Abstract
Companies need to develop new products towards customer's satisfaction in order to survive in the boom and bust cycle in todays’ economy. The capturing of customer satisfaction depends on customer needs, and generally, understanding emotions has a challenge for designers. Kansei engineering is a type of methodology to help customers and designers analyze needs and emotion for the new product development. Producing new product design with Kansei data increases customer satisfaction and helps to reach market goals. In this study, a market-oriented baby cradle design methodology is proposed, and we obtain the new product strategies with association rule extraction by using rough set theory. To obtain efficient rules, beforehand we selected sales knowledge-related Kansei words with our proposed approach: cost-based and multiclass decision-theoretic rough set (DTRS) attribute reduction. The new product design strategies which are obtained with proposed design methodology are consistent with customer expectations (mood space) and expert opinions (design team).
Highlights
Today, all producers have technological advances in product development, and manufacturers have to catch customer expectations to achieve product satisfaction among the same product properties as in terms of performance, functional features, and price. e customers have difficulty to choose products with the same characteristics and different brands.e success of product design depends on understanding the needs of consumers
Kansei engineering is a kind of methodology which is transformed from customer needs to design elements for pleasure from the product [1]. is method was founded by Mitsuo Nagamachi at Hiroshima University for customer satisfaction [2]
We present a cost-based multiclass decision-theoretic rough set (MCDTRS) attribute reduction algorithm which can obtain the relevant cost for deferment decisions based on the 3 × m × m cost matrix
Summary
All producers have technological advances in product development, and manufacturers have to catch customer expectations to achieve product satisfaction among the same product properties as in terms of performance, functional features, and price. e customers have difficulty to choose products with the same characteristics and different brands. Cost-based multiclass decision-theoretic rough set (MCDTRS) attribute reduction algorithm with DTRS that is a kind of rough set theory is utilized to translate efficiently Kansei of customers into product characteristics in a furniture sector. 2. The Proposed Cost-Based MCDTRS Attribute Reduction is section describes the idea of defining associated rules between product parameters and customer requirements on a customer satisfaction which are modeled with cost-sensitive learning method and apriori algorithm. By considering the special case, where λ(aPi | Ci) λ(aNi | Cj) 0, where i ≠ j, cost is assumed zero, and we defined the decision costs of all rules and the Bayesian expected cost of each rule for the multiclass dataset which has 3 × m × m loss function: cost of positive rule for class Ci:. O4 o 1, o6, o8, o15 o 2, o5, o7, o9, o11, o12, o14, o16, o18, o19 o 3, o10, o13, o17, o20 o4 o 1, o6, o8, o15 o 2, o5, o7, o9, o11, o12, o14, o16, o18, o19 o 3, o10, o13, o17, o20 o4 o 1, o6, o8, o15 o4 o 2, o5, o7, o9, o11, o12, o14, o16, o18, o19 o 3, o10, o13, o17, o20
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