Should Optimal Designers Worry About Consideration?

Consideration set formation using non-compensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via non-compensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to the data, and then optimize design decisions using the estimated models. Model predictive power, design "error", and profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply better design decisions; that is, different model forms can provide "descriptive" rather than "predictive" information that is useful for design.Comment: 5 figures, 26 pages. In Press at ASME Journal of Mechanical Design (as of 3/17/15

Consideration behavior and design decision making

Over the past decade, design engineering has developed a systematic framework to coordinate with consumer behavior models. Traditional consumer models applied in the past has mainly focused on the preference of compensatory trade-offs in the choice decisions. Recent marketing research has become interested in developing consumer models that are representative in that they reflect realistic human decision processes. One important example is consideration : the process of quickly screening out many available alternatives using non-compensatory rules before trading off the value of different feature combinations. Is capturing consideration important for design? This research investigates the impact of modeling consideration behavior to design engineering, aiming at constructing consideration models that can inform strategic decisions. The study includes several features absent in existing research: quantifying the mis-specifications of the underlying choice process, tailoring survey instruments for particular models, and exploring the models\u27 strategic value on product profitability and design feature differences. First, numerical methods are explored to address the discontinuity in the profit-oriented optimization problem introduced by the consideration models. Methods based on complementarity constraints, smoothing functions and genetic algorithms are implemented and evaluated with a vehicle design case study. Second, a simulation experiment based on synthetic market data compares consideration models and a variety of conventional choice models in the process of model estimation and design optimization. The simulation finds that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. The synthetic experiment framework then further extends the comparison to include the survey design process guided by the different assumptions behind considerations and traditional models. A product line design case study reveals that even though both compensatory models and consideration models show robustness in profitability, using consideration models leads to optimal portfolios with higher feature diversity while reducing the risk of overestimating profits. Finally, the research explores how to use consideration models to analyze the market penetration of newly designed product in a case study of a consideration maximization problem. It is the hope that this research will arouse the attention of designers to the informative power of consideration models, expand the understanding of consumer behavior modeling from the predictive power in the marketing field to the strategic impacts to design decisions, and provide technical support to the future application of consideration models in design engineering

Lithofacies Characteristics and Sweet Spot Distribution of Lacustrine Shale Oil: A Case Study from the Dongying Depression, Bohai Bay Basin, China

AbstractLacustrine shale is characterized by rapid lithofacies transformation and compositional heterogeneity, which present challenges in shale oil sweet spot evaluation and distribution prediction and should be systematically studied. Field emission-scanning electron microscopy (FE-SEM), low-pressure adsorption isotherm analysis, mercury intrusion porosimetry (MIP), and triaxial compression testing were employed to comprehensively analyze the oil-bearing capacity, reservoir properties, fluidity, and frackability of different lithofacies. Via analyses of mineral composition, total organic carbon (TOC) content, and sedimentary structure, seven lithofacies were identified: organic-rich calcareous shale (L1), organic-rich laminated calcareous mudstone (L2), organic-rich laminated carbonate-bearing mudstone (L3), intermediate-organic laminated calcareous mudstone (L4), organic-poor laminated calcareous mudstone (L5), organic-poor thin-bedded calcareous mudstone (L6), and organic-rich laminated silty mudstone (L7). Considered together, the oil-bearing capacity, reservoir properties, fluidity, and frackability suggested that the L1 and L7 lithofacies were high-quality sweet spots, with satisfactory oil-bearing capacity (TOC>3.5%; S1>10 mgHC/grock), well-developed pores and microfractures, notable fluidity (as indicated by a high oil saturation index value), and suitable brittleness. The sweet spot distribution was predicted according to multiresolution graph-based clustering analysis of well logs. The results indicate that comprehensive research of the key factors for shale oil and lithofacies prediction can promote sweet spot prediction and enhance shale oil exploration

