Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major paradigm shift for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are. The purpose of this research is to further explore the MBSE adoption process in the context of the organization. There were three research objectives designed to address the research question: how does organizational structure influence the adoption and implementation of MBSE? Research objective one was: relate organizational structure characteristics to MBSE adoption and implementation measures. Research objective two was: discover how organizational factors contribute to decisions made and other aspects of the MBSE adoption process. Research objective three was: connect different organizational structure and adoption variables together to derive critical variables in the adoption process. Research objective one was carried out using a survey as the instrument. The objective of the survey was to examine what the effects of organizational structure are on MBSE adoption and implementation. Organizational structure was represented by seven variables: Size, Formalization, Centralization, Specialization, Vertical Differentiation, Flexibility, and Interconnectedness. These are different characteristics of organizational structure that can be measured on a scale. MBSE adoption and implementation was represented by one adoption and three implementation variables. These include Adoption Process, Maturity of MBSE, Use of MBSE, and Influence on organizational outcomes. A total of 51 survey responses were received that met the inclusion criteria. Factor analysis was done for variables with multi-item measures. The factors were then analyzed using pairwise correlations to determine which relationships were significant. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables. Size and Vertical Differentiation had a negative correlation with Use of MBSE (implementation). Centralization was found to have negative correlations with adoption and implementation. Specialization did not have any significant correlations. Research objective two utilized semi-structured interviews as the main instrument. Survey participants had the opportunity to provide more detailed explanations of their organizations' experiences in the form of follow-up interviews. Eighteen survey participants agreed to this follow-up interview focused on MBSE adoption. Two of the participants shared failed adoption experiences, with the rest were at various stages of the adoption process. One of the most emergent themes out of the interviews was the idea of integration. Integration needs to occur at the organizational level, and the technical level. The technical level refers to the fact that tools, models, and/or data repositories need to be linked together in some way. Integration also has to occur at the organizational level, because a lot of different functional areas need to come together for MBSE. The way that organizations can address the issue of integration is through coordination mechanisms. The ultimate goal is to achieve implicit coordination through the use of connected models, but getting to that point will require coordination between different subunits. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority. The final research objective of this work was carried out through a causal analysis using the data obtained from the survey and interviews. The purpose of this analysis was to visualize and better understand the adoption process. According to the calculated measures of centrality, the important nodes in this model are Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools. Improved organizational outcomes is part of a key loop in the causal model. Improved organizational outcomes contributes to leaders and employees' willingness to support and use MBSE methods and tools, which contribute to actual use of tools and methods. This creates more Improved organizational outcomes, completing the loop. The survey results showed that Formalization, Decentralization, Flexibility, and Interconnectedness all have positive correlations with the Influence on organizational outcomes. So these organizational structure components are external factors that can be used to positively impact the adoption loop. Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structure was shown to have significant correlations with adoption and implementation of MBSE. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Improved organizational outcomes was shown to be a critical variable in the adoption process as an avenue for organizational structure factors to have a positive effect on the adoption process. / Doctor of Philosophy / Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major mindset change for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are. This research looks into how organizational structure may have an impact on MBSE adoption and implementation. This research was carried out with the use of three different methods: an online survey, semi-structured interviews, and a causal analysis. The data obtained from the survey and interviews was used to construct a causal model depicting the MBSE adoption process. Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structural variables were shown to have significant correlations with adoption and implementation of MBSE. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables, while Centralization had negative correlations with adoption and implementation. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority. The causal analysis showed that Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools were the critical variables in the MBSE adoption process.

