scholarly journals Knowledge-Based Decision Support for Concept Evaluation Using the Extended Impact Model of Modular Product Families

2022 ◽  
Vol 12 (2) ◽  
pp. 547
Author(s):  
Erik Greve ◽  
Christoph Fuchs ◽  
Bahram Hamraz ◽  
Marc Windheim ◽  
Christoph Rennpferdt ◽  
...  
Keyword(s):  
Decision Support ◽  
Product Architecture ◽  
Impact Model ◽  
Product Families ◽  
Support Tool ◽  
Knowledge Based ◽  
Modular Product ◽  
Concept Evaluation ◽  
The Impact ◽  
Selection Of

The design of modular product families enables a high external variety of products by a low internal variety of components and processes. This variety optimization leads to large economic savings along the entire value chain. However, when designing and selecting suitable modular product architecture concepts, often only direct costs are considered, and indirect costs as well as cross-cost center benefits are neglected. A lack of knowledge about the full savings potential often results in the selection of inferior solutions. Since available approaches do not adequately address this problem, this paper provides a new methodological support tool that ensures consideration of the full savings potentials in the evaluation of modular product architecture concepts. For this purpose, the visual knowledge base of the Impact Model of Modular Product Families (IMF) is used, extended and implemented in a model-based environment using SysML. The newly developed Sys-IMF is then applied to the product family example of electric medium-voltage motors. The support tool is dynamic, expandable and filterable and embedded in a methodical procedure for knowledge-based decision support. Sys-IMF supports decision makers in the early phase of interdisciplinary product development and enables the selection of the most suitable modular solution for the company.

scholarly journals Assessment of alternative land management practices using hydrological simulation and a decision support tool: Arborea agricultural region, Sardinia

2007 ◽  
Vol 11 (6) ◽  
pp. 1811-1823 ◽  
Author(s):  
P. Cau ◽  
C. Paniconi
Keyword(s):  
Decision Support ◽  
Land Management ◽  
Quantitative Methods ◽  
Management Practices ◽  
Water Movement ◽  
Agricultural Practices ◽  
Treated Wastewater ◽  
Hydrological Simulation ◽  
Support Tool ◽  
The Impact

Abstract. Quantifying the impact of land use on water supply and quality is a primary focus of environmental management. In this work we apply a semidistributed hydrological model (SWAT) to predict the impact of different land management practices on water and agricultural chemical yield over a long period of time for a study site situated in the Arborea region of central Sardinia, Italy. The physical processes associated with water movement, crop growth, and nutrient cycling are directly modeled by SWAT. The model simulations are used to identify indicators that reflect critical processes related to the integrity and sustainability of the ecosystem. Specifically we focus on stream quality and quantity indicators associated with anthropogenic and natural sources of pollution. A multicriteria decision support system is then used to develop the analysis matrix where water quality and quantity indicators for the rivers, lagoons, and soil are combined with socio-economic variables. The DSS is used to assess four options involving alternative watersheds designated for intensive agriculture and dairy farming and the use or not of treated wastewater for irrigation. Our analysis suggests that of the four options, the most widely acceptable consists in the transfer of intensive agricultural practices to the larger watershed, which is less vulnerable, in tandem with wastewater reuse, which rates highly due to water scarcity in this region of the Mediterranean. More generally, the work demonstrates how both qualitative and quantitative methods and information can assist decision making in complex settings.

Using a decision support tool to screen for pro-poor policies: Application of Extrapolate to smallholder dairy systems in East Africa

2005 ◽  
Vol 2005 ◽  
pp. 28-28
Author(s):  
P. K. Thornton ◽  
P. J. Thorne ◽  
C. Quiros ◽  
D. Sheikh ◽  
R. L. Kruska ◽  
...  
Keyword(s):  
Decision Support ◽  
Poverty Reduction ◽  
Intervention Mapping ◽  
Decision Support Tool ◽  
Target Groups ◽  
Support Tool ◽  
Policy Alternatives ◽  
Ex Ante ◽  
Policy Measures ◽  
The Impact

Extrapolate (EX-ante Tool for RAnking POLicy AlTErnatives) is a decision support tool to assess the impact of policy measures on different target groups. It is designed to serve as a “filter” that, given the broad characteristics of the population, allows the user to sift through different policy measures to assess ex ante the broad potential impacts of these before deciding to look at particular policy options in more detail. Extrapolate models, in a very simple way, the impact of changes on constraints facing potential beneficiary groups, and how these may affect outcomes and their livelihood status. Extrapolate now makes use of mapping facilities from another decision-support tool, PRIMAS (Poverty Reduction Intervention Mapping in Agricultural Systems), that allows the user to match characteristics of particular technological options and constraints with the spatial characteristics of particular target groups in the landscape.

scholarly journals Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience

2020 ◽  
Vol 21 (6) ◽  
pp. 375-386 ◽  
Author(s):  
Christina L Aquilante ◽  
David P Kao ◽  
Katy E Trinkley ◽  
Chen-Tan Lin ◽  
Kristy R Crooks ◽  
...  
Keyword(s):  
Decision Support ◽  
Health System ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Support Tool ◽  
University Of Colorado ◽  
The University ◽  
The Impact

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

scholarly journals BEETSOIL: a decision support tool for forecasting the impact of soil conditions on sugar beet harvest

2019 ◽  
Vol 191 ◽  
pp. 131-141
Author(s):  
Miguel A. Gabarron-Galeote ◽  
Jacqueline A. Hannam ◽  
Thomas Mayr ◽  
Patrick J. Jarvis
Keyword(s):  
Decision Support ◽  
Sugar Beet ◽  
Decision Support Tool ◽  
Soil Conditions ◽  
Support Tool ◽  
The Impact

scholarly journals Estimating the impact of deploying an electronic clinical decision support tool as part of a national practice improvement project

2019 ◽  
Vol 26 (7) ◽  
pp. 630-636 ◽  
Author(s):  
Ellen K Kerns ◽  
Vincent S Staggs ◽  
Sarah D Fouquet ◽  
Russell J McCulloh
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Market Area ◽  
Improvement Project ◽  
Support Tool ◽  
Febrile Infant ◽  
The Impact ◽  
Electronic Clinical Decision Support

Abstract Objective Estimate the impact on clinical practice of using a mobile device–based electronic clinical decision support (mECDS) tool within a national standardization project. Materials and Methods An mECDS tool (app) was released as part of a change package to provide febrile infant management guidance to clinicians. App usage was analyzed using 2 measures: metric hits per case (metric-related screen view count divided by site-reported febrile infant cases in each designated market area [DMA] monthly) and cumulative prior metric hits per site (DMA metric hits summed from study month 1 until the month preceding the index, divided by sites in the DMA). For each metric, a mixed logistic regression model was fit to model site performance as a function of app usage. Results An increase of 200 cumulative prior metric hits per site was associated with increased odds of adherence to 3 metrics: appropriate admission (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.18), appropriate length of stay (OR, 1.20; 95% CI, 1.12-1.28), and inappropriate chest x-ray (OR, 0.82; 95% CI, 0.75-0.91). Ten additional metric hits per case were also associated: OR were 1.18 (95% CI, 1.02-1.36), 1.36 (95% CI, 1.14-1.62), and 0.74 (95% CI, 0.62-0.89). Discussion mECDS tools are increasingly being implemented, but their impact on clinical practice is poorly described. To our knowledge, although ecologic in nature, this report is the first to link clinical practice to mECDS use on a national scale and outside of an electronic health record. Conclusions mECDS use was associated with changes in adherence to targeted metrics. Future studies should seek to link mECDS usage more directly to clinical practice and assess other site-level factors.

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