Orchestrating Smart Movement Through Live Intelligence
DOI:
https://doi.org/10.5281/zenodo.20828517Keywords:
Real-time big data stream processing; Supply Chain; Logistics Optimization; Inventory Management; Fleet Management; Traffic Data; Routing; Decision Making; Real-Time Feedback; Reinforcement Learning; feedback System; Emerging Trends; Supply Chain Automation.Abstract
The logistics and supply chain domain is a hotspot for Internet-of-Things (IoT) applications and Big Data. Logistics processes are increasingly instrumented, resulting in the generation of massive amounts of data. For humankind, this is an opportunity to achieve sustainability and efficiency increases through the development of Smart Logistics. The Big Data stream processing field offers tools to process data in near real-time and/or at large scales. Yet, the requirement for Smart Logistics is not merely the processing of Big Data streams, but rather the development of a Smart Logistics Operations System that continuously adapts to new information and subsystems. Such an application demands high availability, scalability, reliability, fault tolerance, and the ability to process streams of data in real-time, allowing learned knowledge to be applied instantaneously.
Two key decisions must be made for building a stream system that supports these same hard requirements in terms of implementation and solution development. The processing unit, implemented in the Cloud or at the Edge, must guarantee the lowest possible delay, while the use of Microbatch or True Streaming Processing should provide the best response time feasible, without sacrificing, at least at the design stage, reliability and fault tolerance. A third concern, occurring in the Data Ingestion layer of the Smart Logistics stack, is the heterogeneous nature of the data sources and their integration into a common Near Real-Time stream for the application. Finally, the development of a Smart Logistics Operations System relies on true Real-Time Analytics that naturally support decision-making, since optimal strategy changes are executed and orchestrated in the Streams.
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