Este trabalho desenvolve um método para a comparação de soluções logísticas de armazenamento e recuperação, com aplicações em centros de distribuição, depósitos, armazéns e demais estruturas equivalentes. Tais soluções podem implicar desde o uso de paleteiras manuais e empilhadeiras contra-balanceadas, até em arranjos mais complexos, envolvendo trans-elevadores operando em corredores de prateleiras com vários metros de altura. A literatura existente para o design e a escolha de tais soluções ressalta o prevalecimento de métodos proprietários e ad-hoc, auxiliados por ferramentas de software demasiadamente genéricas. Assim, o método aqui proposto é elaborado seguindo os princípios da Engenharia de Sistemas Baseada em Modelos (MBSE), sendo expresso através da linguagem OMG SysMLTM, e montado com o auxílio de ferramenta de software CASE (computer aided systems engineering) disponível comercialmente. Utilizando-se das técnicas mencionadas, este trabalho demonstra o passo-apasso da construção do método proposto, incluindo a formulação de um template de requisitos e de um modelo de referência, orientado a objetos, para sistemas logísticos de armazenamento e recuperação. Concluída a apresentação do método, o mesmo é aplicado em dois exemplos de estudos de viabilidade (trade-studies) que determinam soluções ótimas para um dado conjunto de requisitos de negócio. No primeiro exemplo tem-se como fator limitante o no de endereços de armazenamento, e no segundo a área disponível para construção do armazém. O principal resultado obtido com esse trabalho é capacidade de simular, em um único ambiente, escolhas de soluções logísticas de armazenamento que consideram parâmetros do sistema como um todo, e não apenas de seus sub-sistemas isoladamente. Isto tornou possível mensurar como alterações nas especificações de um dado ponto de vista, como o estrutural, impactam na satisfação dos requisitos de outros pontos de vista, como o dinâmico ou financeiro. A MBSE, entretanto, ainda não pode ser considerada uma disciplina madura. As ferramentas de software que a ela dão suporte, bem como as listas de melhores práticas de suas aplicações estão em constante evolução e aprimoramento. Dessa forma, a aplicação dos princípios da MBSE no design e seleção de soluções logísticas de armazenamento, com adoção da orientação a objetos, pode ser tida como uma ideia inovadora. / This work presents a method for warehouse storage solutions comparison. The existing research regarding the design and selection of such logistic solutions highlights the predominance of ad-hoc procedures, as well as the use of generic software tools. Therefore, the method herein presented shall be developed according to the model-based systems engineering (MBSE) principles, being describe through the system modeling language (SysML), and built inside a computer-aided system engineering (CASE) software tool, commercially available. The method\'s steps shall be thoroughly detailed, including the creation of a reference model for warehouse storage systems, and its further use in trade studies execution. Once the method is properly described, its validation is demonstrated through two case studies designed to compare storage solutions according to the number of pallet-positions oered, and its dimensional footprint. This work\'s main achievement is the possibility to simulate, in a single environment, warehouse storage solution\'s options that take into account parameters of the system as a whole, and not only its sub-systems separately. With that, it is possible to measure how changes in the specifications of a given view point, such as structural, impact the requirement\'s satisfaction of other view points, such as dynamic or financial. The MBSE, however, still can not be considered a mature discipline. The software tools that support it, as well as the lists of best practices of its applications are constantly evolving and improving. Thus, the application of MBSE\'s principles in the design, and comparison, of warehouse storage solutions, with the adoption of object orientation, can be considered an innovative idea.

Este trabalho desenvolve um método para a comparação de soluções logísticas de armazenamento e recuperação, com aplicações em centros de distribuição, depósitos, armazéns e demais estruturas equivalentes. Tais soluções podem implicar desde o uso de paleteiras manuais e empilhadeiras contra-balanceadas, até em arranjos mais complexos, envolvendo trans-elevadores operando em corredores de prateleiras com vários metros de altura. A literatura existente para o design e a escolha de tais soluções ressalta o prevalecimento de métodos proprietários e ad-hoc, auxiliados por ferramentas de software demasiadamente genéricas. Assim, o método aqui proposto é elaborado seguindo os princípios da Engenharia de Sistemas Baseada em Modelos (MBSE), sendo expresso através da linguagem OMG SysMLTM, e montado com o auxílio de ferramenta de software CASE (computer aided systems engineering) disponível comercialmente. Utilizando-se das técnicas mencionadas, este trabalho demonstra o passo-apasso da construção do método proposto, incluindo a formulação de um template de requisitos e de um modelo de referência, orientado a objetos, para sistemas