Making Sense of Elegant Complexity in Design

level. It also helps build function failure logic, facilitating the reasoning about potential faults and their propagation through the system. FFIP reveals mappings that are otherwise difficult to see in complex systems and provides an elegant simulation environment for early design stage analysis, and as such, makes reliability and risk analysis possible during the qualitative stages of design. As we move into the more quantitative stages of design, additional complex research issues emerge as presented in the following section. 3.2 Modeling, Simulation, and Optimization of Complex Systems in Later Design Stages. As noted by the editors of the recent special edition of the Journal of Mechanical Design on Designing Complex Engineering System, multidisciplinary systems are complex and multifaceted, they have emergent and unpredictable behavior, and their solutions must integrate knowledge from multiple disciplines while managing a wide range of risks and uncertainties [40]. Unfortunately, common approaches to solving these problems are ad hoc and reductionist, often resulting in cost over-runs, schedule delays, and solutions that perform poorly. We have clearly reached the limits of what these approaches can do. To make progress, we need a more rigorous and deeper understanding of complex engineered systems and how they should be designed; we need firmer foundations for a science of design. The approaches presented in this section are all motivated by providing rigorous approaches for complex systems design from which elegant simplicity emerges in the accompanying formulation, solution, and/or insights. Not only have products and systems become more complex, but the number and type of issues that must be accounted for in a design process is staggering. For instance, while life-cycle product analysis has been an active of research, intentionally designing products for rapid and easy recovery has become a rapidly growing research field. Considering product recovery requirements when the recovery is years, if not decades away, is in itself a very complex problem to model and solve. At the core of this issue are the following questions: How do design differences in complex products impact product recovery and what architectural characteristics are desirable to facilitate recovery? The links between product design and the product recovery process have largely been unknown. However, Kwak and Kim develop a framework to address these questions by optimizing the product architecture while simultaneously considered the recovery network [41]. As a result, recovery profit is estimated based on the optimal reprocessing options for a product, as well as on the optimal recovery network design. The simple yet elegant result of the study is that differences in product design have a great influence on potential profit from product recovery. For example, the study demonstrated that a modular design for a cell phone handset is more preferred than an integrated design when a high rate of defects in the LCD screen is expected. This result elegantly implies that part composition has a greater impact on handset recovery profit than does assembly structure or weight, especially when the part has a relatively high cost. While this framework effectively addresses a complex issue with a simple answer, demonstrating an example of elegant complexity, Kwak and Kim point out that many recovery network features are uncertain. In addition, the application of such a framework to other complex systems will demand the integration of considerations from multiple disciplines. Handling uncertainty across multiple disciplines in a system optimization process presents another difficult question that researchers are attempting to find simple answers for: How can disciplinary consistency be maintained in the design of complex engineering systems under uncertainty? In a deterministic case when a multidisciplinary analysis is decomposed, each subsystem should eventually have the same value for the coupling variables. Otherwise, the subsystems are said not to be consistent [42–44]. Typical approaches such as using an Interdisciplinary Consistency Constraint [44] are not effective when input uncertainty is introduced because the uncertainty in the inputs leads to a range rather than a single value for each coupling variable. An elegant solution to this complex problem is offered in the form of multiobjective collaborative robust optimization (McRO) [45]. The McRO can find robust solutions for multiobjective multilevel coupled optimization problems in which uncontrollable variations exist not only in parameters in each subsystem but also in coupling variables. McRO requires a tolerance region for uncertain parameters and an acceptable variation range for objective functions and coupling variables [46] which provides a cushion to absorb the variation in coupling variables. In this case, as long as the variation in coupling variables is within the tolerance region of targets, all subsystems are said to be collaboratively consistent. This elegant approach to absorb variation in the mathematical formulation of a complex system now allows fully coupled multidisciplinary design optimization problems with interval uncertainty to be solved. Moreover, the solutions are comparable to a single-disciplinary robust optimization approach. Although ensuring disciplinary consistency under uncertainty is a significant accomplishment, additional sources of complexity are introduced when the system itself is required to undergo transformation or reconfiguration. In this case, not only are the operating conditions changing, but the system is as well. Reconfigurable systems are emerging as an effective way to perform at a high level in different operating environments. However, researchers and practitioners are realizing that they can be one of the most complex systems to model, design, and deploy. Indeed since many reconfigurable systems are based on a biological principle, mimicking the design of nature becomes a daunting task. Despite the complexity of reconfigurable systems in biological contexts, Singh and his colleagues were able to identify three design principles for the transformation of systems: expand/collapse, expose/cover, and fuse/divide [47]. Haldaman and Fig. 5 The functional-failure identification and propagation framework [36] (with kind permission from Springer Science1Business Media B.V.) Journal of Mechanical Design DECEMBER 2012, Vol. 134 / 120801-5 Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 03/22/2018 Terms of Use: http://www.asme.org/about-asme/terms-of-use Parkinson argue for a fourth principle, reorientation, based on a sample of products whose reconfiguration is not captured by the previous three principles [48]. These researchers have