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Global product platforms can reduce production costs through economies of scale and learning but may decrease revenues by restricting the ability to customize for each market. We model the global platforming problem as a Nash equilibrium among oligopolistic competing firms, each maximizing its profit across markets with respect to its pricing, design, and platforming decisions. We develop and compare two methods to identify Nash equilibria: (1) a sequential iterative optimization (SIO) algorithm, in which each firm solves a mixed-integer nonlinear programming problem globally, with firms iterating until convergence; and (2) a mathematical program with equilibrium constraints (MPEC) that solves the Karush Kuhn Tucker conditions for all firms simultaneously. The algorithms’ performance and results are compared in a case study of plug-in hybrid electric vehicles where firms choose optimal battery capacity and whether to platform or differentiate battery capacity across the US and Chinese markets. We examine a variety of scenarios for (1) learning rate and (2) consumer willingness to pay (WTP) for range in each market. For the case of two firms, both approaches find the Nash equilibrium in all scenarios. On average, the SIO approach solves 200 times faster than the MPEC approach, and the MPEC approach is more sensitive to the starting point. Results show that the optimum for each firm is to platform when learning rates are high or the difference between consumer willingness to pay for range in each market is relatively small. Otherwise, the PHEVs are differentiated with low-range for China and high-range for the US.

We measure the labor-demand effects of two simultaneous forms of technological change—automation of production processes and consolidation of parts. We collect detailed shop-floor data from four semiconductor firms with different levels of automation and consolidation. Using the O*NET survey instrument, we collect novel task data for operator laborers that contains process-step level skill requirements, including operations and control, near vision, and dexterity requirements. We then use an engineering process model to separate the effects of the distinct technological changes on these process tasks and operator skill requirements. Within an occupation, we show that aggregate measures of technological change can mask the opposing skill biases of multiple simultaneous technological changes. In our empirical context, automation polarizes skill demand as routine, codifiable tasks requiring low and medium skills are executed by machines instead of humans, whereas the remaining and newly created human tasks tend to require low and high skills. Consolidation converges skill demand as formerly divisible low and high skill tasks are transformed into a single indivisible task with medium skill requirements and higher cost of failure. We conclude by developing a new theory for how the separability of tasks mediates the effect of technology change on skill demand by changing the divisibility of labor.

Design optimization studies that model competition with other products in the market often use a small set of products to represent all competitors. We investigate the effect of competitor product representation on profit-maximizing design solutions. Specifically, we study the implications of replacing a large set of disaggregated elemental competitor products with a subset of competitor products or composite products. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors when using logit and nested logit models (where preferences are homogeneous). However, this relationship differs in the case of random-coefficients logit models (where preferences are heterogeneous), and we demonstrate that profit-maximizing design solutions using latent-class or mixed-logit models can (but need not always) depend on the representation of competing products. We discuss factors that affect the magnitude of the difference between models with elemental and composite representations of competitors, including preference heterogeneity, cost function curvature, and competitor set specification. We present correction factors that ensure models using subsets or composite representation of competitors have optimal design solutions that match those of disaggregated elemental models. While optimal designs using logit and nested logit models are not affected by ad hoc modeling decisions of competitor representation, the independence of optimal designs from competitors when using these models raises questions of when these models are appropriate to use.

This paper examines the impact of Omitted Variable Bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers’ purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer-choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into biased optimal design solutions and characterize properties of optimization problems that affect the magnitude of this bias. We then demonstrate the impact of OVB on optimal designs using a more-realistic engineering optimization case study of automotive powertrain design. In the demonstration, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5–143%) which propagates up to 21% bias in the optimal engine size.