logísticos de armazenamento e recuperação. Concluída a apresentação do método, o mesmo é aplicado em dois exemplos de estudos de viabilidade (trade-studies) que determinam soluções ótimas para um dado conjunto de requisitos de negócio. No primeiro exemplo tem-se como fator limitante o no de endereços de armazenamento, e no segundo a área disponível para construção do armazém. O principal resultado obtido com esse trabalho é capacidade de simular, em um único ambiente, escolhas de soluções logísticas de armazenamento que consideram parâmetros do sistema como um todo, e não apenas de seus sub-sistemas isoladamente. Isto tornou possível mensurar como alterações nas especificações de um dado ponto de vista, como o estrutural, impactam na satisfação dos requisitos de outros pontos de vista, como o dinâmico ou financeiro. A MBSE, entretanto, ainda não pode ser considerada uma disciplina madura. As ferramentas de software que a ela dão suporte, bem como as listas de melhores práticas de suas aplicações estão em constante evolução e aprimoramento. Dessa forma, a aplicação dos princípios da MBSE no design e seleção de soluções logísticas de armazenamento, com adoção da orientação a objetos, pode ser tida como uma ideia inovadora. / This work presents a method for warehouse storage solutions comparison. The existing research regarding the design and selection of such logistic solutions highlights the predominance of ad-hoc procedures, as well as the use of generic software tools. Therefore, the method herein presented shall be developed according to the model-based systems engineering (MBSE) principles, being describe through the system modeling language (SysML), and built inside a computer-aided system engineering (CASE) software tool, commercially available. The method\'s steps shall be thoroughly detailed, including the creation of a reference model for warehouse storage systems, and its further use in trade studies execution. Once the method is properly described, its validation is demonstrated through two case studies designed to compare storage solutions according to the number of pallet-positions oered, and its dimensional footprint. This work\'s main achievement is the possibility to simulate, in a single environment, warehouse storage solution\'s options that take into account parameters of the system as a whole, and not only its sub-systems separately. With that, it is possible to measure how changes in the specifications of a given view point, such as structural, impact the requirement\'s satisfaction of other view points, such as dynamic or financial. The MBSE, however, still can not be considered a mature discipline. The software tools that support it, as well as the lists of best practices of its applications are constantly evolving and improving. Thus, the application of MBSE\'s principles in the design, and comparison, of warehouse storage solutions, with the adoption of object orientation, can be considered an innovative idea.

Yes / The process of generating FMEA following document-centric approach is tedious and susceptible to human error. This paper presents preliminary methodology for robotic manufacturing process modelling in MBSE environment with a scope of automating multiple steps of the modelling process using ontology. This is followed by the reasoning towards automatic generation of process FMEA from the MBSE model. The proposed methodology allows to establish robust and self-synchronising links between process-relevant information, reduce the likelihood of human error, and scale down time expenses.

Technische Änderungen sind im Produktentstehungsprozess allgegenwärtig und beanspruchen einen hohen Anteil an vorhandenen Forschungs- und Entwicklungskosten. Gleichzeitig wird ca. ein Drittel aller technischen Änderungen aufgrund der hohen Systemkomplexität als kritisch eingestuft. Für den Umgang mit hoher Systemkomplexität werden Ansätze des Model-Based Systems Engineering (MBSE) als vielversprechend gesehen. MBSE stellt einen formalisierten Ansatz zur Erstellung und Analyse von domänenübergreifenden Systemmodellen dar, die u.a. in den Bereichen Anforderungsmanagement, Verifikation und Validierung sowie Analyse und Synthese unterstützen können. Die Entwicklung von technischen Systemen erfolgt in Generationen. Mit Hilfe des Beschreibungsmodells der Systemgenerationsentwicklung - SGE nach ALBERS können u.a. technische und ökonomische Risiken von technischen Änderungen auf Basis der Herkunft von Referenzsystemelementen sowie der Übernahme- und Neuentwicklungsanteilen eingeschätzt werden. Es existieren zudem viele weitere Ansätze im technischen Änderungsmanagement, die sich auf einzelne Aspekte der Ausbreitungs- und Auswirkungsanalyse fokussieren. Die modellbasierte Methodik AECIA - Advanced Engineering Change Impact Approach bietet eine ganzheitliche Unterstützung im technischen Änderungsmanagement. Erste Veröffentlichungen untersuchen insbesondere Aktivitäten zur Prüfung der Validität sowie die Modellierung und Analyse der Ausbreitung von technischen Änderungen. Ziel dieser Veröffentlichung ist es die AECIA-Methodik um ein Vorgehen zur Bewertung von technischen Änderungen zu erweitern und dieses am Beispiel einer Sondermaschine anzuwenden und zu evaluieren.

