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The Discipline of Systems Engineering
Published in Lory Mitchell Wingate, Systems Engineering for Projects, 2018
Although the processes that ultimately make up systems engineering were practiced in various forms throughout history, the first documented use of an approach that took the system into consideration was in Bell Telephone Laboratories. Their use of “systems engineering” as a term to reflect their methods was documented in the 1940s.1 Bell Laboratories was involved in military action optimization studies during World War II. Scientists and engineers there were using operations research methods, specifically optimization modeling using calculus, linear algebra, and other techniques, as well as stochastic processes such as queuing theory and probability theory. It was not until 1962, however, that “Arthur Hall published his first book on systems engineering: A Methodology for Systems Engineering. Hall was an executive at Bell Laboratories and was one of the people who were responsible for the implementation of systems engineering at the company.”2 During the same era, the RAND Corporation, ideated by a newly formed United States Air Force, developed a process for systems analysis that would become an important part of the systems engineering discipline. Systems analysis is an approach that reviews a problem in logical steps, and describes the system thoroughly and explicitly. Using computing resources to perform systems analysis and optimization modeling provides a solid scientifically based approach for performing systems engineering.
Designing Case Studies Using a Systems Analysis Approach
Published in Peter Berggren, Staffan NäHlinder, Erland Svensson, Assessing Command and Control Effectiveness, 2017
There are several definitions of the term ‘systems analysis’, but any definition usually involves some kind of procedure, more or less formal, for collecting and organizing data about an empirical phenomenon into a system model. There are a variety of systems analysis techniques and approaches, such as ‘task analysis’ (Annett et al. 1971; Drury et al. 1987), ‘job analysis’ (Harvey 1991), ‘content analysis’ (Weber 1990; Kolbe 1991), ‘action analysis’ (Singleton 1979) and ‘cognitive systems engineering’ (Hollnagel and Woods 1983; Rasmussen et al. 1994). Despite the fact that these techniques differ somewhat when it comes to perspectives and procedures, they are rather similar. They are related to a scientific style of approaching a certain phenomenon analytically, in order to treat or analyse reality as a systematically connected set of elements (Gasparski 1991).
The Fundamentals of Systems Engineering to Inform a Whole System Approach
Published in Peter Stasinopoulos, Michael H. Smith, Karlson ‘Charlie’ Hargroves, Cheryl Desha, Whole System Design, 2013
Peter Stasinopoulos, Michael H. Smith, Karlson ‘Charlie’ Hargroves, Cheryl Desha
Analysis of systems involves an investigation of the multiple relationships of elements that comprise a system. Systems analysis uses diagrams, graphs and pictures to describe and structure inter-relationships of elements and behaviours of systems. Every element in a system is called a variable, and the influence of one element on another element is called a link; this can be represented by drawing an arrow from the causing element to the affected element. In analysis of systems, links always comprise a ‘circle of causality’ or a feedback loop, in which every element is both cause and effect. For example, take the urban expansion/induced traffic issue depicted below (Figure 2.7). To relieve traffic congestion in cities (variable #1), freeways are added or extended (variable #2). By adding more/extending freeways, people are able to live further out from the city, and hence more residential properties are built further out from the city (variable #3). More people living further out means more people drive into the city via the new freeways, hence contributing even more to the traffic congestion problem (feedback loop).
Embracing complexity: a sociotechnical systems approach for the design and evaluation of higher education learning environments
Published in Theoretical Issues in Ergonomics Science, 2020
Eduardo Navarro-Bringas, Graeme Bowles, Guy H. Walker
With UK’s HE current levels of capital investments on estates, the retrofit of existing estates through technology-enhanced, informal and social learning initiatives offer significant opportunities for the application of STS methodologies, such as CWA. These investments include both formal learning facilities (classrooms, lecture theatres tutorial or seminar rooms), informal learning facilities (library spaces, learning commons or social learning spaces) and other recreational ones (like sport facilities, religious facilities, restaurants or cafés). Most of these spaces were developed with the principles of didactic-learning behavioural models and efficiency/capacity as criteria. However, new technologies and the prioritisation of socio-constructivist learning and its principles (self-directed, collaborative, active learning, problem-solving etc.), provide a new landscape of opportunities to explore new and innovative designs that acknowledge the role and impact that design can have on learning and its associated cognitive processes. It is in this complex context, where STS and CWA provide an opportunity to explore new designs, based on an understanding of systemic constraints, of the environment, institutional cultures, ethos and structure, the pedagogic activities and the constraints underlying user’s competences and preferences. Campus development and management are complex sociotechnical matters, which involve the views of multiple stakeholder, sometimes conflicting between each other. A systems analysis offers opportunities to understand ways in which new developments can impact the overall functioning of the system, in this case the university and its campus, while integrating the views of relevant stakeholders and perspectives. Including, amid others, from estates and facilities managers, learning technologists, academic developers, or information services. Thus, complementing recent approaches focused on understanding end-user learning experiences and micro-interactions, including those in the context of designing successful learning environments such as courses, academic libraries or museums (Shapiro, Hall, and Owens 2017; Johnson and Khoo 2018).
A Semantic Model for Enterprise Digital Transformation Analysis
Published in Journal of Computer Information Systems, 2023
Traditionally, systems analysts apply systems analysis and design methods and tools to analyze the system by specifying the requirements and to design the system by detailing the specifications for implementation. During the past decades of enterprise digital transformation, the roles of systems analysts have been shifted from “blueprint making” for systems construction to coordination for systems acquisition.60 Specifically, the major roles of systems analysts for enterprise digital transformation are to coordinate the development activities of all stakeholders involved in the digital transformation process and to assist the decision makers of the organization to monitor the gaps between the current system and the future system during the digital transformation process to ensure the implementation of transformation strategies. To support systems development coordination and supervision, systems analysts have used reference models.61 A reference model is an abstract framework or domain-specific ontology consisting of an interlinked set of clearly defined concepts to encourage clear communication. Our proposed semantic model is a reference model for enterprise digital transformation, and has the following advantages over the traditional systems development “blueprint making” tools such as UML. The semantic model integrates the semantic relationships between all organizational aspects of enterprise digital transformation. The model is able to represent key organizational aspects in enterprise digital transformation, including costs and benefits, system infrastructure, business process and business rules, data architecture, and users’ roles.The proposed enterprise digital transformation model can serve as a devise to integrate managerial requirements and technical specifications.The model is scalable and maintainable through adding, deleting, and modifying the organizational aspects and the relationships between them for a particular enterprise digital transformation.The semantic model can be computerized and readily incorporated into a web portal, as demonstrated in the next section of this paper.The semantic model is easy to understand for all decision makers and system developers in the organization to share knowledge about the enterprise digital transformation.