Michael M. Whiston is a Postdoctoral Research Associate in the Department of Engineering and Public Policy at Carnegie Mellon University. As a postdoctoral researcher, Michael has elicited the assessments of over 100 fuel cell experts and presented his findings on Capitol Hill and at international conferences. Michael has published research in the Proceedings of the National Academy of Sciences, Journal of Power Sources, and Journal of Fuel Cell Science and Technology. Michael received his Ph.D. from the University of Pittsburgh in Mechanical Engineering. During his graduate work, Michael performed cost analyses of SOFC systems and developed a dynamic SOFC model. Professor Ines M.L. Azevedo is an Associate Professor of Energy Resources Engineering at Stanford University. Her work focuses on the transitions to sustainable and low-carbon energy systems. She has published 70+ journal publications and graduated 26 Ph.D. students. Since 2010, she has been serving as PI/co-PI of the NSF sponsored Climate and Energy Decision Making Center (CEDM). Professor Azevedo participated as a committee member and co-author on several reports from the U.S. National Academy of Sciences. She was awarded the “40 Scientists under 40” award by the World Economic Forum (WEF) (2014) and the C3E Research Award for Women in Energy (2017). Shawn Litster is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University in Pittsburgh, PA. He received his Ph.D. in Mechanical Engineering from Stanford University and his bachelor’s and master’s degrees from the University of Victoria in Canada. His current research focus is micro- and nano-scale transport phenomena in energy conversion technologies where electrochemistry and electrokinetics play a dominant role, including fuel cells, batteries, and ultra-capacitors. He is the author of over 60 journal publications on fuel cells and batteries. Constantine Samaras is an Associate Professor of Civil and Environmental Engineering and a Fellow in the Wilton E. Scott Institute for Energy Innovation at Carnegie Mellon University. His research spans energy, automation, and climate change. He has contributed to U.S. National Academies of Sciences reports, the Fourth National Climate Assessment, and the Global Energy Assessment. He received a Ph.D. in Civil and Environmental Engineering and Engineering and Public Policy from Carnegie Mellon University and a M.P.A. in Public Policy from New York University. He was named 2018 Professor of the Year by the American Society of Civil Engineers Pittsburgh Section. Kate S. Whitefoot is an Assistant Professor of Engineering and Public Policy and of Mechanical Engineering at Carnegie Mellon University. Professor Whitefoot's research bridges engineering design theory and analysis with that of economics to understand how product and process design affects enterprise and social-welfare objectives, such as expected profits, customer adoption, productivity, energy efficiency, emissions, and consumer welfare. Her work is featured in the Wall Street Journal, Washington Post, Popular Mechanics, and Bloomberg Business and referenced in the 2017–2025 Corporate Average Fuel Economy rulemaking. Professor Jay F. Whitacre started his career at the California Institute of Technology/Jet Propulsion Laboratory. Since joining Carnegie Mellon University in 2007, Dr. Whitacre has focused on the synergistic fields of energy storage and energy system techno-economic assessment. He has developed a novel battery chemistry/design that is manufactured and sold by Aquion Energy, a company he founded in 2008. He is the recipient of multiple awards, including the 2017 Leigh Ann Conn Prize for Renewable Energy, the 2015 Lemelson-MIT Prize, and the 2014 Caltech Resnick Institute Resonate Award, and has been named a Fellow of the National Academy of Inventors.

A cost-effective and compact hydrogen storage system could advance fuel cell electric vehicles (FCEVs). Today's commercial FCEVs incorporate storage that is projected to be heavier, larger, and costlier than targets set by the U.S. Driving Research and Innovation for Vehicle efficiency and Energy sustainability Partnership (U.S. DRIVE). To inform research and development (R&D), we elicited 31 experts' assessments of expected future costs and capacities of storage systems. Experts suggested that systems would approach U.S. DRIVE's ultimate capacity targets but fall short of cost targets at a high production volume. The 2035 and 2050 median costs anticipated by experts were $13.5 and $10.53/kWhH2, gravimetric capacities of 5.2 and 5.6 wt %, and volumetric capacities of 0.93 and 1.33 kWhH2/L, respectively. To meet U.S. DRIVE's targets, experts recommended allocating the majority of government hydrogen storage R&D funding to materials development. Furthermore, we incorporated experts' cost assessments into a levelized cost of driving model. Given technical and fuel price uncertainty, FCEV costs ranged from $0.38 to $0.45/mile ($0.24-$0.28/km) in 2020, $0.30 to $0.33/mile ($0.19-$0.21/km) in 2035-2050, and $0.27 to $0.31/mile ($0.17-$0.19/km) in 2050. Depending on fuel, electricity, and battery prices, our findings suggest that FCEVs could compete with conventional and alternative fuel vehicles by 2035.

