A System Method to Elicit Innovative Knowledge Based on Chance Discovery for Innovative Product Design

Introduction

For responding to global competition, increasing activities in product design has been changing from investment-driven landscape to an innovation-driven economy. Companies have to transform themselves from production-focused to value-added design-focused businesses (Yan et al., 2009). Based on radical innovations, Lin and Luh (2009) developed a vision-based approach to explore potential opportunities that lead us to new perspectives for product design, where Visioning thinking, TRIZ (the theory of innovative problem solving), Image Scale, and Morphological Analysis are integrated for developing designs with a balance of rational/logical analyses and intuitive/creative thinking.

In the last few years, chance discovery has been widely applied in various research areas, especially in business. Chance discovery is a human-computer interaction process to detect rare but important chances for decision making. A chance in chance discovery means to understand an unnoticed event or situation which can be uncertain but significant for a decision by human beings (Ohsawa & McBurney, 2003). KeyGraph is a visualization tool with the KeyGraph algorithm which can generate scenario map for aiding human’s value cognition in the process of chance discovery (Ohsawa, Benson, & Yachida, 1998). In the case of marketing, product designers designed new products with KeyGraph, and made real business profits (Ohsawa & Fukuda, 2002). However, researchers gradually recognized a new problem in chance discovery where KeyGraph failed to visualize important events which are neither visible nor observed as data (Maeno & Ohsawa, 2007). In fact, only observable part of the real world as visible knowledge is presented in data. For scattered data, such as incomplete and ill-structured data, Ohsawa (2005) proposed a breaking-through method named data crystallization which aimed at presenting the hidden structure by inserting dummy items corresponding to unobservable, i.e., hidden events, into the given data recording past events. Hidden events will be visualized as the output of data crystallization. Horie, Maeno, and Ohsawa (2007) applied the method of data crystallization for designing new products in a real company. The results illustrated its effect on industrial decision making. Wang et al. (2010) proposed an effective method to discover emerging chance based on AHP and Grey Relational Analysis (GRA).

However, there are some problems in the mentioned above research as follows:

• 1.

  There are fewer approaches proposed under a systematic or united framework for the purpose of innovative product design from innovative knowledge elicitation to evaluate knowledge representation.

• 2.

  It’s imperative to integrate qualitative and quantitative analysis in a unified framework. Most existing methods have been developed by qualitative guidelines rather than quantitative evaluation. Actually, we human beings are good at qualitative data and weak in quantitative data (Matsumura, 2006).

This paper is organized as follows: A united systematic framework for innovative knowledge elicitation and evaluation is proposed. In this framework, there are three significant modules: scenario generation module, innovative knowledge elicitation module and innovative knowledge selection module. These three modules are presented in the following sections. Finally, a case study for innovative product design is used for system illustration.

A Framework For Innovative Knowledge Elicitation System

A framework of the proposed innovative knowledge elicitation system (IKES) is shown in Figure 1, which includes three main functional modules: (1) scenario generation module, (2) innovative knowledge elicitation module, and (3) innovative knowledge selection module.

Figure 1.

A framework for innovative knowledge elicitation system