توسعه محصول با استفاده از تکنیک های داده کاوی : مطالعه موردی در طراحی دوربین های دیجیتال
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی|
|2777||2011||7 صفحه PDF||27 صفحه WORD|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 8, August 2011, Pages 9274–9280
Many enterprises have been devoting a significant portion of their budget to product development in order to distinguish their products from those of their competitors and to make them better fit the needs and wants of customers. Hence, businesses should develop product designing that could satisfy the customers’ requirements since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper investigates the following research issues in the development of new digital camera products: (1) What exactly are the customers’ “needs” and “wants” for digital camera products? (2) What features is more importance than others? (3) Can product design and planning for product lines/product collection be integrated with the knowledge of customers? (4) How can the rules help us to make a strategy during we design new digital camera? To investigate these research issues, the Apriori and C5.0 algorithms are methodologies of association rules and decision trees for data mining, which is implemented to mine customer’s needs. Knowledge extracted from data mining results is illustrated as knowledge patterns and rules on a product map in order to propose possible suggestions and solutions for product design and marketing.
With the ever-changing information technology and the current consumption patterns change, product life cycle becomes shorter and shorter. Enterprises must master the ever-changing market trends, and create high value business activities continuing to develop of new products designed to enhance the competitiveness of enterprises. To satisfy customers’ needs, customer-specific products should be produced. However, the latter increases production costs and the product market price. Manufacturing cost can be reduced by standardizing products to realize the benefits of the economy of scale. Concurrent engineering is a management procedure for the traditional sequential engineering arising out of the product development loss. The concept which in its product design stage can be considered as thinking the problems may faced before the product life cycle processes, the problem such as manufacturing, assembly, cost and reliability other factors, and then reached the purpose of shortening the design time and reducing development costs. Concurrent engineering is a systematic approach to integrate product development that emphasizes the response to customer expectations. It embodies team values of co-operation, trust and sharing in such a manner that decision making is by consensus, involving all perspectives, from the beginning of the product life cycle. Accordingly, the entire product life cycle related activities can all be fully taken into account early in product development, not only to reduce development costs and shorten the time to market but also to increase product and process quality, lower costs and enhance the competitiveness of the new product. At present, the development and research of concurrent engineering in many areas of integration have many good results; for example, with design for manufacturing, with design for assembly, with design for reliability, with design for quality, with design for cost and so on (Boothroyd et al., 2001 and Parsaei, 1993). However, with the design for customer on the integration of the design, there is not much written. A new product development cannot only be pursuant to the business of the design and manufacturing capability one also has to consider the customer’s needs and preferences and translate then into the design map. Cooper and Kleinschmidt (1993) also pointed out that with customer-oriented enterprises, when developing new products, one must be fully aware of the needs of customers, market competition and the nature of the market as these are critical success factor to new any product. The model of product development driven by sales has been gradually replaced by the customer and market orientation. If an enterprise can exactly understand what the customer wants, preferences and buying behavior will provide clues to the development of new products. This study applies association rule and decision tree techniques to analyze customer preferences portfolio information and make a new product to customers. This will bring fast and accurate feed back to the product designers; the enterprises can make a quick response for short-lived product life cycle, and grasp the real needs of customers. This paper investigates the following research issues in the development of new digital camera products: (1) What exactly are the customers’ “needs” and “wants” for digital camera products? (2) What features is more importance than others? (3) Can product design and planning for product lines/product collection be integrated with the knowledge of customers? (4) How can the rules help us to make a strategy during we design new digital camera? To investigate these research issues, the Apriori and C5.0 algorithms are methodologies of association rules and decision trees for data mining, which is implemented to mine customer’s needs. Knowledge extracted from data mining results is illustrated as knowledge patterns and rules on a product map in order to propose possible suggestions and solutions for product design and marketing. The remainder of this paper is structured as follows. Section 2 presents a research background review focused on the new product development using data mining techniques. Section 3 presents a research framework and analysis procedure. Section 4 presents data preparation and analysis. Some experimental results are presented and analyzed in Section 5, and finally our concluding remarks are provided in Section 6.
نتیجه گیری انگلیسی
Customer needs and wants are sensitive and complex. If a firm can understand them and make efforts to fulfill customer demands and provide friendly service, then customers will be more supportive and loyal to the enterprise. During the process of development from the product concept to the actual product, the customer can only passively receive new information, and can only select from the products that are currently on sale in the market. No matter which type of product, the customer cannot individually come up with a product concept and then develop it. Furthermore, buying what is available on the market does not mean that customers are satisfied with the current product, because the customer’s experiences and preferences were not considered in developing the product so they can only accept the product as it is. As a result, a business should develop products that fulfill the customer’s needs and wants, since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. Data mining technology can make dramatic changes to business practice which gathering of information about the customer to analyze and integrate, provide the engine to realize the knowledge of customer’s requirements. In this paper, mining customer information for new product development is an example of implementing a data mining approach for analyzing and providing decision supports. Data mining techniques should be implemented using the on data mining process in order to enhance data analysis capabilities for classification, clustering, and prediction analysis. In this study, the functional attributes of digital cameras that influence the digital camera purchase were found and emphasized to increase the digital camera repurchase rate and to present a product sales strategy for digital camera manufacturers and relevant researchers. This paper suggested that integrated rules were extracted from the association rules and C5.0 algorithm, which is implemented for mining product knowledge from customers. Knowledge extraction from data mining results is shown as rules in order to propose suggestions and solutions for new product development and possible marketing solutions. Despite the many findings from this study, it has some limitations. Firstly, the results from the study should be generalized. It would be better to investigate more products in order to generalize the results of this study. Secondly, the sample of this study is mainly on undergraduate and full-time MBA students. Actually, we should extend the range and amount of the sample to get more data if that we can get more detail information, the rules will be better than now. The result of the study also should be analyzed with the engineers to research the feasibility of the rules. For future study, we could focus on combine the rules and the customers in details and use other data mining techniques such as neural networks, genetic algorithms, and support vector machines by analyzing past years data to predict future new product design direction.