We examine optimal-profit product design platforming problems for products sold across multiple markets. Firms have an incentive to platform to take advantage of cost reductions that are possible with increased production quantity, such as learning-by-doing. However, platforming may decrease sales compared to if the designs were customized for each market. The problem can be represented as a Nash equilibrium between multiple competing firms, each with a nonconvex mixed-integer nonlinear programing (MINLP) problem for maximizing their individual profits. We derive the Karush-Kuhn-Tucker (KKT) conditions for the problem and compare results from two algorithmic approaches: (1) an iterative MINLP approach that uses the BARON algorithm to solve each firm’s design and platforming problem and iterates until convergence to an equilibrium, and (2) an approach that solves the KKT conditions directly holding platforming decisions fixed, and compares profits for these platforming decisions to find an equilibrium. Results are presented for a case study of plug-in hybrid electric vehicles (PHEVs) where firms choose whether or not to platform the battery pack across the U.S. and China, and set the optimal battery capacity. We vary the learning rate and the difference in consumer willingness to pay for all-electric range between China and the U.S. Both algorithms agree on the same equilibrium solution in 98.4% of the cases. Results show that the optimum for each firm is to platform when learning rates are low, or the difference between optimal battery capacity in each market is relatively small.

While fuel cell electric vehicles (FCEVs) fueled by hydrogen produced using low-carbon processes could considerably reduce carbon emissions from transportation, FCEVs are produced at low volume, are expensive to manufacture, and lack widespread refueling infrastructure. To inform advancement pathways for FCEVs, we conducted an expert elicitation on vehicle costs and performance at anticipated production volumes, governmental actions to advance FCEVs, anticipated sales of FCEVs equipped with an automated driving system (ADS), and anticipated infrastructure deployments. Between 2020 and 2035, experts assessed a three-fold decline in fuel cell system costs to $60/kW and over an order of magnitude increase in production volume to 225,000 systems/year. Levelized costs of driving were assesed at $0.25–$0.90/mile and $0.17–$0.65/mile in 2035 and 2050, respectively. FCEVs could constitute a considerable share of ADS-equipped vehicle sales depending on cost and performance trajectories of automated driving technology and electric vehicles. Experts identified regulatory and incentive-based policies as important governmental actions to advance FCEVs and recommended hydrogen, fuel cells, and technology deployment activities each receive at least 20% of government research and development funding. Medians of experts' U.S. refueling station deployment assessments were 500 and 2,000 stations cumulative in 2030 and 2040, respectively. The middle 50% of respondents anticipated 2030 cumulative FCEV deployments in China of 100,000–1 million.

Vehicle choice modelers often use composite alternatives, which are simplified representations of a larger, diverse group of vehicle options—a practice known as choice set aggregation. Although this practice has been justified by computational tractability and data constraints, it can introduce arbitrary changes to choice-share predictions. We isolate and characterize the implications of using composite vehicles for choice prediction, given exogenously determined model parameters. We first identify correction factors needed for composite models to predict choice shares that are consistent with those from models that use the full set of disaggregated elemental alternatives. We then assess the distortion of choice-share predictions under various composite specifications and partial corrections using two case studies based on models in the literature used in transportation and energy policymaking: (1) we examine a logit model without alternative-specific constants (ASCs) and find that the distortion in share predictions due to composite specification is substantial and can be larger than variation due to parameter uncertainty; (2) we examine counterfactual predictions of a nested logit model with ASCs based on the NEMS and LVChoice models and find that composite models using ASCs can mitigate or eliminate distortion in some, but not all, counterfactual scenarios. In particular, the distortion is larger when the scenario significantly affects the differences in elemental membership or utility heterogeneity between composite groups. We provide explicit correction factors for composite models with and without ASCs that can be used to take advantage of the tractability of composite models while ensuring that their choice-share predictions exactly match those of their corresponding elemental models in counterfactual and forecasting scenarios.

