This paper presents a five-phase taxonomy of systematic procedures to enable flexibility in the design and management of engineering systems operating under uncertainty. The taxonomy integrates contributions from surveys, individual articles, and books from the literature on engineering design, manufacturing, product development, and real options analysis obtained from professional e-index search engines. Thirty design procedures were classified based on the kind of early conceptual activities they support: baseline design, uncertainty recognition, concept generation, design space exploration, and process management. Each procedure is evaluated based on ease of use to enable flexibility analysis, whether it can be used directly in collaborative design activities, and has a proven applicability record in industry and research. The organizing principles integrate the procedures into a cohesive and systematic design framework. Demonstration applications on engineering systems case studies show that it helps designers select relevant procedures in different phases of the design process, depending on the context, available analytical resources, and objectives. In turn, the case studies show that the design framework helps generate concepts with improved lifecycle performance compared to baseline concepts. The taxonomy provides guidance to organize ongoing research efforts, and highlights potential contribution areas in this field of engineering design research.

The design of engineering systems like airports, communication infrastructures, and real estate projects today is growing in complexity. Designers need to consider sociotechnical uncertainties, intricacies, and processes in the longterm strategic deployment and operations of these systems. Flexibility in engineering design provides ways to deal with this complexity. It enables engineering systems to change in the face of uncertainty to reduce impacts from downside scenarios (e.g., unfavorable market conditions) while capitalizing on upside opportunities (e.g., new technology). Many case studies have shown that flexibility can improve anticipated lifecycle performance (e.g., expected economic value) compared to current design and evaluation approaches. It is a difficult process requiring guidance and must be done at an early conceptual stage. The literature offers little guidance on procedures helping designers do this systematically in a collaborative context. This study investigated the effects of two educational training procedures on flexibility (current vs. explicit) and two ideation procedures (free undirected brainstorming vs. prompting) to guide this process and improve anticipated lifecycle performance. Controlled experiments were conducted with ninety participants working on a simplified engineering systems design problem. Results suggest that a prompting mechanism for flexibility can help generate more flexible design concepts than free undirected brainstorming. These concepts can improve performance significantly (by up to 36 %) compared to a benchmark design-even though users did not expect improved quality of results. Explicit training on flexibility can improve user satisfaction with the process, results, and results quality in comparison with current engineering and design training on flexibility. These findings give insights into the crafting and application of simple, intuitive, and efficient procedures to improve lifecycle performance by means of flexibility and performance that may be left aside with existing design approaches. The experimental results are promising toward further evaluation in a real-world setting.

This paper presents an innovative flexibility analysis as a practical, effective procedure to improve the expected value of large-scale, capital-intensive projects when there is market uncertainty. Its novelty lies in its approach and scope. Its approach develops understanding of the drivers of the value of flexibility, so as to build acceptance among decision-makers. Its scope explicitly considers the combined effects of uncertainty, economies of scale, learning, and geographic distribution. It demonstrates how these factors combine to impact the benefits of flexibility in the early stages of design and project evaluation in the context of uncertainty. It makes this point through a specific example: the long-term deployment of liquefied natural gas (LNG) technology to supply the transportation market. It contrasts the base case fixed design (a big centralized production facility) with flexible modular designs that phase capacity additions over time and space. The proposed flexibility method compares design alternatives based on several indicators of economic lifecycle performance (Net Present Value (NPV), Initial Capex, etc.). Results indicate that flexible modular deployment strategies can significantly improve the economic performance of large, expensive projects. As sensitivity analyses show, the improvements can be significant over a wide range of analytical assumptions. An important insight is that higher learning rates increase the benefits of flexibility, counteracting the effects of economies of scale. Overall, the study shows that flexibility in engineering design of major production facilities such as LNG plants has multiple, supporting advantages due to uncertainty, learning, and location. C⃝ 2015 Wiley Periodicals, Inc. Syst Eng 18: 253-268, 2015

