Kelly X. Campo, T. Teper, Casey E. Eaton, Anna M. Shipman, Garima Bhatia, Bryan L. Mesmer
Although Model‐Based Systems Engineering (MBSE) is quickly becoming adopted in Systems Engineering (SE), there have not been many studies evaluating literature to determine the perceived value of implementing MBSE. This research first identifies and discusses previous studies on the justification or rejection of MBSE. This study investigates how the SE community perceives the value of MBSE by coding and analyzing positive and negative descriptions of MBSE; perceived benefits and drawbacks of implementing MBSE; and the evidence and metrics used to substantiate and measure each claim about MBSE. From 60 academic sources, this study collected and coded over 2900 claims on MBSE. Our findings determine the most positive attributes of MBSE to be Verification & Validation Capability, Consistency, Reasoning, and Risk & Error Manageability, while the most negative attributes were Approach Understandability, Acceptability, Familiarity, and Approach Complexity. The most‐stated benefits were Reduced Time, Better Communication/Information Sharing, Reduced Costs, and Better Analysis Capability. The most claimed drawbacks were Increased Costs, Increased Time, Increased Effort, and Worsened Capability. A large share of claims (47%) about MBSE was based on author opinions. Most claims (86%) were not substantiated by a metric.
{"title":"Model‐based systems engineering: Evaluating perceived value, metrics, and evidence through literature","authors":"Kelly X. Campo, T. Teper, Casey E. Eaton, Anna M. Shipman, Garima Bhatia, Bryan L. Mesmer","doi":"10.1002/sys.21644","DOIUrl":"https://doi.org/10.1002/sys.21644","url":null,"abstract":"Although Model‐Based Systems Engineering (MBSE) is quickly becoming adopted in Systems Engineering (SE), there have not been many studies evaluating literature to determine the perceived value of implementing MBSE. This research first identifies and discusses previous studies on the justification or rejection of MBSE. This study investigates how the SE community perceives the value of MBSE by coding and analyzing positive and negative descriptions of MBSE; perceived benefits and drawbacks of implementing MBSE; and the evidence and metrics used to substantiate and measure each claim about MBSE. From 60 academic sources, this study collected and coded over 2900 claims on MBSE. Our findings determine the most positive attributes of MBSE to be Verification & Validation Capability, Consistency, Reasoning, and Risk & Error Manageability, while the most negative attributes were Approach Understandability, Acceptability, Familiarity, and Approach Complexity. The most‐stated benefits were Reduced Time, Better Communication/Information Sharing, Reduced Costs, and Better Analysis Capability. The most claimed drawbacks were Increased Costs, Increased Time, Increased Effort, and Worsened Capability. A large share of claims (47%) about MBSE was based on author opinions. Most claims (86%) were not substantiated by a metric.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"26 1","pages":"104 - 129"},"PeriodicalIF":2.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48639835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Model‐based systems engineering is a powerful methodology to develop safety‐critical systems. The use of the system model as a single source of truth for risk and dependability analysis results in a consistent and complete assessment. Besides, representation and logging of the assessment within the model result in a complete and up‐to‐date single source of information that can be used during the device certification as well. This paper aims to provide a comprehensive risk management SysML profile that includes interconnected safety analysis [functional hazard assessment (FHA), fault tree, and failure mode and effect analysis (FTA, FMEA)], control measure, and evaluation model elements in compliance with the medical standards. Model‐based risk assessment of a point‐of‐care diagnostic device for sepsis has been shown as a case study to show the implementation of the profile. This device is a standalone unit and the test results obtained directly affect the patient. Therefore, both the top‐down (FHA and FTA) and bottom‐up (FMEA) safety assessment methods have been used. Another objective of the study is to define a systematic and holistic method to perform fault tree analysis, not only from the system architecture models but also from the functional, activity, and sequence diagrams of the system model.