As the global population of people with dementia is projected to reach 139 million by 2050, there is a growing focus on strategies supporting their Health and Care Outcomes (HCOs), one of which is dementia-friendly design in healthcare facilities. The built environment of healthcare facilities plays a key role in dementia care. To design healthcare facilities that better align with the HCOs for people with dementia, it is beneficial to assess the effects of Architectural Variables (AVs) on HCOs. The more extensive the consideration of AVs’ effects in design, the greater the capacity to achieve alignment between the built environment and HCOs. For this purpose, this PhD thesis develops a new assessment software which assesses the effects of AVs on HCOs more effectively, shifting from traditional and manual assessment tools in architecture towards systematic and digital approaches. Its development is guided by a methodology that addresses correspondingly three main challenges in previous assessment tools including lack of standard set of AVs and HCOs with widespread consensus, limitation in the holistic and systematic coverage of their interactions in the assessment calculations, and application difficulties of assessment tools. Firstly, this thesis creates a new set of AVs and HCOs through terminology analysis and introduces a new structure of classification for allocating and positioning the AVs and HCOs. In the terminology analysis, AVs and HCOs were extracted from a source of Evidence-Based Design (EBD) studies, then through frequency analysis and statistical tests, representative terms with the most potential for consensus were identified. For the structure of classification, a new structure was developed for AVs and HCOs based on both theoretical and practical investigation approaches to meet a set of fundamental classification criteria. Secondly, Model-Based Systems Engineering (MBSE), a subset of Systems Engineering, is utilized to model the interactions between AVs and HCOs. This approach enables the consideration of all different types of interactions between AVs and HCOs. It considers both direct interactions (AV-HCO) and indirect interactions, such as AV-HCO-HCO (an AV affects an HCO, which in turn affects another HCO) and AV-AV-HCO (an AV affects another AV, which then affects an HCO). Through systematic modeling with MBSE, a logical model has been developed that automates assessment calculations. Thirdly, the application difficulties of the previous assessment tools are addressed through considerations in the software features and capabilities. Namely, the logical model obtained in the second step is integrated into the computational engine of the software to support it as a calculative engine without any need for manual intervention by users. Users can enter the specifications of the facilities supposed to be assessed through AVs in the software, then the assessment is carried out through data exchange between the computational engine and its logical model on the backend. The results of the assessment are displayed online through quantitative and qualitative analysis. Users are informed about how many negative or positive effects each HCO receives from which AVs. It also provides root cause analysis through the impact chains of direct and indirect interactions to clarify why an effect, whether positive or negative, occurs. The total result for all of the HCOs is also available. Currently, the software conducts the assessment based on 396 interactions between AVs and HCOs, extracted from a source of previous studies. However, the model obtained through implementing MBSE is so developed that new findings could be added into the model and subsequently automatically into the software, along with all relevant assessment calculations. This makes the software dynamic and adaptable to new findings. Moreover, the software was implemented in two real-world case assessments in Cambridge, UK. Additionally, expert feedback was gathered through a series of feedback sessions.:Table of content SUMMARY OF THESIS KURZFASSUNG TABLE OF CONTENT GLOSSARY INTRODUCTION CHAPTER 1.BUILT ENVIRONMENT AND HUMAN OUTCOMES 1.1. Introduction to built environment and human outcomes 1.1.1. Definition of built environment and human outcomes 1.1.2. The impact of