Big Data Driven Supply Chain Network Design - Insights, Impacts, and a Framework for Assessment

There is a huge amount of hype associated with the term “Big Data” (Walker, 2014; Chen, Chiang & Storey, 2012; Chen, Mao & Liu, 2014). Its applications to industry appear limitless (Chen, Alspaugh & Katz, 2012). However, research applying big data to the supply chain management domain fails to demonstrate useful results (Richey et al., 2016). We therefore propose the following research objective: Does big data have a practical impact on the future of supply chain management? In this article, we argue that the insights gathered from big data cannot effectively be used to improve supply chain performance unless the supply chain is contextually robust. Supply chain robustness is defined as “the ability of a system to maintain its operational capabilities under different circumstances” (De Neufville, 2004). In this scenario, if a supply chain is unable to adapt, decision makers attempting to rapidly react to insights may actually disrupt supply chain performance instead of improving it. To effectively incorporate these insights, we must first evaluate the underlying traditional supply chain network design [SCND] with the purpose of understanding vulnerabilities and then attempting to discover processes that are more flexible, adaptive and robust. We hypothesise that seminal supply chain structures are incapable of effectively utilising big data, significantly diminishing supply chain performance in this context. To initially evaluate our hypothesis, we illustrate a scenario that utilises the Lambda architecture’s speed layer to process our data Fig. 1. Traditional Supply Chain Response To Real-Time/Stream Data