a healthcare organisation by incorporating key metrics that span the organisation.
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Laszlop, Ádám. "Production Strategic Success Factors in Yield Monitoring Technologies." Acta Periodica, no. 23 (2021): 65–71. http://dx.doi.org/10.47273/ap.2021.23.65-71.
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Crop production is only profitable when all information is known about a specific crop. This information is turned into profitable yield through efficient management technologies and critical decision-making. Efficient management can only be done with the help of yield monitoring technologies, with the goal to optimize natural, human, and material resources while maximizing crop yield efficiency. Yield monitoring technologies works using sensors systems and ensure accuracy of yield. The sensor system detects every aspect of a potential yield in seconds and has also the ability to measure yield values from raw data. and set formulas. The final measurement (yield) is calculated and displayed on the dashboard of relevant technologies and acts to help decision-making and methodology.
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Hartzler, Andrea L., Jason P. Izard, Bruce L. Dalkin, Sean P. Mikles, and John L. Gore. "Design and feasibility of integrating personalized PRO dashboards into prostate cancer care." Journal of the American Medical Informatics Association 23, no. 1 (August 9, 2015): 38–47. http://dx.doi.org/10.1093/jamia/ocv101.
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Abstract Objective Patient-reported outcomes (PROs) are a valued source of health information, but prior work focuses largely on data capture without guidance on visual displays that promote effective PRO use in patient-centered care. We engaged patients, providers, and design experts in human-centered design of “PRO dashboards” that illustrate trends in health-related quality of life (HRQOL) reported by patients following prostate cancer treatment. Materials and Methods We designed and assessed the feasibility of integrating dashboards into care in 3 steps: (1) capture PRO needs of patients and providers through focus groups and interviews; (2) iteratively build and refine a prototype dashboard; and (3) pilot test dashboards with patients and their provider during follow-up care. Results Focus groups ( n = 60 patients) prioritized needs for dashboards that compared longitudinal trends in patients’ HRQOL with “men like me.” Of the candidate dashboard designs, 50 patients and 50 providers rated pictographs less helpful than bar charts, line graphs, or tables ( P < .001) and preferred bar charts and line graphs most. Given these needs and the design recommendations from our Patient Advisory Board ( n = 7) and design experts ( n = 7), we built and refined a prototype that charts patients’ HRQOL compared with age- and treatment-matched patients in personalized dashboards. Pilot testing dashboard use ( n = 12 patients) improved compliance with quality indicators for prostate cancer care ( P < .01). Conclusion PRO dashboards are a promising approach for integrating patient-generated data into prostate cancer care. Informed by human-centered design principles, this work establishes guidance on dashboard content, tailoring, and clinical use that patients and providers find meaningful.
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Kumar, Lokku Guru, Gangireddy Harinatha Reddy, Payasam Venkata Sai, Sk Irfan, and K. Pushpa Pujitha. "Real Time Remote Monitoring, Control and Reporting Dashboard System to Avoid Industrial Disasters Using Industrial IOT." Advances in Science and Technology 106 (May 2021): 143–49. http://dx.doi.org/10.4028/www.scientific.net/ast.106.143.
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In today’s global scenario, with the evolution of new technologies and robust ideas, the world gets more involved and embed the advancements of wireless communication with information technology. An ongoing Gartner report assesses that, by 2021, there will be 25.1 billion web associated gadgets, developing at a pace of 32% every year. Bounties of automation are minimizing the human assistance, intervention and reduced risk factor in industry. Here Industrial Automation is used to control systems or things such as computers or robots or machines or sensors with the help of Internet protocol and cloud computing. In this paper six parameters viz., vibration, temperature, humidity, air quality, sound rate and pressure are monitored and controlled remotely using cloud computing. The system performance automatically changes on the basis of sensor data being collected at regular intervals with a feedback mechanism, thereby allowing the system to control or monitor various devices using internet protocols. The threshold values for all the sensors are set as per the industrial standards. These automation techniques find extensive applications in various control mechanisms to operate the equipment under production processes like boilers and heat-treating ovens, steering and stabilization, pressure exerted by ideal gases in confined containers, vibrations by machinery, air pollution released from chemical composites etc.,
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Jeong, Heejin, and Yili Liu. "Development and Evaluation of a Computational Human Performance Model of In-vehicle Manual and Speech Interactions." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1642. http://dx.doi.org/10.1177/1541931218621372.
