Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

“…As with the case of research in the SCM area [6], an interdisciplinary perspective is needed to gain an increased understanding of BDA in SCM research. Though there exist quite a few literature reviews on BDA in SCM [7][8][9][10][11][12][13][14][15][16], there is a lack of an interdisciplinary focus in the prior works.…”

“…Moreover, it also reveals avenues for gaining additional insights by further reviewing the published research, and they are as follows. First, existing reviews either took technical (e.g., [7,15]) or organizational perspectives (e.g., [13,17]). However, Arunachalam et al [18] highlight that the challenges in using BDA in SCM involve both technical and organizational aspects.…”

“…This study also includes research publications from IEEE eXplore and ACM Digital Library to provide a more inclusive view of organizational and technical aspects in the BDA research in the SCM area. Third, some of the past review articles relied on specific areas in the use of big data analytics in supply chains (e.g., cold chain: [19]), or specific techniques of big data analytics in supply chain management (e.g., machine learning: [15]). Hence, a review of the research corpus into the whole big data analytics in SCM is warranted.…”

“…For example, Yudhistyra et al [13] found a wide variety of BDA techniques used in prior research, and the level of use of these techniques too differed between SCM and logistics journals publications. However, Aamer et al [15], in their review of big data analytics, especially machine learning in demand forecasting, found that though there were many techniques used and neural networks/artificial neural networks were used in almost half of the studies they reviewed. Hence, there are differences in the use of BDA techniques between SCM's sub-areas and within particular techniques in BDA.…”

Machine Learning Applications in Supply Chain Management: A Deep Learning Model Using an Optimized LSTM Network for Demand Forecasting

“…[41] are among the first researchers who have carried out the systemic research of ML works in the discipline of supply chain, they have shown by analyzing 123 articles published in five large databases during the period between (1998/01/01) and (2018/12/31) that 87% of the applications were through supervised learning and that among the 10 ML algorithms that were found to be frequently used the methods of neural networks and its variants occupy 54% of the global total of applications. In the same way, [42] established a systemic study of 79 articles on ML applications in the field of supply chain and more precisely demand forecasting, published during the last ten years and coming from five databases. The authors concluded that neural network-based applications represent 54% of the studies reviewed, 65% of which were applied in the industrial sector.…”