• 산업경영시스템학회지
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• 제41권1호
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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.

• 대한안전경영과학회지
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• 제18권4호
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• pp.161-169
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• 2016

In this research, we propose efficient demand forecasting scheme for intermittent demand. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods such as Croston method and Syntetos-Boylan approximation, then using these findings we propose the new demand forecasting method. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this end, we adopt combining forecasting method that utilizes unbiased forecasting methods such as simple exponential smoothing and simple moving average. Various simulation results show that the proposed forecasting method performed better than the existing forecasting methods.

• 대한산업공학회지
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• 제20권4호
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• pp.135-149
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• 1994

Demand forecasting is to estimate the demand of customers for products and services. Since the future is uncertain in nature, it is too difficult for us to predict exactly what will happen. Therefore, when the forecasting is performed upon the uncertain future, it is realistic to estimate the value of demand as an interval or a fuzzy number instead of a crisp number. In this paper, we propose a demand forecasting method using the standard back-propagation algorithm and then we extend the method to the case of interval inputs. Next, we demonstrate that the proposed method using the interval neural networks can represent the fuzziness of forecasting values as intervals. Last, we propose a demand forecasting method using the transformed input variables that can be obtained by taking account of the degree of influence between an input and an output.

• 대한안전경영과학회지
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• 제19권3호
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• pp.97-107
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• 2017

This research studies on the demand forecasting for service parts considering parts life cycle, that gets relatively less attentions in the field of forecasting. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods, then we propose the new demand forecasting method by using these findings and reinforcement leaning technique. Using simulation experiments, we proved that the proposed forecasting method is better than the existing methods under various experimental environments.

• Journal of Preventive Medicine and Public Health
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• 제16권1호
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• pp.113-120
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• 1983

The main objective of this case study is to develop demand forecasting model for durg inventory control in a university hospital. This study is based on the pertinent records during the period of January 1975 to August 1981 in the pharmacy and stock departments of the hospital. Through the analysis of the above records the author made some major findings as follows: 1. In A.B.C. classification, the biggest demand (A class) consists of 9 items which include 6 items of antibiotics. 2. Demand forecasting level of an index or discrepancy in A class drug compared with real demand for 6 months is average 30.4% by X-11 Arima method and 84.6% by Winter's method respectively. 3. After the correcting ty the number of bed, demand forecasting of drug compared with real demand for 6 months is average 23.1% by X-11 Arima method and 46.6% by Winter's method respectively.

• 제어로봇시스템학회논문지
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• 제12권7호
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• pp.631-636
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• 2006

In this paper, an algorithm of time series function forecasting using GMDH(group method of data handling) algorithm that gives more weight to the recent data is proposed. Traditional methods of GMDH forecasting gives same weights to the old and recent data, but by the point of view that the recent data is more important than the old data to forecast the future, an algorithm that makes the recent data contribute more to training is proposed for more accurate forecasting. The average error rate of electric power demand forecasting by the traditional GMDH algorithm which does not use data weighting algorithm is 0.9862 %, but as the result of applying the data weighting GMDH algorithm proposed in this paper to electric power forecasting demand the average error rate by the algorithm which uses data weighting algorithm and chooses the best data weighting rate is 0.688 %. Accordingly in forecasting the electric power demand by GMDH the proposed method can acquire the reduced error rate of 30.2 % compared to the traditional method.

• 대한임베디드공학회논문지
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• 제9권3호
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• 전기학회논문지P
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• 제64권2호
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• 디자인학연구
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• 제14권1호
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• pp.93-101
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• 2001

This study suggested how to apply it decision-making of product development rapidly by design evaluation process to objectify and the result to quantify with viewpoint of design evaluation sets to marketability. Coverage of this method limited to the evaluation stage of design concept. The procedure of study, first of all, referred to some type of design evaluation method and their feature. And next, referred to some kinds of demand forecasting for marketing. Above an, this study focused on the method of demand forecasting by buying intentions surveys proper to the marketability evaluation of new product design. On a case study, I had investigated preference survey and buying intentions surveys about the design proposal of "language master audio". I selected the best design proposal through the conjoint analysis and also investigated demand forecasting. First, on the basis of buying intentions surveys, choose population and had produced buying demand, awareness demand, potential demand. I could estimate some profit to take out expense and cost from the buying demand. This estimated profit is marketability judgement data of product design at the design concept stage and can be utilized to measurable data for decision-making of product development. Through the case study, this method could forecast a target demand, and even if it is some difference between real sales volume, but the case study could verified that this method is effective to the evaluation of marketability in case of completely new product got on the typical category and the product category could be set up the population clearly.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 춘계학술대회
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• pp.833-836
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• 2012

최대수요전력 관리 장치는 현재 사용전력을 모니터링하여 예측 전력을 계산해 수용가에서 설정한 목표전력을 초과하지 않게 부하를 제어하는 장치이다. 부하의 제어는 예측된 최대수요전력이 목표전력을 초과할 경우 경보를 발생하고 부하를 차단하는 방식을 사용하기 때문에 최대수요전력에 대한 정확한 예측이 중요하다. 전력 변동이 심한 수용가에서는 기존의 예측 방법을 사용할 경우 최대수요전력 관리가 안정적이지 못하다는 단점이 있다. 본 논문에서는 기존의 최대수요전력 예측 방법 및 지수평활방법을 살펴보고 칼만 필터를 사용한 예측 방법을 제안한다.