Kaushik Sinha

A network-based structural complexity metric for engineered complex systems

The complexity of todays highly engineered products is rooted in the interwoven architecture defined by its components and their interactions. Such structures can be viewed as the adjacency matrix of the associated dependency network representing the product architecture. To evaluate a complex system or to compare it to other systems, numerical assessment of its structural complexity is mandatory. In this paper, we develop a quantitative measure for structural complexity and apply the same to real-world engineered systems like gas turbine engine. It is observed that low topological complexity implies centralized architecture and it increases as one marches towards highly distributed architectures. We posit that the development cost varies non-linearly with structural complexity. Some empirical evidences of such behavior are presented from the literature and preliminary results from simple experiments involving assembly of simple structures strengthens our hypothesis.

Empirical Validation of Structural Complexity Metric and Complexity Management for Engineering Systems

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.

Correlating Integrative Complexity With System Modularity

Korea (South). Ministry of Education, Science and Technology (MEST) (National Research Foundation of Korea. NRF-2016R1D1A1A09916273)

Exploring Early Stage Cost-Estimation Methods Using Off-the-Shelf Tools: A Preliminary Study

Cost analysis is challenging for multiple reasons, one of which is the lack of historical data due to proprietary issues, or significant work required to make it useful for a particular application and domain of interest. In addition, to support system engineering methods such as Design Space Exploration, both component- and engine-level costs must be supported. This paper presents the results of a preliminary study on a tool that can be used to estimate the development cost for a set of airplane-engine architecture models using publicly available off-the-shelf tools. Our tool focuses on supporting complex system engineering tool chains and methods that require strong interoperability with different tools in a networked environment. The tool, through its architecture, allows the inclusion of supports for early stage cost analysis without directly using historical data, and both system- and component-level cost generations. We describe our approach, tools, estimation process and possible use cases.

Integrative Complexity: An Alternative Measure for System Modularity

Korea (South). Ministry of Education, Science and Technology (MEST) (National Research Foundation of Korea. Grant NRF2016R1D1A1A09916273)

Complexity Management for Engineered Systems Using System Value Definition

Quantitative and objective management of complexity is essential for effective design of engineered complex systems. In this paper, we develop a quantitative framework for complexity management. This includes a measure of system value that explicitly considers system complexity. The system design goal is to maximize the system value. Using a simple, representative mathematical model linking performance to system complexity, we show analytically that there exists a regime where we have an optimal level of complexity that leads to maximization of system value. Existence of this regime is dependent on two rate parameters that link the complexity-performance-development cost triad for engineered systems. Outside of this regime one has to always aim for reducing system complexity in order to maximize system value. The framework is subsequently applied to a case study involving a set of aircraft engine architectures.

A Simplified Mathematical Model for Two-Sided Market Systems With an Intervening Engineered Platform

A two-sided market involves two different user groups whose interactions are enabled over a platform that provides a distinct set of values to either side. In such market systems, one side’s participation depends on the value created by presence of the other side over the platform. Two-sided market platforms must acquire enough users on both sides in appropriate proportions to generate value to either side of the user market. In this paper, we present a simplified, generic mathematical model for two-sided markets with an intervening platform that enables interaction between the two different sets of users with distinct value propositions. The proposed model captures both the same side as well as cross-side effects (i.e., network externalities) and can capture any behavioral asymmetry between the different sides of the two-sided market system. The cross-side effects are captured using the notion of affinity curves while same side effects are captured using four rate parameters. We demonstrate the methodology on canonical affinity curves and comment on the attainment of stability at the equilibrium points of two-sided market systems. Subsequently a stochastic choice-based model of consumers and developers is described to simulate a two-sided market from grounds-up and the observed affinity curves are documented. Finally we discuss how the two-sided market model links with and impacts the engineering characteristics of the platform.Copyright