Solid oxide fuel cells (SOFCs) can efficiently generate continuous electricity for buildings but face technical and economic challenges to achieving mass market acceptance. We conducted a workshop comprising 23 experts to assess near-term markets, system cost and production scale, and system and stack degradation rates. Experts identified commercial- and residential-scale systems as the most favorable entry-level markets in the U.S. and world, respectively. The 2020, 2035, and 2050 median 250 kW electric-only system costs assessed by experts were $2,400/kW, $1,909/kW, and $841/kW (2018 USD) at production volumes of 210, 4000, and 10,000 systems per year, respectively. For small-scale combined heat and power applications, median 1 kW system cost assessments were $11,000/kW, $7,750/kW, and $5,750/kW at 50,000, 80,000 and 175,000 systems per year, respectively. Experts suggested voltage degradation could achieve 0.2%/1000 hrs in 2035–2050 and identified chromium poisoning, microstructural cathode degradation, and Ni agglomeration and coarsening as the most substantial barriers to mitigating degradation. To assess lifecycle performance, we incorporated experts' assessments into a cost of electricity (COE) model. The 250 kW system COE competed with anticipated internal combustion engine, microturbine, and U.S. grid COEs in 2035–2050. The 1 kW system COEs were three times anticipated grid prices in 2035–2050; however, 1 kW systems may be better suited to countries with higher spark spreads. Our results could inform business decisions, funding prioritizations, and technology roadmap development.

The theme of this symposium is the labor outcomes of technological change, and the mechanisms by which these outcomes are generated. The four papers presented address questions of skill and occupat...

The ability of automakers to improve the fuel economy of vehicles using engineering design modifications that compromise other performance attributes, such as acceleration, is not currently considered when setting fuel economy and greenhouse-gas emission standards for passenger cars and light trucks. We examine the role of these design tradeoffs by simulating automaker responses to recently reformed vehicle standards with and without the ability to adjust acceleration performance. Results indicate that acceleration tradeoffs can be important in two respects: (1) they can reduce the compliance costs of the standards, and (2) they can significantly reduce emissions associated with a particular level of the standards by mitigating incentives to shift sales toward larger vehicles and light trucks relative to passenger cars. We contrast simulation-based results with observed changes in vehicle attributes under the reformed standards. We find evidence that is consistent with firms using acceleration tradeoffs t...

This research presents a method of optimizing the consolidation of parts in an assembly using metal additive manufacturing (MAM). The method generates candidates for consolidation, filters them for feasibility and structural redundancy, finds the optimal build layout of the parts, and optimizes which parts to consolidate using a genetic algorithm. Results are presented for both minimal production time and minimal production costs, respectively. The production time and cost models consider each step of the manufacturing process, including MAM build, post-processing steps such as support structure removal, and assembly. It accounts for costs affected by part consolidation, including machine costs, material, scrap, energy consumption, and labor requirements. We find that developing a closed-loop filter that excludes consolidation candidates that are structurally redundant with others dramatically reduces the number of candidates, thereby significantly reducing convergence time. Results show that when increasing the number of parts that are consolidated, the production cost and time at first decrease due to reduced assembly steps, and then increase due to additional support structures needed to uphold the larger, consolidated parts. We present a rationale and evidence justifying that this is an important tradeoff of part consolidation that generalizes to many types of assemblies. Subsystems that are smaller, or can be oriented with very little support structures or have low material costs or fast deposition rates can have an optimum at full consolidation; for other subsystems, the optimum is less than 100%. The presented method offers a promising pathway to minimize production time and cost by consolidating parts using MAM. In our test-bed results for an aircraft fairing produced with powder-bed electron beam melting, the solution for minimizing production cost (time) is to consolidate 17 components into four (two) discrete parts, which leads to a 20% (25%) reduction in unit production cost (time).

