Fuzzy dual experience-based design evaluation model for integrating engineering design into customer responses

Abstract

Experience-based design is a recently emerging method used to capture the emotional content of customer experiences. Both the engineer's experiences and customer's experiences for dual experiences are important in delivering high quality user-centred product design. To assess dual experiential design optimization, fuzzy decision tree and fuzzy cognitive map are integrated in engineering design perspectives. This study aims at optimizing complex interactions and experiential design system with imprecise relationships while quantifying the performance impact of engineering design efficiency on customer satisfaction. The experiment is conducted by utilizing sensitivity analysis of the three degrees of fuzzy membership function using a product mix-experience problem. The evaluation results show that this dual experience-based design approach can help R&D design, deliver high quality product development experiences and co-create value with customers to yield a high performance engineering design.

Human-centred design in industry 4.0: case study review and opportunities for future research

“…Basically, one of the key measurements to understand the degree to which the design of HMI meets usage requirements is its usability, which focuses on functional indicators: usefulness, efficiency, effectiveness, and the learning curve of the user interface. The deeper concept of user multidimensional experience—which considers users’ emotional and psychological responses—is getting increasing attention and is also known as the core concept of user-centred design (UCD) (Chen, 2016 ; Kymäläinen et al, 2017 ; Lin, 2018 ; Paelke et al, 2015 ; Zheng et al, 2018 ). UCD, also interchangeably called user-centrality , embraces the user’s needs and involvement as the centre of the co-designing development process (Mazali, 2018 ; Wu et al, 2016 ) in order to enhance user acceptability and acceptance.…”

“…Similarly, Leng and Jiang ( 2017 ) clustered similar individual service design processes into homogeneous bundles of services by applying a granular computing method—fuzzy set theory combined with quotient space theory for classification (or clustering) of uncertain complex problem (Zhang & Zhang, 2010 ). Taking both customer and engineering subjective ideas, Chen ( 2016 ) carried out the fuzzy analytic hierarchy process (AHP) to develop good quality design based on the imprecise relationship between engineering experience (robust design, design optimization, design cognition) and customer experience (requirements management, ergonomics design). Based on that, the author also proposed a linear programming model to optimize the total profit of the product mix-experience portfolio, taking economic considerations into account.…”

“…Figure 6 structures those success factors as a triangular decision-making diagram: Stakeholder networks : the organizational, social, and environmental contexts—which involve stakeholders (e.g., users, customers, employees, suppliers, distributors, partners, regulators, etc.) through the life-cycle design process—are essential for enhancing the credibility of information and promoting the sharing of transdisciplinary knowledge as valuable design inputs (Chen, 2016 ; Mazali, 2018 ; Schulze et al, 2005 ; Witschel et al, 2019 ). The diversity in interests and expectations of the stakeholders needs to be respected and analysed to comprehend the impact of stakeholder interactions and their features at different life-cycle design phases: design, production, delivery, service, maintenance and end-of-life cycle (Mourtzis et al, 2018 ; Turetken et al, 2019 ; Zhang et al, 2020 ).…”

“…This product family design allows designers to not only utilize existing design methods from the product platform to form a series of products, but also gain inspiration from different ethnic groups—human diversity with distinct cultural traits—to extract ideal design elements. In another aspect, Chen ( 2016 ) emphasized directly the cost–benefit analysis of design quality, taking into account two economic elements: estimated profit; total cost comprising R&D cost, market capital, and design quality for market share. The reported figures prove the robustness and performance of a system—human diversity, ergonomics, economics, manufacturability, sustainability—can be achievable with the approaches of HCD.…”

Portfolio optimization based on fuzzy entropy

“…Moreover; some of previous studies have employed fuzzy and entropy in different scientific studies. For example, Kundu and Islam [13], proposed a new interactive approach to solve entropy based fuzzy reliability optimization models, Chen [14], developed a Fuzzy dual decisional model to evaluate performance impacts of engineering design on customer satisfaction., Yari et al [15,16] utilized it in option pricing and also in portfolio optimization, Song et al [17] used fuzzy logics in studying children's intuitionistic fuzzy emotions as a psychology course and Farnoosh et al [18] introduced a method in image processing based on intuitionistic fuzzy entropy.…”