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Systems, Volume 7, Issue 1 (March 2019)

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Open AccessArticle Economic Analysis of Model-Based Systems Engineering
Received: 26 December 2018 / Revised: 14 February 2019 / Accepted: 15 February 2019 / Published: 20 February 2019
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Abstract
In the face of ever-increasing complexity of systems and system development programs, several aerospace, automotive, and defense organizations have already begun or are contemplating the transition to model-based systems engineering (MBSE). The key challenges that organizations face in making this decision are determining [...] Read more.
In the face of ever-increasing complexity of systems and system development programs, several aerospace, automotive, and defense organizations have already begun or are contemplating the transition to model-based systems engineering (MBSE). The key challenges that organizations face in making this decision are determining whether it is technically feasible and financially beneficial in the long-run to transition to MBSE, and whether such transition is achievable given budgetary constraints. Among other cost drivers of this transition, are a new digital infrastructure, personnel training in MBSE, and cost-effective migration of legacy models and data into the new infrastructure. The ability to quantify gains from MBSE investment is critical to making the decision to commit to MBSE implementation. This paper proposes a methodological framework for analyzing investments and potential gains associated with MBSE implementation on large-scale system programs. To this end, the MBSE implementation problem is characterized in terms of: system complexity, environment complexity and regulatory constraints, and system lifespan. These criteria are applied to systems in twelve major industry sectors to determine MBSE investment and expected gains. Results from this cost-benefit analysis are used to justify investment in MBSE implementation where warranted. This approach is generic and can be applied to different sectors for economic evaluation of costs and benefits and justification of transition to MBSE if warranted. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Model-Based Approach to Engineering Resilience in Multi-UAV Systems
Received: 26 November 2018 / Revised: 3 February 2019 / Accepted: 12 February 2019 / Published: 15 February 2019
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Abstract
Multi-UAV Operations are an area of great interest in government, industry, and research community. In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue or disaster relief. As multi-UAV systems operate in [...] Read more.
Multi-UAV Operations are an area of great interest in government, industry, and research community. In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue or disaster relief. As multi-UAV systems operate in an open operational environment, many disrupting events can occur. To this end, resilience of these systems is of great importance. The research performed and reported in this paper utilizes simulation-based research methodology and demonstrates that resilience of multi-UAV systems can be achieved by real-time evaluation of resilience alternatives during system operation. This evaluation is done using a dynamic utility function where priorities change as a function of context. Simulation results show that resilience response can in fact change depending on the context. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Gamification Risks to Enterprise Teamwork: Taxonomy, Management Strategies and Modalities of Application
Received: 1 January 2019 / Revised: 29 January 2019 / Accepted: 10 February 2019 / Published: 13 February 2019
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Abstract
Gamification corresponds to the use of game elements to encourage certain attitudes and behaviours in a serious context. When applied to enterprise teamwork, gamification can lead to negative side-effects which compromise its benefits. For example, applying competitive elements such as leaderboard may lead [...] Read more.
Gamification corresponds to the use of game elements to encourage certain attitudes and behaviours in a serious context. When applied to enterprise teamwork, gamification can lead to negative side-effects which compromise its benefits. For example, applying competitive elements such as leaderboard may lead to clustering amongst team members and encourage adverse work ethics such as intimidation and pressure. Despite the recognition of the problem in the literature, the research on concretising such gamification risks is scarce. There is also a lack of methods to identify gamification risks and their management strategies. In this paper, we conduct a multi-stage qualitative research and develop taxonomy of risks, risk factors and risk management strategies. We also identify the modalities of application of these strategies, including who should be involved and how. Finally, we provide a checklist to help the risk identification process as a first step towards a comprehensive method for eliciting and managing gamification risks to teamwork within enterprises. Full article
(This article belongs to the Special Issue Enterprise Systems & Gamification)
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Open AccessArticle A Bibliographic and Visual Exploration of the Historic Impact of Soft Systems Methodology on Academic Research and Theory
Received: 12 December 2018 / Revised: 4 February 2019 / Accepted: 7 February 2019 / Published: 13 February 2019
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Abstract
Soft systems methodology (SSM), an analytic method commonly employed in engineering and business research, produces models focused on human activities and relevant structures used to explain complex, engineered systems. The original version of SSM involves seven stages; five address real-world aspects and observable [...] Read more.
