• Journal of Digital Convergence
• /
• v.18 no.2
• /
• pp.205-211
• /
• 2020

The method of predicting the future may be predicted by technical characteristics or technical performance. Therefore, technology prediction is used in the field of strategic research that can produce economic and social benefits. In this study, we predicted the future market through the study of how to predict the future with these technical characteristics. The future prediction method was studied through the prediction of the time when the market occupied according to the demand of special product. For forecasting market demand, we proposed the future forecasting model through comparison of representative quantitative analysis methods such as CAGR model, BASS model, Logistic model and Gompertz Growth Curve. This study combines Rogers' theory of innovation diffusion to predict when products will spread to the market. As a result of the research, we developed a methodology to predict when a particular product will mature in the future market through the spread of various factors for the special product to occupy the market. However, there are limitations in reducing errors in expert judgment to predict the market.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.562-563
• /
• 2008

경쟁적 전력시장에서는 이익주체의 다양화로 송전망확장은 개별 시장참여자의 경제적 편익에 큰 영향을 미칠 수 있다. 송전설비 투자계획은 미래 전력 시장 및 계통의 예측을 바탕으로 하기 때문에 예측의 불확실성에서 발생하는 설비투자의 과잉.과소투자의 최소화 방안이 필요하다. 따라서 본 논문은 송전망확장사업의 경제적 가치를 평가하는 방법에 대해 연구였고 미래 시장 및 계통의 불확실성을 반영하기 위해 전력수요와 연료가격의 과거 예측오차의 표준편차를 이용한 예측값의 확률밀도함수의 모델링 방법을 이용한 송전망확장의 경제성 평가 방법을 제시한다. Monte Carlo Sampling을 이용, 송전망확장으로 인한 시장참여자의 경제적 편익 변화의 기대값과 편익 변화의 범위를 산출함으로써 설비투자의 리스크와 잠재효과에 대해 분석한다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10a
• /
• pp.508-510
• /
• 2003

주식 가격의 결정은 시장 내 수요와 공급에 의해서 결정되며, 가격 변동은 일정한 패턴으로 움직인다고 가정한다. 이러한 패턴을 찾아내어 주식가격의 변동을 예측하는 분석 방법을 기술적 분석이라 한다. 기술적 분석에서는 수요.공급의 변화에 의해 추세가 변동되고, 모든 형태의 주가모형은 반복하려는 경향을 보인다고 가정한다. 이러한 가정하에 본 논문에서는 한국주가지수 200의 과거지수와 거래량을 분석하고, 일정한 패턴을 이용하여 미래의 지수를 예측하는 방법을 연구하였다.

• The Journal of the Korea Contents Association
• /
• v.20 no.3
• /
• pp.209-222
• /
• 2020

The purpose of this paper is to predict the future changes of housing through the Delphi technique. The targets to predict were set by housing type, housing space, housing demand, and architectural technology. The results were as follows: ① The influences of social and value perspectives on the change of housing type, space, and demand would be high, on the other hands, the influence of political perspective would be low. ② In terms of housing type, the increase in demand for downsizing housing for high-rise buildings and the possibility of realizing remote medical support services and homecare using big data are highly predicted. That is, ③ it is anticipated that IoTs will have a significant influences on future housing changes, and ④ enactment of co-housing and related laws by the sharing economy, services for maintenance through the supply of high-rise and high-density homes, housing support for residents, and advanced lease markets by developed architectural technology are expected as anticipated forms of future housing.

