머신러닝 기법을 활용한 서비스 산업의 일단위 수요예측 연구

• 영문명: A Study on Daily Demand Forecasting in the Service Industry Using Machine Learning Techniques
• 발행기관: 한국무역연구원
• 저자명: 유성용(Sung-Yong Yoo) 박민영(Min-Young Park)
• 간행물 정보: 『무역연구』제20권 제5호, 495~509쪽, 전체 15쪽
• 주제분류: 경제경영 > 무역학
• 파일형태: PDF
• 발행일자: 2024.10.31

국문 초록

Purpose - As the volume of data has increased due to advancements in supply chain management and IT technology, there is growing interest in demand forecasting using various types of time series data. This paper studies daily demand forecasting by comparing traditional time series prediction methods with machine learning-based prediction techniques to improve forecasting accuracy. Design/Methodology/Approach - Using daily sales data from Bakery A’s hierarchy ERP system, a Hidden Markov Model (HMM) algorithm was applied to estimate the number of hidden state and perform daily demand forecasting. The accuracy of the HMM-based forecasts was then compared and analyzed against existing time series prediction methods. Findings - The application of the Hidden Markov Model (HMM) at the end-item level demonstrated higher forecasting accuracy when compared to traditional time series methods across all forecast horizons. HMM proved to be highly effective in maintaining both efficiency and accuracy in daily demand forecasting, making it a suitable method for companies that require daily operational planning. Research Implications - This study reviews existing research on time series prediction methods and introduces a new machine learning-based approach. By proposing an enhanced Hidden Markov Model, this research offers a method that can predict future daily demand by inferring state changes within data. Further research is needed to explore daily demand forecasting models in similar industries or service sectors, with a focus on comparing and evaluating applicability.

목차

Ⅰ. 서론

Ⅱ. 선행연구 및 이론적 고찰

Ⅲ. 연구방법론

Ⅳ. 실증분석 결과

Ⅴ. 결론

References

키워드

ARIMA Demand Forecasting Hidden Markov Model Holt-winters Exponential Smoothing Supply Chain Management