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Improved supply chain management based on hybrid demand forecasts
Luis Aburto
, Richard Weber
Faculty of Engineering and Science
Research output
:
Contribution to journal
›
Article
›
peer-review
245
Scopus citations
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Keyphrases
Hybrid Demand
100%
Replenishment Systems
100%
Demand Forecast
100%
Supply Chain Management
100%
Chilean
50%
Neural Network
50%
Network Model
50%
Prediction Accuracy
50%
Supermarkets
50%
Forecasting Techniques
50%
Autoregressive Integrated Moving Average (ARIMA)
50%
Sales Failure
50%
Demand Forecasting
50%
Hybrid Intelligent Systems
50%
Inventory Level
50%
Hybrid System
50%
Future Demand
50%
INIS
management
100%
demand
100%
supply
100%
chains
100%
hybrids
100%
forecasting
75%
levels
25%
comparative evaluations
25%
accuracy
25%
neural networks
25%
inventories
25%
solutions
25%
sales
25%
failures
25%
hybrid systems
25%
Social Sciences
Supply Chain Management
100%
Demand Forecast
100%
Sales
50%
Neural Network
50%
Computer Science
Supply Chain
100%
Neural Network
50%
Moving Average
50%
Inventory Level
50%
forecasting accuracy
50%
Economics, Econometrics and Finance
Inventory Model
100%
ARMA Model
50%