We investigate the effect of competitor product representation on optimal design results in profit-maximization studies. Specifically, we study the implications of replacing a large set of product alternatives available in the marketplace with a reduced set of selected competitors or with composite alternatives, as is common in the literature. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors under the logit and nested logit models (where preference coefficients are homogeneous), but optimal design results may depend on competitor representation in latent class and mixed logit models (where preference coefficients are heterogeneous). In a case study of automotive powertrain design using mixed logit demand, we find some change in the optimal acceleration performance value when competitors are modeled using a small set of alternatives rather than the larger set. The magnitude of this change depends on the specific form and parameters of the cost and demand functions assumed, ranging from 0% to 3% in our case study. We find that the magnitude of the change in optimal design variables induced by competitor representation in our case study increases with the heterogeneity of preference coefficients across consumers and changes with the curvature of the cost function.

This research presents a method of evaluating and optimizing the consolidation of parts in an assembly using metal additive manufacturing (MAM). The method generates candidates for consolidation, filters them for feasibility and structural redundancy, finds the optimal build layout of the parts, and optimizes which parts to consolidate using a genetic algorithm. Optimal results are presented for both minimal production time and minimal production costs, respectively. The production time and cost model considers each step of the manufacturing process, including MAM build, post-processing steps such as support-structure removal, and assembly. It accounts for costs affected by parts consolidation, including machine costs, material, scrap, energy consumption, and labor requirements. We find that developing a closed-loop filter that excludes consolidation candidates with structural redundancy dramatically reduces the number of candidates to consider, thereby significantly reducing convergence time. Results show that, when increasing the number of parts that are consolidated, the production cost and time at first decrease due to reduced assembly steps, and then increase due to additional support structures needed to uphold the larger, consolidated parts. We present a rationale and evidence justifying that this is an inherent tradeoff of parts consolidation that generalizes to most types of assemblies. Subsystems that can be oriented with very little support structures, or have low material costs or fast deposition rates can have an optimum at full consolidation; otherwise, the optimum is likely to be less than 100%. The presented method offers a promising pathway to minimize production time and cost by consolidating parts using MAM. In our test-bed results on an aircraft fairing produced with powder-bed electron-beam melting, the solution for minimizing time is to consolidate 48 components into three discrete parts, which leads to a 33% reduction in unit production time. The solution for minimizing production costs is to consolidate the components into five discrete parts, leading to a 28% reduction in unit costs.

Nonassembled products, which are produced from a raw material and post-processed to a final form without any assembly steps, form a large and potentially growing share of the manufacturing sector. However, the design for manufacturing literature has largely focused on assembled products and does not necessarily apply to nonassembled products. In this paper, we review the literature on design for nonassembly (DFNA) and the broader literature on design for manufacturing that has design guidelines and metrics applicable to nonassembled products, including both monolithic single-part products and nonassembly mechanisms. Our review focuses on guidelines that apply across multiple manufacturing processes. We identify guidelines and metrics that seek to reduce costs as well as provide differentiated products across a product family. We cluster the guidelines using latent semantic analysis and find that existing DFNA guidelines fall into four main categories pertaining to (1) manufacturing process, (2) material, (3) tolerance, and (4) geometry. We also identify existing product family metrics that can be modified for nonassembled products to measure some aspects of these categories. Finally, we discuss possible future research directions to more accurately characterize the relationships between design variables and manufacturing costs, including investigating factors related to the complexity of operations at particular process steps and across process steps.