{"title":"Integration of systems design and risk management through model‐based systems development","authors":"Y. Uludağ, Ersin Evin, Nazan Gözay Gürbüz","doi":"10.1002/sys.21643","DOIUrl":"https://doi.org/10.1002/sys.21643","url":null,"abstract":"Model‐based systems engineering is a powerful methodology to develop safety‐critical systems. The use of the system model as a single source of truth for risk and dependability analysis results in a consistent and complete assessment. Besides, representation and logging of the assessment within the model result in a complete and up‐to‐date single source of information that can be used during the device certification as well. This paper aims to provide a comprehensive risk management SysML profile that includes interconnected safety analysis [functional hazard assessment (FHA), fault tree, and failure mode and effect analysis (FTA, FMEA)], control measure, and evaluation model elements in compliance with the medical standards. Model‐based risk assessment of a point‐of‐care diagnostic device for sepsis has been shown as a case study to show the implementation of the profile. This device is a standalone unit and the test results obtained directly affect the patient. Therefore, both the top‐down (FHA and FTA) and bottom‐up (FMEA) safety assessment methods have been used. Another objective of the study is to define a systematic and holistic method to perform fault tree analysis, not only from the system architecture models but also from the functional, activity, and sequence diagrams of the system model.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"26 1","pages":"48 - 70"},"PeriodicalIF":2.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48907043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Systems engineering tools and methodologies are increasingly being used in urban planning and sustainable development applications. Such tools were previously extensively used for urban planning during the 1960s and 1970s in the United States, only to result in high profile failures and pushback from urban planners, politicians, and the public. In order to better understand why this occurred, what has changed, and how we can avoid such failures moving forward, this study conducts a systematic review and an integrative review of the systems engineering and critical literature. These reviews are used to identify eight common pitfalls and organize them into key themes. Technological and methodological developments that may address each of these pitfalls are considered and recommendations are made for future applications of systems engineering to planning contexts. Finally, examples are provided of systems engineering being used productively in a way consistent with these recommendations for sustainable development applications.
{"title":"Systems engineering applied to urban planning and development: A review and research agenda","authors":"Jack Reid, D. Wood","doi":"10.1002/sys.21642","DOIUrl":"https://doi.org/10.1002/sys.21642","url":null,"abstract":"Systems engineering tools and methodologies are increasingly being used in urban planning and sustainable development applications. Such tools were previously extensively used for urban planning during the 1960s and 1970s in the United States, only to result in high profile failures and pushback from urban planners, politicians, and the public. In order to better understand why this occurred, what has changed, and how we can avoid such failures moving forward, this study conducts a systematic review and an integrative review of the systems engineering and critical literature. These reviews are used to identify eight common pitfalls and organize them into key themes. Technological and methodological developments that may address each of these pitfalls are considered and recommendations are made for future applications of systems engineering to planning contexts. Finally, examples are provided of systems engineering being used productively in a way consistent with these recommendations for sustainable development applications.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"26 1","pages":"103 - 88"},"PeriodicalIF":2.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45347960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of complex systems is an important aspect of systems engineering. Previous research derived six integration principles and qualitatively validated four of them using a data set of 14 systems. Of the two non‐validated principles, one was determined to be confounded with two of the four validated principles and is hence not considered in this research. This paper describes the quantitative validation of the resulting five integration principles based on an expanded data set of 52 systems. This expanded data set is analyzed statistically, and the interactions between integration principles are also evaluated. This research quantitatively validates four of the five integration principles and identifies three principle interactions that are significantly related to integration success, solidifying validity of the principles, and identifying three cases where the principles interact that must be further explored.
