產品創新輔助系統的開發

Abstract

本論文提出CBR(case-based reasoning)技術，從產品資料庫中，針對給定產品(稱為基準產品)提昇其功能，產生新產品創意。在此資料庫中，產品利用100屬性向量來編碼，其中87個利用使用情境來表達，以及使用13個製造/回收特徵(features)來描述。根據使用情境屬性與依據模糊AHP(Analytic Hierarchy Process)技術決定的相對權重，以模糊CBR擷取機制來檢索產品創意，以提昇「基準產品」的功能為方向。根據製造/回收特徵，以模糊CBR機制所發展篩除機制，可濾除與「基準產品」不相容的產品創意，得到更高比例有價值的產品創意。我們所提出的執行雛形系統叫作產品創新輔助系統(product innovation aiding system ; 簡稱PIAS)。實驗顯示，「檢索」機制其創意績效比人為的「腦力激盪法」來的好。而以產生高比例有價值產品創意的觀點，「檢索與濾除」機制所產生效率與效果又勝過單獨採用「檢索」機制。

This dissertation presents a fuzzy CBR (case-based reasoning) technique for generating new product ideas from a product database for enhancing the functions of a given product (called the baseline product). In the database, a product is modeled by a 100-attribute vector, 87 of which are used to model the use-scenario and 13 are used to describe the manufacturing/recycling features. Based on the use-scenario attributes and their relative weights—determined by a fuzzy AHP (Analytic Hierarchy Process) technique, a fuzzy CBR retrieving mechanism is developed to retrieve product-ideas that tend to enhance the functions of the baseline product. Based on the manufacturing/recycling features, a fuzzy CBR mechanism is developed to screen the incompatible products ideas with baseline product in order to obtain a higher ratio of valuable product ideas. A prototype system of the proposed approach, called PIAS (product innovation aiding system), has been implemented. Experiments show that the PIAS outperforms the brainstorming approach in generating creative and valuable product ideas. In addition, experiments indicate that the retrieving-and-filtering mechanism outperforms the retrieving-only mechanism in terms of generating a higher ratio of valuable product ideas.