What is Supply Chain Analytics?

Published

October 22, 2026

Last updated

October 22, 2026

Definition

Supply chain analytics is the use of data from sourcing, production, inventory, logistics, and demand to understand, predict, and optimize supply chain performance. It turns operational records, such as purchase orders, production runs, stock levels, shipments, and sales, into measures teams can act on.

Supply chain analytics covers 4 types. Descriptive analytics shows what happened, diagnostic analytics explains why, predictive analytics estimates what will happen, and prescriptive analytics recommends what to do. Predictive work often relies on predictive forecasting, and its value depends on forecast accuracy.

In business planning and FP&A, supply chain analytics connects operational supply chain data to financial plans. Finance and operations teams can model scenarios, such as a supplier delay or a demand spike, and quantify cost and service trade-offs before they commit to a supply plan.

Related terms

Supply Planning

Forecast Accuracy

Predictive Forecasting

Sustainable Supply Chain

Frequently Asked Questions

How does supply chain analytics differ from supply planning?

Supply chain analytics measures and explains supply chain performance, while supply planning decides how much to buy, make, and move to meet forecast demand. Analytics supplies the evidence, and planning turns that evidence into a supply plan.

What are the main types of supply chain analytics?

The 4 main types are descriptive, diagnostic, predictive, and prescriptive analytics. They answer, in order, what happened, why it happened, what will happen, and what to do next.

Which metrics does supply chain analytics track?

Common metrics include forecast accuracy, inventory turnover, fill rate, on-time delivery, and lead time. Finance teams also track cost metrics, such as cost to serve and inventory carrying cost, to link operational performance to margin and cash.