Big data analytics in supply chain management: A state-of-the-art literature review
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., 2015a, 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).
Among those 5Vs, veracity and value, which represent the rigorousness of Big Data Analytics (BDA), are particularly important because without data analysis, other BD processing aspects such as collection, storage, and management would not create much value (Huang et al., 2015, Chen and Zhang, 2014b, Babiceanu and Seker, 2016).
BDA involves the use of advanced analytics techniques to extract valuable knowledge from vast amounts of data, facilitating data-driven decision-making (Tsai et al., 2015). Supply chain management (SCM) has been extensively applying a large variety of technologies, such as sensors, barcodes, RFID, IoT, etc. to integrate and coordinate every linkage of the chain. Therefore, not surprisingly, supply chains (SCs) have been revolutionized by BDA and its application in SCM has been reported in a number of special issues (Wamba et al., 2015a, Gunasekaran et al., 2016, Wamba et al., 2017). Indeed, BDA is reported to be an emerging SC game changer (Fawcett and Waller, 2014; Dubey et al., 2016), enabling companies to excel in the current fast-paced and ever-changing market environment. Empirical evidence demonstrates multiple advantages of BDA in SCM including reduced operational costs, improved SC agility, and increased customer satisfaction (Sheffi, 2015, Ramanathan et al., 2017) and consequently, there is an increasing interest in identifying a specific skill set for SCM data scientists (Waller and Fawcett, 2013, Schoenherr and Speier‐Pero, 2015). Although the expectation of BDA adoption to enhance SC performance is rather high, a recent report found that only 17% of enterprises have implemented BDA in one or more SC function (Wang et al., 2016a). The main reasons for low uptake are the lack of understanding of how it can be implemented, the inability to identify suitable data (Schoenherr and Speier-Pero, 2015), low acceptance, routinization and assimilation of BDA by organizations and SC partners (Gunasekaran et al., 2017), and data security issues (Fawcett and Waller, 2014; Dubey et al., 2016). This motivates our exploration of the existing research and the applications of BDA in SCM.
There are a number of literature reviews of BDA applications in the SCM context, but most of them tend to focus on a specific operational function of the SC. For instance, O'Donovan et al., 2015, Dutta and Bose, 2015, and Babiceanu and Seker (2016) conducted literature reviews on material flow in manufacturing operations while Wamba et al. (2015)b focused on logistics applications. A literature review that takes a broad perspective of SC as a whole and cross-maps with BDA techniques in SCM is yet scarce (Olson, 2015, Addo-Tenkorang and Helo, 2016, Hazen et al., 2016, Wang et al., 2016b, Mishra et al., 2016). Our literature review develops a classification framework, which identifies and connects SC functions with levels of analytics, BDA models and techniques. Our review scope aims to provide a full picture of where and how BDA has been applied in SCM, by mapping BDA models and techniques to SC functions.
To obtain the objective, the literature review attempts to address the following 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 paper is structured as follows. Section 2 describes the review methodology used for the literature search and delimitation. It develops the classification framework for this review. Section 3 undertakes a review in line with the developed framework. Section 4 discusses the findings. Section 5 provides the avenues for future research. Section 6 concludes the review and presents the research limitations.
Section snippets
Review methodology
To address the research questions, the review methodology is based on the content analysis approach proposed by Mayring (2008). This approach has been adopted by a number of highly cited review papers in SCM literature, such as Seuring and Müller, 2008, Seuring, 2013, and Govindan et al. (2015). In particular, the review is systemically conducted in accordance with the four-step iterative process:
- -
Step 1: Material collection, which entails a structured process of search and delimitation of
Reviewing by SC functions
The distribution of BDA studies in each SC function is shown in Fig. 5 and the classification of the examined literature by key application topics is summarized in Table 2.
As seen in Fig. 5, logistics/transportation and manufacturing have extremely dominated over the current literature on this topic, together taking up more than half of the publications. Research on the other three fundamental SC functions – warehousing, demand management and procurement – are limited, but relatively well
In what areas of SCM is BDA being applied?
In logistics, transportation management prevails, with particular focus on three fundamental functions of ITS: routing optimization, real-time traffic operation monitoring, and proactive safety management. It is noteworthy that the BDA-driven routing problem is mainly studied in static environments based on historical databases (Ehmke et al., 2016, Zhang et al., 2016), while the use of BDA for dynamic routing optimization in real-time contexts is only conceptualized in some theoretical
Future direction
The findings discussed above suggest some future directions to capitalize the research development of BDA applications in the SCM context.
Conclusion
Based on the content analysis methodology of Mayring (2008), this literature review examined 88 journal papers to provide a full picture of where and how BDA has been applied within the SCM context. In particular, we developed a classification framework based on 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? And, finally, (4) what BDA techniques are employed to
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