scholarly journals Systems Engineering Modelling and Simulation to Support Defence Acquisition System

Hadmérnök ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. 17-42
Author(s):  
Rodrigo Guajardo
Keyword(s):  
Systems Engineering ◽  
Process Development ◽  
Modelling And Simulation ◽  
Acquisition System ◽  
Engineering Discipline ◽  
Essential Tool ◽  
Modelling And Simulations ◽  
Product And Process ◽  
Acquisition Cycle

Modelling and Simulations have been applied in various engineering disciplines since the 1990s, especially in the area of defence, being an essential tool in all phases of the cycle of acquisition and in all applications whose main objective are to reduce the time, resources, and risks associated with acquisition, to enable the integrated product and process development, and to improve the quality of the fielded product. One of the main purposes of this research is to demonstrate the importance of Systems Engineering (INCOSE) as part of the defence acquisition system and how this engineering discipline has incorporated the use of Modelling and Simulations into their acquisition-cycle phases and processes.

Advances in Computers and Information in Engineering Research, Volume 2

2021 ◽  
Author(s):  
John G. Michopoulos ◽  
Christiaan J.J. Paredis ◽  
David W. Rosen ◽  
Judy M. Vance
Keyword(s):  
Systems Engineering ◽  
Process Development ◽  
Naval Research ◽  
Engineering Practice ◽  
Book Series ◽  
Georgia Institute ◽  
Engineering Research ◽  
Product And Process ◽  
Institute Of Technology

Book Series Editorial Board John Michopoulos, Naval Research Laboratory David Rosen, Georgia Institute of Technology Chris Paredis, Georgia Institute of Technology Judy Vance, Iowa State University This is the second volume in this book series that aims to capture advances in computers and information in engineering research, especially by researchers and members of ASME’s Computers & Information in Engineering (CIE) Division. The series is focusing on advances in computational methods, algorithms, tools, and processes on the cutting edge of research and development as they have evolved and/or have been reported during the last three to five annual CIE conferences. The series will provide a resource for enhancing engineering practice by enabling the understanding and the application of evolving and emerging technologies that impact critical engineering issues related to the topics and themes under CIE’s technical committees areas of interest, but not limited to: Advanced Modeling and Simulation; Computer-Aided Product and Process Development; Systems Engineering, Information and Knowledge Management; Virtual Environments and Systems.

scholarly journals THE DEVELOPMENT OF THE QUALITY MANAGEMENT SYSTEM IN TERMS OF KNOWLEDGE MANAGEMENT / KOKYBĖS VADYBOS SISTEMŲ TOBULINIMAS ŽINIŲ VADYBOS ASPEKTU

2011 ◽  
Vol 3 (4) ◽  
pp. 113-119
Author(s):  
Romualdas Vitkauskas
Keyword(s):  
Knowledge Management ◽  
Quality Management ◽  
Quality Management System ◽  
Analysis Method ◽  
Continual Improvement ◽  
Correlation Analysis Method ◽  
Product And Process ◽  
Quality Of Products ◽  
The Relationship

