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Systems provide value through their ability to fulfill stakeholders' needs and wants. These needs evolve over time and may diverge from a fielded system's capabilities. Thus, a system's value to its stakeholders diminishes over time. As a result, systems are replaced or upgraded at substantial cost and disruption. If a system is designed to be changed and upgraded easily, however, this adaptability adds to its lifetime value. How can adaptability be designed into systems so that they will provide maximum value to stakeholders throughout their lifetime? This paper describes the problem and an approach to its mitigation.We adopt the concept of real options from the field of economics and extend it to the field of systems architecture. We coin the term architecture options for this next-generation method and the associated tools for the design of flexible systems. Architecture options provide a quantitative means of implementing the optimal degree of design flexibility in a system to maximize its lifetime value for varied stakeholders. Based on initial research to date, we believe that implementing this aspect of design for adaptability can increase a system's overall stakeholder value by 15% at a very conservative minimum. We also present an extension of a method for measuring the dynamic value of a system.

Developing products that are more easily adaptable to future requirements can increase their overall value. Product adaptability is largely determined by choices about product architecture, especially modularity. Because it is possible to be too modular and/or inappropriately modular, deciding how and where to be modular in a cost‐effective way is an important managerial decision. In this article, we gather data from four case studies to model effects of firms’ product architecture decisions at the component level. We optimize an architecture adaptability value (AAV) measure that accounts for both the benefits of more architecture options and the costs of interfaces. The optimal architecture prompted each firm to rearchitect an existing product to increase its expected future profitability. Several insights emerged from the case evidence during this research. (i) Although decomposing an architecture into an increasing number of modules increases product adaptability, the amount of modularity is an insufficient predictor of the adaptability value of a system. AAV, which also accounts for interface costs, provides an improved measure of appropriate modularity. (ii) Managers can influence the path of architectural evolution in the direction of increased value. This influence may diminish but does not disappear as products become more mature. Also, modularity and innovations coevolved, as the new modularizations suggested by AAV optimization prompted and guided searches for further innovations. (iii) When presented with the concepts of options, interface costs, and AAV, the firms’ designers and managers were initially skeptical. However, in each case, the modelers were able to rearchitect an actual product not only with increased AAV by our model (theoretical improvement) but also with actual future benefits for their firm. Postproject reports from each firm confirmed that the AAV modeling and optimization approaches were indeed helpful, equipping them to increase the adaptability, cost‐efficiency, lifespan, and overall value of actual products. The evidence suggests that firms can benefit from designing products for adaptability, but that how they do so matters. This study expands our understanding of modularity and adaptability by illuminating managerial decisions and insights about appropriate approaches to each.

The cost of large systems' Verification, Validation, and Testing (VVT) is in the neighborhood of 40% of the total life cycle cost. The cost associated with systems' failures is even more dramatic, often exceeding 10% of industrial organizations turnover. There is a great potential benefit in streamlining and optimizing the VVT process. The first step in accomplishing this aim is to define a VVT strategy and then to quantify the cost and risk associated with carrying it out. This paper provides an overview of the methodologies for risk and cost monitoring for VVT and proposes a novel approach for modeling VVT strategies as decision problems. A quantitative VVT process and risk model is proposed. Due to the nondeterministic nature of risk, simulation is used to generate distributions of possible costs, schedules, and risk outcomes. These distributions represent a probabilistic approach and are analyzed in relation to impact events. The model provides means to explore different VVT strategies for optimizing relevant decision parameters. To demonstrate the proposed procedure the paper describes a case study depicting a planned avionics suite upgrade program for a fighter aircraft. Some simplified partial quantitative results are also presented. © 2003 Wiley Periodicals, Inc. Syst Eng 6: 135–151, 2003

Systems provide value through their ability to fulfill stakeholders’ needs. These needs evolve and often diverge from an original system's capabilities. Thus, a system's value to its stakeholders diminishes over time. Consequently, systems have to be periodically upgraded or replaced. Since replacement costs are often prohibitive, system adaptability is valuable. Adaptability entails the ability to modify an existing system or design of a system's architecture, such as changing, adding, removing, or replacing relevant elements as well as adjusting their reciprocal interactions. In 2008, Engel and Browning proposed a Design for Adaptability concept based on Architecture Option (AO) theory. AO fuses Financial Options and Transaction Cost theories, seeking to design systems for optimal lifetime value. They asserted that designers should balance the benefits of adaptability against its affordability. More modularity is not always better; the amount of modularity alone is an insufficient and even misleading cause of value. This follow‐up paper reports on a project, aimed at enhancing the AO theory and validating its applicability within diverse industrial environments. Through interactions with practicing system developers, the AO model was simplified and a modified Black–Scholes model was adapted into the engineering domain and successfully practiced. Six case studies were conducted within: food packaging, machine tools, automotive, aerospace, communication, and optoelectronics industries. All six industrial participants estimated, among other improvements, over 15% benefits in (1) reducing systems’ lifetime cost, (2) reducing systems’ upgrade cycle‐time, and (3) increased systems’ lifespan. These results demonstrate the industrial applicability and validity of the AO theory.