데이터 구조와 수요예측 정확도와의 상관관계

• 영문명: The Correlation of the Data Structure with Demand Forecasting Accuracy
• 발행기관: 한국무역연구원
• 저자명: 유성용(Sung-Yong Yoo) 박민영(Min-Young Park) 공윤택(Yoon-Taek Kong)
• 간행물 정보: 『무역연구』제16권 제5호, 467~483쪽, 전체 17쪽
• 주제분류: 경제경영 > 무역학
• 파일형태: PDF
• 발행일자: 2020.10.30

영문 초록

Purpose - This paper analyzes that the correlation between the hierarchy of the entity’s product sales data and the accuracy of demand forecasting was studied to reduce the entity’s demand forecast error. Because each hierarchy of sales data has different characteristics, we propose a new approach for hierarchical forecasting, which represents that each tier characteristic should be considered. Design/Methodology/Approach - Based on sales data from consumer goods A company and the hierarchy in ERP, the accuracy of hierarchical forecasting such as Top-down, Bottom-up and Middle-out approaches was compared, and the accuracy of time series analysis methods such as Exponential Smoothing and ARIMA was also done. Findings - In order to estimate the accuracy of the proposed hierarchy, the simulation study is performed. Results demonstrate that when comparing existing and optimal hierarchy, “optimal” middle-out approach significantly outperforms existing bottom-up one. Beside comparison with hierarchical forecasting, ARIMA model shows the lowest forecast error. Thus, we reveal that the optimal hierarchy taking into account the characteristics of each level could improve the accuracy of demand forecasting over the existing hierarchy. Research Implications - This study reviews the existing studies on identifying factors that intensifies forecasting inaccuracy and the adopted hierarchical approach method. Then we suggest a forecasting model considering the data with hierarchical features and structure.

목차

Ⅰ. 서론

Ⅱ. 이론적 배경

Ⅲ. 연구방법론

Ⅳ. 실증분석 결과

Ⅴ. 결론

References

키워드

ARIMA Exponential Smoothing Hierarchical Forecasting Supply Chain Management