The interaction of quality management and knowledge man­agement are analyzed quite widely in scientific literature and discussed with reference to various models integrating these two management theories. However, there are not enough precise tools that could provide a possibility of improving the quality of products and services through knowledge management. To this end, the article examines the already existing models for the interaction between quality management and knowledge management. The results of a survey on Lithuanian industry show what organizational knowledge is the most important while implementing quality management principles aimed at improving quality. The correlation analysis method showed the relationship between quality management principles (process approach, continual improvement, management, facts) and the factors defining the product and process quality (the extent of determining process execution time, process resources, costs of process stages, indicators for products and/or services, the dura­tion of the process, the extent of measuring indicators, the extent of collecting information on indicators, the extent of collecting information about the costs of the process). Santrauka Kokybės vadybos ir žinių vadybos sąveika mokslinėje literatūroje analizuojama gana plačiai, aptariami įvairūs sąveikos modeliai integruojant šias dvi vadybos teorijas. Tačiau nepakankamai nagrinėjamos konkrečios priemonės, kuriomis būtų galima pagerinti produktų ir paslaugų kokybę pasitelkiant žinių vadybą. Tuo tikslu straipsnyje analizuojami jau egzis­tuojantys kokybės vadybos ir žinių vadybos sąveikos modeliai bei pateikiami Lietuvos pramonės įmonių tyrimo rezultatai, parodantys, kokios organizacinės žinios svarbiausios įgyvendinant kokybės vadybos principus, skirtus kokybei gerinti. Taikant koreliacinės analizės metodą nustatyta, kad egzistuoja ryšys tarp kokybės vadybos principų (procesinio požiūrio, nuolatinio gerinimo, valdymo remiantis faktais) įgyvendinimo lygio ir veiksnių, apibūdinančių produktų ir procesų kokybę (procesų įvykdymo laiko nustatymas, procesų išteklių nustatymas, procesų etapų išlaidų nustatymas, produktų ir (ar) paslaugų rodiklių nustatymas, rodiklių matavimas, rodiklių informacijos rinkimas, informacijos apie proceso trukmę nustatymas ir informacijos apie proceso išlaidas rinkimas).

TOWARD A THEORY OF SYSTEMS ENGINEERING

2021 ◽  
pp. 1-11
Author(s):  
George A. Hazelrigg ◽  
Donald G. Saari
Keyword(s):  
Systems Engineering ◽  
Engineering Community ◽  
Engineering Discipline ◽  
Abstract Approach ◽  
Core Set ◽  
Engineering Practices ◽  
Physical Laws ◽  
Simple Preference ◽  
Sizable Fraction ◽  
Current Systems

Abstract The derivation of a theory of systems engineering has long been complicated by the fact that there is little consensus within the systems engineering community regarding precisely what systems engineering is, what systems engineers do, and what might constitute reasonable systems engineering practices. To date, attempts at theories fail to accommodate even a sizable fraction of the current systems engineering community, and they fail to present a test of validity of systems theories, analytical methods, procedures or practices. This paper presents a more theoretical and more abstract approach to the derivation of a theory of systems engineering that has the potential to accommodate a broad segment of the systems engineering community and present a validity test. It is based on a simple preference statement: “I want the best system I can get.” From this statement, it is argued that a very rich theory can be obtained. Whereas most engineering disciplines are framed around a core set of widely accepted physical laws, to the authors' knowledge, this is the first attempt to frame an engineering discipline around a preference.

Product-Oriented Sensitivity Analysis for Multistation Compliant Assemblies

2006 ◽  
Vol 129 (8) ◽  
pp. 844-851 ◽  
Author(s):  
Jianpeng Yue ◽  
Jaime A. Camelio ◽  
Melida Chin ◽  
Wayne Cai
Keyword(s):  
Product Performance ◽  
Sensitivity Index ◽  
Final Assembly ◽  
Sensitivity Indices ◽  
Dimensional Variation ◽  
Product And Process ◽  
Pattern Sensitivity ◽  
Sheet Metal Assembly

Dimensional variation in assembled products directly affects product performance. To reduce dimensional variation, it is necessary that an assembly be robust. A robust assembly is less sensitive to input variation from the product and process components, such as incoming parts, subassemblies, fixtures, and welding guns. In order to effectively understand the sensitivity of an assembly to input variation, an appropriate set of metrics must be defined. In this paper, three product-oriented indices, including pattern sensitivity index, component sensitivity index, and station sensitivity index, are defined. These indices can be utilized to measure the variation influence of a pattern, an individual part, and/or component, and components at a particular station to the dimensional quality of a final assembly. Additionally, the relationships among these sensitivity indices are established. Based on these relationships, the ranges of the sensitivity indices are derived. Finally, a case study of a sheet metal assembly is presented and discussed to illustrate the applicability of these metrics.

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