built environment on human outcomes, with a focus on older occupants 1.2. Theories linking the built environment and human outcomes 1.2.1. Overview of theories linking built environment and human outcomes 1.2.2. Environmental gerontology 1.3. EBD: An approach to design for the theories linking built environment and human outcomes 1.3.1. Role of EBD in healthcare facility design 1.3.2. Role of EBD in environmental gerontology CHAPTER 2.DEMENTIA-FRIENDLY DESIGN IN NURSING HOMES 2.1. Understanding dementia: Definition to consequences 2.2. The built environment of people with dementia 2.3. Definition and history of dementia-friendly design 2.4. Effects of dementia-friendly design on people with dementia 2.5. Principles of dementia-friendly design in nursing homes CHAPTER 3.ASSESSMENT TOOLS IN DEMENTIA-FRIENDLY DESIGN 3.1. Role of assessment tools in dementia-friendly design 3.2. Overview of previous assessment tools 3.3. Analyzing the previous assessment tools CHAPTER 4.RESEARCH DESIGN AND METHODOLOGY 4.1. Research gap, objectives, and questions 4.2. Scope and boundaries 4.3. Methodology CHAPTER 5.TERMINOLOGY ANALYSIS FOR CONSISTENCY 5.1. Extraction of terminology through concept-based approach 5.1.1. Conducting content analysis of source studies 5.1.2. Application of a concept-based approach 5.2. Dataset generation of the extracted terminologies 5.3. Frequency analysis and statistical tests 5.3.1. Frequency analysis and chi-square test of the concepts for AVs 5.3.2. Frequency analysis and chi-square test of the concepts for HCOs 5.4. Selection of representative terms 5.5. Scenarios for establishing comprehensive standardized terminology Chapter 6. DEVELOPMENT OF CLASSIFICATION STRUCTURE 6.1. Development of the classification structure 6.1.1. Expected efficacies and importance of the classification structure 6.1.2. Criteria for the development of classification structure 6.1.3. Nature of classification criteria 6.1.4. Investigation approaches 6.1.5. Creating the structure of classifications for AVs and HCOs 6.2. Allocation of AVs and HCOs to their corresponding classifications 6.3. Extraction of the interactions between AVs and HCOs 6.4. Considerations for interactions between AVs and HCOs CHAPTER 7.IMPLEMENTING MODEL-BASED SYSTEMS ENGINEERING 7.1. The role and benefits of MBSE in the assessment software 7.2. Introduction to the Model-Based Systems Engineering (MBSE) 7.2.1. The foundation of MBSE: Systems Engineering (SE) 7.2.2. The core principles of MBSE 7.3. Implementing MBSE 7.3.1. Operational analysis phase 7.3.2. System analysis phase 7.3.3. Logical architecture phase 7.3.4. Physical architecture phase CHAPTER 8.DEVELOPMENT OF WEB-BASED ASSESSMENT SOFTWARE AND ITS IMPLEMENTATION IN PRACTICE 8.1. Overview of the software structure 8.2. Technical structure and key technologies 8.3. Key features and functionalities 8.3.1. Accessibility 8.3.2. Registration 8.3.3. Management of assessment cases 8.3.4. Creation of a new assessment case 8.3.5. Design assessment questionnaire 8.3.6. Displaying the assessment results 8.4. Considerations for interactions in the assessment software 8.4.1. Reliability awareness 8.4.2. Reflection of AV-HCO direct vs. indirect distinctions 8.4.3. Clarification of conflicts in studies’ findings 8.5. Case analysis 8.5.1. On-site assessment and data collection for AVs’ specifications 8.5.2. Assessment results of case analysis 8.5.3. Comparative analysis 8.6. Experts’ feedback 8.6.1. Selection of participants 8.6.2. Content of the feedback sessions 8.6.3. Feedback session process and outcomes CHAPTER 9.DISCUSSION AND CONCLUSION 9.1. Thesis implications for dementia-friendly design assessment 9.1.1. Standardization and organization of AVs and HCOs 9.1.2. Systematic consideration of interactions 9.1.3. Application capabilities 9.2. Limitations 9.2.1. Scope of interactions and benchmarking 9.2.2. Limitation in qualitative nature of EBD findings 9.2.3. Practical application and validation 9.2.4. Standardization of terminology 9.2.5. Stakeholder interplay 9.3. Future directions 9.3.1. Expanding scope of interactions 9.3.2. Expanding practical application and user feedback 9.3.3. Extending standardization of terminology 9.3.4. Region-specific versions of the assessment software 9.4. Conclusion APPENDICES TABLE OF TABLES TABLE OF