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Articles by Kuo-Qin Yan
Total Records ( 2 ) for Kuo-Qin Yan
  Kuo-Qin Yan and Shu-Ching Wang
  For a manufacturer which bases make-to-order (MTO) as its product positioning strategy, in such competitively growing business in which customers demand a shorter and shorter delivery time, it`s important for the manufacturer to avoid unnecessary inventory of semi-finished goods but improve the flexibility of delivery and to minimize the uncertain interferences in the middle of manufacturing in order to better respond to the customers. We propose a four-step method. In the first step, employing RFM indexing method to find the most potential profit-maximizing product from the product line for export. Second step is to do a mining on sequence pattern of the bill of material (BOM) which is picked from the RFM to obtain the large frequent sequence pattern of processing. From these largest sequence patterns, in the third step, create the corresponding sequence rules and present in visualization the common parts BOM and critical parts. Fourth step is to generate a feasible as well as safe optimized quantity of production. Our proposed method can, provide as an assistance to the enterprises that they can better control the commonness of their products and processes and help them find the most appropriate parts or semi-finished goods for make-to-stock during the safe planning horizon so that they can effectively and accurately increase the inventory of these critical parts and minimize the uncertain interferences in the middle of manufacturing in order to better respond to the customers.
  Kuo-Qin Yan , Shu-Ching Wang and Chia-Hui Wei
  Due to the internationalization of the domestic business environment nowadays, competitions that every company has to survive have come not merely from the challenges of other local companies but from everywhere around the world. In order to support high quality service to contribute the most to business interest so that companies can stay highly competitive, optimized, standardized and flexible advertising recommend mechanism must be developed. With a view to exploring how to digitally turn the customer transaction information into real value for business organizations, in this study, we shall focus on the establishment of mobile advertising recommend mechanism. In this paper, we propose a two level personalized mobile advertising recommend mechanism. The method of Genetic Algorithm (GA) is used at first and then the method of Back Propagation Network (BPN) is used to extract the customer characteristic information and increase the accuracy in the learning rate. To start with using e-ticket shopping that it raises security while add cryptography, then employ mobile agent will collect and merge the information of e-ticket transaction records and personal local site. On these grounds, the proposed model can reduce the cost of manpower, promote the service quality and performance and offer automatically proper solution to increase the customer satisfaction
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