• The Journal of Information Systems
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• v.23 no.4
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• pp.25-47
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• 2014

The purpose of this study is to identify and comprehensively explain the SCM-related factors involved in the realization of RTE, including the quality of information concerning SCM, the quality of SCM partnerships, and relationship controlling factors (planning control, behavioral control, and outcome control). This study systematizes the interactions among these factors into a form of a model, elucidating how these interactions affect and shape RTE characteristics. To validate the research model proposed in the study, filed survey was conducted with 79 staffs in Korean company using SCM. The findings of the study can be summarized as follows: First, the quality of information concerning SCM had significant effect on the quality of SCM partnerships, planning control, behavioral control, and outcome control. Not only does the quality of SCM information directly influence the quality of SCM partnerships, planning control, behavioral control, and outcome control; but it also indirect influence on forecasting, agility, and visibility of SCM though its influence on the different forms of relationship control. Second, the quality of SCM partnerships also had significant effect on planning control, behavioral control, and outcome control. In addition to directly influencing these forms of relationship control, the quality of SCM partnerships also indirectly influenced forecasting, agility, and visibility though its influence on relationship control. Third, planning control significantly influenced forecasting, agility and visibility. Fourth, behavioral control significantly influenced forecasting, but neither agility nor visibility. Fifth, outcome control had significant influence on forecasting, agility, and visibility.

• The Journal of Information Systems
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• v.20 no.3
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• pp.161-186
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• 2011

To date, SCM research has mainly focused on the effects of controlled variables on SCM success and emphasized adoption strategies and critical success factors. Consequently, the effects of more uncontrolled variables such as partnership between SCM partners have been largely ignored. The purpose of this study, therefore, is to examine the effects of both controlled variables and uncontrolled variables on SCM performance through affecting RTE characteristics. The six factors examined in this study include Quality of information, partnership quality, Forecasting, Agility, Visibility, and SCM performance. In this study, SCM Performance was divided into three categories: Quality Performance, Cost Performance, Delivery Performance. All factors were examined from the perspective of part suppliers. The results of this study can be summarized as follows. First, SCM information quality positively affected SCM partnership quality, Forecasting, Agility, Visibility. Second, SCM partnership quality positively affected Forecasting, Agility. But, SCM partnership quality showed no significant effect on Visibility. Third, Forecasting had a significant impact on SCM performance. According to the detailed result of measuring SCM performance with Quality Performance, Cost Performance, Delivery Performance, although Forecasting affects Cost Performance, Delivery Performance directly, it does not affect Quality Performance directly. Fourth, Agility also had a significant impact on SCM performance. According to the detailed result of measuring SCM performance, Agility has significant impact on Quality Performance, Cost Performance, Delivery Performance. Fifth, Visibility, as expected, had a significant impact on SCM performance. According to the detailed result of measuring SCM performance, Visibility has significant impact on Quality Performance, Cost Performance, Delivery Performance.

• IE interfaces
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• v.18 no.3
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.658-663
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.103-111
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• 2013

As the demand-oriented management has been getting important in Supply Chain Management (SCM), various forecasting methods have been suggested including regression analyses. However, dependency structures among variables have been captured by a correlation coefficient, only. It results in inaccurate demand predictions. This paper suggests a new and effective forecasting modeling framework using student's t-copula function. In order to show overall modeling procedures framework, heavy tail typed numerical data and its copula estimations are provided. The suggested methodology can contribute to decrease the bullwhip effect and to stabilize volatile environment in a supply chain network.

• The Journal of Society for e-Business Studies
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• v.7 no.3
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• pp.121-137
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• 2002

SCM(Supply Chain Management) have been introduced in many companies for integrated management and improvement of business process. Recently, as internet and e-business concept are spread globally, the SCM concept is expanded from one internal company process to inter-company process, it makes a Global SCM concept. In this paper, we discuss the implementation of the Global SCM concept using e-business infrastructure, and propose SCM portal models. Four types of the SCM portal model are discussed, which are forecasting information sharing model, e-Marketplace-typed model, collaboration model and logistics information sharing model. The major concept of the SCM portal is to share information of supply chain process, it provide merits of scale to company. The result of this paper can be summarized as follows : First, the information sharing is very useful in the Global SCM. Second, the e-business infrastructure, especially e-Marketplace can be usefully used for implementation of SCM portal. Third, the M2M(Market to Market) function of e-Marketplace is a major function for implementing SCM portal.

• IE interfaces
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• v.17 no.1
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• pp.84-92
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• 2004

We have discussed the importance of collaborative forecasting and the difficulties that can arise during its implementation. We have also proposed the detail process of collaborative forecasting and the system requirement on each step of the process so that the proposed detail process can be easily applied to real life scenario. Lastly, we have talked about a case study of a telecommunication equipment manufacturer that has implemented the proposed collaborative forecasting process that verify the feasibility of the process.

• Asia pacific journal of information systems
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• v.8 no.3
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• pp.51-73
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• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• Journal of the Korea Safety Management & Science
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• v.3 no.1
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• pp.45-56
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• 2001

In the supply chain, The ATP function doesn't only give customers to confirmation of delivery. It can be used by the core function with ATP rule that can reconcile supplies and demands on the supply chain. Therefore We can acquire the conformation about accuracy on the due date of supplier by using the ATP function of management about real and concurrent access on the supply chain, also can decide the affect about product availability due to forecasting or customer's orders through the ATP. This study analyze the data concerned with ATP and define the necessity on a SCM solution. Under the these environments, after defining the ATP rule that can improve the customer value and data flow related the CRM, we propose the advanced ATP model that proposes the method and classification system that can flexibly aggregate the ATP data with ATP rule on the supply chain.

• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.