• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.527-530
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• 2015

2014 년 4 월 TTA 에서 개정된 "HTML5 기반 스마트 TV 플랫폼(TTAK.KO-07.0111/R1)" 표준의 멀티스크린 서비스는 UNnP, mDNS 등의 ZeroConf 네트워크 기술을 기반으로 스마트 TV 수신기가 스마트 폰이나 태블릿과 같은 다양한 컴패니언 디바이스를 발견하고 서로 연결하여 디바이스 간 연동 서비스를 구현할 수 있는 기술을 정의하고 있다. 또한, 이 멀티스크린 기술을 활용하여 개발된 서비스(앱)가 표준을 준수하는 다양한 수신기에서 동일한 사용자 경험(UX)으로 실행되기 위해서는 수신기가 멀티스크린 기술 요구 사항을 준수하여 구현되었는지를 검증하는 적합성 시험이 반드시 필요하다. 본 논문에서는 이러한 적합성 시험을 위한 시험 기준과 수행절차, 판정 기준 등을 정의하고 있는 " HTML5 기반 스마트 TV 플랫폼 수신기 적합성 시험(TTAK.KO-07.0119/R1)" 표준에 멀티스크린 시험을 위하여 추가된 시험 항목과 이를 실제로 실행하여 수신기를 검증하기 위하여 개발된 테스트케이스를 소개한다. 아울러, 컴패니언 디바이스에서 실행되면서 수신기의 테스트케이스와 연동하여 수신기 기능을 검증하는 데 활용하는 모바일 테스트 프레임워크와 이를 기반으로 하는 모바일 테스트케이스의 구현을 설명한다. 아울러, 본 논문에서는 TTA 에 구축된 멀티스크린 시험 환경에서 표준 기반 참조 수신기를 대상으로 멀티스크린 시험을 수행한 사례를 통해 시험환경의 유효성을 확인하고 향후 개선 방향을 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.85-96
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• 2024

As users of paid media readers' satisfaction with usage directly affects the promotion and development of paid media in China. Based on relevant research literature on user sustained use behavior, using the Expectation Confirmation Model as the framework, China paid media Caixin is used as a content product to construct a sustained use model for paid media. At the same time, the operational definition and theoretical assumptions of the variables in the model were provided, laying the foundation for subsequent empirical research on the effectiveness of the model.The research results show that paid media's social influence and performance expectations have a positive impact on Caixin App readers' adoption behavior,perceived usefulness and expection confirmation has a positive impact on Caixin App readers' satisfaction . Adoption behavior and satisfaction has a positive effect on continue using intention, what's more,the perceived usefulness also has a positive effect on continue using intention.