Effective communication in product development organizations is widely recognized to be a key element of product development performance. Furthermore, management of product architecture knowledge by the development organization provides important competitive advantage for established firms facing architectural innovation. This research studies how the combination of product architecture and organizational structure determines technical communication in development teams. By documenting and analyzing both the design interfaces between the components that comprise a product and the technical interactions between the teams that design each of these components, we learn how the architecture of the product and the layout of the organization drive development team interactions. Several hypotheses are formulated to explain the unexpected cases when: 1) known design interfaces are not matched by team interactions, and 2) observed team interactions are not predicted by design interfaces. We test the hypothesized effects due to organizational and system boundaries, and design interface strength. Hypotheses are tested using both categorical data analysis and log-linear network analysis. The research is conducted using data collected describing a large commercial aircraft engine development process.
Complex engineered systems tend to have architectures in which a small subset of components exhibits a disproportional number of linkages. Such components are known as hubs. This paper examines the degree distribution of systems to identify the presence of hubs and quantify the fraction of hub components. We examine how the presence and fraction of hubs relate to a system’s quality. We provide empirical evidence that the presence of hubs in a system’s architecture is associated with a low number of defects. Furthermore, we show that complex engineered systems may have an optimal fraction of hub components with respect to system quality. Our results suggest that architects and managers aiming to improve the quality of complex system designs must proactively identify and manage the use of hubs. Our paper provides a data-driven approach for identifying appropriate target levels of hub usage.
The value of a system usually diminishes over its lifetime, but some systems depreciate more slowly than others. Diminished value is due partly to the increasing needs and wants of the system’s stakeholders and partly to its decreasing capabilities relative to emerging alternatives. Thus, systems are replaced or upgraded at substantial cost and disruption. If a system is designed to be changed and upgraded easily, however, this adaptability may increase its lifetime value. How can adaptability be designed into a system so that it will provide increased value over its lifetime? This paper describes the problem and an approach to its mitigation, adopting the concept of real options from the field of economics, extending it to the field of systems architecture, and coining the term architecture options for this next-generation method and the associated tools for design for adaptability. Architecture options provide a quantitative means of optimizing a system architecture to maximize its lifetime value. This paper provides two quantitative models to assess the value of architecture adaptability. First, we define three metrics—component adaptability factors, component option values, and interface cost factors—which are used in a static model to evaluate architecture adaptability during the design of new systems. Second, we enhance a dynamic model to evaluate architecture adaptability over the maintenance and upgrade lifetime of a system, formulating a Design for Dynamic Value (DDV) optimization model. We illustrate both models with quantitative examples and also discuss how to obtain the socio-economic data required for each model.
To gain competitive leverage, firms that design and develop complex products seek to increase the efficiency and predictability of their development processes. Process improvement is facilitated by the development and use of models that account for and illuminate important characteristics of the process. Iteration is a fundamental but often unaddressed feature of product development (PD) processes. Its impact is mediated by the architecture of a process, i.e., its constituent activities and their interactions. This paper integrates several important characteristics of PD processes into a single model, highlighting the effects of varying process architecture. The PD process is modeled as a network of activities that exchange deliverables. Each activity has an uncertain duration and cost, an improvement curve, and risks of rework based on changes in its inputs. A work policy governs the timing of activity execution and deliverable exchange (and thus the amount of activity concurrency). The model is analyzed via simulation, which outputs sample cost and schedule outcome distributions. Varying the process architecture input varies the output distributions. Each distribution is used with a target and an impact function to determine a risk factor. Alternative process architectures are compared, revealing opportunities to trade cost and schedule risk. Example results and applications are shown for an industrial process, the preliminary design of an uninhabited combat aerial vehicle. The model yields and reinforces several managerial insights, including: how rework cascades through a PD process, trading off cost and schedule risk, interface criticality, and occasions for iterative overlapping.
A goal of systems development is to produce enduringly valuable product systems— i.e., systems that are valuable when delivered to their users and which continue to be attractive to their stakeholders over time. However, quantifying the life-cycle value (LCV) provided by a system has proven elusive. In this paper, we propose an approach to quantifying a system’s LCV based on the key parameters that have perceived value to the system’s stakeholders. For this, we draw upon insights from the management, marketing, product development, value engineering, and systems engineering literature. We then demonstrate our proposed approach with an example of a cellular telephone system. By designing systems for maximum LCV, systems architects and engineers will provide dramatically increased value to their organizations and other stakeholders. However, to provide maximum LCV, a system may need to be designed to facilitate adaptability to changing circumstances and stakeholder preferences. We conclude the paper with discussions of some of the major difficulties in measuring LCV and some of the opportunities for further research in this area.