مقاله انگلیسی رایگان در مورد تجزیه و تحلیل کلان داده در مدیریت زنجیره تامین – الزویر 2018

1- Introduction

2- Review methodology

3- Material evaluation

4- Results and discussion

5- Future direction

6- Conclusion

References

Abstract

The rapidly growing interest from both academics and practitioners in the application of big data analytics (BDA) in supply chain management (SCM) has urged the need for review of up-to-date research development in order to develop a new agenda. This review responds to the call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structurally based on the content analysis method of Mayring (2008), addressing four research questions: (1) in what areas of SCM is BDA being applied? (2) At what level of analytics is BDA used in these SCM areas? (3) What types of BDA models are used in SCM? (4) What BDA techniques are employed to develop these models? The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

Introduction

With the fast-paced and far-reaching development of information and communication technologies (ICTs), big data (BD) has become an asset for organizations. BD has been characterized by 5Vs: volume, variety, velocity, veracity, and value (Wamba et al., 2015; Assunção et al., 2015; Emani et al., 2015). Volume refers to the magnitude of data, which has exponentially increased, posing a challenge to the capacity of existing storage devices (Chen and Zhang, 2014). Variety refers to the fact that data can be generated from heterogeneous sources, for example sensors, Internet of things (IoT), mobile devices, online social networks, etc., in structured, semi-structured, and unstructured formats (Tan et al., 2015). Velocity refers to the speed of data generation and delivery, which can be processed in batch, real-time, nearly real-time, or streamlines (Assunção et al., 2015). Veracity stresses the importance of data quality and level of trust due to the concern that many data sources (e.g. social networking sites) inherently contain a certain degree of uncertainty and unreliability (Gandomi and Haider, 2015; IBM, 2012; White, 2012). Finally, Value refers to the process of revealing underexploited values from BD to support decision-making (IDC, 2012; Oracle, 2012).