What is supply chain analytics?

Supply chain analytics is the practice of combining and analyzing data from procurement, inventory, order management, warehousing, fulfillment and shipping systems to understand and optimize how goods and services reach customers. It gives decision-makers a complete, up-to-date view of the supply chain so they can reduce costs, avoid stockouts and respond to disruptions.

Supply chain analytics refers to the process by which organizations seek to understand and optimize their supply chains — the set of activities involved in delivering goods and services to customers. Understanding the supply chain typically involves gathering and analyzing information from multiple operational systems. These include systems used for procurement, inventory management, order management, warehousing, fulfillment, shipping and more. A supply chain analytics platform provides decision-makers with a comprehensive and up-to-date view of their supply chains to help them make more informed business decisions.

Supply chain analytics is about more than raw goods. While supply chain management (SCM) is often associated with the raw materials and sub-assemblies that serve as inputs to a manufacturing process, this is only part of the story. A supply chain analytics platform also needs to help analysts understand the nature of various facilities, mechanisms for transporting goods, and the routes between facilities and end customers. All of these factors can impact schedules, costs and organizational efficiency.

Decisions around supply chains are complex. Besides component availability, organizations need to consider order lead times, inventory levels, pending orders, shipping costs and historical supplier performance.

How to use analytics in your supply chain management

While each of these functional applications typically has its own reporting capabilities, supply chain analytics is most effective when data from all these sources is integrated and viewed holistically. A supply chain analytics platform enables data from all of these sources to be combined in a single place to deliver a complete end-to-end view of the health of an organization's supply chain. With a better understanding of their supply chain, organizations can:

• Minimize inventory carrying costs
• Optimize order fulfillment
• Proactively manage material spending
• Optimize supply chain planning
• Avoid stockouts and optimize safety stock levels
• Manage suppliers and purchases

With a supply chain analytics platform, organizations can identify trends based on patterns in historical data. These insights can be used to develop predictive models that reduce costs and devise strategies to cope with possible future scenarios and minimize the chances of costly supply chain disruptions.

Types of supply chain analytics

Supply chain analytics is commonly grouped into four types, each answering a different question. Many organizations use all four, progressing from understanding what happened toward deciding what to do next.

• Descriptive analytics answers "what happened?" It summarizes historical and current data, such as last quarter's on-time delivery rate, inventory on hand by warehouse or freight spend by carrier.
• Diagnostic analytics answers "why did it happen?" Analysts drill from a summary metric into transaction-level detail to find root causes, for example tracing a spike in late shipments to a single distribution center, carrier or supplier.
• Predictive analytics answers "what is likely to happen?" Using historical patterns, it forecasts demand, lead times and the likelihood of stockouts or supplier delays.
• Prescriptive analytics answers "what should we do?" It recommends actions such as reorder points, safety stock levels, sourcing changes or alternate shipping routes, and can support automated responses through agentic AI.

Key supply chain metrics and KPIs

Supply chain analytics platforms typically track a core set of KPIs that span the end-to-end flow of goods:

• Perfect order rate: the share of orders delivered complete, on time, undamaged and with accurate documentation.
• Fill rate: the percentage of customer demand met from available stock without backorders.
• On-time delivery (OTD): the share of orders or shipments that arrive by the promised date, measured for both suppliers (inbound) and customers (outbound).
• Inventory turns: how many times inventory is sold and replaced over a period, a key indicator of working capital efficiency.
• Days of inventory on hand: how long current inventory will last at the current rate of demand.
• Order cycle time: the elapsed time from order receipt to delivery.
• Cash-to-cash cycle time: the time between paying suppliers and collecting payment from customers.
• Supplier lead time and lead time variability: how long suppliers take to deliver and how consistent they are.
• Freight cost per unit: transportation cost relative to volume shipped.

Supplier and spend metrics such as purchase price variance (PPV) are often monitored in a dedicated procurement analytics dashboard, while production metrics such as overall equipment effectiveness (OEE) are covered by manufacturing analytics.

Supply chain analytics vs. supply chain management

The two terms are related but not interchangeable. Supply chain management (SCM) is the discipline and the set of operational systems used to plan, source, make, store and deliver goods: running purchase orders, moving inventory and scheduling shipments. Supply chain analytics is the layer that measures and interprets how well those activities perform. SCM systems execute transactions; supply chain analytics combines data from SCM, ERP, warehouse and transportation systems to explain performance and guide decisions.

Supply chain analytics also differs from narrower views. Procurement analytics focuses on spend and suppliers, and manufacturing analytics focuses on the shop floor. Supply chain analytics connects these into a single end-to-end view, much like operational analytics does across the broader business.

Challenges of supply chain analytics

• Fragmented data: supply chain data is spread across ERP, SCM, warehouse management, transportation management and supplier systems, often with inconsistent definitions for items, locations and suppliers.
• Multiple ERP instances: acquisitions and regional rollouts leave many organizations with several ERPs, making consistent reporting difficult.
• Data latency: batch reporting can leave teams working from inventory and order status that is a day or more old while conditions continue to change.
• Loss of detail: aggregating data into summary tables can make it hard to drill down to the individual order, shipment or line item that explains a problem.
• External volatility: demand swings, supplier disruptions and transportation constraints make forecasts harder to trust.

Best practices for supply chain analytics

• Integrate data end to end. Bring procurement, inventory, order, warehouse and logistics data together rather than analyzing each function in isolation. Prebuilt data connectors can shorten this work.
• Standardize definitions. Agree on how KPIs such as fill rate and on-time delivery are calculated, ideally in a shared semantic layer.
• Preserve transaction-level detail. Keep the ability to drill from a KPI to the underlying orders and shipments so root causes can be found quickly.
• Refresh data frequently. Fresher data supports faster response to stockouts, late shipments and supplier issues.
• Enable self-service. Give planners, buyers and logistics teams self-service analytics so they can answer their own questions without waiting on IT.
• Progress from descriptive to prescriptive. Start with reliable reporting, then layer in forecasting and recommendations.

Supply chain analytics tools and platforms

Building a supply chain analytics platform in-house can be challenging. Organizations need to interface with multiple operational systems, and extracting and transforming operational data into useful information can involve significant data engineering work. Developing intuitive reports and dashboards that are useful to business users is usually time-consuming, expensive and hard to get right.

The Incorta supply chain analytics platform

Fortunately, Incorta can be used to provide a comprehensive supply chain analytics platform that can be deployed quickly and cost-effectively in the cloud with virtually no impact on production systems. With Incorta supply chain data analytics, customers can leverage prebuilt integrations with popular ERP and SCM systems. They also benefit from dozens of prebuilt dashboards and reports that can help analysts, logistics professionals and supply management teams answer business-level questions about their supply chain.

By combining data from purchasing, sales and operations in a single data environment, supply chain analytics enables organizations to gain a full picture of customer orders, inventory levels, raw materials, work in progress (WIP) and finished goods, in addition to production schedules. By leveraging Incorta's data applications (formerly Blueprints), supply chain teams can accelerate the deployment of full supply chain dashboards and reports.