successfully extracted simple principles from complex behavior in natural systems, giving designers a set of elegant solutions to complex problems. For instance, Ferguson and his research team demonstrate the effectiveness of using reconfigurable principles to accommodate system uncertainty while also increasing important aspects of the system’s performance [49]. Their elegant solution provided innovative insights into the development of complex systems. A bottom-up approach to developing reconfigurable systems is to consolidate two (or more) existing single-state static products into an integrated product that provides multiple functions. This requires the integration of two or more already complex systems into a new system with increased functionality. At the core of this research challenge is the following question: How can two or more complex system be consolidated into a multifunctional product? In a general sense, the idea of consolidation is motivated by simplification. However, when increased functionality and more complex operation is the result of product consolidation, simplification is difficult. Yet, Kalyanasundaram and Lewis have developed an approach based on function sharing and flow analysis that simplifies the consolidation process to a quantitative assessment of component re-use [50]. Functional similarity is quantified to identify the functions that can be shared by the same components. The information obtained from the function structure is then mapped to the components of two existing products to analyze their roles in the final reconfigurable product architecture. Each component in the original systems is allocated into one of three categories: common, representing similar components that perform the same function in the systems; dormant, representing components that perform a function in one state of operation, but are nonfunctional in another state; and conflicting, representing components that must undergo some type of reconfiguration in order to remain in the new multifunctional product. The prominence of each of these three categories of components is quantified and represented elegantly with a prism as illustrated in Fig. 6. The superscripts on the “dormant” and “conflicting” axes correspond to which product the axis represents (product 1 or product 2). The height of the prism (the common core axis) represents the potential for component sharing between the products. While reconfigurable systems transform to accommodate changes in operating conditions, changes in technology also create need for resilient products and systems. At some point, most electromechanical systems will be impacted by new technology, whether it is upgraded software, higher quality components, or new technology modules. Studying the impact of the infusion of such new technology into systems and products is an important research challenge and is captured in the following question: What is the impact of the integration of new technologies into existing product lines? de Weck and colleagues have studied this complex issue and developed a repeatable and scalable process that extracts simple insights into the lifecycle design of large systems [51,52]. Their systematic framework supports the assessment of the impact of technology infusion early in the product planning cycle. In addition, it quantitatively predicts the impact of technology infusion through the use of a design structure matrix (DSM) and the subsequent creation of a delta DSM (DDSM) describing the changes to the original system due to the infused technology. In Fig. 7, a complete DSM representation of a baseline printing system is shown [52]. The DSM consists of 84 elements, and shows physical connections, mass flows, energy flows, and information flows within the system. The cost for technology infusion is then estimated from the DDSM, and the potential market impact of the technology is calculated based on customer value, expressed through utility curves for system technical performance measures. Finally, probabilistic analysis is performed to predict the change in the net present value of a newly infused technology. The answers that are found to the original question are strikingly simple. For instance, the researchers found that the greater the range of uncertain operating conditions the more lifecycle value an adaptable system can deliver. By generating simple answers to complex questions, this research demonstrates the essence of elegant complexity. While the design of multidisciplinary systems provides complex challenges, introducing globalization and sociotechnical issues creates another set of complex questions. 3.3 Addressing the Complexity of Globalization and Sociotechno Interfaces. Globalization has created a wealth of additional challenges for design researchers. As noted by Augustine [53], new issues in complexity are emerging in the flatter world we find ourselves in because of the increase of social influences, decentralization, and cultural forces now impacting the development of products and systems. For example, it has become important to be able to capture user preferences and then link them to a system optimization model [54–57]. Since this is a very difficult task given the potential variability in population preferences, many times the number of variables in a preference model is kept small. However, there are a number of qualitative aspects of design that demand a large number of variables to accurately model, including stylistic issues. As a result, a complex question that has recently been posed is as follows: How does one quantitatively capture user preferences on qualitative aspects of design? Fig. 6 Graphical depiction of reconfigurable metrics Fig. 7 Xerox iGen3 baseline DSM [52] (with kind permission, VC 2009 Wiley Periodicals, Inc.) 120801-6 / Vol. 134, DECEMBER 2012 Transactions of the ASME Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 03/22/2018 Terms of Use: http://www.asme.org/about-asme/terms-of-use Ren and Papalambros address this complex issue by developing an efficient global optimization algorithm to both converge to a preferred design quickly yet still exploit potentially preferred new designs [58]. The algorithm is applied to an automotive styling problem of high geometric dimension and is able to support the identification of target (preferred) designs. This approach is able to elegantly guide users to preferred designs through an interactive optimization process, effectively addressing what can be a very complex problem of preference solicitation in high-dimensional space. Not only are user choice preferences critical to capture before product/system deployment, but the dynamics of the user interaction with the product/system many times are essential to model and simulate. Since user interactions with the product/system involve feedback using