Supply Chain Analytics, Definitions, Characteristics and Applications: A Systematic Literature Review

While there is a high uptake of BDA in the realm of supply chain management, in the view of automation supply chains and improving their value proposition by providing more accurate data for demand forecasting. There are material knowledge gaps on the SC-specific analytics applied to match demand, albeit the existing knowledge could be more amorphous. From this backdrop, the study endeavored to analyze extant literature within the ambit of BDA to unpack the current trends and possible future research directions to foster the application of BDA in SC contexts. The study adopted a systematic literature review of the extant literature published between 2014-2023. The study adopted the five-stage iterative procedure used in the systematic review methodology. The review's findings depict extensive use of big data analytics in matching demand and supply and supply chain optimization. The findings of this study adduce almost non-rebuttable evidence that big data analytics can be applied in procurement, inventory control, logistics, and order processing. Under the auspices of BDA is the SCA. Arguably, extant research has demonstrated the capability of SCA in mitigating contemporary SC risks such as mismatches between demand and supply, sub-optimal SCs, and underutilization of the supply chain infrastructures at the cusps of various echelons.