FIGURES DECLARATION REFERENCES / Bis zum Jahr 2050 wird die Weltbevölkerung voraussichtlich 139 Millionen Menschen mit Demenz erreichen. Infolgedessen liegt der Schwerpunkt zunehmend auf Lösungen zur Unterstützung ihrer Gesundheits- und Pflegeergebnisse (HCOs), zu denen auch die demenzfreundliche Gestaltung von Gesundheitseinrichtungen gehört. Um Gesundheitseinrichtungen zu gestalten, die besser mit den HCOs von Menschen mit Demenz übereinstimmen, ist es notwendig, die Auswirkungen von architektonischen Variablen (AVs) auf HCOs gründlich zu bewerten. Je umfassender die Berücksichtigung der Effekte von AVs im Design ist, desto größer ist die Fähigkeit, eine Übereinstimmung zwischen der gebauten Umgebung und den HCOs zu erreichen. Zu diesem Zweck wird in dieser Dissertation eine neue softwaregesteuerte Bewertungslösung entwickelt, mit der die Auswirkungen von AVs auf HCOs effektiver bewertet werden können, indem von traditionellen und manuellen Instrumenten auf digitale Lösungen umgestellt wird. Die Entwicklung wird von einer Methodik geleitet, die drei Hauptprobleme in früheren Bewertungsinstrumenten behandelt, darunter das Fehlen eines Standardsets von AVs und HCOs mit weitreichendem Konsens, Einschränkungen in der umfassenden und systematischen Abdeckung ihrer Interaktionen in den Bewertungsberechnungen sowie Anwendungsprobleme von Bewertungsinstrumenten. Erstens wird in dieser Arbeit durch eine Terminologieanalyse ein neues Set von AVs und HCOs erstellt und eine neue Klassifikationsstruktur für die Zuordnung und Positionierung der AVs und HCOs eingeführt. Bei der Terminologieanalyse wurden AVs und HCOs aus einer Quelle von Evidence-Based Design (EBD) Studien extrahiert, dann durch statistische und Häufigkeitsanalysen repräsentative Begriffe mit dem größten Konsenspotenzial ermittelt. Für die Struktur der Klassifizierung wurde eine neue Struktur für AVs und HCOs entwickelt, die sowohl auf theoretischen als auch auf praktischen Untersuchungsansätzen basiert, um eine Reihe von grundlegenden Klassifizierungskriterien zu erfüllen. Zweitens wird das modellbasierte System-Engineering (MBSE), ein Teilbereich des Systems-Engineering, zur Modellierung der Interaktionen zwischen AVs und HCOs eingesetzt. Dieser Ansatz ermöglicht die Berücksichtigung aller verschiedenen Arten von Interaktionen zwischen AVs und HCOs. Es berücksichtigt sowohl direkte Interaktionen (AV-HCO) als auch indirekte Interaktionen wie AV-HCO-HCO (ein AV beeinflusst ein HCO, das wiederum ein anderes HCO beeinflusst) und AV-AV-HCO (ein AV beeinflusst ein anderes AV, das wiederum ein HCO beeinflusst). Durch systematische Modellierung mit MBSE wurde ein logisches Modell entwickelt, das die Bewertungsberechnungen automatisiert. Drittens werden die Anwendungsprobleme der vorherigen Bewertungsinstrumente durch Überlegungen zu den Softwarefunktionen und -fähigkeiten behandelt. Insbesondere wird das im zweiten Schritt erhaltene logische Modell in den Berechnungsmotor der Software integriert, um es als einen rechnerischen Motor zu unterstützen, ohne dass Benutzer manuell eingreifen müssen. Benutzer können die Spezifikationen der Einrichtungen, die durch AVs der Software bewertet werden sollen, eingeben, und die Bewertung erfolgt durch den Datenaustausch zwischen dem Berechnungsmotor und seinem logischen Modell auf dem Backend. Die Ergebnisse der Bewertung werden online durch quantitative und qualitative Analysen angezeigt. Benutzer werden darüber informiert, wie viele negative oder positive Auswirkungen jede HCO von welchen AVs erhält. Es bietet auch Ursachenanalyse, um zu klären, warum ein Effekt, sei er positiv oder negativ, auftritt. Das Gesamtergebnis für alle HCOs ist ebenfalls verfügbar. Aktuell führt die Software die Bewertung auf der Grundlage von 396 Interaktionen zwischen AVs und HCOs durch, die aus einer Quelle früherer Studien extrahiert wurden. Das durch die Implementierung von MBSE erhaltene Modell ist jedoch so entwickelt, dass neue Erkenntnisse problemlos in das Modell und anschließend automatisch in die Software und alle relevanten Bewertungsberechnungen integriert werden können. Dies macht die Software dynamisch und anpassungsfähig für neue Erkenntnisse. Darüber hinaus wurde die Software in zwei realen Fallbewertungen in Cambridge, Großbritannien, implementiert. Zusätzlich wurde