Understanding the Value of Systems Engineering
Understanding the Value of Systems Engineering
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了解系统工程的价值
DOI：
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发表时间：
2004
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通讯作者：
Honourcode
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作者：
E. Honour;Honourcode
系统工程的实践被认为在复杂系统的开发中具有很高的价值。启发式智慧是系统工程（SE）数量和质量的增加可以在提高产品质量的同时减少项目进度。本文探讨了最近关于SE的启发式价值的理论和统计信息。它探讨了项目成本与进度、技术价值、技术规模、技术复杂性和技术质量之间的潜在理论关系，总结了作者之前的工作。然后确定并总结了六项先前的统计研究，得出了与SE值相关的结论。最后，本文提供了INCOSE系统工程卓越中心（SECOE）统计研究的最终结果，该结果显示了支持启发式的明显相关性。结果表明，最佳的SE工作大约是整个项目工作的15-20%。系统工程（SE）这门学科已经被认为是复杂系统发展的关键学科，已有50年的历史。自20世纪50年代被认可以来[Goode 1957]， SE已应用于各种产品，如船舶，计算机和软件，飞机，环境控制，城市基础设施和汽车[SE Applications TC 2000]。系统工程师已经成为一个又一个复杂项目中公认的技术领导者[Hall 1993， Frank 2000]。然而，在许多方面，我们对SE的了解比其他任何工程学科都要少。系统工程可以依靠系统科学和许多领域物理关系来分析产品系统性能。但是系统工程师仍然在为控制系统开发的基本数学关系而奋斗。今天，SE通过使用每个从业者在职业生涯的个人实验中学到的启发式来指导每个系统的开发。每个人知道的启发式是不同的；人们只需要看看SE“标准”和SE认证的支离破碎的发展，就知道它们有多大的不同。由于这种对学科的启发式理解，几乎不可能量化SE对项目的价值[Sheard 2000]。然而，实践者和管理者都直觉地理解这种价值。他们通常在每个复杂的项目中合并一些SE实践。然而，理解上的差异通常会导致对要包含的实践的级别和形式的分歧。预测主义者创建了广泛的标准、手册和成熟度模型，这些标准、手册和模型规定了“应该”包括的实践。描述主义者记录了在给定项目中“成功”遵循的实践。然而，在这两种情况下，实践都不是基于对项目实际价值的量化度量。对SE值的直观理解如图1所示。在传统设计中，没有考虑SE概念，系统产品的创建主要集中在生产、集成和测试上。在“系统思考”的设计中，对系统设计的更多强调创造了更容易、更快速的集成和测试。总体结果是节省了时间和成本，并具有更高质量的系统产品。系统工程概念的主要影响是尽早减少风险，如图2所示。通过早期降低风险，集成和测试的问题可以避免出现，从而降低成本并缩短进度。理解SE价值的挑战在于量化这些直观的理解。研究领域由于对社会科学的认识存在很大的差异，任何理论努力都必须从界定研究领域的术语开始。对于本文来说，“系统工程”是广义上的，它包括将科学和技术（“工程”）应用于元素（“系统”）的相互作用组合的开发的所有努力。由于系统开发团队的跨学科性质，这样的工作经常被描述为同时具有技术和管理部分。[Frank 2000]对良好SE所需的技能广度进行了很好的研究。我们把“SE管理”作为指导和控制系统工程的工作。有明显的重叠(a)“开发工程”，使用特定的工程规程来创建系统的元素，(b)“测试工程”，验证和/或确认系统的工程应用，以及(c)“计划管理”，项目的总体控制。SE通过使用跨学科的协调和元素间的技术分析将自己与开发工程区分开来。应用于系统级别的测试工程被认为是SE的一个子集，而应用于系统元素的测试工程则不是。SE管理在使用技术分析和控制以及强调技术质量（相对于财务和进度问题）方面区别于项目管理。理解SE的价值需要量化它的价值。量化需要理解对SE重要的数值参数。一个实用的数学理论，系统设计，详细设计，生产集成测试传统设计节省时间/成本“系统思维”设计图1。SE的直观值。时间风险时间风险传统设计“系统思维”设计通过SE降低风险。基础SE有助于理解重要参数，特别是那些对SE管理很重要的参数。经常有人提出反对这种数学理论的可行性的论点[Sheard 2000]。首先，每个系统开发都是独一无二的。在提出新的挑战时，每一个这样的发展都可能使任何先前理论的科学基础失效。第二，项目在重要的参数度量上差异很大——规模、进度和可接受的风险。在之前收集的少数数据中[2002年3月]，这种差异导致数据点的差异很大。第三，这种必要性理论包含了开发人员人性的统计表示，这种表示经常被怀疑。第四，SE适用于包含几乎由任何其他工程学科开发的组件的系统[honor 1999]。这种高度多样化的应用可能会挫败编纂。然而，这些论证都不能证明这种数学理论的不可能性。本节总结了作者在SE管理数学理论方面的工作[honor 2002]，并将该理论应用于量化SE的价值。这个总结的目的是引出一个关于SE值的工作假设。
The practices of systems engineering are believed to have high value in the development of complex systems. Heuristic wisdom is that an increase in the quantity and quality of systems engineering (SE) can reduce project schedule while increasing product quality. This paper explores recent theoretical and statistical information concerning this heuristic value of SE. It explores the underlying theoretical relationships among project cost and schedule, technical value, technical size, technical complexity, and technical quality, summarizing prior work by the author. It then identifies and summarizes six prior statistical studies with conclusions that relate to the value of SE. Finally, the paper provides final results of a statistical study by the INCOSE Systems Engineering Center of Excellence (SECOE) that presents evident correlations supporting the heuristics. The results indicate that optimal SE effort is approximately 15-20% of the total project effort. Background The discipline of systems engineering (SE) has been recognized for 50 years as essential to the development of complex systems. Since its recognition in the 1950s [Goode 1957], SE has been applied to products as varied as ships, computers and software, aircraft, environmental control, urban infrastructure and automobiles [SE Applications TC 2000]. Systems engineers have been the recognized technical leaders [Hall 1993, Frank 2000] of complex project after complex project. In many ways, however, we understand less about SE than nearly any other engineering discipline. Systems engineering can rely on systems science and on many domain physics relationships to analyze product system performance. But systems engineers still struggle with the basic mathematical relationships that control the development of systems. SE today guides each system development by the use of heuristics learned by each practitioner during the personal experimentation of a career. The heuristics known by each differ; one need only view the fractured development of SE “standards” and SE certification to see how much they differ. As a result of this heuristic understanding of the discipline, it has been nearly impossible to quantify the value of SE to projects [Sheard 2000]. Yet both