Scalable Cloud Data Pipelines for Supply Chain Analytics and Decision Support

Abstract

Supply chain organizations increasingly draw on cloud-based data pipelines to ingest information from suppliers, logistics providers, and retail partners at a scale and velocity that on-premises infrastructure struggles to match, and they increasingly layer predictive analytics demand forecasting, disruption-risk scoring on top of that ingested data to support operational decisions. Each of these capabilities has a substantial literature of its own, but a gap persists between them: a cloud-native ingestion pipeline built for throughput and elasticity typically does not preserve the provenance of each data point back to the specific supplier, shipment, or network tier it came from, while predictive analytics for disruption risk and demand forecasting is most useful to a decision-maker precisely when its output can be traced back to the originating part of the network that is driving a forecast or an alert. Without that traceable link, a cloud pipeline can scale ingestion effectively while still leaving a decision-maker unable to act on a risk alert with confidence about where in the network the underlying signal originated. This paper proposes SCOPE (Supply Chain Observability and Prediction Engine), an architecture in which an elastic cloud ingestion and streaming layer, a predictive risk-and-demand analytics layer, and a traceable decision-support layer are coordinated by a provenance-linked analytics coordinator that tags every prediction and alert with the specific network node, tier, or shipment driving it. Following a design-science research approach, SCOPE is illustrated, not empirically validated, through a comparative capability table, a short case scenario, and two illustrative figures, with every quantitative value explicitly labeled as an illustrative estimate rather than measured production data. The paper situates SCOPE against the literature on cloud-native and stream-processing infrastructure, big-data analytics for logistics and supply chain management, supply chain risk and digital-twin research, demand forecasting, and blockchain- and IoT-based traceability.