Despite decades of development, proton exchange membrane fuel cells (PEMFCs) still lack wide market acceptance in vehicles. To understand the expected trajectories of PEMFC attributes that influence adoption, we conducted an expert elicitation assessment of the current and expected future cost and performance of automotive PEMFCs. We elicited 39 experts' assessments of PEMFC system cost, stack durability, and stack power density under a hypothetical, large-scale production scenario. Experts assessed the median 2017 automotive cost to be $75/kW, stack durability to be 4,000 hours, and stack power density to be 2.5 kW/L. However, experts ranged widely in their assessments. Experts' 2017 best cost assessments ranged from $40 to $500/kW, durability assessments ranged from 1,200 to 12,000 hours, and power density assessments ranged from 0.5 to 4 kW/L. Most respondents expected the 2020 cost to fall short of the 2020 target of the US Department of Energy (DOE). However, most respondents anticipated that the DOE's ultimate target of $30/kW would be met by 2050 and a power density of 3 kW/L would be achieved by 2035. Fifteen experts thought that the DOE's ultimate durability target of 8,000 hours would be met by 2050. In general, experts identified high Pt group metal loading as the most significant barrier to reducing cost. Recommended research and development (R&D) funding was allocated to "catalysts and electrodes," followed in decreasing amount by "fuel cell performance and durability," "membranes and electrolytes," and "testing and technical assessment." Our results could be used to inform public and private R&D decisions and technology roadmaps.

Metals-additive manufacturing (MAM) is enabling unprecedented design freedom and the ability to produce significantly lighter weight parts with the same performance, offering the possibility of significant environmental and economic benefits in many different industries. However, the total production costs of MAM will need to be reduced substantially before it will be widely adopted across the manufacturing sector. Current topology optimization approaches focus on reducing total material volume as a means of reducing material costs, but they do not account for other production costs that are influenced by a part's structure such as machine time and scrap. Moreover, concurrently optimizing MAM process variables with a part's structure has the potential to further reduce production costs. This paper demonstrates an approach to use process-based cost modeling (PBCM) in MAM topology optimization to minimize total production costs, including material, labor, energy, and machine costs, using cost estimates from industrial MAM operations. The approach is demonstrated on various 3D geometries for the electron beam melting (EBM) process with Ti64 material. Concurrent optimization of the part structures and EBM process variables is compared to sequential optimization, and to optimization of the structure alone. The results indicate that, once process variables are considered concurrently, more cost effective results can be obtained with similar amount of material through a combination of (1) building high stress regions with lower power values to obtain larger yield strength and (2) increasing the power elsewhere to reduce the number of passes required, thereby reducing build time. In our case studies, concurrent optimization of the part's structure and MAM process parameters lead to up to 15% lower estimated total production costs and 21% faster build time than optimizing the part's structure alone.

The Integrated Network for Social Sustainability (INSS) is a research-coordination network supported by the National Science Foundation that is currently in its third year of activities. Individual and institutional members, representing a wide range of fields and interests, are devoted to addressing social sustainability as an important, understudied issue under the broader rubric of sustainability and sustainable development. The INSS has developed a number of affinity groups and a set of activities to facilitate its development. An annual conference draws members together to review and report on their efforts. At the first conference, a group interested in developing a research agenda formed. This Community Essay shares its members’ perspectives about priorities for future research and education on social sustainability, highlighting efforts for greater inclusion of marginalized populations in research.

Researchers have developed several methods to assess lifecycle environmental impacts of decisions in product design and policymaking. A major challenge is that whether impacts are reduced or exacerbated depends on market effects such as how the design change or policy influences the demand, use, and end-of-life of the relevant product and other products. However, little guidance is available to determine when market effects matter and how to model them. This paper identifies four categories of market effects and presents a framework to help researchers identify a priori whether these effects significantly influence environmental impacts and select an appropriate method.

