Application of machine learning techniques for supply chain demand forecasting

DOI10.1016/J.EJOR.2006.12.004zbMath1141.90441OpenAlexW1988277750MaRDI QIDQ2384865

Kevin Laframboise, Rustam M. Vahidov, Réal A. Carbonneau

Publication date: 10 October 2007

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2006.12.004

zbMATH Keywords

neural networksforecastingsupport vector machinesbullwhip effectSupply chain management

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Management decision making, including multiple objectives (90B50) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (11)

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system ⋮ Easy, reliable method for mid-term demand forecasting based on the Bass model: a hybrid approach of NLS and OLS ⋮ A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment ⋮ On the value of shipment consolidation and machine learning techniques for the optimal design of a multimodal logistics network ⋮ Support vector regression for warranty claim forecasting ⋮ Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research ⋮ Distributional regression for demand forecasting in e-grocery ⋮ A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches ⋮ A data-driven newsvendor problem: from data to decision ⋮ A combination selection algorithm on forecasting ⋮ Iterative pre-conditioning for expediting the distributed gradient-descent method: the case of linear least-squares problem

Uses Software

• Matlab
• DYNAMO
• LS-SVMlab
• Neural Network Toolbox

Cites Work

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• Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand
• Improving supply-chain performance by sharing advance demand information
• Measuring and avoiding the bullwhip effect: A control theoretic approach
• Information systems in supply chain integration and management
• Agile supply chain capabilities: determinants of competitive objectives
• Information Distortion in a Supply Chain: The Bullwhip Effect
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• Noisy time series prediction using recurrent neural networks and grammatical inference

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