Adaptive Big Data-Driven Supply Chain Optimization for National Food Service Wholesale Using Federated AWS, Azure, and GCP Analytics

Abstract

National level supply chain optimization demands federated analytics across multiple sovereign clouds to respect regulatory needs and avoid privacy concerns. In traditional centralized models, neither such aspects nor the sensitivity to data latency can be suitably considered. A real-world wholesaler of the food service sector engaged in the development of demand forecasting, inventory optimization, and transportation planning models from different data domains and sources, routed through AWS, Azure, and GCP. Data sharing agreements enforced mandatory storage duration and data sharing policies to satisfy ownership rules and a business partnering model was used to coordinate application developments. State-of-the-art algorithms were applied for the federated learning building blocks, and the communication overheads associated with model exchanges were assessed. Today's business world suffers from the lack of information about critical events that occur far away and could have a significant positive or negative influence on business outcomes. Big data opens up the possibility of having more information for analysis but brings with it new challenges and costs, especially when dealing with processing and scripting these big data analytics. The need for specialized know-how and costs are important factors that dictate the success of an analytics process and the return on investment. However, successful and careful analysis of data has its rewards. Developing scalable models on three major public clouds (AWS, Azure, and GCP) for national-level supply chain optimization and representing data and models accurately under privacy and security regulations are still in their infancy.