{"title":"Quantitative validation of complex systems integration principles","authors":"Joshua Logan Grumbach, L. Thomas","doi":"10.1002/sys.21641","DOIUrl":"https://doi.org/10.1002/sys.21641","url":null,"abstract":"The integration of complex systems is an important aspect of systems engineering. Previous research derived six integration principles and qualitatively validated four of them using a data set of 14 systems. Of the two non‐validated principles, one was determined to be confounded with two of the four validated principles and is hence not considered in this research. This paper describes the quantitative validation of the resulting five integration principles based on an expanded data set of 52 systems. This expanded data set is analyzed statistically, and the interactions between integration principles are also evaluated. This research quantitatively validates four of the five integration principles and identifies three principle interactions that are significantly related to integration success, solidifying validity of the principles, and identifying three cases where the principles interact that must be further explored.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"26 1","pages":"32 - 47"},"PeriodicalIF":2.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43914728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kaitlin Henderson, T. McDermott, E. V. Aken, A. Salado
Model‐based systems engineering (MBSE) is an increasingly accepted practice in the Systems Engineering (SE) community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented for feedback to subject‐matter experts from a working group developing the first DE measurement framework. This group is a joint effort with industry, academia, and the USA government to develop DE metric standards. Once the causal map was finalized, a case study was used to partially validate the causal model. Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model. The potential metric candidates include: system quality, defects, time, rework, ease of making changes, system understanding, Effort, accessibility of information, collaboration, project methods/processes, and use of DE/MBSE tools. We believe a concerted effort across the industry to focus on measuring these variables is the most effective way to establish proof of the value of MBSE and DE.
{"title":"Towards Developing Metrics to Evaluate Digital Engineering","authors":"Kaitlin Henderson, T. McDermott, E. V. Aken, A. Salado","doi":"10.1002/sys.21640","DOIUrl":"https://doi.org/10.1002/sys.21640","url":null,"abstract":"Model‐based systems engineering (MBSE) is an increasingly accepted practice in the Systems Engineering (SE) community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented for feedback to subject‐matter experts from a working group developing the first DE measurement framework. This group is a joint effort with industry, academia, and the USA government to develop DE metric standards. Once the causal map was finalized, a case study was used to partially validate the causal model. Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model. The potential metric candidates include: system quality, defects, time, rework, ease of making changes, system understanding, Effort, accessibility of information, collaboration, project methods/processes, and use of DE/MBSE tools. We believe a concerted effort across the industry to focus on measuring these variables is the most effective way to establish proof of the value of MBSE and DE.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"26 1","pages":"3 - 31"},"PeriodicalIF":2.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47355150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Engineering‐heavy new product development (NPD) projects face unplanned design iterations, which can cause failure in terms of missed targets for cost, schedule, quality, and customer satisfaction. These unplanned design iterations can be understood as the occurrence of a specific category of engineering project risks. As a result, companies employ structured actions to mitigate these risks in projects. However, even with such strategies in place, projects can still struggle to achieve their targets. This study aims to explore how companies employ risk mitigation actions to manage risks in engineering‐based NPD projects. To investigate this topic, a survey of employees in the aerospace and defense industries was conducted. We analyzed the responses using statistical methods. The results indicate that risk mitigation actions are used according to thematic clusters, in line with our findings from the literature. Furthermore, the selected mitigation measures show collective explanatory power for handling engineering project risks, suggesting that while some projects that employ mitigation actions may still fail, their use of such measures does still reduce the overall impact of risks. Interestingly, the results of the statistical analysis show no significant difference in the employment of risk mitigation actions in engineering‐based NPD projects, whether they employ waterfall or agile NPD methods, or a mixture of both. These results suggest that companies should consider all classes of risk mitigation actions to manage engineering project risks. On this basis, the wider contextualization of individual mitigating actions should be taken into account when planning risk mitigation for engineering‐based NPD projects.