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Usability evaluation traditionally relies on costly and time-consuming human-subject experiments, which typically involve developing physical prototypes, designing usability experiment, and recruiting human subjects. To minimize the limitations of human-subject experiments, computational human performance models can be used as an alternative. Human performance models generate digital simulations of human performance and examine the underlying psychological and physiological mechanisms to help understand and predict human performance. A variety of in-vehicle information systems (IVISs) using advanced automotive technologies have been developed to improve driver interactions with the in-vehicle systems. Numerous studies have used human subjects to evaluate in-vehicle human-system interactions; however, there are few modeling studies to estimate and simulate human performance, especially in in-vehicle manual and speech interactions. This paper presents a computational human performance modeling study for a usability test of IVISs using manual and speech interactions. Specifically, the model was aimed to generate digital simulations of human performance for a driver seat adjustment task to decrease the comfort level of a part of driver seat (i.e., the lower lumbar), using three different IVIS controls: direct-manual, indirect-manual, and voice controls. The direct-manual control is an input method to press buttons on the touchscreen display located on the center stack in the vehicle. The indirect-manual control is to press physical buttons mounted on the steering wheel to control a small display in the dashboard-cluster, which requires confirming visual feedback on the cluster display located on the dashboard. The voice control is to say a voice command, “ deflate lower lumbar” through an in-vehicle speaker. The model was developed to estimate task completion time and workload for the driver seat adjustment task, using the Queueing Network cognitive architecture (Liu, Feyen, & Tsimhoni, 2006). Processing times in the model were recorded every 50 msec and used as the estimates of task completion time. The estimated workload was measured by percentage utilization of servers used in the architecture. After the model was developed, the model was evaluated using an empirical data set of thirty-five human subjects from Chen, Tonshal, Rankin, & Feng (2016), in which the task completion times for the driver seat adjustment task using commercial in-vehicle systems (i.e., SYNC with MyFord Touch) were recorded. Driver workload was measured by NASA’s task load index (TLX). The average of the values from the NASA-TLX’s six categories was used to compare to the model’s estimated workload. The model produced results similar to actual human performance (i.e., task completion time, workload). The real-world engineering example presented in this study contributes to the literature of computational human performance modeling research.
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Toman, Michael. "The need for multiple types of information to inform climate change assessment." Journal of Benefit-Cost Analysis 5, no. 03 (December 2014): 469–85. http://dx.doi.org/10.1515/jbca-2014-9005.
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Abstract:Many critics of economic analysis of climate change impacts and response options claim that information is needed on ecosystem characteristics as well as on economic values to fully inform decisions about how climate change affects human well-being. Information on the irreversibility of impacts also is important, critics argue, because it relates to how society evaluates implications for intergenerational equity. In addition, because climate change is subject to a large degree of Knightian uncertainty, it is useful to understand both the information available for assessing climate change risks, and how individuals themselves perceive and evaluate risks. The paper discusses rationales for using these types of information as important complements to benefit-cost analysis for evaluating climate change risks and responses. Ideally such information could be available in a “dashboard” for decision makers assessing social and economic impacts, although limits on currently available information are a significant barrier to using that approach.
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Ismail, Ahmad, Kumar Karagaratnan, and Kumaran Kadirgama. "Thermal comfort findings: Scenario at Malaysian automotive industry." Thermal Science 17, no. 2 (2013): 387–96. http://dx.doi.org/10.2298/tsci111111015i.