A 2018 analysis discarded at least $112 billion in benefits

We measure the labor-demand effects of two simultaneous forms of technological change—automation of production processes and consolidation of parts. We collect detailed shop-floor data from four semiconductor firms with different levels of automation and consolidation. Using the O*NET survey instrument, we collect novel task data for operator laborers that contains process-step level skill requirements, including operations and control, near vision, and dexterity requirements. We then use an engineering process model to separate the effects of the distinct technological changes on these process tasks and operator skill requirements. Within an occupation, we show that aggregate measures of technological change can mask the opposing skill biases of multiple simultaneous technological changes. In our empirical context, automation polarizes skill demand as routine, codifiable tasks requiring low and medium skills are executed by machines instead of humans, while the remaining and newly created human tasks tend to require low and high skills. Consolidation converges skill demand as formerly divisible low and high skill tasks are transformed into a single indivisible task with medium skill requirements and higher cost of failure. We conclude by developing a new theory for how the separability of tasks mediates the effect of technology change on skill demand by changing the divisibility of labor.

The ability of automakers to improve the fuel economy of vehicles using engineering design modifications that compromise other performance attributes, such as acceleration, is not currently considered when setting fuel economy and greenhouse-gas emission standards for passenger cars and light trucks. We examine the role of these design trade-offs by simulating automaker responses to recently reformed vehicle standards with and without the ability to adjust acceleration performance. Results indicate that acceleration trade-offs can be important in two respects: (1) they can reduce the compliance costs of the standards, and (2) they can significantly reduce emissions associated with a particular level of the standards by mitigating incentives to shift sales toward larger vehicles and light trucks relative to passenger cars. We contrast simulation-based results with observed changes in vehicle attributes under the reformed standards. We find evidence that is consistent with firms using acceleration trade-offs to achieve compliance. Taken together, our analysis suggests that acceleration trade-offs play a role in automaker compliance strategies with potentially large implications for both compliance costs and emissions.

Metals-additive manufacturing (MAM) is enabling unprecedented design freedom and the ability to produce significantly lighter weight parts with the same performance, offering the possibility of significant environmental and economic benefits in many different industries. However, the total production costs of MAM will need to be reduced substantially before it will be widely adopted across the manufacturing sector. Current topology optimization approaches focus on reducing total material volume as a means of reducing material costs, but they do not account for other production costs that are influenced by a part’s structure such as machine time and scrap. Moreover, concurrently optimizing MAM process variables with a part’s structure has the potential to further reduce production costs. This paper demonstrates an approach to use process-based cost modeling in MAM topology optimization to minimize total production costs, including material, labor, energy, and machine costs, using cost estimates from actual MAM operations. The approach is demonstrated in a simple case study of a Ti64 cantilever produced with electron beam melting (EBM). Results of a concurrent optimization of the part structure and EBM process variables are compared to an optimization of the part structure alone. The results show that, once process variables are considered, it is more cost effective to include more material in the part through a combination of (1) building additional thin trusses with a faster laser speed and (2) increasing the thickness of other truss members and decreasing laser velocity to create larger melt pools that reduce the number of passes required, thereby reducing build time. Concurrent optimization of the part’s structure and MAM process parameters leads to 7% lower estimated total production costs and approximately 50% faster build time than optimizing the part’s structure alone.

We separate and directly measure the labor-demand effects of two simultaneous forms of technological change–automation and parts consolidation. We collect detailed shop-floor data from four semicon...

The recently amended U.S. Corporate Average Fuel Economy (CAFE) standards determine fuel-economy targets based on the footprint (wheelbase by track width) of vehicles such that larger vehicles have lower fuel-economy targets. This paper considers whether these standards create an incentive for firms to increase vehicle size by presenting an oligopolistic-equilibrium model in which automotive firms can modify vehicle dimensions, implement fuel-saving technology features, and trade off acceleration performance and fuel economy. Wide ranges of scenarios for consumer preferences are considered. Results suggest that the footprint-based CAFE standards create an incentive to increase vehicle size except when consumer preference for vehicle size is near its lower bound and preference for acceleration is near its upper bound. In all other simulations, the sales-weighted average vehicle size increases by 2–32%, undermining gains in fuel economy by 1–4 mpg (0.6–1.7 km/L). Carbon-dioxide emissions from these vehicles are 5–15% higher as a result (4.69×10 11 –5.17×10 11 kg for one year of produced vehicles compared to 4.47×10 11 kg with no size changes), which is equivalent to adding 3–10 coal-fired power plants to the electricity grid each year. Furthermore, results suggest that the incentive is larger for light trucks than for passenger cars, which could increase traffic safety risks.

