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Optimal Machine Learning-Based Demand Prediction Model Performance Comparison by Demand Pattern
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수요 패턴 별 최적 머신러닝 수요예측 모델 성능 비교

논문 기본 정보

Type
Academic journal
Author
Won Hee Chung (세종대학교) Da Woon Jeong (세종대학교) Kang Ah Young (세종대학교) Yeong Hyeon Gu (세종대학교) Yoo Seong joon (세종대학교)
Journal
한국차세대컴퓨팅학회 한국차세대컴퓨팅학회 논문지 한국차세대컴퓨팅학회 논문지 제16권 제6호 KCI Accredited Journals
Published
2020.1
Pages
76 - 89 (14page)

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Optimal Machine Learning-Based Demand Prediction Model Performance Comparison by Demand Pattern
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Abstract· Keywords

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Demand forecasting is a way to manage resources by forecasting demands for products, so it has direct impacts on corporate resources and budget management. Based on these reasons, research on improving forecasting performances of demand forecasting models. In this research, 4 demand patterns for items were analyzed to improve demand prediction performance, and the optimal model was proposed. The data used to compare the performance were the demand data from each quarter for maintenance items for a T-50 aircraft of Republic of Korea air force. First, the demand patterns for the items adopted average demand interval(ADI) and coefficient of variation(CV) and were categorized into smooth, lumpy, intermittent, and erratic items. In this research, to compare the performance of demand forecasting models derived from different algorithms, 5 types of machine learning algorithms and 2 types of deep learning algorithms were used to construct demand forecasting models. In machine learning algorithms, there are ensemble learning such as random forest regression, adaboost, extra trees regression, bagging, gradient boosting regression and deep learning algorithm such as long-short term memory(LSTM) and deep neural network(DNN). We can confirm that item accuracy is 0.61% and quantity accuracy is 0.09% better than that of consistent models when the demand forecast results are derived by selecting models suitable for four types according to demand patterns. We expect that efficient demand management by experts will be achieved if the application of the proposed model.
#장비수요예측 #군집화 #수요패턴분석

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References (43)

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Hua, Z. S. / 2007 / A new approach of forecasting intermittent demand for spare parts inventories in the process industries / Journal of the Operational Research Society 58(1):52~61 google schola Moon, S. / 2012 / The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy —A case study / International Journal of Production Economics 140(2):794~802 google schola Ghobbar, A. A. / 2003 / Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model / Computers & Operations Research 30(14):2097~2114 google schola Esperon-Miguez, M. / 2013 / A review of integrated vehicle health management tools for legacy platforms: challenges and opportunities / Progress in Aerospace Sciences 56:19~34 google schola Structural Integrity Management of German Combat and Mission Aircraft google schola

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