{"title":"The role of risk mitigation actions in engineering projects: An empirical investigation","authors":"A. Shafqat, J. Oehmen, T. Welo, G. Ringen","doi":"10.1002/sys.21639","DOIUrl":"https://doi.org/10.1002/sys.21639","url":null,"abstract":"Engineering‐heavy new product development (NPD) projects face unplanned design iterations, which can cause failure in terms of missed targets for cost, schedule, quality, and customer satisfaction. These unplanned design iterations can be understood as the occurrence of a specific category of engineering project risks. As a result, companies employ structured actions to mitigate these risks in projects. However, even with such strategies in place, projects can still struggle to achieve their targets. This study aims to explore how companies employ risk mitigation actions to manage risks in engineering‐based NPD projects. To investigate this topic, a survey of employees in the aerospace and defense industries was conducted. We analyzed the responses using statistical methods. The results indicate that risk mitigation actions are used according to thematic clusters, in line with our findings from the literature. Furthermore, the selected mitigation measures show collective explanatory power for handling engineering project risks, suggesting that while some projects that employ mitigation actions may still fail, their use of such measures does still reduce the overall impact of risks. Interestingly, the results of the statistical analysis show no significant difference in the employment of risk mitigation actions in engineering‐based NPD projects, whether they employ waterfall or agile NPD methods, or a mixture of both. These results suggest that companies should consider all classes of risk mitigation actions to manage engineering project risks. On this basis, the wider contextualization of individual mitigating actions should be taken into account when planning risk mitigation for engineering‐based NPD projects.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"25 1","pages":"584 - 608"},"PeriodicalIF":2.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43840609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Systems engineering (SE) is an interdisciplinary domain that can benefit from incorporating contributions from fields not typically associated with technical disciplines, including integrating relevant research from social sciences. The study of innovation has produced the diffusion of innovation theory, which identifies variables that affect the adoption rate of innovations. Of these variables, the perceived attributes of the innovation have been shown to have the most significant impact on the adoption rate of innovations. Shaping the innovation attributes of relative advantage, compatibility, complexity, trialability, and observability and how they are perceived can accelerate its adoption rate. This theory has the potential to accelerate the adoption rate of SE innovations. Model‐based systems engineering (MBSE) is an SE innovation that, despite its benefits, has not been adopted generally. An evaluation of the attributes of MBSE as defined by the diffusion of innovation theory can aid in understanding its slow diffusion and inform methods to accelerate its adoption. Since there is some evidence to suggest that this theory is applicable to SE and MBSE, additional research should be conducted to determine the best way to utilize its principles.
{"title":"Applicability of the diffusion of innovation theory to accelerate model‐based systems engineering adoption","authors":"Daniel R. Call, D. Herber","doi":"10.1002/sys.21638","DOIUrl":"https://doi.org/10.1002/sys.21638","url":null,"abstract":"Systems engineering (SE) is an interdisciplinary domain that can benefit from incorporating contributions from fields not typically associated with technical disciplines, including integrating relevant research from social sciences. The study of innovation has produced the diffusion of innovation theory, which identifies variables that affect the adoption rate of innovations. Of these variables, the perceived attributes of the innovation have been shown to have the most significant impact on the adoption rate of innovations. Shaping the innovation attributes of relative advantage, compatibility, complexity, trialability, and observability and how they are perceived can accelerate its adoption rate. This theory has the potential to accelerate the adoption rate of SE innovations. Model‐based systems engineering (MBSE) is an SE innovation that, despite its benefits, has not been adopted generally. An evaluation of the attributes of MBSE as defined by the diffusion of innovation theory can aid in understanding its slow diffusion and inform methods to accelerate its adoption. Since there is some evidence to suggest that this theory is applicable to SE and MBSE, additional research should be conducted to determine the best way to utilize its principles.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"25 1","pages":"574 - 583"},"PeriodicalIF":2.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45452433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. McKay, Richard Chittenden, Tom Hazlehurst, A. Pennington, Richard Baker, T. Waller
The architectures of extended enterprises, including the supply networks that design, develop and support large, complex, engineered products, often reflect system‐level design decisions made very early in the product development process. Design tools used at this, preliminary design, stage focus on the physics and optimization of product system behaviors. Comparable tools for the consideration of extended enterprise perspectives at this stage are not available despite the costs of non‐quality often attributed to supply chain issues related to early design decisions. This paper introduces an interface to a discrete event simulation package that derives supply chain processes from product system architectures, so enabling the quantification and visualization of supply chain risk in early design decisions. The interface uses input data, in the form of a product architecture and associated make‐buy scenarios, which are available in the preliminary design process. Supplier data needed to drive the simulations is predefined and editable by users. Results from a proof‐of‐concept software prototype demonstrate the feasibility of generating enterprise architectures from product architectures and coupling these with a systems design vee model to create executable simulation models that can be used to identify, quantify and visualize engineering supply chain process operations and consequential risks.
