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About Demand Forecasting in Supply Chain The Impact of D
This document discusses demand forecasting in supply chains when demand is volatile due to promotions. It analyzes 843 real demand time series with different levels of volatility measured by the coefficient of variation (CoV). The authors propose a hybrid model that decomposes demand into baseline and promotional components to more accurately forecast volatile demand series. They compare the performance of statistical and machine learning models like ARIMA, ETS, SVR and DLR across demand series with different CoV values. The authors aim to identify the most suitable forecasting model for different levels of demand volatility caused by promotions.
Detailed Information
| Author: | ['Oding'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 19 |
| Language: | English |
| Format: | |
| Price: | FREE |
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