• Proceedings of the KIEE Conference
• /
• 2006.07a
• /
• pp.349-350
• /
• 2006

정부의 배전분할 유보결정에 따라 전력거래소에서는 현행 발전경쟁시장의 효율성 향상을 위한 많은 사업을 추진하고 있다. 그중 우선적으로 추진하였던 사업이 도매전력시장을 대비해 구축한 시장운영시스템(MOS; Market Operation System)을 현행 계통운영을 담당하고 있는 급전 자동화시스템(EMS; Energy Management System)과의 연계활동이다. 이는 시장운영시스템(MOS)에 내재되어 있는 최첨단 기능중에서 일부를 현행 전력시장에 적용함으로써 계통운영의 효율성 향상뿐만 아니라 계통운영기술을 선진화하는 것이다. 특히, 기존 급전시스템에 비해 가장 두드러지는 점은 발전기들에 대한 에너지 및 예비력 급전계획을 사전적으로 5분단위로 최적화하여 수립하고 이를 발전기들에게 송신함으로써 경제적이며 안정적인 전력계통운영을 가능하게 해 줄 수 있는 것이다. 이를 가능하게 하기 위해서는 바로 정확한 미래 수요수준의 결정이 필수불가결한 요소이다. 따라서 본 논문에서는 MOS-EMS 연계활용에서의 실시간 수요예측의 중요성과 기존 제작사의 알고리즘과 국내 전력수요특성간의 불일치점 그리고 이를 해결하고자 모색했던 방안들에 대하여 설명하고자 한다.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.10a
• /
• pp.139-145
• /
• 1996

A forecast on the past output data sets of small bar steels is very important information to make a decision on the future production quantities. In many cases, however, it has been mainly determined by experience (or rule of thumb). In this paper, past basic data sets of each small bar steels are statistically analyzed by some graphical and statistical forecasting methods. This work is mainly done by SAS. Among various quantitative forecasting methods in SAS, STEPAR forecasting method was best performed to the above data sets. By the method, the future production quantities of each small bar steels are forecasted. As a result of this statistical analysis, 95% confidence intervals for future forecast quantities are very wide. To improve this problem, a suitable systematic database system, integrated management system of demand-production-inventory and integrated computer system should be required.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.10
• /
• pp.2923-2934
• /
• 2009

This paper presents study on technological forecasting of Next-Generation Display technology. Next-Generation Display technology is one of the emerging technologies lately. So databases on patent documents of this technology were analyzed first. And patent analysis was performed for finding out present technology trend. And the forecast for this technology was made by growth curves which were obtained from forecast models using patent documents. In previous study, Gompertz, Logistic, Bass were used for forecasting diffusion of demand in market. Gompertz, Logistic models which were often used for technological forecasting, too. So, two models were applied in this study. But Gompertz, Logistic models only consider internal effect of diffusion. And it is difficult to estimate maximum value of growth in two models. So, Bass model which considers both internal effect and external effect of diffusion was also applied. And maximum value of growth in Gompertz, Logistic models was estimated by Bass model.

• The Journal of the Korea Contents Association
• /
• v.21 no.11
• /
• pp.203-212
• /
• 2021

The high-tech industry, a highly knowledge-intensive industry based on advanced technologies such as electronics, new materials, and IT, is developing rapidly centering on the semiconductor, display, and battery fields. The market size of this industry is continuously increasing, and various challenges are coming forward due to various factors such as changes in the market, changes in demand, and the requirements of the clients. Many strategies are being implemented to advance the start-up time of factories, such as fast-track construction and basic line construction. Therefore, construction of high-tech factory is required to respond to various types of construction plans and early decision making, and an accurate and reliable method of calculating construction costs is needed. In this study the existing construction type was classified into the overall line configuration considering the total production of the factory, and a basic line configuration for quick production start-up. The correction ratio/value for each detailed construction required to calculate the construction cost of the basic line configuration type was derived. Finally, reliability and accuracy were verified by applying the correction ratio suggested in this study to a new high-tech factory construction project.

• Journal of Korea Port Economic Association
• /
• v.26 no.1
• /
• pp.20-42
• /
• 2010

This study is aimed at evaluating transshipment competitiveness of Incheon New Port which will open in 2013. For this reason, we used Conjoint Analysis(CA) for a methodology of this research as CA had been frequently adopted for empirical analysis of new container terminal in previous studies. We have provided the questionnaires to the stake holders of the port such as experts in Port Authority(PA), logistics companies, and terminal operators. The result showed that transshipment competitiveness of Incheon New Port was subordinate to transshipment costs and port costs of Busan and Shanghai. Overall, we hope this study could help draw up the policies on a New Port that will attract transshipment cargoes and set up marketing plan for an early settlement.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.1-23
• /
• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.