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To find the system with minimum investment and best quality performance that is capable of producing all of the product variants, assessing the complexity of designing assembly system at the early concept stage is an essential step, which helps and instructs a designer to create a product- and system-oriented assembly solution with the least complexity. The purpose of this paper is to propose a quantifying measurement of complexity in the design of a modular automated assembly system.

The configurable assembly system is becoming a trend, which enables companies to quickly respond to changes caused by different product variants but without a large investment. One of the enabling factors is the availability of modular solutions of assembly modules that can be configured according to different technical requirements. This paper develops a methodology using fuzzy evaluation to calculate the design complexity in the design phase for a modular automatic assembly system. Fuzzy linguistic variables are used to measure the interaction among the influence factors, to deal with the uncertainty of the judgement. The proposed method investigates three matrices to present how the function-based assembly modules, design complexity factors, part attributes and product components, which are regarded as the main influence factors, complicate the construction of a modular assembly system. The design complexity is derived and quantified based on these assessments.

The proposed approach presents a formal quantification to evaluate the design complexity with regard to a modular assembly system from beginning, which can be identified and used as criteria to indicate the quality of performance and investment cost in advance. A mathematical model based on the fuzzy logic is established to provide both theoretical and practical guidance for the paper. To validate the predictive model, the statistic relationships between the assessed system design complexity, real assembly defect rate and investment cost are estimated based on regression analysis. The application of the presented methodology is demonstrated with regard to a traditional rear drive unit in the automotive industry.

This paper presents a developed method, which addresses the measures of complexity found in the design of a modular assembly system. It would help to run the design process with better resource allocation and cost estimation in a quantitative approach.

Complexity is the main challenge for present and future manufacturers. Assembly complexity heavily affects a product’s final quality in the fully automated assembly system. This paper aims to propose a new method to assess the complexity of modern automated assembly system at the assembly design stage with respect to the characteristics of both manufacturing system and each single component to be mounted. Aiming at validating the predictive model, a regression model is additionally presented to estimate the statistic relationship between the real assembly defect rate and predicted complexity of the fully automated assembly system.

The research herein extends the S. N. Samy and H. A. ElMaraghy’s model and seeks to redefine the predictive model using fuzzy evaluation against a fully automated assembly process at the assembly design stages. As the evaluation based on the deterministic scale with accurate crisp number can hardly reflect the uncertainty of the judgement, fuzzy linguistic variables are used to measure the interaction among influence factors. A dependency matrix is proposed to estimate the assembly complexity with respect to the interactions between mechanic design, electric design and process factors and main functions of assembly system. Furthermore, a complexity attributes matrix of single part is presented, to map the relationship between all individual parts to be mounted and three major factors mentioned in the dependency matrix.

The new proposed model presents a formal quantification to predict assembly complexity. It clarifies that how the attributes of assembly system and product components complicate the assembly process and in turn influence the manufacturing performance. A center bolt valve in the camshaft of continue variable valve timing is used to demonstrate the application of the developed methodology in this study.

This paper presents a developed method, which can be used to improve the design solution of assembly concept and optimize the process flow with the least complexity.

Existing models of product assembly scheme design often ignore the constraint relations among design thinking. In order to grasp the functions of each part and the constraint relations among them in product assembly system macroscopically, further design and variation of product assembly system should be made according to design thinking. This paper seeks to address these issues.

Through analyzing the similarity between biological organization system and product system and taking biology knowledge for reference, product assembly system was expressed as product function gene, product constraint gene, product function protein, product constraint protein and product cell and so on in this paper. The product gene model composed of product function gene groups and constraint genes was established and a modeling method based on it was proposed.

The author applied this method to model the 5‐DOF manipulator of complex diamond manufacturing special equipment with good results which proved the effectiveness of this modeling method.

By identifying constraint relations and design thinking in the gene model, the system makes the modification process which is conducted by the designers automatically identified and varied to achieve computer‐aided design and assembly.

The aim of this research is to examine the application of the stereotype content model (SCM) in the field of marketing from macro (the country of origin), meso- (corporate image) and micro (service providers, brands, advertising and promotions) levels.

This paper collects, reviews and summarizes the relevant literature, and prospects future research directions from three levels on this research topic.

First, the authors contend that competence primacy popularizes in the early work but warmth becomes more influential recently. Second, they identify and discuss two doubts of the SCM in marketing, namely the moral dimension and the link between brands as intentional agents framework (BIAF) and the brand personality theory. Finally, they suggest several research avenues for the use of SCM in marketing research, including research on nation branding, emerging global brands and Confucianist cultures at macro level, artificial intelligence and warmth-as-competence strategy at meso-level, and brand personality and the brand animal logo at micro level.

As an established framework in social psychology, the SCM has been increasingly applied in marketing research and a literature review in this light appears timely. This paper conducts for the first time a comprehensive review of the SCM in the marketing field on three levels, projects promising research directions, and thus contributes to the academia of marketing.