원문정보
초록
영어
Demand forecasting is a crucial task for an online retail where has to manage daily fresh foods effectively. Failing in forecasting results loss of profitability because of incompetent inventory management. This study investigated the optimal performance of different forecasting models for a very short shelf-life product. Demand data of 13 perishable items with aging of 210 days were used for analysis. Our comparison results of four methods: Trivial Identity, Seasonal Naïve, Feed-Forward and Autoregressive Recurrent Neural Networks (DeepAR) reveals that DeepAR outperforms with the lowest MAPE. This study also suggests the managerial implications by employing coefficient of variation (CV) as demand variation indicators. Three classes: Low, Medium and High variation are introduced for classify 13 products into groups. Our analysis found that DeepAR is suitable for medium and high variations, while the low group can use any methods. With this approach, the case can gain benefit of better fill-rate performance.
목차
1. Introduction
2. Literature Review
2.1 Perishable Products
2.2 Demand Forecasting
2.3 Time-series Forecasting Technique
2.4 Performance Metrics
3. Method
3.1 Data preparation
3.2 Data Characteristics
3.3 Technique
3.4 Model configuration
4. Splitting Strategy
5. Result
5.1 Product Classification
6. Model Performance
7. Discussion
8. Conclusion
8.1 Financial Performance
8.2 Enhancing Service Level
References