Cloud-Driven Big Data Harmonization for Real-Time Demand Forecasting in Hybrid Retail-Manufacturing Supply Chains

Abstract

Driven by the Fourth Industrial Revolution, the traditional linear supply chain is evolving into a hybrid supply chain, in which manufacturing and retail interact with and complement each other. This shift is due to consumers’ increasing focus on direct purchasing and experiential consumption, as well as manufacturers' growing emphasis on omnichannel sales. Through the hybrid supply chain, enterprises can better attend to their customers and increase their agility and connectivity to effectively deal with market changes. Supply chain partners, however, still find it challenging to efficiently integrate various big data along the hybrid supply chain to better manage real-time demand forecasting and enhance supply chain responsiveness. To harness the opportunities of the hybrid supply chain, we propose a cloud-driven big data harmonization framework consisting of a cloud computing environment and eight ingredients. Then, we develop a cloud-based demand forecasting model to demonstrate how our framework can fulfill the unique demand forecasting requirement of hybrid retail manufacturing supply chains.

Our model contributes to the hybrid supply chain literature in three aspects. First, it highlights the integration of customer and product sensing big data stored at supply chain partners in increasing the quality and efficiency of demand forecasting. Second, it emphasizes the significance of data collaboration for demand forecasting and proposes an innovative solution that allows for greater retail manufacturing collaboration in the cloud-driven era. Third, it incorporates multiple data formats and predictive analytics to resolve real-time big data challenges and develop a “one forecast” approach that delivers timely and quality demand forecasting. Overall, we provide an initial point of departure for research on hybrid supply chain management by addressing the demand side.