Soft systems methodology (SSM), an analytic method commonly employed in engineering and business research, produces models focused on human activities and relevant structures used to explain complex, engineered systems. The original version of SSM involves seven stages; five address real-world aspects and observable data, while two stages leverage a systems thinking viewpoint. This approach allows the development of a simplified depiction of complex systems representative of the multi-perspective lenses used to comprehend the systemic complexity of a problem and provide a clearer picture to analysts and decision makers. This bibliometric meta-analysis of 286 relevant publications in engineering, business, and other social sciences fields explores the historic impacts of SSM on academic research and systems thinking in relevant publications that described or employed SSM for research from 1980–2018. This study produced descriptive narrative outcomes and data visualizations including information about top SSM authors, author citation impacts, common dissemination outlets for SSM work, and other relevant metrics commonly used to measure academic impact. The goal of this piece is to depict who, what, why, when, and where SSM had the greatest impact on research, systems thinking, and methodology after nearly 40 years of use, as we look towards its future as a methodological approach used to comprehend complex problem situations. Full article
(This article belongs to the Special Issue Systems Thinking)
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Open AccessArticle Big Data: From Forecasting to Mesoscopic Understanding. Meta-Profiling as Complex Systems
Received: 1 September 2018 / Revised: 6 December 2018 / Accepted: 28 January 2019 / Published: 8 February 2019
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Abstract
We consider Big Data as a phenomenon with acquired properties, similar to collective behaviours, that establishes virtual collective beings. We consider the occurrence of ongoing non-equivalent multiple properties in the conceptual framework of structural dynamics given by sequences of structures and not only [...] Read more.
We consider Big Data as a phenomenon with acquired properties, similar to collective behaviours, that establishes virtual collective beings. We consider the occurrence of ongoing non-equivalent multiple properties in the conceptual framework of structural dynamics given by sequences of structures and not only by different values assumed by the same structure. We consider the difference between modelling and profiling in a constructivist way, as De Finetti intended probability to exist, depending on the configuration taken into consideration. The past has little or no influence, while events and their configurations are not memorised. Any configuration of events is new, and the probabilistic values to be considered are reset. As for collective behaviours, we introduce methodological and conceptual proposals using mesoscopic variables and their property profiles and meta-profile Big Data and non-computable profiles which were inspired by the use of natural computing to deal with cyber-ecosystems. The focus is on ongoing profiles, in which the arising properties trace trajectories, rather than assuming that we can foresee them based on the past. Full article
Open AccessArticle Leveraging Digital Twin Technology in Model-Based Systems Engineering
Received: 6 November 2018 / Revised: 22 December 2018 / Accepted: 22 January 2019 / Published: 30 January 2019
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Abstract
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, [...] Read more.
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Open AccessArticle Categorification of the Müller-Wichards System Performance Estimation Model: Model Symmetries, Invariants, and Closed Forms
Received: 25 October 2018 / Revised: 11 December 2018 / Accepted: 11 December 2018 / Published: 24 January 2019
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Abstract
The Müller-Wichards model (MW) is an algebraic method that quantitatively estimates the performance of sequential and/or parallel computer applications. Because of category theory’s expressive power and mathematical precision, a category theoretic reformulation of MW, i.e., CMW, is presented in this paper. The CMW [...] Read more.
The Müller-Wichards model (MW) is an algebraic method that quantitatively estimates the performance of sequential and/or parallel computer applications. Because of category theory’s expressive power and mathematical precision, a category theoretic reformulation of MW, i.e., CMW, is presented in this paper. The CMW is effectively numerically equivalent to MW and can be used to estimate the performance of any system that can be represented as numerical sequences of arithmetic, data movement, and delay processes. The CMW fundamental symmetry group is introduced and CMW’s category theoretic formalism is used to facilitate the identification of associated model invariants. The formalism also yields a natural approach to dividing systems into subsystems in a manner that preserves performance. Closed form models are developed and studied statistically, and special case closed form models are used to abstractly quantify the effect of parallelization upon processing time vs. loading, as well as to establish a system performance stationary action principle. Full article
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Open AccessArticle The Epistemological Implications of Critical Complexity Thinking for Operational Research
Received: 7 December 2018 / Accepted: 11 January 2019 / Published: 21 January 2019
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Abstract
Classical operational research (OR) is mainly concerned with the use of mathematical techniques and models used in decision-making situations. The basic assumptions of OR presuppose that the structure of the world is one of order and predictability. Although this positivistic approach produces significant [...] Read more.
Classical operational research (OR) is mainly concerned with the use of mathematical techniques and models used in decision-making situations. The basic assumptions of OR presuppose that the structure of the world is one of order and predictability. Although this positivistic approach produces significant results when levels of certainty and initial conditions are stable, it is limited when faced with an acknowledgement of the complex nature of the real world. This paper aims to highlight that by drawing on a general understanding of complexity theory, classical OR approaches can be enriched and broadened by adopting an epistemology based on the assumption that the underlying mechanisms governing the world are complex. It is argued that complexity theory (as interpreted by the philosopher Paul Cilliers) acknowledges the complex nature of the real world and helps to identify the characteristics of complex phenomena. By aligning OR epistemologies with the acknowledgment of complexity, new modelling methods could be developed. In addition, the implications for knowledge generating processes through boundary setting, as well as the provisional nature of such knowledge and what (ethical) responsibilities accompany the study of complex phenomena, will be discussed. Examples are presented to highlight the epistemological implications of complexity thinking for OR. Full article
Open AccessReview Complexity Theory: An Overview with Potential Applications for the Social Sciences
Received: 22 December 2018 / Revised: 10 January 2019 / Accepted: 15 January 2019 / Published: 19 January 2019
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Abstract
Systems theory has been challenged in the recent literature due to its perceived disconnection from today’s research and practice demands. Moving away from the reductionist frameworks and the complicated domain predominated by known unknowns and order, a call is being made to the [...] Read more.