Summary This article describes the development of a consequential life cycle assessment (cLCA) with endogenous market-driven design (MDD). Incorporation of MDD within cLCA (cLCAMDD) is beneficial because design decisions, influenced by market forces, are a major source of environmental emissions and resource consumption in many life cycle systems. cLCAMDD captures the environmental impact of these design responses resulting from industrial and policy decisions. We begin by developing the concept of cLCA-MDD, then present a case study that demonstrates how design responses can be endogenously captured in a cLCA analysis. The case study is in two parts: First, we incorporate endogenous design responses into a cLCA of a mid-size vehicle and, second, we conduct a policy analysis using a cLCA-MDD approach. The case study illustrates that cLCA-MDD can capture multiple “ripple effects” resulting from an industrial decision (e.g., downsizing a vehicle’s engine) or a policy decision (e.g., raising gasoline taxes) and that these effects significantly influence results. A key challenge of the approach is appropriately managing and communicating uncertainties associated with the choice of economic parameters or models. We discuss sources of uncertainty in cLCA-MDD and demonstrate a presentation scheme to facilitate communication of result sensitivity to uncertainties from input parameters, models, and model structure.

Product design success depends on the engineering performance of the product and also on the reaction of external stakeholders such as customers, retailers, and policymakers. This article illustrates how an early-stage engineering design performance model can be incorporated into a decision framework representing customers, retailers, and policymakers to assess the revenue potential for different technologies. Sensitivity analysis is performed for revenue and other stakeholder decision criteria with respect to the design performance measures. We illustrate our approach for photovoltaic panels in the context of the residential solar electricity generation system market in New South Wales, Australia that experienced a variety of federal and state government incentive programs between 2010 and 2012. The analysis is based on engineering performance modeling, discrete choice demand modeling, and cost modeling all with simplifying assumptions.

Prizes are receiving increasing attention in policy and entrepreneurial communities as means to promote innovation, but their distinguishing features remain inadequately understood. Models of patents treat winning a patent as winning a prize; other models distinguish prizes primarily as public lump-sum (re)purchase of a patent. We examine advantages of prizes based on the ability to customize rewards, manage competition, generate publicity, and cover achievements otherwise not patentable. We propose a two-dimensional comparative framework based first on whether the procuring party knows its needs and technology, its needs but not its technology, or neither. The second dimension is the risk that the investment in research will prove profitable, where the greater the risk, the more the procuring party should share in it through ex ante cost coverage or payment commitment. Such a framework may be extended to cover other means of technology inducement, including grants, customized procurement, and off-the-shelf purchase.

Success in achieving environmental goals is intrinsically dependent on policy decisions, firm decisions, and consumer decisions. Understanding how consumer product adoption jointly depends on policy incentives and firm design decisions is necessary for both firms and governments to make optimal decisions. This paper demonstrates a methodology for assessing the linkage between policy incentives and firm decisions on the level of consumer adoption of a particular technology. A policy optimization is formulated and technology-adoption indifference curves are constructed to allow firms to identify the most profitable direction for product development given the policy environment, and similarly to allow government organizations to set policies that maximize technology adoption given firm decisions. As an example we use the residential solar electricity industry in New South Wales, Australia. Consumer choice is modeled using a mixed logit choice model estimated with hierarchical Bayes techniques from stated preference experiment data.Copyright © 2011 by ASME