Element Extraction and Evaluation of Packaging Design using Computational Kansei Engineering Approach

Abstract

Currently packaging design needs more a computational processing roles and became the fundamental selling art of products. Design of packaging is very subjective and company needs to understand customer’s behavior, perception and attractiveness. Challenges arise when marketing in fast moving consumer goods is getting very dynamic and competitive. Computational needs to identify customer’s perception and attractiveness is unavoidable. In this paper we proposed new methodology to extract and evaluate information elements of packaging design from customer preferences using computational Kansei Engineering (KE) approach. The elements of packaging design were extracted from group discussion and evaluate centrality and novelty metrics using Key Element Extraction (KEE) algorithm. Correlation of packaging design elements and Kansei words was obtained with association rule mining (ARM). This formulation enabled us to define which packaging design elements are strongly correlated with each Kansei/affective words and gives recommendation to designer what kind of packaging to design. In short this proposed methods become a quantification of the art of packaging design that ease a reliable design.