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This paper discusses the findings of thermal comfort assessment at Malaysian automotive industry. Nine critical workstations were chosen as subjects for the study in order to determine the thermal comfort among workers at Malaysian automotive industry. The human subjects for the study comprises of the operators from tire receiving, dashboard assembly, drum tester, body assembly, seat assembly, door check assembly, stamping workstation, engine sub assembly and paint shop of the factory. The environmental factors such as Wet Bulb Globe Temperature (WBGT), relative humidity, air velocity, illuminance were measured using BABUC A apparatus and Thermal Comfort Measurement equipment. Through questionnaire survey, the demographic data of subjects and their perceptions on thermal comfort at each workstation were assessed based on ISO Standard 7730 and thermal sensation scale using Predicted Mean Vote (PMV). Then, Predicted Percentage of Dissatisfied (PPD) is used to estimate the thermal satisfaction of occupants. The results indicated that most of the workstations of the automotive industry are considered as uncomfortable. Tire receiving station is considered having better working environment compared to other stations with lowest PMV index of 1.09 to 1.41 and PPD of 46%. Meanwhile, the engine sub assembly station and paint shop of assembly are considered the worst thermal environment with the PMV index values ranging between 2.1 to 2.9 and PPD values of 81% to 99%. Therefore, these two workstations are considered not comfortable because the thermal sensation scale is warm and almost hot.
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Tranter, Morgan, Svenja Steding, Christopher Otto, Konstantina Pyrgaki, Mansour Hedayatzadeh, Vasilis Sarhosis, Nikolaos Koukouzas, Georgios Louloudis, Christos Roumpos, and Thomas Kempka. "Environmental hazard quantification toolkit based on modular numerical simulations." Advances in Geosciences 58 (November 22, 2022): 67–76. http://dx.doi.org/10.5194/adgeo-58-67-2022.
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Abstract. Quantifying impacts on the environment and human health is a critical requirement for geological subsurface utilisation projects. In practice, an easily accessible interface for operators and regulators is needed so that risks can be monitored, managed, and mitigated. The primary goal of this work was to create an environmental hazards quantification toolkit as part of a risk assessment for in-situ coal conversion at two European study areas: the Kardia lignite mine in Greece and the Máza-Váralja hard coal deposit in Hungary, with complex geological settings. A substantial rock volume is extracted during this operation, and a contaminant pool is potentially left behind, which may put the freshwater aquifers and existing infrastructure at the surface at risk. The data-driven, predictive tool is outlined exemplary in this paper for the Kardia contaminant transport model. Three input parameters were varied in a previous scenario analysis: the hydraulic conductivity, as well as the solute dispersivity and retardation coefficient. Numerical models are computationally intensive, so the number of simulations that can be performed for scenario analyses is limited. The presented approach overcomes these limitations by instead using surrogate models to determine the probability and severity of each hazard. Different surrogates based on look-up tables or machine learning algorithms were tested for their simplicity, goodness of fit, and efficiency. The best performing surrogate was then used to develop an interactive dashboard for visualising the hazard probability distributions. The machine learning surrogates performed best on the data with coefficients of determination R2>0.98, and were able to make the predictions quasi-instantaneously. The retardation coefficient was identified as the most influential parameter, which was also visualised using the toolkit dashboard. It showed that the median values for the contaminant concentrations in the nearby aquifer varied by five orders of magnitude depending on whether the lower or upper retardation range was chosen. The flexibility of this approach to update parameter uncertainties as needed can significantly increase the quality of predictions and the value of risk assessments. In principle, this newly developed tool can be used as a basis for similar hazard quantification activities.
Book chapters on the topic "Human values dashboard"
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Bayram, Alper, and Antonino Marvuglia. "A Web-Based Dashboard for Estimating the Economic and Ecological Impacts of Land Use Class Changes for Key Land Patches." In Computational Science and Its Applications – ICCSA 2022 Workshops, 281–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10545-6_20.
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AbstractThe increasing pressure on land coming from the raising needs of a fast-growing population puts public and private landowners and decision makers in front of difficult choices concerning the best use of limited land resources. On one hand, agricultural land and grassland need to be used to support human food requirements. On the other hand, these land uses create trade-offs with other ecosystem functions, assets and services, such as ecological connectivity, biodiversity and natural habitat maintenance. In this paper a prototype web-based dashboard is presented, that aims at allowing a fully-fledged calculation of the economic and environmental trade-offs between different land uses of any land patch (excluding urban areas and infrastructures) and in the Grand Duchy of Luxembourg. An agent-based model (ABM) coupled with life-cycle assessment (LCA) runs on the background of the dashboard. The coupled model allows the simulation of the farm business and the calculation of the revenues made by farmers in every land patch under different farm management scenarios. Crossing the information coming from the model with other tools would also allow to integrate local environmental trade-offs, such as degradation of local habitats or ecological connectivity, and not only global ones defined in a non-spatialized way. The dashboard has a potentially high value to inform policy, strategies, or specific actions (e.g., environmental stewardship programs that integrate economic convenience as a condition) and has the necessary flexibility to integrate new aspects related to territorial analyses as they become available.
