Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities
Abstract Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BD...
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Online Access: | http://link.springer.com/article/10.1186/s40537-020-00329-2 |
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doaj-1dcc322f4f62461a894fd63ac48142e92020-11-25T03:09:18ZengSpringerOpenJournal of Big Data2196-11152020-07-017112210.1186/s40537-020-00329-2Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunitiesMahya Seyedan0Fereshteh Mafakheri1Concordia Institute for Information Systems Engineering (CIISE), Concordia UniversityConcordia Institute for Information Systems Engineering (CIISE), Concordia UniversityAbstract Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.http://link.springer.com/article/10.1186/s40537-020-00329-2Demand forecastingSupply chain managementClosed-loop supply chainsBig data analyticsMachine-learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mahya Seyedan Fereshteh Mafakheri |
spellingShingle |
Mahya Seyedan Fereshteh Mafakheri Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities Journal of Big Data Demand forecasting Supply chain management Closed-loop supply chains Big data analytics Machine-learning |
author_facet |
Mahya Seyedan Fereshteh Mafakheri |
author_sort |
Mahya Seyedan |
title |
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities |
title_short |
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities |
title_full |
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities |
title_fullStr |
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities |
title_full_unstemmed |
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities |
title_sort |
predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities |
publisher |
SpringerOpen |
series |
Journal of Big Data |
issn |
2196-1115 |
publishDate |
2020-07-01 |
description |
Abstract Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research. |
topic |
Demand forecasting Supply chain management Closed-loop supply chains Big data analytics Machine-learning |
url |
http://link.springer.com/article/10.1186/s40537-020-00329-2 |
work_keys_str_mv |
AT mahyaseyedan predictivebigdataanalyticsforsupplychaindemandforecastingmethodsapplicationsandresearchopportunities AT fereshtehmafakheri predictivebigdataanalyticsforsupplychaindemandforecastingmethodsapplicationsandresearchopportunities |
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