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.

1. 서 론
 2. 간헐적 수요를 위한 단기예측방법
 3. 이항가중 지수평활 방법
  3.1 이항가중 지수평활 방법
  3.2 진부화를 고려한 이항가중 지수평활 방법
  3.3 이항가중 지수평활 방법의 편향
 4. 모의실험 환경
  4.1 간헐적 수요
  4.2 평가척도
  4.3 초기 예측값과 평활계수 설정
 5. 평가결과
  5.1 최우수 성능 비중
  5.2. 수요의 특성에 따른 영향
  5.3 평활계수 최적화의 영향
  5.4 진부화의 영향
 6. 결 론
 References

• 하정훈(홍익대학교 정보컴퓨터공학부 산업공학전공) | Chunghun Ha (School of Information & Computer Engineering, Hongik University) Corresponding Author