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Ferreira, Luís, Goran Putnik, Maria Manuela Cruz-Cunha, Zlata Putnik, Hélio Castro, Catia Alves, and Vaibhav Shah. "Dashboard Services for Pragmatics-Based Interoperability in Cloud and Ubiquitous Manufacturing." In Cloud Technology, 435–49. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6539-2.ch020.
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The real Cloud and Ubiquitous Manufacturing systems require effectiveness and permanent availability of resources, their capacity and scalability. One of the most important problems for applications management over cloud based platforms, which are expected to support efficient scalability and resources coordination following SaaS implementation model, is their interoperability. Even application dashboards need to easily incorporate those new applications, their interoperability still remains a big problem to override. So, the possibility to expand these dashboards with efficiently integrated communicational cloud based services (cloudlets) represents a relevant added value as well as contributes to solving the interoperability problem. Following the architecture for integration of enriched existing cloud services, as instances of manufacturing resources, this paper: a) proposes a cloud based web platform to support dashboard integrating communicational services, and b) describe an experimentation to sustain the theory that the effective and efficient interoperability, especially in dynamic environments, could be achieved only with human intervention.
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Patro, ChandraSekhar. "Impulsion of Information Technology on Human Resource Practices." In Advances in Business Strategy and Competitive Advantage, 231–54. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1886-0.ch013.
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In recent years, adoption of Information Technology (IT) mechanism has had an intense effect on Human Resources (HR) processes and practices. IT has revolutionized the way in which the organizations execute their day-to-day activities, particularly in the HRM domain, where technology has redefined the way in which HR departments perform their operational, relational and transformational functions. Organizations have realized the emergent value of using IT in leveraging their Human Resource functions and the way they function in the market. Today the organizations are facing more challenges than they ever did due to the rapid and dynamic growth of e-businesses which has lead companies to seek greater opportunities to run HR functions more effectively by implementing technology in the HRM. The chapter provides a conceptual framework on the role of IT in HRM. It examines the impact of technology on HR practices and the factors influencing the effectiveness of human resource dashboards. It also investigates the effect of technology on organizational and work force productivity.
Conference papers on the topic "Human values dashboard"
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Nurwidyantoro, Arif, Mojtaba Shahin, Michel Chaudron, Waqar Hussain, Harsha Perera, Rifat Ara Shams, and Jon Whittle. "Towards a Human Values Dashboard for Software Development." In ESEM '21: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3475716.3475770.
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Toader, Lucian, Paulinus Abhyudaya Bimastianto, Shreepad Purushottam Khambete, Suhail Mohammed Al Ameri, Erwan Couzigou, Adel A/Rahman Al-Marzouqi, Wiliem Pausin, et al. "Automated Drilling Variances Detection Through Smart Alarms System." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207995-ms.
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Abstract In a drive to enhance drilling operational awareness, the Real-Time Operations Center (RTOC) has developed a State-of-the-Art event detection algorithm that consistently highlights the deviations of critical parameters by actively comparing real-time values against comprehensive physical models and alerting the users through a dashboard. The process relies on different levels of frequency and severity in order to detect events at their onset and prevent developing into a situation that compromises the operations. The first pillar of the solution consists of deterministic modelling of the expected values for a series of parameters in order to provide the basis for comparison and diagnostics. The main parameters sought to be modelled consist of the Standpipe Pressure, the Rotary Torque and the Hook load, which respectively are generated through individual methods taking into consideration actual conditions as well as relevant contextual data to ensure accuracy. The second pillar of the solution consists of visual alerts, triggered and displayed on a dashboard based on frequency and severity levels, as percentage of deviation from accepted operational envelope. The solution has been initially implemented during drilling operations where different issues were expected to take place, finding that whenever such occurrences took place, the algorithms were able to signal potential events in most of the cases. Some challenges were observed mainly due to sensor calibration and behavior since the expected model values not necessarily match reality, including residual pressure when the pumps are off or when the string is set on slips but the hook load values still present some variance. Also, it has been observed during transient periods where flow and rotation are changed drastically, that the stabilization to a steady state present with high variance, which has demanded the introduction of further logics within the algorithms to account for these effects and avoid the generation of false indications of issues. The solution has given encouraging results thus far in signaling different dysfunctions on the drilling process without the need of immediate human interpretation of data, which has allowed to move forward in the digitalization of operations, not only by timely signaling the onset of issues, but as well by providing the basis to further develop real time diagnosis of the problems to accelerate their resolution. The conception of the event detection based on deterministic real time analysis of individual channels against robust physical models from the existing digital twin solution has proven an immediate asset for operations on its own. By providing clear signaling of issues, while providing a solid framework to ultimately develop a diagnostic solution to translate a potential event into a proactive approach to support decision making process.
