سیستم پشتیبانی نوآوری برای طراحی محصول خلاق بر اساس کشف فرصت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2262||2012||8 صفحه PDF||سفارش دهید||5020 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 5, April 2012, Pages 4890–4897
Under a turbulently changing and highly competitive market, discovery of a chance is always significant for many companies to launch new and creative products or services in time, fulfilling consumers demands for occupying more market share. Many available methods on market research for designing new products are more focused on the analysis process, so that product designers run short of ideas discovery. In this paper, we present a novel innovation support system (ISS) based on chance discovery with data crystallization to assist human innovation in designing new products, especially creative products. The ISS is a human-centric system to enable value cognition and follows the following process: (1) visualized scenario graph generation, (2) human value cognition, (3) value co-creation based on shared knowledge, and (4) emerging chances evaluation. The result of a case study validates the effectiveness of ISS.
In the dynamic and competitive market, many companies have to release new and creative products or services to fulfill the consumer demands for occupying much more market share. Many methods available on market research for designing new products, such as detailed questionnaires followed up with further analysis, are more focused on the analysis process, which makes the product designers suffering from fewer requirements. An interview and requirement meeting is an effective communication technique between product designers and customers (Carrol, 2000). However, the requirements obtained from customers are usually incomplete information. The reasons are as follows: 1. The designers failed to understand real customer needs. Sometimes the requirements from customers are too ambiguous or abstract to understand for designers. 2. The designers misunderstand real consumer needs. The original requirements of customers are too vague. Kushiro and Ohsawa (2005) established a new scenario elicitation method to solve the above issues by combining the chance discovery and the requirements engineering methods. Chance discovery is to become aware of a chance and to explain its significance, especially if the chance is rare and its significance is unnoticed (Ohsawa, 2002). 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 making a decision (Ohsawa, 2003a and Ohsawa, 2003b). Keygraph is a text mining visualization tool and used to build scenario graph with the Keygraph algorithm to assist the process of chance discovery (Ohsawa, 2003a and Ohsawa, 2003b). That is, Keygraph can recognize and display relations between events and event clusters in a document, and event clusters can be read as special meaningful scenarios by domain experts. Through human–computer interaction for some times, a decision from decision makers is to choose one from possible scenarios in the future. In the last few years, chance discovery has been widely applied in various research areas, especially in creative product design (Horie and Ohsawa, 2005, Horie et al., 2007 and Ohsawa and Usui, 2006). However, researchers have recognized a new problem of how to discover the important events that are not in the data, which is beyond scope of the chance discovery. Such invisible events are named dark events. Ohsawa (2005) extended theory and method of chance discovery where dummy nodes are inserted into the original data corresponding to the dark events. The data including dummy nodes is visualized and the dark events are understood by human cognitive process. For human easily understanding the visual scenario graph, Maeno and Ohsawa (2007) proposed human–computer interactive annealing method (HIAM) to discover invisible dart events. Horie, Maeno and Ohsawa (2007) applied the data crystallization with HIAM to design products in a real company and got good performance in the market. How to develop a systematic method, which not only can capture important but low or even zero frequency data, but also combine visual technologies with the methods of raising cognition capability of human, to support human to discover future chances such as creative products, has become an important research topic. In this paper, we present a novel innovation support system (ISS) based on chance discovery with data crystallization to assist human innovation in designing new products, especially creative products. The ISS is a human-centric system to enable value cognition and follows the following process: (1) visualized scenario generation, (2) human value cognition, (3) value co-creation based on shared knowledge, (4) emerging chances evaluation. The rest of paper is organized as follows. In Section 2, we present a method for making scenario based on chance discovery with data crystallization. A tool for aiding innovative thoughts and communication is introduced in Section 3. A method to effectively evaluate these emerging chances (creative ideas) is proposed in Section 4. In Section 5, we use a real example to illustrate the process of ISS. The conclusion is discussed in Section 6.
نتیجه گیری انگلیسی
A successful and profitable product starts with a good idea. However, usually it’s difficult for product designers to create a new idea using existing methods. Creative insight of human is a fruit of the interaction of mental process and the social environment. In this paper, we propose a systematic method to support human innovation. Firstly, scenario graph is generated by Keygraph with data crystallization. Based on the scenario graph, product designers create new ideas by dynamic interaction and communication between them. Secondly for further improving innovation process, innovators market game (IMG) is organized to simulate a real market. Finally, we evaluate these creative ideas as emerging chances in the future for decision making. Furthermore, a case study validates the effectiveness of our method.