Towards data exchange interoperability in building lifecycle management

Author(s):  
Sylvain Kubler ◽  
Manik Madhikermi ◽  
Andrea Buda ◽  
Kary Framling ◽  
William Derigent ◽  
...  
2005 ◽  
Vol 5 (3) ◽  
pp. 227-237 ◽  
Author(s):  
Ravi M. Rangan ◽  
Steve M. Rohde ◽  
Russell Peak ◽  
Bipin Chadha ◽  
Plamen Bliznakov

The past three decades have seen phenomenal growth in investments in the area of product lifecycle management (PLM) as companies exploit opportunities in streamlining product lifecycle processes, and fully harnessing their data assets. These processes span all product lifecycle phases from requirements definition, systems design/ analysis, and simulation, detailed design, manufacturing planning, production planning, quality management, customer support, in-service management, and end-of-life recycling. Initiatives ranging from process re-engineering, enterprise-level change management, standardization, globalization and the like have moved PLM processes to mission-critical enterprise systems. Product data representations that encapsulate semantics to support product data exchange and PLM collaboration processes have driven several standards organizations, vendor product development efforts, real-world PLM implementations, and research initiatives. However, the process and deployment dimensions have attracted little attention: The need to optimize organization processes rather than individual benefits poses challenging “culture change management” issues and have derailed many enterprise-scale PLM efforts. Drawn from the authors’ field experiences as PLM system integrators, business process consultants, corporate executives, vendors, and academicians, this paper explores the broad scope of PLM, with an added focus on the implementation and deployment of PLM beyond the development of technology. We review the historical evolution of engineering information management/PLM systems and processes, characterize PLM implementations and solution contexts, and discuss case studies from multiple industries. We conclude with a discussion of research issues motivated by improving PLM adoption in industry.


Author(s):  
Joa˜o P. M. A. Silva ◽  
Ricardo Jardim-Goncalves ◽  
Adolfo Steiger-Garc¸a˜o ◽  
Anto´nio A. C. Monteiro

Recently, computational design aiding tools resources are undertaken in modern companies, enhancing high quality product definition development. However, accurate digital product descriptions are attained through multiple software applications, each one seeking to solve focused needs. Regardless significant advances, there still remains a substantial computational deficiency in how these systems interact with each other between the several PLC stages. Plural issues with different origin and nature contribute to such state, increasing the research community interest to contribute with solution that minimizes the problem. In particular, one main issue refers to product and process knowledge exchange along PLC stages. According to this scenario, and with market pressure to increase profits and reduce redundancies, an efficient coordination and management of all the activities taking place along the Production Process must be performed. Hence, promising technologies of Product Lifecycle Management are considered strategic to manage capture of product knowledge along its life, from initial conception to retirement. This paper proposes the use of an ontology to be used in a knowledge-based system, giving support to a comprehensive product model to improve integration and data exchange capabilities trough entire PLC. The capture, handle and re-use of knowledge from multiple disciplines during PLC (e.g. design, manufacture or maintenance), extending capabilities of existent product and process models is the promising main benefit of ontologies development.


Author(s):  
Igor Ilin ◽  
Anastasia Levina ◽  
Konstantin Frolov

The COVID-19 pandemic has severely tested humanity, revealing the need to develop and improve the medical, economic, managerial, and IT components of vaccine management systems. The vaccine lifecycle includes vaccine research and development, production, distribution, and vaccination of the population. To manage this cycle effectively the proper organizational and IT support model of the interaction of vaccine lifecycle management stakeholders is needed—which are an innovation ecosystem and an appropriate virtual platform. A literature review has revealed the lack of methodological basis for the vaccine innovation ecosystem and virtual platform. This article is devoted to the development of a complex approach for the development of an innovation ecosystem based on vaccine lifecycle management and a virtual platform which provides the data exchange environment and IT support for the ecosystem stakeholders. The methodological foundation of the solution, developed in the article, is an enterprise architecture approach, CALS technologies, supply chain management and an open innovation philosophy. The results, presented in the article, are supposed to be a reference set of models for the creation of a vaccine innovation ecosystem, both during pandemics and periods of stable viral load.


2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364


Author(s):  
Scot D. Weaver ◽  
Thomas E. Lefchik ◽  
Marc I. Hoit ◽  
Kirk Beach

2019 ◽  
Vol 21 (3) ◽  
pp. 25 ◽  
Author(s):  
Muyu Liu ◽  
Lei Liang ◽  
Hao Wu ◽  
Gang Xu ◽  
Qian Li

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