Regular Paper This paper provides a foundation for modeling the set of activities and their relationships by which systems are engineered, or, more broadly, by which products and services are developed. It provides background, motivations, and formal definitions for process modeling in this specialized environment. We treat the process itself as a kind of system that can be engineered. However, while product systems must be created, the process systems for developing complex products must, to a greater extent, be discovered and induced. Then, they tend to be reused, either formally as standard processes, or informally by the workforce. We distinguish and clarify several important concepts in modeling processes, including: product development versus repetitive business processes, descriptive versus prescriptive processes, activities as actions versus deliverables as interactions, standard versus deployed processes, centralized versus decentralized process modeling, “as is ” versus “to be ” process modeling, and multiple phases in product development. We also present a basically simple yet highly extendable and generalized framework for modeling product development processes. The framework enables building a single model to support a variety of purposes, including project planning (scheduling, budgeting, resource loading, and risk management) and control, and it provides the scaffolding for knowledge management and organizational

Technical and operational uncertainties dynamically change environments for engineering systems. Flexibility allows systems to continue delivering value as the uncertainty unfolds. Uncertainty can better be managed by embedding flexibility into the system. However, system designers do not have a tool or metric that identifies which components within the system to focus embedded flexibility efforts. They rely on intuition developed through experience and expertise to build in system flexibility, often leading to disagreement between system stakeholders (both designers and customers) about where to focus efforts due to the differing perspectives and inability to assess

ABSTRACT: Projects are dealing with bigger stakes and facing an ever-growing complexity. Project risks have then increased in number and criticality. Lists of identified project risks thus need to be decomposed, for smaller clusters are more manageable. Existing techniques are mainly based on a well-known parameter such as the nature of the risk or its criticality. But some limits have appeared since project risk interactions are not properly considered. Project interdependent risks are indeed often considered and managed just as if they were independent. We thus propose an interactions-based clustering method with its associated algorithms and heuristics. Our objective is to group risks, so that the project risk interaction rate is maximal inside clusters and minimal outside. The final objective of this study is to facilitate the coordination of complex projects by reducing interfaces when dealing with risks. We first model project risk interactions through a matrix representation. Then, the overall mathematical formulation of the problem is presented. A case study in the construction industry is finally presented and permits us to propose global recommendations, conclusions and perspectives. KEYWORDS: Project management, Risk, Complexity, Interactions, Clustering. 1

Abstract — Within any incremental development paradigm, there exists a tension between the desire to deliver value to the customer early and the desire to reduce cost by avoiding architectural refactoring in subsequent releases. What is lacking, however, is quantifiable guidance that highlights the potential benefits and risks of choosing one or the other of these alternatives or a blend of both strategies. In this paper, we assert that the ability to quantify architecture quality with measurable criteria provides engineering guidance for iterative release planning. We demonstrate the use of propagation cost as a proxy for architectural health with dependency analysis of design structure and domain mapping matrices as a quantifiable basis for iteration planning.

Complex systems and enterprises, such as those typical in the aerospace industry, are subject to uncertainties that may lead to suboptimal performance or even catastrophic failures if unmanaged. This work focuses on flexibility as an important means of managing uncertainties and leverages real options analysis that provides a theoretical foundation for quantifying the value of flexibility. Real options analysis has traditionally been applied to the valuation of capital investment decisions by considering managerial flexibility. More recently, real options have been applied to the

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Abstract. The scope and complexity of engineered systems are ever-increasing as burgeoning global markets, unprecedented technological capabilities, rising consumer expectations and ever-changing social requirements present difficult design challenges that often extend beyond the traditional engineering paradigm. These challenges require engineers and technical managers to treat the technological systems as a part of a larger whole. Existing system modeling frameworks are limited in scope for representing the information about engineering systems. This paper presents a conceptual framework and an improved modeling framework for engineering systems. Its value is that it allows engineers and managers an improved means to visually arrange information and structure discourse in a way that facilitates better systems engineering. It augments the existing literature by providing a clear and concise framework for an engineering system, and provides a methodology for engineers to tag and organize systems information in