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Technical organizations increasingly rely on innovation contests to find novel ideas for designing complex systems. These activities involve outsiders in the early stages of the design process, leading to ground-breaking designs that often surpass expectations. Here, the contest's rules document plays a crucial role: this design artifact communicates the organization's problem and the desired system performance to the participants--significantly impacting the resulting solutions. However, the contest's nature amplifies the challenges of communicating complex design problems across boundaries. Existing strategies for formulating--i.e., requirement and objective allocation--might not suit this context. We developed an inductive model of their formulation process based on a multiyear field study of five complex innovation contests. We found that a formulation team (or "seeker") balanced the need to communicate their problem in detail with the risk of excluding valuable participants. Here, they chose among three approaches--incentivize, impose, or subsume--depending on their knowledge of potential solutions and the participants' capabilities. Notably, the seeker formulated more granularly than the literature describes, employing multiple approaches within each rules document. These findings shed light on a poorly understood aspect of innovation contests, shed new light on a longstanding debate in the engineering design literature, and guide practitioners' formulation processes. [ABSTRACT FROM AUTHOR]

Engineering design has proven to be a rich context for applying artificial intelligence (AI) methods, but a categorization of such methods applied in AI-based design research works seems to be lacking. This paper presents a focused literature review of AI-based methods mapped to the different stages of the engineering design process and describes how these methods assist the design process. We surveyed 108 AI-based engineering design papers from peer-reviewed journals and conference proceedings and mapped their contribution to five stages of the engineering design process. We categorized seven AI-based methods in our dataset. Our literature study indicated that most AI-based design research works are targeted at the conceptual and preliminary design stages. Given the open-ended, ambiguous nature of these early stages, these results are unexpected. We conjecture that this is likely a result of several factors, including the iterative nature of design tasks in these stages, the availability of open design data repositories, and the inclination to use AI for processing computationally intensive tasks, like those in these stages. Our study also indicated that these methods support designers by synthesizing and/or analyzing design data, concepts, and models in the design stages. This literature review aims to provide readers with an informative mapping of different AI tools to engineering design stages and to potentially motivate engineers, design researchers, and students to understand the current state-of-the-art and identify opportunities for applying AI applications in engineering design. [ABSTRACT FROM AUTHOR]

Abstract: Open innovation methods, which broadly include: prize competitions, grand challenges, and collaborative communities, are increasingly being applied to complex systems problems. While external solvers (i.e., the crowd) have historically contributed some important innovations, there appears to be a limit to the kind of systems where open innovation can be successful. We contend that open innovation activities can be more broadly effective if seekers (i.e., problem owners) formulated their system architectures with “openability” in mind. In this paper, we build on existing theory to categorize the kinds of problems that are most amenable to crowd contributions, based on the mechanisms of solving. We call these “openable” problems. We then develop and demonstrate an architecting process that uses a facilitated expert workshop to elicit “openable” subproblems within a given systems‐level problem. It treats the need for some openable subproblems as a constraint on the architecture. While not all systems should be designed with “openability” in mind, many organizations have faced political‐level pressure to better leverage the crowd. In that context, we provide guidance on how to do so effectively. We demonstrate the potential value of our approach through application to the NASA Asteroid Grand Challenge—a highly complex system. The paper concludes by reflecting on the implications of this exercise for open innovation and system decomposition in general. [ABSTRACT FROM AUTHOR]

This paper examines the decentralization of European space technology research and development through the joint lenses of policy, systems architecture, and innovation contexts. It uses a detailed longitudinal case history of the development of a novel astrophysics instrument to explore the link between policy-imposed institutional decomposition and the architecture of the technical system. The analysis focuses on five instances of collaborative design decision-making and finds that matching between the technical and institutional architectures is a predictor of project success, consistent with the mirroring hypothesis in extant literature. Examined over time, the instances reveal stability in the loosely coupled nature of institutional arrangements and a trend towards more integral, or tightly coupled, technical systems. The stability of the institutional arrangements is explained as an artifact of the European Hultqvist policy and the trend towards integral technical systems is related to the increasing complexity of modern space systems. If these trends persist, the scale of the mismatch will continue to grow. As a first step towards mitigating this challenge, the paper develops a framework for balancing upfront decomposition and in-process coordination in collaborative development projects. The astrophysics instrument case history is used to illustrate how collaborations should be defined for a given inherent system complexity. [ABSTRACT FROM AUTHOR]

This article leverages a unique field experiment to characterize who in the crowd is willing and able to solve complex engineering design problems for the prospect of a prize. Contrary to popular skepticism, we find that the crowd is highly capable of not only providing quality point solutions but also contributing valuable engineering insight. However, although a diverse population of more than 9000 registrants showed initial interest in the competitions, of the 255 judgeable solutions received, the majority of good solutions came from solvers with prior within-discipline experience. Of those, more of the good solutions came from solvers who perceived the relevant problem as “at the boundary” of their expertise. This suggests that while complex engineering design problems do require some threshold of relevant training and skills, prize competitions still play an important role in attracting solvers who perceive themselves to be adjacent to the problem. Furthermore, the submissions revealed a role for solvers in the crowd in augmenting traditional concept generation and tradespace exploration processes, by leveraging the independence of solvers in the crowd. These findings contribute an empirical basis for resolving how and when the crowd can be used most effectively, as part of an innovation toolkit. [ABSTRACT FROM AUTHOR]