• IE interfaces
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• v.24 no.4
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• pp.373-378
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• 2011

High level of accuracy in forecasting heat demand of each district is required for operating and managing the district heating efficiently. Heat demand has a close connection with the demands of the previous days and the temperature, general demand forecasting methods may be used forecast. However, there are some exceptional situations to apply general methods such as the exceptional low demand in weekends or vacation period. We introduce a new method to forecast the heat demand to overcome these situations, using the linearities between the demand and some other factors. Our method uses the temperature and the past 7 days' demands as the factors which determine the future demand. The model consists of daily and hourly models which are multiple linear regression models. Appling these two models to historical data, we confirmed that our method can forecast the heat demand correctly with reasonable errors.

• Journal of Information Technology Services
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• v.7 no.2
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• pp.41-57
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• 2008

The development of service-based software and web-based application aims for providing user-demand service. On-demand software is emerging for same reason. Software delivery models like on-demand software is expected to change the software industry as an important technical revolution with the firm's strategy. Few research, however, has been done on the on-demand software. While much research on ASP and SaaS focused on firm' use, this study intends to examine the intention of using on-demand software targeting personal user. The intention to use of on-demand software was empirically examined in terms of quality, user characteristics, usefulness, easy of use, and security. Results shows that usefulness and easy of use are most significantly related to the intention of using on-demand software. Other factors are also found to affect the intention to use of on-demand software. This study contributes to improve the understanding and interest in on-demand software and it is expected to spread widely for individual user.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• Journal of the Korean Professional Engineers Association
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• v.44 no.2
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• pp.29-33
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• 2011

In recent years, maximum electric power demand has been increasing steadily. But, Electric Power Supply & Demand problem is occurring due to lack of electric power reserve ratio caused by electric power peak. For this reason, I investigated the current status of the Electric Power Supply & Demand and established Electric Power Supply & Demand and established Electric Power Supply & Demand measures. I will expect that this paper will be contributed balanced and stable Electric Power Supply & Demand management.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1282-1289
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• 2011

The rail passenger demand for the railroad operations required a short-term demand rather than a long-term demand. The rail passenger demand can be classified according to the purpose. First, the rail passenger demand will be use to the restructure of line planning on the current operating line. Second, the rail passenger demand will be use to the line planning on the new line and purchasing the train vehicles. The objective of study is to analyze the travel behavior of rail passenger for modeling of short-term demand forecasting. The scope of research is the passenger of KTX. The travel behavior was analyzed the daily trips, origin/destination trips for KTX passenger using the ANOVA and the clustering analysis. The results of analysis provide the directions of the short-term demand forecasting model.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.103-117
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• 1988

Replacement demand plays an important role to forecast the total demand of durable goods, while most of the diffusion models deal with only adoption data, namely initial purchase demand. This paper presents replacement demand forecasting models incorporating repurchase rate, multi-ownership, and dynamic product life to complement the existing diffusion models. The performance of replacement demand forecasting models are analyzed and practical guidelines for the application of the models are suggested when life distribution data or adoption data are not available.

• The Journal of Fisheries Business Administration
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• v.47 no.4
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• pp.57-63
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• 2016

The purpose of this study was to raise some questions about the supply and demand statistics of fisheries products and to find implications for food supply and demand. There are three problems in the statistics of fisheries supply and demand. First, it is a structural problem of supply and demand statistics. Supply and demand statistics are not accurate because the feed, the amount of loss, and the waste rate are not surveyed. Second, the amount of fish used as a moist pellet is missing. Third, although some of the seaweed and kelp production is used as abalone feed, it is not classified as feed. Taking these results into consideration, at least 300,000 tons should be classified as feed for fisheries supply and demand statistics. As mentioned above, the current statistics on the supply and demand of fisheries are incomplete and structural improvement is needed.

• Korean Management Science Review
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• v.25 no.1
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• pp.93-106
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• 2008

This study is focused on the problem of seat allocation for group travel demand in airlines. We first explain the characteristic of group demand and its seat allocation process. The group demand in air travel markets can be classified into two types : incentive and package groups. Allocating seats for group demand depends on the types of group demand and the relationship between airlines and travel agents. In this paper we concentrate on the package group demand and develop an optimization model for seat allocation on the demand to maximize the total revenue. With some assumptions on the demand distribution and the linear approximation technique, we develop a mixed IP model for solving our problem optimally. From the computational experiments, we can find our optimization model can be applied well for real-world application.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.37-40
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• 2002

It is to show the problems of the existing techniques to estimate agricultural water demand and to suggest the new methods considering the water demand for non-irrigated area and decrease of water loss in canal. It is to suggest the methods to improve the techniques for estimating agricultural water demand and to analyze the water demand and supply according to the facilities capacity. Until now, the concept of per the unit used to estimate agriculture water demand is useful to estimate demand, but is insufficient to cope with the variations of conditions in future. And the paddy area of government is not realistic against a trend of decrease. Water demand decrease is caused by constructions of irrigation facilities as constructing of irrigation canal, but application loss ratio is fixed. Increase of the water demand owing to the increase of the yield per the unit area is also the actual condition which is not considered. The guide-line must contain these contents for a demand estimate.