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The "traditional" way of designing constellations of communications satellites in low Earth orbit is to optimize the design for a specified global capacity. This approach is based on a forecast of the expected number of users and their activity level, both of which are highly uncertain. This can lead to economic failure if the actual demand is significantly smaller than the one predicted. This paper presents an alternative flexible approach. The idea is to deploy the constellation progressively, starting with a smaller, more affordable capacity that can be increased in stages as necessary by launching additional satellites and reconfiguring the existing constellation in orbit. It is shown how to find the best reconfigurable constellations within a given design space. The approach, in effect, provides system designers and managers with real options that enable them to match the system evolution path to the actual unfolding demand scenario. A case study demonstrates significant economic benefits of the proposed approach, when applied to Low Earth Orbit (LEO) constellations of communications satellites. In the process, life cycle cost and capacity are traded against each other for a given fixed per-channel performance requirement. The benefits of the staged approach demonstrably increase, with greater levels of demand uncertainty. A generalized framework is proposed for large capacity systems facing high demand uncertainty.

In this paper, a methodology is presented to determine the optimum number of product platforms to maximize overall product family profit with simplifying assumptions. This methodology is attempting to aid various manufacturing industries who are seeking ways to reduce product family manufacturing costs and development times through implementation of platform strategies. The methodology is based on a target market segment analysis, market leader’s performance vs. price position, and a two-level optimization approach for platform and variant designs. The proposed methodology is demonstrated for a hypothetical automotive vehicle family that attempts to serve seven different vehicle market segments. It is found that the use of three distinct platforms maximizes overall profit by pursuing primarily a horizontal leveraging strategy.

Quantitative assessment of structural complexity is essential for characterization of engineered complex systems. In this paper, we describe a quantitative measure for structural complexity, conduct an empirical validation study of the structural complexity metric, and introduce a complexity management framework for engineering system development. We perform empirical validation of the proposed complexity metric using simple experiments using ball and stick models and show that the development effort increases superlinearly with increasing structural complexity. The standard deviation of the build time for ball and stick models is observed to vary superlinearly with structural complexity. We also describe a generic statistical procedure for building such cost estimation relationships with structural complexity as the independent variable. We distinguish the notion of perception of complexity as an observer‐dependent property and contrast that with complexity, which is a property of the system architecture. Finally, we introduce the notion of system value based on performance‐complexity trade space and introduce a complexity management framework for system development.

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The use of terms such as “Engineering Systems”, “System of systems” and others have been coming into greater use over the past decade to denote systems of importance but with implied higher complexity than for the term systems alone. This paper searches for a useful taxonomy or classification scheme for complex Systems. There are two aspects to this problem: 1) distinguishing between Engineering Systems (the term we use) and other Systems, and 2) differentiating among Engineering Systems. Engineering Systems are found to be differentiated from other complex systems by being human‐designed and having both significant human complexity as well as significant technical complexity. As far as differentiating among various engineering systems, it is suggested that functional type is the most useful attribute for classification differentiation. Information, energy, value and mass acted upon by various processes are the foundation concepts underlying the technical types.

Customization and market uncertainty require increased functional and physical bandwidth in product platforms. This paper presents a platform design process in response to such future uncertainty. The process consists of seven iterative steps and is applied to an automotive body-in-white (BIW) where 10 out of 21 components are identified as potential candidates for embedding flexibility. The method shows how to systematically pinpoint and value flexible elements in platforms. This allows increased product family profit despite uncertain variant demand and specification changes. We show how embedding flexibility suppresses change propagation and lowers switch costs, despite an increase of 34% in initial investment for equipment and tooling. Monte Carlo simulation results for 12 future scenarios reveal the value of embedding flexibility.