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Markopoulos, Evangelos, Emmanuel Querrec, and Mika Luimula. "A strategic partner selection decision-making support methodology in the business modelling phase for startups in the pre-incubation phase." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001529.
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Partner choice is an important element for any business throughout its lifecycle. It is even more strategic in early startup stages, when the business model is set in the pre-incubation phase.Entrepreneurs are confronted to take decisions on which partners to choose. Those strategic decisions on which partners to commit with, and defining their roles, can be made more or less formally, with the risk of relying on “gut feelings” when there is complex data to be taken in consideration and when there is pressure, constraints, limited resources and no proper methodology for the entrepreneur to base its decision on.Confronted to such a situation, it is interesting to consider building a decision-making support methodology for strategic partner choice for in the business modelling phase of a startup pre-incubation phase. This can offer support to the entrepreneur and make its leadership anchored in more formal approach to decision-making.This research presents a methodological framework that can support early startups, while still in the pre-incubation phase, to select the most suitable strategic business partner(s) and develop, based on that, their business operations, management, development and commercialization models. The methodology offers an initial approach which allows an entrepreneur to make more formal investigation and be assisted in the decision-making process on choosing the partners and defining their roles and contribution in the strategy of the start-up. Specifically, the methodology intends to provide support on selecting the most relevant and feasible data types that need to be collected for the effective partner evaluation and selection. Furthermore, it provides a data collection mechanism and algorithm, a partner evaluation procedure, support on identifying the strategic intend or need from a specific partner, the analysis of the potential partner based on the partnership needs, a scoring tableau based on several parameters per partner selection criteria and finally the calculation of the potential partner’s score. The research conducted evaluated twenty-one potential partners for a VR training startup that intends to operate in the following months and it is currently at the partnerships establishment phase. The partners that have been analyzed derive from eight, related to the start-up, professional sectors, from five countries, and with more than fifty unique activities that cover the fourteen key parameters of the partner evaluation methodology. The paper presents the overall methodological approach in stages and the procedure (steps) of each stage. It indicates the goal setting approach, the evaluation of the partner’s activities, the partner’s evaluation scorecard, the computation of the scoring process and the visualization of the scoring results in tables and charts that create a partner’s evaluation dashboard for effective partners comparison in total or in specific partnership requirements as set in the partnerships strategy and objectives. It must be noted that the proposed methodology is not an optimal tool but more of a heuristic exploratory tool. Further research has been scheduled to be extend the testing of the methodology with more cases, to increase the number of partner evaluation parameters and to link several of the related parameter metrics with sources than can provide more subjective values.
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Di Gironimo, Giuseppe, Antonio Lanzotti, Kenan Melemez, and Fabrizio Renno. "A Top-Down Approach for Virtual Redesign and Ergonomic Optimization of an Agricultural Tractor’s Driver Cab." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82947.