multiple senses, developing a comprehensive analytical model of such complex interactions remains a future research challenge. However, modeling and simulating these interactions provide a way to extract simpler heuristics and principles that can guide the design of systems characterized by significant user interaction, as noted in the following research question: How can complex socio-technical system use interactions be captured and utilized in the design of more effective products? Consumer choice modeling has become prominent in engineering design research and while what a consumer chooses can be modeled, why they make those choices is a much more complex issue. To gain insight into this issue, Chen and her research team quantify the impact of usage context on consumer choice using a hybrid electric vehicle (HEV) case study, representing a product where the purchase motives are very important to capture and understand [59]. This enhanced understanding of a complex and dynamic environment allows engineering designers to determine optimal performance targets for product development. These targets represent a relatively simple vector of scalar values that subsequently provide a mechanism to design products with enhanced usage in complex heterogeneous markets. The model was further expanded to include product, consumer, and social network interactions in HEV market choices [60]. While this increased the complexity of the fundamental research issues, the insights are wonderfully simple. For instance, the research team made conclusions on the influence of HEV-owners on their friends, and the impact of education level on HEV preferences. Sociotechnical interactions are also prominent in emergency scenarios where systems, such as aircraft, subways, and buildings must support optimal evacuation performance. The design of such systems must account for complex interactions among occupants and between occupants and their surroundings. Mesmer and Bloebaum have developed a simulation tool to identify principles based on the emergent behavior of sociotechnical systems including aircraft, buildings, and their surrounding environments. One element of the simulation tool—the use and impact of personal communication devices—has been the focus of recent studies [61]. Based on this study and others, some simple yet powerful insights can be gained from the simulation of complex sociotechnical systems. For instance, in some scenarios the presence of groups (e.g., families, co-workers) detracted from the evacuation process as members of the group moved against the evacuation flow to find their group members. In other scenarios, groups with strong leaders significantly helped the evacuation process. These insights along with others can inform the design of such systems and their surrounding environments by extracting simple principles from very complex sociotechnical interfaces. When designing complex sociotechnical systems, many times the design decisions and tasks are allocated to disciplinary subsystem teams who then must communicate and coordinate their subsystem solutions. There are a number of different allocation, coordination, and convergence strategies for such distributed design problems. There are also many process architectures for such problems, including parallel tasks, optimal sequencing of tasks, or a hybrid structure. Until recently, the process architecture was thought to impact the rate of convergence to a particular solution, but not whether the solution process would ultimately converge or not. A recent study challenged this assumption and asked the following question: In a complex network of coupled subsystem optimization problems, what impact does the process architecture have on the final system solution? There are a number of frameworks that have studied these coupled subsystem optimization problems in the context of multidisciplinary design optimization including analytical target cascading (ATC) [62], concurrent subspace optimization (CSSO) [63,64], bilevel integrated system synthesis (BLISS) [65], and collaborative optimization (CO) [66]. While these approaches each have certain advantages in terms of global optimality, convergence speed, or compatibility constraints, the process flow structure is assumed to be consistent in most approaches. While the original ATC formulations were hierarchical in nature, formulations that accommodate nonhierarchical subproblems [67] and separable subproblems to facilitate parallel computation [68] have been developed. However, these developments focused on providing additional flexibility and computational efficiency rather than studying the impact on whether or not the design optimization problem converged. Lewis and Devendorf found that process architecture not only impacts the convergence speed (which was already known), but that it can determine whether the process even converges or not [69]. As a result, a parallel equivalent model was created, allowing for a straightforward determination of the convergence properties of any complex decentralized network of coupled design optimization problems. Many times, these distributed design problems are allocated to teams across the globe who are trying to determine the best product strategy for a number of global markets. Consequently, not only are issues of decentralization, convergence, stability, sociotechnical interactions, and optimality present, but additional considerations of regional cultures, global economics, and international regulations must be considered. In global product design, there are numerous factors to consider and the interactions between all these factors create a highly uncertain environment (see Fig. 8) [70]. Because of the volume of issues to consider in such a complex product design problem, there could be hundreds of possible design strategies and thousands of resulting solutions. As a result, a fundamental research question being asked is the following: In global product design, what is the best product design strategy to use when integrating user, technological, and regulatory requirements? This question is addressed by Simpson, Parkinson, and their team who identify a set of elegant solutions centered around determining whether to create a single global platform of products, a flexible global platform of products, or a series of unique products for each region [70]. This top-down approach can help simplify a Fig. 8 The interaction of user, business, and regulation concerns in global product design [70] Journal of Mechanical Design DECEMBER 2012, Vol. 134 / 120801-7 Downloaded From: http://mechanicaldesign.asmedigitalcollection.asme.org/ on 03/22/2018 Terms of Use: http://www.asme.org/about-asme/terms-of-use very complex task that many companies are currently facing as global markets emerge and holistic product design opportunities arise.