Consideration behavior and design decision making

Over the past decade, design engineering has developed a systematic framework to coordinate with consumer behavior models. Traditional consumer models applied in the past has mainly focused on the preference of compensatory trade-offs in the choice decisions. Recent marketing research has become interested in developing consumer models that are "representative" in that they reflect realistic human decision processes. One important example is "consideration": the process of quickly screening out many available alternatives using non-compensatory rules before trading off the value of different feature combinations. Is capturing consideration important for design? This research investigates the impact of modeling consideration behavior to design engineering, aiming at constructing consideration models that can inform strategic decisions. The study includes several features absent in existing research: quantifying the mis-specifications of the underlying choice process, tailoring survey instruments for particular models, and exploring the models' strategic value on product profitability and design feature differences. First, numerical methods are explored to address the discontinuity in the profit-oriented optimization problem introduced by the consideration models. Methods based on complementarity constraints, smoothing functions and genetic algorithms are implemented and evaluated with a vehicle design case study. Second, a simulation experiment based on synthetic market data compares consideration models and a variety of conventional choice models in the process of model estimation and design optimization. The simulation finds that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. The synthetic experiment framework then further extends the comparison to include the survey design process guided by the different assumptions behind considerations and traditional models. A product line design case study reveals that even though both compensatory models and consideration models show robustness in profitability, using consideration models leads to optimal portfolios with higher feature diversity while reducing the risk of overestimating profits. Finally, the research explores how to use consideration models to analyze the market penetration of newly designed product in a case study of a consideration maximization problem. It is the hope that this research will arouse the attention of designers to the informative power of consideration models, expand the understanding of consumer behavior modeling from the predictive power in the marketing field to the strategic impacts to design decisions, and provide technical support to the future application of consideration models in design engineering.</p

The Evolution of Carbon Nanotube Network Structure in Unidirectional Nanocomposites Resolved by Quantitative Electron Tomography

Carbon nanotube (CNT) reinforced polymers are next-generation, high-performance, multifunctional materials with a wide array of promising applications. The successful introduction of such materials is hampered by the lack of a quantitative understanding of process–structure–property relationships. These relationships can be developed only through the detailed characterization of the nanoscale reinforcement morphology within the embedding medium. Here, we reveal the three-dimensional (3D) nanoscale morphology of high volume fraction (Vf) aligned CNT/epoxy-matrix nanocomposites using energy-filtered electron tomography. We present an automated phase-identification method for fast, accurate, representative rendering of the CNT spatial arrangement in these low-contrast bimaterial systems. The resulting nanometer-scale visualizations provide quantitative information on the evolution of CNT morphology and dispersion state with increasing Vf, including network structure, CNT alignment, bundling and waviness. The CNTs are observed to exhibit a nonlinear increase in bundling and alignment and a decrease in waviness as a function of increasing Vf. Our findings explain previously observed discrepancies between the modeled and measured trends in bulk mechanical, electrical and thermal properties. The techniques we have developed for morphological quantitation are applicable to many low-contrast material systems.Airbus GroupBoeing CompanyEMBRAERLockheed MartinSaab (Firm)Hexcel (Firm)Toho TenaxANSYS, Inc.NECST Consortiu

Targeting treatment of bladder cancer using PTK7 aptamer-gemcitabine conjugate

Abstract Background Gemcitabine (GEM) is one of the first-line chemotherapies for bladder cancer (BC), but the GEMs cannot recognize cancer cells and have a low long-term response rate and high recurrence rate with side effects during the treatment of BC. Targeted transport of GEMs to mediate cytotoxicity to tumor and avoid the systemic side effects remains a challenge in the treatment of BC. Methods Based on a firstly confirmed biomarker in BC-protein tyrosine kinase 7 (PTK7), which is overexpressed on the cell membrane surface in BC cells, a novel targeting system protein tyrosine kinase 7 aptamer-Gemcitabine conjugate (PTK7-GEMs) was designed and synthesized using a specific PTK7 aptamer and GEM through auto-synthesis method to deliver GEM against BC. In addition, the antitumor effects and safety evaluation of PTK7-GEMs was assessed with a series of in vitro and in vivo assays. Results PTK7-GEMs can specifically bind and enter to BC cells dependent on the expression levels of PTK7 and via the macropinocytosis pathway, which induced cytotoxicity after GEM cleavage from PTK7-GEMs respond to the intracellular phosphatase. Moreover, PTK7-GEMs showed stronger anti-tumor efficacy and excellent biosafety in three types of tumor xenograft mice models. Conclusion These results demonstrated that PTK7-GEMs is a successful targeted aptamer-drug conjugates strategy (APDCs) to treat BC, which will provide new directions for the precision treatment of BC in the field of biomarker-oriented tumor targeted therapy