{"title":"The derivation and visualization of supply network risk profiles from product architectures","authors":"A. McKay, Richard Chittenden, Tom Hazlehurst, A. Pennington, Richard Baker, T. Waller","doi":"10.1002/sys.21622","DOIUrl":"https://doi.org/10.1002/sys.21622","url":null,"abstract":"The architectures of extended enterprises, including the supply networks that design, develop and support large, complex, engineered products, often reflect system‐level design decisions made very early in the product development process. Design tools used at this, preliminary design, stage focus on the physics and optimization of product system behaviors. Comparable tools for the consideration of extended enterprise perspectives at this stage are not available despite the costs of non‐quality often attributed to supply chain issues related to early design decisions. This paper introduces an interface to a discrete event simulation package that derives supply chain processes from product system architectures, so enabling the quantification and visualization of supply chain risk in early design decisions. The interface uses input data, in the form of a product architecture and associated make‐buy scenarios, which are available in the preliminary design process. Supplier data needed to drive the simulations is predefined and editable by users. Results from a proof‐of‐concept software prototype demonstrate the feasibility of generating enterprise architectures from product architectures and coupling these with a systems design vee model to create executable simulation models that can be used to identify, quantify and visualize engineering supply chain process operations and consequential risks.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"25 1","pages":"421 - 442"},"PeriodicalIF":2.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48894101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents novel approaches to investigate aspects of cognition in organization and team‐based decision making. The authors draw on recent literature in team and organizational learning, cognition, behavior, knowledge management, macrocognition, and human factors to support this approach. The authors discuss five novel cognitive approaches based on this literature. The authors draw parallels with the philosophical “4E cognition” of organism model, which describes four types of cognition present in organisms. The authors note that previous literature has discussed analogous behavior between organizations and organisms, and that organizations can be described as complex systems mimicking organism structure and behavior. The authors discuss applications of these approaches in systems engineering contexts, including complex system design, decision making, and knowledge management that future research should pursue.
{"title":"New conceptual approaches to cognition in systems engineering: applying the 4 E's of cognition","authors":"V. M. Rao, R. Francis","doi":"10.1002/sys.21637","DOIUrl":"https://doi.org/10.1002/sys.21637","url":null,"abstract":"This paper presents novel approaches to investigate aspects of cognition in organization and team‐based decision making. The authors draw on recent literature in team and organizational learning, cognition, behavior, knowledge management, macrocognition, and human factors to support this approach. The authors discuss five novel cognitive approaches based on this literature. The authors draw parallels with the philosophical “4E cognition” of organism model, which describes four types of cognition present in organisms. The authors note that previous literature has discussed analogous behavior between organizations and organisms, and that organizations can be described as complex systems mimicking organism structure and behavior. The authors discuss applications of these approaches in systems engineering contexts, including complex system design, decision making, and knowledge management that future research should pursue.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"25 1","pages":"609 - 617"},"PeriodicalIF":2.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46219423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Component and subsystem reuse has been an important tool in controlling the cost and schedule requirements of developing new aerospace systems. Although the mere utilization of component reuse cannot be shown to influence system integration success significantly, previous research has shown that interactions between reuse and other integration principles do significantly influence integration success. The research described in this paper leverages historical system data to characterize these interactions and assess the influence of these interactions on integration success. This research identifies four characterizations of interactions between reuse and the other principles that significantly influence system integration success when component reuse is included in the system design.
{"title":"Systems integration implications of component reuse","authors":"Joshua Logan Grumbach, L. Thomas","doi":"10.1002/sys.21636","DOIUrl":"https://doi.org/10.1002/sys.21636","url":null,"abstract":"Component and subsystem reuse has been an important tool in controlling the cost and schedule requirements of developing new aerospace systems. Although the mere utilization of component reuse cannot be shown to influence system integration success significantly, previous research has shown that interactions between reuse and other integration principles do significantly influence integration success. The research described in this paper leverages historical system data to characterize these interactions and assess the influence of these interactions on integration success. This research identifies four characterizations of interactions between reuse and the other principles that significantly influence system integration success when component reuse is included in the system design.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":"25 1","pages":"561 - 573"},"PeriodicalIF":2.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42639380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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