Review and analysis of artificial intelligence methods for demand forecasting in supply chain management-Reference-Cited by-同舟云学术

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Review and analysis of artificial intelligence methods for demand forecasting in supply chain management

Published:2022 Issue: Volume:107 Page:1126-1131
ISSN:2212-8271
Container-title:Procedia CIRP
language:en
Short-container-title:Procedia CIRP

Author:

Mediavilla Mario Angos,Dietrich Fabian,Palm Daniel

Publisher

Elsevier BV

Subject

General Medicine

Reference35 articles.

1. Grundzüge der Beschaffung, Produktion und Logistik;Kummer,2018

2. Chopra S, Meindl P. Supply Chain Management: Strategy, Planning, and Operation; 2016.

3. Hoeffding Tree Method with Feature Selection for Forecasting Daily Demand Orders;Alsanad;IEEE,2020

4. Demand Forecasting for an Automotive Company with Neural Network and Ensemble Classifiers Approaches;Bottani,2021

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1. To err is human: Bias salience can help overcome resistance to medical AI;Computers in Human Behavior;2024-12

2. A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting;Expert Systems with Applications;2024-11

3. The Impact Time Series Selected Characteristics on the Fuel Demand Forecasting Effectiveness Based on Autoregressive Models and Markov Chains;Energies;2024-08-21

4. A Systematic Literature Review on the Use of Simulators to Train Undergraduate Students in Logistics;2024 Portland International Conference on Management of Engineering and Technology (PICMET);2024-08-04

5. A New Hybrid Approach for Product Management in E-Commerce;Applied Sciences;2024-07-01

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