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This paper presents and applies a simulation-based methodology to assess the value of flexible decentralized engineering systems design (i.e., the ability to flexibly expand the capacity in multiple sites over time and space) under uncertainty. This work differs from others by analyzing explicitly the tradeoffs between economies of scale (EoS)—which favors designing large capacity upfront to reduce unit cost and accommodate high anticipated demand—and the time value of money—which favors deferring capacity investments to the future and deploying smaller modules to reduce unit cost. The study aims to identify the best strategies to design and deploy the capacity of complex engineered systems over time and improve their economic lifecycle performance in the face of uncertainty by exploiting the idea of flexibility. This study is illustrated using a waste-to-energy (WTE) system operated in Singapore. The results show that a decentralized design with the real option to expand the capacity in different locations and times improves the expected net present value (ENPV) by more than 30% under the condition of EoS  α  = 0.8 and discount rate λ   = 8%, as compared to a fixed centralized design. The results also indicate that a flexible decentralized design outperforms other rigid designs under certain circumstances since it not only reduces transportation costs but also takes advantage of flexibility, such as deferring investment and avoiding unnecessary capacity deployment. The modeling framework and results help designers and managers better compare centralized and decentralized design alternatives facing significant uncertainty. The proposed method helps them analyze the value of flexibility (VOF) in small-scale urban environments, while considering explicitly the tradeoffs between EoS and the time-value of money.

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Designing an engineering system that is both environmentally and economically sustainable is a challenging task. Designers need to cope with socio-technical uncertainties and design systems to provide high performance during long lifecycles. Flexibility in engineering design provides ways to address such challenges by making engineering systems changeable in the face of uncertainty. It is difficult, however, to identify suitable system elements for designing flexibility, especially when subjected to multiple sources of uncertainty and complex interdependency between socio-technical and systems elements. This paper considers embedding flexibility into the engineering design as a mechanism to ensure better sustainability and to improve economic performance in long-term lifecycles. The main contribution is a novel methodology to identify valuable opportunities to embed flexibility as a way to deal proactively with uncertainty in market and environment. The proposed methodology integrates Bayesian network into engineering system design to effectively model complex change propagation in the flexibility identification process. It helps structure concept generation activities by identifying candidate areas to embed flexibility in the system. It compares favorably to other concept generation methods (e.g., prompting, brainstorming) that require modeling and evaluation of a large number of concepts generated in order to identify the ones offering better performance. It differs from other flexibility enabler identification methods by considering indirect as well as direct dependencies, in addition to the probabilistic nature and risk resulting from possible changes. Another contribution is the demonstration application of the proposed methodology through the analysis of a waste-to-energy technology in Singapore based on anaerobic digestion. Results show that the expected net present value of the flexible design concepts provides more than 10 % improvement over a fixed benchmark design in terms of economic lifecycle performance. This design is conducive of better economic sustainability via additional power generation and better use of resources. Results also indicate that the flexible design can reduce downside risks and capitalize on upside opportunities significantly.Keywords Sustainability Á Uncertainty management Á Flexibility in engineering design Á Waste-to-energy system Á Real options analysis Á Change propagation Abbreviations AD Anaerobic digestion ADOS Anaerobic digestion of organic slurry BN Bayesian network CPA Change propagation analysis CPI Change propagation index CPM Change prediction method DCF Discounted cash flow DSM Design structure matrix ENPV Expected net present value ESM

This study proposes a systematic approach for planning and operating a one-way vehicle-sharing system (VSS) under demand uncertainty. It investigates the distribution of parking spaces and vehicles considering stochastic demand and interactions with the major operational decisions, namely vehicle redistribution activities. An optimization model is formulated that aims to determine the best deployment strategy for minimizing overall cost while achieving a certain level of service (LoS). Then, a simulation-based solution approach based on a discrete-event simulator (DES), Particle Swarm Optimization (PSO), and Optimal Computing Budget Allocation (OCBA) is devised to solve the mathematical model. The methodology is then applied to a car-sharing system in Singapore. Results demonstrate that considering rebalancing activities is imperative in making deployment strategies. The case study also provides managerial insights regarding designing and operating one-way VSS.