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Nowadays, economical, technical and ergonomic factors have a great importance on the design of the agricultural tractors. The paper illustrates the use and the management of heterogeneous product information (manual measurements and drafts, 2D drawings, technical documentation, photos), advanced CAD modeling tools and digital human models, for the redesign and the ergonomic optimization of an agricultural tractor’s driver cab. The project development has been organized using a top–down approach in a collaborative environment. At first, a manual measurement with gauges allowed to realize a technical draft of the whole agricultural tractor and of each component part of the driver cab. Then a main skeleton has been created in Catia V5 environment in order to specify all the datum elements necessary to model each sub-assembly of the tractor. Cabin, platform, engine, tires, seat, dashboard and controls have been organized separately and modeled considering the details related to the manual measurements and to the technical standards. Once obtained the 3D CAD model of the tractor, an opportune questionnaire was prepared and a test campaign was carried out with real operators in order to define the more critical control devices within the driver cab, as regards to usability and ergonomic issues. An “Ergonomics’ Evaluation Index” (EEI) was defined taking into account the posture angles of the operator and the Rapid Upper Limb Assessment analysis tool available in the “Ergonomics Design & Analysis” module of Catia V5 based on the use of a digital human model. The index was validated comparing the results of tests carried out using virtual manikins of different percentiles performing a specific driving task, with the results of tests carried out by real operators, of the same percentiles, performing the same driving task. Critical values of the EEI obtained during some driving tasks in virtual environment, suggested to modify the shape and the position of some control devices in order to optimize the ergonomics of the driver cab. The adoption of the top-down modeling based approach allowed each change on a singular component part to be automatically propagated on the whole assembly, making easy the changes on the virtual prototype.
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Robertson, Alex. "Predicting Project Outcomes with the Association of Project Management." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/210795-ms.
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Abstract Project professionals place great value in accurately predicting project outcomes. Itis therefore unsurprising that there has been a rapid acceptance of a new suite of tools promising to forecastproject outcomes better than ever before. Within a few years the use of project data analytics has become widespread throughout project delivery organisations; they have become the norm. Most closely associated with dashboards,project data analytics is transforming projects’ ability to see problems sooner and act quicker. Project data analytics however has not yet been ‘professionalised’. No single organisation has yet solved how to holistically get the very best out of analytics to deliver more predictable projects, but many are trying across multiple industries. Some organisations have great solutions and ideas, but itis not yet encoded in how the profession delivers. Petrofac as part of the Project Data Analytics Task Force [1&2], a cross industry working group, have collaborated with the Association of Project Management (APM) [3] to publish a ground-breaking guide[4] designed to help project delivery organisations get started in project data analytics. In addition, a five step framework*is offered which is designed for project delivery organisations who are further long their journey with analytics. This framework recommends the steps organisations can take to improve their project predictability from basic systems, through to dashboards and onto machine learning and artificial intelligence. It also references the capabilities organisations need to consider forthe benefits to become embedded. The paper explores how superior project performance is best achieved when project data analytics is blended with the insights and actions our people bring to delivering projects; to improve resultsthis blend of data and people is noted as being essential. The paper highlights this as the most significant factor as to why data analytics programmesmay not bring the value organisations expect and why step 4 (automated performance) of the model is seen as the pivot to success. On realisation of step 5 (intelligent performance), an organisation would be expected to have embraced the market leading approaches to maximising project performance and be well placed to achieve market leading returns on investment and margin. The paper advocates that organisations should: Adopt a 3-click rule to project informationSend the right action, to the right person, at the right timeBlend human and data insights by quantifying perceptions and makinginsights actionableAutomate Project Data Analytics into the working rhythm of project delivery This paper is deliberately aimed at the project professional and not the data science community. It simplifiesthe typical technical jargon around analytics and provides a wide variety of examples, tips and graphics that the project professional can easily relate to.
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Vimercati, Silvia, Emanuele Vignati, Pamela Mariotti, Sabatino Severino, Lorenzo Raimondi, and Christian Onzaca. "Application of Natural Language techniques in Reservoir Management Framework." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209961-ms.
