Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network based approach to modeling and analyzing the evolution of product competitions using multi-year product survey data. We adopt Separate Temporal Exponential Random Graph Model (STERGM) as the statistical inference framework because it considers the evolution of dynamic networks as two separate processes: formation and dissolution. This treatment allows designers to investigate why two products enter into competition and why a competitive relationship preserves or dissolves over time. In an open market, the available products to customers are continuously changing over the time, posing challenges for conventional modeling methods concerning fixed product input. Consequently, we propose to leverage “structural zeros” in STERGM to tackle the problem of modeling varying product competitors as nodes in dynamic networks. We use China’s automotive market as a case study to illustrate the implementation of the proposed approach and its benefits compared to the static network modeling approach based on Exponential Random Graph Model (ERGM). The results show that our approach identifies the driving factors associated with product attributes and current market competition structures for the change of competition in both formation and dissolution processes. The insights gained from this paper can help designers better interpret the temporal changes of product competition relations and make product design decisions with the aid of dynamic network-based models.
Customer preferences are found to evolve over time and correlate with geographical locations. Studying spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for a thorough understanding of preference trend. However, existing analytical models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. To fill this research gap, a spatial panel modeling approach is developed in this study to investigate the spatiotemporal heterogeneity of customer preferences by introducing engineering attributes explicitly as model inputs in support of demand forecasting in engineering design. In addition, a step-by-step procedure is proposed to aid the implementation of the approach. To demonstrate this approach, a case study is conducted on small SUV in China’s automotive market. Our results show that small SUVs with lower prices, higher power, and lower fuel consumption tend to have a positive impact on their sales in each region. In understanding the spatial patterns of China’s small SUV market, we found that each province has a unique spatial specific effect influencing the small SUV demand, which suggests that even if changing the design attributes of a product to the same extent, the resulting effects on product demand might be different across different regions. In understanding the underlying social-economic factors that drive the regional differences, it is found that Gross Domestic Product (GDP) per capita, length of paved roads per capita and household consumption expenditure have significantly positive influence on small SUV sales. These results demonstrate the potential capability of our approach in handling spatial variations of customers for product design and marketing strategy development. The main contribution of this research is the development of an analytical approach integrating spatiotemporal heterogeneity into demand modeling to support engineering design.
Automakers are interested in creating optimal car shapes that can visually convey environmental friendliness and safety to customers. This research examined the influence of vehicle form on perceptions based on two subjective inference measures: safety and perceived environmental friendliness (PEF). A within-subjects study was conducted in 2009 (Study 1) to study how people would evaluate 20 different vehicle silhouettes created by designers in industry. Participants were asked to evaluate forms on several scales, including PEF, safety, inspired by nature, familiarity, and overall preference. The same study was repeated in 2016 (Study 2). The results from the first study showed an inverse relationship between PEF and perceptions of safety. That is, vehicles that appeared to be safe were perceived to be less environmentally friendly, and vice versa. Participants in the second study showed a similar trend, but not as strongly as the 2009 participants. Several shape variables were identified to be correlated with participants' PEF and safety ratings. The changes in the trend of participants' evaluations over seven years were also discussed. These results can provide designers with insights into how to create car shapes with balanced PEF and safety in the early design stage.
Research on decision making in engineering design has focused primarily on how to make decisions using normative models given certain information. However, there exists a research gap on how diverse information stimuli are combined by designers in decision making. In this paper, we address the following question: how do designers weigh different information stimuli to make decisions in engineering design contexts? The answer to this question can provide insights on diverse cognitive models for decision making used by different individuals. We investigate the information gathering behavior of individuals using eye gaze data from a simulated engineering design task. The task involves optimizing an unknown function using an interface which provides two types of information stimuli, including a graph and a list area. These correspond to the graphical stimulus and numerical stimulus, respectively. The study was carried out using a set of student subjects. The results suggest that individuals weigh different forms of information stimuli differently. It is observed that graphical information stimulus assists the participants in optimizing the function with a higher accuracy. This study contributes to our understanding of how diverse information stimuli are utilized by design engineers to make decisions. The improved understanding of cognitive decision making models would also aid in improved design of decision support tools.
Mobile robots are being widely used in smart manufacturing, and efficient task assignment and path planning for these robots is an area of high interest. In previous studies, task assignment and path planning are usually solved as separate problems, which can result in optimal solutions in their respective fields, but not necessarily optimal as an integrated problem. Meanwhile, precedence constraints exist between sequential processing operations and material delivery tasks in the manufacturing environment. Thus, those planning methods developed for warehousing and logistics may not simply apply to the environment of smart factories. In this paper, we propose an integrated task and path planning approach based on Looking-backward Search Strategy (LSS) and Regret-based Search Strategy (RSS). In the stage of task assignment, the real paths for mobile robots are identified based on the Cooperative A* (CA*) algorithm and the time and energy consumed by mobile robots and machining centers are calculated. Then a greedy strategy working with LSS or RSS is used to search reasonable task assignments in time-series, which can generate a joint optimal solution for both task assignment and path planning. We verify the validity of the proposed approach in a simulated smart factory and the results show that our approach can improve the operation efficiency of the smart factory and save the time and energy consumption effectively.
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