Systems theory has been challenged in the recent literature due to its perceived disconnection from today’s research and practice demands. Moving away from the reductionist frameworks and the complicated domain predominated by known unknowns and order, a call is being made to the social sciences to begin adopting complexity theory and newer connectionist methods that better address complexity and open social systems. Scholars and scholar-practitioners will continue to find the need to apply complexity theory as wicked problems become more prevalent in the social sciences. This paper differentiates between general systems theory (GST) and complexity theory, as well as identifies advantages for the social sciences in incorporating complexity theory as a formal theory. Complexity theory is expanded upon and identified as providing a new perspective and a new method of theorizing that can be practiced by disciplines within the social sciences. These additions could better position the social sciences to address the complexity associated with advancing technology, globalization, intricate markets, cultural change, and the myriad of challenges and opportunities to come. Full article
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Open AccessArticle Regulatory Limits to Corporate Sustainability: How Climate Change Law and Energy Reforms in Mexico May Impair Sustainability Practices in Mexican Firms
Received: 2 November 2018 / Revised: 7 January 2019 / Accepted: 11 January 2019 / Published: 17 January 2019
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Abstract
This paper aims to show that sustainable behavior by firms may be impaired by regulatory restrictions. We challenge the assumption that regulation aimed at curbing greenhouse gas emissions (GHG) in the form of a target to meet the Country’s GHG emissions commitments will [...] Read more.
This paper aims to show that sustainable behavior by firms may be impaired by regulatory restrictions. We challenge the assumption that regulation aimed at curbing greenhouse gas emissions (GHG) in the form of a target to meet the Country’s GHG emissions commitments will promote sustainable corporations. We argue that, in fact, such regulation may impair sustainability practices because it creates unintended consequences. This paper tackles the efficiency of the institutional framework chosen through the lenses of the analytical themes of fit, scale, and interplay, then we use a systems dynamic approach to represent how regulation in the arenas of energy efficiency and GHG emissions reduction may withhold competitive business outcomes and corporate sustainability schemes. We exemplify and simulate a single regulation scheme: a clean energy target for firms; and found that as a result of such scheme, the system is dominated by negative feedback processes resulting in lesser outcomes that would be better tackled by firms not being subject to the restrictions imposed by the regulation. Full article
(This article belongs to the collection System Dynamics)
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Open AccessEditorial Acknowledgement to Reviewers of Systems in 2018
Published: 16 January 2019
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Abstract
Rigorous peer-review is the corner-stone of high-quality academic publishing [...] Full article
Open AccessConcept Paper An MBSE Approach for Development of Resilient Automated Automotive Systems
Received: 26 November 2018 / Revised: 28 December 2018 / Accepted: 7 January 2019 / Published: 10 January 2019
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Abstract
Advanced driver assistance and automated driving systems must operate in complex environments and make safety-critical decisions. Resilient behavior of these systems in their targeted operation design domain is essential. In this paper, we describe developments in our Model-Based Systems Engineering (MBSE) approach to [...] Read more.
Advanced driver assistance and automated driving systems must operate in complex environments and make safety-critical decisions. Resilient behavior of these systems in their targeted operation design domain is essential. In this paper, we describe developments in our Model-Based Systems Engineering (MBSE) approach to develop resilient safety-critical automated systems. An MBSE approach provides the ability to provide guarantees about system behavior and potentially reduces dependence on in-vehicle testing through the use of rigorous models and extensive simulation. We are applying MBSE methods to two key aspects of developing resilient systems: (1) ensuring resilient behavior through the use of Resilience Contracts for system decision making; and (2) applying simulation-based testing methods to verify the system handles all known scenarios and to validate the system against potential unknown scenarios. Resilience Contracts make use of contract-based design methods and Partially Observable Markov Decision Processes (POMDP), which allow the system to model potential uncertainty in the sensed environment and thus make more resilient decisions. The simulation-based testing methodology provides a structured approach to evaluate the operation of the target system in a wide variety of operating conditions and thus confirm that the expected resilient behavior has indeed been achieved. This paper provides details on the development of a utility function to support Resilience Contracts and outlines the specific test methods used to evaluate known and unknown operating scenarios. Full article
(This article belongs to the Special Issue Model-Based Systems Engineering)
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