Abstract
AbstractThis article proposes the solver-aware system architecting framework for leveraging the combined strengths of experts, crowds and specialists to design innovative complex systems. Although system architecting theory has extensively explored the relationship between alternative architecture forms and performance under operational uncertainty, limited attention has been paid to differences due towhogenerates the solutions. The recent rise in alternative solving methods, from gig workers to crowdsourcing to novel contracting structures emphasises the need for deeper consideration of the link between architecting and solver-capability in the context of complex system innovation. We investigate these interactions through an abstract problem-solving simulation, representing alternative decompositions and solver archetypes of varying expertise, engaged through contractual structures that match their solving type. We find that the preferred architecture changes depending on which combinations of solvers are assigned. In addition, the best hybrid decomposition-solver combinations simultaneously improve performance and cost, while reducing expert reliance. To operationalise this new solver-aware framework, we induce two heuristics for decomposition-assignment pairs and demonstrate the scale of their value in the simulation. We also apply these two heuristics to reason about an example of a robotic manipulator design problem to demonstrate their relevance in realistic complex system settings.
Highlights
This article proposes a theoretical framework for how to organise complex system design and development activities in a way that actively considers both strategies for breaking up the technical work and the capabilities of potential solvers – both inside and outside the organisation
System architecting theory has focused on optimally grouping tasks or subproblems based on attributes of the technical design space (Browning 2001; Crawley, Cameron & Selva 2015; Eppinger & Ulrich 2015), and strategies for hedging against changing operating environments, for example, through modularity (Ulrich 1995), commonality (Boas, Cameron & Crawley 2013), flexibility and changeability (Ulrich 1995; Brusoni & Prencipe 2001; Fricke & Schulz 2005; Brusoni et al 2007)
We argue that golf provides a useful basis for analytical generalizability (Eisenhardt 1989; Yin 2003) in terms of the focal impact on decomposition and solver capability
Summary
This article proposes a theoretical framework for how to organise complex system design and development activities in a way that actively considers both strategies for breaking up the technical work and the capabilities of potential solvers – both inside and outside the organisation. The ‘gig’ economy and other forms of ad hoc work are taking off, with nontraditional players, including crowds of amateurs, increasingly being leveraged through nontraditional contracting mechanisms, such as open competitions (Poetz & Schreier 2012; Franzoni & Sauermann 2014; Gustetic et al 2015; Suh & de Weck 2018; Lifshitz-Assaf, Lebovitz & Zalmanson 2021) Successes in these areas call into question the notion that talent and expertise only reside within traditional organisations and professions (Chesbrough 2003; Baldwin & Von Hippel 2011; Gambardella, Raasch & von Hippel 2016; Lifshitz-Assaf 2018). To test and elaborate this idea, this article develops an abstract simulation model to study the relationship between problem architecture, solver characteristics, and how that interaction drives solution efficacy We discuss the relevance of these findings to real-world engineering systems design and management
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