Next-Generation Order Management with Generative AI for Edge Computing and Hybrid Cloud Data Centers

Abstract

Next-generation order management with generative AI is a timely concept aligned with ongoing developments in edge computing and hybrid cloud data centers. Most applications contemplate online inference of generative neural network models without strict requirements for low latency. Workloads that demand fast, real-time responses and support distributed execution of edge services, however, are encountering challenges with respective latency-sensitive applications. Reliable, low-delay order management for such settings can greatly benefit from generative AI. The active exploration of Generative AI methods for operational decision automation, particularly in areas such as order scheduling and work order forecasting, may enhance responsiveness and shorten time-to-decision.

Implementation or use-case analyses often remain sketchy or even absent. Performance considerations are commonly limited to standard generative approaches, namely the ability of generative methods to provide accurate outputs. Generic Latency metrics, however, are equally important. Although generative methods are frequently described as costly relative to their discriminative counterparts, this argument merits careful scrutiny in decision-automation scenarios, especially when local deployment at the edge is considered.