practitioners and managers intuitively understand that value. They typically incorporate some SE practices in every complex project. The differences in understanding, however, just as typically result in disagreement over the level and formality of the practices to include. Presciptivists create extensive standards, handbooks, and maturity models that prescribe the practices that “should” be included. Descriptivists document the practices that were “successfully” followed on given projects. In neither case, however, are the practices based on a quantified measurement of the actual value to the project. The intuitive understanding of the value of SE is shown in Figure 1. In traditional design, without consideration of SE concepts, the creation of a system product is focused on production, integration, and test. In a “system thinking” design, greater emphasis on the system design creates easier, more rapid integration and test. The overall result is a savings in both time and cost, with a higher quality system product. The primary impact of the systems engineering concepts is to reduce risk early, as shown in Figure 2. By reducing risk early, the problems of integration and test are prevented from occurring, thereby reducing cost and shortening schedule. The challenge in understanding the value of SE is to quantify these intuitive understandings. Field of Study Because there is wide variance in the perceptions of SE, any theoretical effort must start with a definition of terms that bounds the field of study. For this paper, “systems engineering” is taken in a broad sense that includes all efforts that apply science and technology (“engineering”) to the development of interacting combinations of elements (“systems”). Such efforts are frequently characterized as having both technical and management portions because of the interdisciplinary nature of system development teams. The breadth of skills necessary for good SE was studied well by [Frank 2000]. We take “SE management” to be the efforts that guide and control the systems engineering. There are obvious overlaps with (a) “development engineering,” the use of specific engineering disciplines to create the elements of a system, (b) “test engineering,” the application of engineering to verify and/or validate the system, and (c) “program management,” the overall control of a project. SE distinguishes itself from development engineering by the use of interdiscipline coordination and inter-element technical analyses. Test engineering when applied at the system level is taken to be a subset of SE, while test engineering applied to system elements is not. SE management is distinguished from program management in the use of technical analysis and control and in the emphasis on technical quality as opposed to financial and schedule concerns. Underlying Mathematical Theory Understanding the value of SE requires quantifying that value. Quantification requires understanding the numerical parameters that matter to SE. A useable mathematical theory that SYSTEM DESIGN DETAIL DESIGN PRODUCTION INTEGRATION TEST Traditional Design Saved Time/ Cost “System Thinking” Design Figure 1. Intuitive Value of SE. Time Risk Time Risk Traditional Design “System Thinking” Design Figure 2. Risk Reduction by SE. underlies SE can contribute to an understanding of the important parameters, particularly those important to SE management. There are frequently stated arguments against the feasibility of such a mathematical theory [Sheard 2000]. First, each system development is one of a kind. In presenting new challenges, each such development might invalidate the scientific basis of any prior theory. Second, projects vary widely in important parametric measures – size, schedule, and acceptable risk. This variance leads, in the few data collected previously [Mar 2002], to wide variance in the data points. Third, such a theory of necessity includes a statistical representation of the human nature of the developers, a representation that is frequently viewed with skepticism. Fourth, SE applies itself to systems that contain components developed by virtually any other engineering discipline [Honour 1999]. This highly varied application might defeat codification. Yet none of these arguments prove the impossibility of such a mathematical theory. This section summarizes the author’s work [Honour 2002] toward a mathematical theory of SE management and applies that theory to quantifying the value of SE. The intent of this summary is to lead up to a working hypothesis for the value of SE.