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Abstract Reservoir Management requires the definition of a strategy that analyzes the risk assessment to identify surveillance activities and corrective measures. In Eni, a best practice has been established to regulate this strategy through the Reservoir Management Plan (RMP). This paper will present the application of Natural Language Processing (NLP) technologies to improve the definition of RMP. Results will be discussed, highlighting benefits and advantages in extracting cross-document information and running comparisons between different RMPs. Natural Language Processing refers to the branch of computer science concerned with the ability to understand text and spoken words, like human beings. NLP algorithms have been applied to RMPs to pull structured information adding useful numerical data. The main strategy for managing unstructured data, re-elaborating information upon specific request and implementing cross-document queries are discussed. Hundreds of RMP documents have been collected, hoarding extensive information in compartmentalized storages. NLP algorithms can be applied to unlock this hidden potential by capitalizing lessons learnt between Business Units and partaking the experiences acquired in similar assets. The information extracted from unstructured data includes insights on well and reservoir surveillance activities and corrective measures in different assets. A tool has been developed to enable a rapid screening of the key parameters in different assets, highlighting how similar risks can be mitigated. The tool analyzes documents and forms, looking for data and relationships among entities in the text. In particular, the NLP model extracts tables, table cells, and the items within and returns JSON formatted results. This task is performed by using open-source NLP libraries and custom logic, in order to adapt common algorithms to RMP application. The procedure is orchestrated by a cloud based ETL (Extract Transform and Load) and data integration service to create workflows for moving and transforming data. The workflow is scheduled and executed for each RMP document collected. Insights are collected and presented in a dashboard. Two main applications will be discussed to present the additional value brought by NLP algorithms in Reservoir Management. The mitigation strategies and relevant lessons learnt from analogues can be efficiently collected, analyzed and integrated in the planning and scheduling of reservoir management activities. To the authors’ knowledge, this paper presents the first successful implementation of NLP techniques to Reservoir Management. Natural Language Processing algorithms allow unstructured data to be searched, organized and mined, highlighting the strength of combining data and leveraging the main insights without having to read through all the RMP documents.
Reports on the topic "Human values dashboard"
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Marshall, Amber, Krystle Turner, Carol Richards, Marcus Foth, Michael Dezuanni, and Tim Neale. A case study of human factors of digital AgTech adoption: Condamine Plains, Darling Downs. Queensland University of Technology, December 2021. http://dx.doi.org/10.5204/rep.eprints.227177.
Full textAbstract:
As global agricultural production methods and supply chains have become more digitised, farmers around the world are adopting digital AgTech such as drones, Internet of Things (IoT), remote sensors, blockchain, and satellite imagery to inform their on-farm decision-making. While early adopters and technology advocates globally are spruiking and realising the benefits of digital AgTech, many Australian farmers are reluctant or unable to participate fully in the digital economy. This is an important issue, as the Australian Government has said that digital farming is essential to meeting its target of agriculture being a $100billion industry by 2030. Most studies of AgTech adoption focus on individual-level barriers, yielding well-documented issues such as access to digital connectivity, availability of AgTech suppliers, non-use of ICTs, and cost-benefit for farmers. In contrast, our project took an ‘ecosystems’ approach to study cotton farmers in the Darling Downs region in Queensland, Australia who are installing water sensors, satellite imagery, and IoT plant probes to generate data to be aggregated on a dashboard to inform decision-making. We asked our farmers to map their local ecosystem, and then set up interviewing different stakeholders (such technology providers, agronomists, and suppliers) to understand how community-level orientations to digital agriculture enabled and constrained on-farm adoption. We identified human factors of digital AgTech adoption at the macro, regional and farm levels, with a pronounced ‘data divide’ between farm and community level stakeholders within the ecosystem. This ‘data divide’ is characterised by a capability gap between the provision of the devices and software that generate data by technology companies, and the ability of farmers to manage, implement, use, and maintain them effectively and independently. In the Condamine Plains project, farmers were willing and determined to learn new, advanced digital and data literacy skills. Other farmers in different circumstances may not see value in such an undertaking or have the necessary support to take full advantage of the technologies once they are implemented. Moreover, there did not seem to be a willingness or capacity in the rest of the ecosystem to fill this gap. The work raises questions about the type and level of new, digital expertise farmers need to attain in the transition to digital farming, and what interventions are necessary to address the significant barriers to adoption and effective use that remain in rural communities. By holistically considering how macro- and micro-level factors may be combined with community-level influences, this study provides a more complete and holistic account of the contextualised factors that drive or undermine digital AgTech adoption on farms in rural communities. This report provides insights and evidence to inform strategies for rural ecosystems to transition farms to meet the requirements and opportunities of Agriculture 4.0 in Australia and abroad.