supply chain analytics

Contributor(s): David Essex

Supply chain analytics is the application of mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns and knowledge in order, shipment and transactional and sensor data. An important goal of choosing supply chain analytics software is to improve forecasting and efficiency and be more responsive to customer needs. For example, predictive analytics on point-of-sale terminal data stored in a demand signal repository can help a business anticipate consumer demand, which in turn can lead to cost-saving adjustments to inventory and faster delivery.

Achieving true end-to-end supply chain analytics, which starts with the procurement of raw materials and extends through production, distribution and aftermarket services, depends on effective integration between the many SCM and supply chain execution platforms that make up a typical company's supply chain. The goal of such integration is supply chain visibility: the ability to view data on goods at every step in the supply chain. 

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Supply chain analytics software

Supply chain analytics software is generally available in two forms: embedded in supply chain software, or in a separate, dedicated business intelligence and analytics tool that has access to supply chain data. Most ERP vendors offer supply chain analytics features, as do vendors of specialized supply chain management software.

Some ERP and SCM vendors have begun applying complex event processing (CEP) to their platforms for real-time supply chain analytics. Most ERP and SCM vendors have one-to-one integrations but there is no standard. However, the Supply Chain Operations Reference (SCOR) model provides standard metrics for comparing supply chain performance to industry benchmarks.

Ideally, supply chain analytics software would be applied to the entire chain, but in practice, it is often focused on key operational subcomponents, such as demand planning, manufacturing production, inventory management or transportation management. For example, supply chain finance analytics can help identify increased capital costs or opportunities to boost working capital and procure-to-pay analytics can help identify the best suppliers and provide early warning of budget overruns in certain expense categories, and transportation analytics software can predict the impact of weather on shipments. 

This was last updated in October 2017

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