durch eine Reihe von Feedback-Sitzungen Expertenfeedback gesammelt.:Table of content SUMMARY OF THESIS KURZFASSUNG TABLE OF CONTENT GLOSSARY INTRODUCTION CHAPTER 1.BUILT ENVIRONMENT AND HUMAN OUTCOMES 1.1. Introduction to built environment and human outcomes 1.1.1. Definition of built environment and human outcomes 1.1.2. The impact of built environment on human outcomes, with a focus on older occupants 1.2. Theories linking the built environment and human outcomes 1.2.1. Overview of theories linking built environment and human outcomes 1.2.2. Environmental gerontology 1.3. EBD: An approach to design for the theories linking built environment and human outcomes 1.3.1. Role of EBD in healthcare facility design 1.3.2. Role of EBD in environmental gerontology CHAPTER 2.DEMENTIA-FRIENDLY DESIGN IN NURSING HOMES 2.1. Understanding dementia: Definition to consequences 2.2. The built environment of people with dementia 2.3. Definition and history of dementia-friendly design 2.4. Effects of dementia-friendly design on people with dementia 2.5. Principles of dementia-friendly design in nursing homes CHAPTER 3.ASSESSMENT TOOLS IN DEMENTIA-FRIENDLY DESIGN 3.1. Role of assessment tools in dementia-friendly design 3.2. Overview of previous assessment tools 3.3. Analyzing the previous assessment tools CHAPTER 4.RESEARCH DESIGN AND METHODOLOGY 4.1. Research gap, objectives, and questions 4.2. Scope and boundaries 4.3. Methodology CHAPTER 5.TERMINOLOGY ANALYSIS FOR CONSISTENCY 5.1. Extraction of terminology through concept-based approach 5.1.1. Conducting content analysis of source studies 5.1.2. Application of a concept-based approach 5.2. Dataset generation of the extracted terminologies 5.3. Frequency analysis and statistical tests 5.3.1. Frequency analysis and chi-square test of the concepts for AVs 5.3.2. Frequency analysis and chi-square test of the concepts for HCOs 5.4. Selection of representative terms 5.5. Scenarios for establishing comprehensive standardized terminology Chapter 6. DEVELOPMENT OF CLASSIFICATION STRUCTURE 6.1. Development of the classification structure 6.1.1. Expected efficacies and importance of the classification structure 6.1.2. Criteria for the development of classification structure 6.1.3. Nature of classification criteria 6.1.4. Investigation approaches 6.1.5. Creating the structure of classifications for AVs and HCOs 6.2. Allocation of AVs and HCOs to their corresponding classifications 6.3. Extraction of the interactions between AVs and HCOs 6.4. Considerations for interactions between AVs and HCOs CHAPTER 7.IMPLEMENTING MODEL-BASED SYSTEMS ENGINEERING 7.1. The role and benefits of MBSE in the assessment software 7.2. Introduction to the Model-Based Systems Engineering (MBSE) 7.2.1. The foundation of MBSE: Systems Engineering (SE) 7.2.2. The core principles of MBSE 7.3. Implementing MBSE 7.3.1. Operational analysis phase 7.3.2. System analysis phase 7.3.3. Logical architecture phase 7.3.4. Physical architecture phase CHAPTER 8.DEVELOPMENT OF WEB-BASED ASSESSMENT SOFTWARE AND ITS IMPLEMENTATION IN PRACTICE 8.1. Overview of the software structure 8.2. Technical structure and key technologies 8.3. Key features and functionalities 8.3.1. Accessibility 8.3.2. Registration 8.3.3. Management of assessment cases 8.3.4. Creation of a new assessment case 8.3.5. Design assessment questionnaire 8.3.6. Displaying the assessment results 8.4. Considerations for interactions in the assessment software 8.4.1. Reliability awareness 8.4.2. Reflection of AV-HCO direct vs. indirect distinctions 8.4.3. Clarification of conflicts in studies’ findings 8.5. Case analysis 8.5.1. On-site assessment and data collection for AVs’ specifications 8.5.2. Assessment results of case analysis 8.5.3. Comparative analysis 8.6. Experts’ feedback 8.6.1. Selection of participants 8.6.2. Content of the feedback sessions 8.6.3. Feedback session process and outcomes CHAPTER 9.DISCUSSION AND CONCLUSION 9.1. Thesis implications for dementia-friendly design assessment 9.1.1. Standardization and organization of AVs and HCOs 9.1.2. Systematic consideration of interactions 9.1.3. Application capabilities 9.2. Limitations 9.2.1. Scope of interactions and benchmarking 9.2.2. Limitation in qualitative nature of EBD findings 9.2.3. Practical application and validation 9.2.4. Standardization of terminology 9.2.5. Stakeholder interplay 9.3. Future directions 9.3.1. Expanding scope of interactions 9.3.2. Expanding practical application and user feedback 9.3.3. Extending standardization of terminology 9.3.4. Region-specific versions of the assessment software 9.4. Conclusion APPENDICES TABLE OF TABLES TABLE OF FIGURES DECLARATION REFERENCES

