• Management Science and Financial Engineering
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• v.11 no.3
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• pp.125-136
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• 2005

As the importance of time-based competition is increasing, information systems for supporting the immediate decision making is strongly required. Especially high -tech product firms are under extreme pressure of rapid response to the demand side due to relatively short life cycle of the product. Therefore, the objective of our research is proposing a framework of estimating demand curve based on e-auction data, which is extremely easy to access and well reflect the limited demand curve in that channel. Firstly, we identify the advantages of using e-auction data for full demand curve estimation and then verify it using Agent-Eased-Modeling and Tobin's censored regression model.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.497-514
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• 2023

Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

• Journal of Korean Society of Rural Planning
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• v.19 no.4
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• pp.33-46
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• 2013

The present study aims to provide an analytical framework for achieving aid and development effectiveness of agricultural cooperation with a demand-oriented perspective. This paper pays particular attention to categorize the stages of agricultural development of African recipients to identify demands for agricultural aid of the categorized groups. To do so, first of all, it establishes theoretical background to apply the demand-oriented concept and utilize the phase of agricultural development as an alternative for aid and development effectiveness. On the basis of the theoretical robustness, it conducts a series of analyses to categorize the African recipients by the development stages, incorporating factor analysis, cluster analysis and comparison between the present-future agricultural development levels. The findings propose analysis indicators for phase of agricultural development and clustered results including 18 countries of KAFACI members and priority countries in Africa. In addition to the practical application of the results, the methodological flow can be used as steps for sketching a future roadmap to construct the demand-oriented ODA(Official Development Assistance) plan. This paper also offers implications regarding ODA strategy of Korea in response to the phase of agricultural development and the aid demands.

• Proceedings of the Korea Association of Information Systems Conference
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• 1997.10a
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• pp.113-125
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• 1997

The objective of this research is to construct and build a software platform to enable collaboration among enterprise headquarters, product designers, software engineers, manufacturing plants, and suppliers which are located at different remote locations via internet. In specific, agent technology is adopted as a software vehicle to automate demand as a software vehicle to automate demand and supply process in the internet environment. Agents are programs that act an behalf of their human users to perform laborious tasks such as information locating, accessing, filtering, integrating, adapting and resolving inconsistencies. Global competition is forcing the present day industry to produce high quality product more fast and inexpensively. In Korea, most labor-intensive industries have moved to China and other Asian countries for cost reduction. The need for fast information exchange has increased among the remote locations for the cooperation and coordination. In this research, a virtual global manufacturing system will be constructed that distributes production schedule among remote places, acts as a bridge between the headquarters and manufacturing plants, distributes tasks and collates different solutions between demand and supply using agent. The external communication protocol takes HTML format, internal message handling requires SGML for document exchange, and KQML for agent implementation. The expected benefits will be : reduced cost of real-time information exchange, realization of global manufacturing environment, the maximum utilization of internet for the enterprise data exchange.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.245-248
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• 1998

In this paper, as a forecasting method, the market survey for forecasting demand is introduced for the estimation of subscriber line demand in the optical access networks. The market survey method for the new multimedia services is attempted to collect information directly from customers using the questionnaires for home-users and business-users in local loops. Analysis rationale of questionnaires is suggested to estimate the number of subscriber lines. Also, two measures are presented to quantify the credibility on survey responses; one is the probability that the customer will use the multimedia services and the other is the rate that the subscriber line demand will be actually realized. The former measure is calculated based on the information on customers and the Logit analysis. The latter is obtained by the degree of customer's knowledge about specific services and the customer's willingness to use the services. Based on the values of two measures, the number of subscriber line demand can be developed for installing the optical access networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.495-498
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• 2007

Recently, as REID Tag and Reader has been attached to, and wireless internet has been added to a mobile phone, the commercialization of Mobile RFID Service to obtain necessary information on daily life and use various applications by using mobile communication infra is drawing nearer. A new returns by Mobile RFID Service can be expected, however, the exact demand forecasting for the Mobile RFID Service is essential to induce mass investment from related communication enterprises. This study tries to get a foothold in enlarging the investment from related communication enterprises through demand forecasting for the Mobile RFID Service and to be helpful to the decision on their investment by predicting the demand on the service various Mobile RFID Service Models.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.73-80
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• 2009

It is difficult that expertise human supply and demand for industry requires by imbalance of industry necessity human and profession organs of education's Skill Mismatch. Industry can prove productivity though reeducate school graduation person in spot and master correct technology in industry special quality. This paper is research that accommodate Demand Education that industry requires and make out full text caution Curriculum Specializing Vocational High School in e-Business field. Analysis e-Business industrial classification and occupational classification. Analysis knowledge and technological level that require in industry about e-Business education and investigate and analyze the demand. Base industry, Support industry, Apply e-Business Curriculum that is examined by practical use industry to learning, Do to estimate satisfaction about Demand Education Curriculum of industry and confirm Success special quality with research and investigation and application wave. Suggested for e-Business Curriculum's basis model in this paper and school subject Curriculum. Wish to contribute in nation development through productivity elevation through e-Business education of industry request.

• Management Science and Financial Engineering
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• v.17 no.1
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• pp.45-63
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• 2011

The aim of this paper is to consider the process of supplying trained workers with knowledge and skills for upcoming business opportunities and the process of training apprentices to be prepared to meet future demands in an IT service firm. As the demand for new workers fluctuates, a firm should employ a buffer workforce such as apprentices or interns. However, as a result of rapid business development, the capacity of the buffer may be exceeded, thus requiring the company to recruit skilled workers from outside the firm. Therefore, it is important for a firm to map out a strategy for manpower planning so as to fulfill the demands of new business and minimize the operation costs related to training apprentices and recruiting experienced workers. First, this paper analyzes the supply and demand of workers for the IT service in a knowledge-intensive field. It then presents optimal human resource planning strategies via the familiar method of stochastic process. Also, we illustrate that our model is applied to the human resource planning of an information security service firm in South Korea.