Big data and predictive analytics applications in supply chain management

With recent revolutions in technology, data are generated much faster and are bigger than ever before (Duan & Xiong, 2015). Big data is characterized by three Vs: volume, velocity, and variety (Zhou, Chawla, Jin, & Williams, 2014). On the basis of the fundamentals of data science, we can argue that data analytics benefits from a large volume of data. Statistical reliability tends to increase with increases in the volume of data (i.e., population size increases). In addition, predictive methods with a greater number of factors have better explanatory power than ones with few factors. Velocity refers to the rate at which data are generated. Today, due to online sales, smartphones, social networks, and sensory devices, the flow of information has significantly increased. Variety refers to the different types of data, such as unstructured data, semi-structured data, and structured data. Hence, we can argue that big data has immense potential to contribute to predictive analytics in two ways: high reliability and high explanatory power. Duan and Xiong (2015) have noted that BDPA has immense potential to revolutionize existing supply chains. BDPA can be extensively used for improving supply chain performance by improving visibility, which is identified as one of the most important organizational capabilities to improve organizational performance (Barratt and Oke, 2007) and to improve resilience and robustness (Brandon-Jones et al., 2014). Columbus (2015) characterizes BDPA as a capability that generates cost savings for SCM processes and contributes to the competitiveness of a firm. Other scholars underline the importance of BDPA for improving organizational performance (OP) (Schoenherr & Speier-Pero, 2015), leveraging decision-making (Bose, 2006), and transforming the supply chain (Jeyraj et al., 2006; Waller & Fawcett, 2013). McGuire, Manyika, and Chui (2012) further argue that innovative firms seek to beat the competition by finding new ways to leverage BDPA for next-generation products and services, increasing information transparency and decision-making effectiveness via data digitization and accessibility, and precisely segmenting their customer base according to the ‘who,’ ‘what,’ ‘when,’ and ‘where’ for various products and services. Therefore, BDPA assists in achieving higher levels of performance (Waller & Fawcett, 2013). Waller and Fawcett (2013) argue that BDPA implies two perspectives: big data, which is characterized by velocity, volume, and variety; and predictive analytics, which focuses on using data to predict the outcome. Hence, the integration of data science and predictive techniques to improve decision-making capabilities to address the existing complexity in the supply chain requires different skill sets (Wang, Gunasekaran, Ngai, & Papadopoulos, 2016). The existing debate surrounding BDPA is more focused on different types of data and the use of predictive techniques to improve decision-making. However, Waller and Fawcett (2013) argue that, in the absence of domain knowledge, data scientists often fail due to limited domain skills. Gunasekaran et al. (2016) argue that BDPA is one of the organizational capabilities that may improve supply chain performance and organizational performance. Hence, using resource-based view logic, we may argue that BDPA is one of the organizational capabilities (Gupta & George, 2016) that may help an organization gain sustainable competitive advantage. While the literature has acknowledged the importance of BDPA (Davenport, 2013; Schoenherr & Speier‐Pero, 2015; Kiron et al., 2014; McAfee & Brynjolfsson, 2012), other scholars have argued that BDPA may be overhyped or that the benefits stemming from its assimilation may not be visible (Finlay, 2014). Schoenherr and Speier‐Pero (2015) argued that there is significant awareness related to BDPA and its effectiveness, however, empirical research focusing on enablers and impending barriers is limited; whereas Hazen, Boone, Ezell, and Jones-Farmer (2014) have argued that knowledge on how to assimilate BDPA and its ensuing influence on supply chain performance and organizational performance is scant. We address this as the gap in current literature. To address this gap, we have organized a special issue (SI). The objective of this SI is to attract research articles that make significant contributions to the existing literature on the applications of BDPA in supply chain management and which may be used by the practitioners to assimilate BDPA to improve decisions related to supply chain management. The remainder of this editorial note is structured as follows: We review eight articles that we have finally included in this issue after a rigorous review process.