Mechatronic design is an enigma. On the one hand, mechatronic products promise enhanced functionality, and better performance at reduced cost. On the other hand, optimizing mechatronic design concepts is a major challenge to overcome during the design process. In the past, less attention has been paid to the life phases of a mechatronic product, and it was assumed that modifications in electronics and software will ensure that the product performs to expectation throughout its life time. However it has been realized that introducing design changes in mechatronics is not easy, since it is difficult to assess the consequences of a design decision, both during the design process of a new product, and during a design modification. It is also realized that there is a strong need to consider the product's life phases during the early phases of product development. Furthermore, it is rather difficult to perform a design optimization since it requires introducing changes across different domains, which is not well supported by the methods and tools available today. This thesis investigates the topic of mechatronic design and attacks some of the major challenges that have been identified regarding the design of mechatronic products. The goal is to provide support to the designers to facilitate better understanding of the consequences of their design choices as early as possible. The work also aims to provide support for assessing alternative design concepts, and for optimizing a design concept based on requirements, constraints and designer preferences at the time of design. The thesis highlights three main challenges related to mechatronic product development: the need for a common language during conceptual design; the inadequate information transfer between engineering domains; and the difficulty in assessing the properties of competing mechatronic concepts. A model-based integration approach is presented, and these key challenges are considered in relation to an integrated modeling and design infrastructure. The approach is illustrated through the design of two mechatronic systems- a two degrees-of-freedom robot, and a hospital bed propulsion system. Initial results provide evidence of good potential for information transfer across mechatronic domains. Although SysML was used for the case studies, some important questions were raised about its suitability as a common language for mechatronics. Suggestions for future work are: to utilize the developed infrastructure and incorporate a capability to model and assess consequences of competing design concepts; provide support for optimizing these concepts; and evaluate the usefulness of the developed infrastructure in a real-world design setting. These efforts should provide ample information to the designer for making adequate decisions during the design process. / QC 20110629

Ein wesentliches Ziel während der Produktentwicklung ist die frühzeitige Absicherung der Produkteigenschaften auf Basis der definierten Produktmerkmale unter Beachtung der äußeren Randbedingungen. Digitale Modelle und Methoden unterstützen den Produktentwickler bei der frühzeitigen virtuellen Evaluation des Produktes. [...] In diesem Beitrag wird eine Methodik präsentiert, die mit Hilfe der SysML-Modelle eine Simulation in VR ermöglicht. Die SysML-Beschreibung wird als Kern der Simulation dienen und das gesamte Simulationsmodell steuern. Weiterhin wird erläutert, wie die SysML-Beschreibung mit einem VR-Tool und einem Physikberechnungstool verbunden werden kann. Die in CAVE und HMD durchgeführten Simulationen wurden im Rahmen von Usability Tests evaluiert. Aus diesen Tests werden Ergebnisse präsentiert, die sich mit Verwendungsschwerpunkten in VR und der Zufriedenheit bei der Beurteilung von Produkten in VR beschäftigt haben. Schlussendlich wird ein Beispiel-Simulationsszenario in der CAVE-VR und einem Head Mounted Display (HMD) diskutiert. [... aus der Einleitung]