Big data analytics and demand forecasting in supply chains: a conceptual analysis

Abstract

Purpose Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy. Design/methodology/approach A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry. Findings The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments. Originality/value So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.