Application of machine learning techniques for supply chain demand forecasting

2008 ◽  
Vol 184 (3) ◽  
pp. 1140-1154 ◽  
Author(s):  
Real Carbonneau ◽  
Kevin Laframboise ◽  
Rustam Vahidov
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Machine Learning Techniques ◽  
Learning Techniques

Application of Machine Learning Techniques in Railway Demand Forecasting

2021 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Miguel F. Anjos ◽  
Neda Etebarialamdari ◽  
Gilles Savard
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Survey on Machine Learning Techniques for Supply Chain Management

2021 ◽  
pp. 24-38
Author(s):  
Amal F.Abd El .. ◽  
◽  
◽  
◽  
Shereen Zaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Management Processes ◽  
Learning Techniques ◽  
Hidden Patterns ◽  
Learning Types ◽  
Discontinuous Data

Machine learning arose from the increasing ability of machines to handle large amounts of data over the last two decades, and some machines could also identify hidden patterns and complicated associations that humans couldn't, allowing them to make rational and precise decisions, especially for disruptive and discontinuous data. In several areas of decision-making, machines could produce more reliable outcomes than humans and have already begun to replace them. Machine learning, which is widely recognized as a breakthrough technology, has recently made significant progress in improving supply chain management processes and efficiency. From planning to delivery, machine learning may be applied at different stages of the supply chain management process. Machine learning types are supervised, unsupervised, reinforcement. Each type has many tools which are discussed below in detail. This paper presents a detailed survey on machine learning techniques for supply chain management including supply chain and supply chain management interpretation, machine learning definition, its types, and some algorithms that belong to it.

Data-driven short-term natural gas demand forecasting with machine learning techniques

2021 ◽  
pp. 108979
Author(s):  
Vinayak Sharma ◽  
Ümit Cali ◽  
Bhav Sardana ◽  
Murat Kuzlu ◽  
Dishant Banga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Gas ◽  
Demand Forecasting ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Learning Techniques ◽  
Natural Gas Demand

scholarly journals Short-term water demand forecasting using machine learning techniques

2018 ◽  
Vol 20 (6) ◽  
pp. 1343-1366 ◽  
Author(s):  
A. Antunes ◽  
A. Andrade-Campos ◽  
A. Sardinha-Lourenço ◽  
M. S. Oliveira
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Maximum Level ◽  
Water Utilities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Learning Techniques

Abstract Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

scholarly journals Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

2022 ◽  
Vol 31 (3) ◽  
pp. 1671-1687
Author(s):  
Naeem Ali ◽  
Taher M. Ghazal ◽  
Alia Ahmed ◽  
Sagheer Abbas ◽  
M. A. Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Machine Learning Techniques ◽  
Supply Chain Collaboration ◽  
Learning Techniques
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