What supply chain analytics tells you — and the best metrics to track

Teams used to rely on outdated reports and spreadsheets for supply chain management. A planner would export last week’s shipment data, and a category manager would look at a reorder point that hadn’t been updated in months. A disruption three tiers upstream would only become clear when a supplier missed a delivery deadline. By the time the numbers reached a decision-maker, the moment to act on them had passed.

Now, many teams use supply chain analytics to spot issues faster. They often use multiple software programs that provide fresher insights into what’s happening across the supply chain.

This guide explores supply chain analytics, the tools and best practices that support it, and how AI is changing what’s possible.

What is supply chain analytics?

Supply chain analytics refers to using supply chain-related data to improve operational decisions. It brings together information from sourcing, production, inventory, transportation, and fulfillment to show what’s happening across the supply chain.

Data analytics for supply chains can explain why something happened and, with predictive techniques, estimate what is likely to happen next. For example, a team might notice repeated shipping delays, find the specific shipping lane behind them, and start rerouting shipments.

Where supply chain analytics data comes from

Analytics programs use three main types of data: internal operations data, external and partner data, and customer and consumption data.

Internal operations data includes inventory levels, production schedules, and shipment records housed in ERP, warehouse management (WMS), and procurement systems.

External and partner data encompasses supplier performance feeds, third-party logistics portals, commodity and market indicators, weather data, and geopolitical signals that affect lanes and lead times.

Customer and consumption data refers to CRM records, purchasing patterns, and industry-specific usage data, such as patient service data in healthcare. 

The most effective approach to supply chain analytics combines internal, external, and customer data to show what’s happening across the entire supply chain. A demand forecast based only on sales history may miss weather disruptions near a supplier, while an external risk score won’t show how much safety stock the business has on hand. 

Supply chain analytics vs. reporting

Reporting reflects what’s already happened, for example, shipment volumes or delivery performance. Analytics uses statistics, algorithms, and machine learning (ML) to explain why those results happened and what may happen next. 

Types of supply chain analytics

Supply chain analytics is divided into four types: descriptive, diagnostic, predictive, and prescriptive. Each answers a different question, from what happened to what a team should do next. 

Descriptive analytics

Descriptive analytics gives teams a clear picture of past and current performance. This could mean showing whether deliveries arrived on time or which regions handled the highest order volumes. 

• Inputs/sources: It uses historical records from ERP, WMS, and transportation management systems (TMS). 
• Outputs: Dashboards, scorecards, and standard operational reports display the data.
• Business value: A reliable performance baseline makes it easier to spot potential issues.

Diagnostic analytics

When costs rise or disruptions occur, diagnostic analytics helps explain why. A team might use it to trace a stockout back to a supplier or pinpoint where a quality defect first appeared. 

• Inputs/sources: Historical transaction data is compared with supplier scorecards and logs.
• Outputs: Root-cause reports are tied to specific SKUs, suppliers, and facilities.
• Business value: Teams can track recurring problems back to the supplier, facility, or process causing them. 

Predictive analytics

Predictive analytics looks ahead. It uses patterns in historical and current data to estimate future demand, identify supplier delivery risks, or warn teams about a possible inventory shortfall. 

• Inputs/sources: Demand history, current inventory, supplier performance data, and external signals such as weather or market conditions are factors.
• Outputs: Teams receive forecasts, risk scores, and early warning alerts. 
• Business value: Planners get more time to prepare for shortages and avoid unnecessary inventory.

Prescriptive analytics

Prescriptive analytics goes a step further than predictive analytics by recommending a response. It can suggest when to reorder or compare several ways to handle a supplier problem.

• Inputs/sources: Cost, capacity, and constraint data inform recommendations.
• Outputs: Teams receive recommended actions, optimization models, and simulated scenarios.
• Business value: Instead of reviewing a risk and deciding from scratch, teams can compare recommended courses of action. 

How to choose the right supply chain analytics tools

Teams often rely on multiple tools to get a complete picture of their supply chain analytics. A comprehensive stack covers six functional capability categories. Before you choose the right supply chain analytics software, you should evaluate how well each tool handles these core functions:

• Data integration and orchestration: Some tools automatically integrate data from ERP, WMS, TMS, and supplier systems into one central location.
• Data quality and master data management: Tools that standardize product, supplier, and location records avoid tracking the same SKU in multiple ways.
• Analytics and ML workbench: Consider the environment where teams build and run forecasting, optimization, and ML models. 
• Real-time monitoring: Some software tracks shipment status and inventory changes as they happen.
• Self-service BI and visualization: These dashboards let planners and IT leaders explore data without waiting for custom reports. 
• Supply chain visibility and control: Control-tower capabilities provide a centralized view of supply chain activity and alert teams to delays, shortages, and other issues as they happen. 

Supply chain analytics best practices

Strong analytics programs account for the way teams already plan and respond to problems. These best practices maximize the value you receive from your data:

• Measurable outcomes and baselines: Before introducing analytics tools, decide what you want to improve and measure where you are now. For example, your team might decide to track key performance indicators such as inventory turnover, on-time delivery, or cost per unit to see how things change over time. 
• Reliable data: Build end-to-end data inputs before modeling. Bring together information from suppliers, manufacturers, distributors, ERP systems, Internet of Things sensors, GPS tracking, and other relevant sources before building models.
• Existing workflows: Analytics ideally support the way teams already work, not force them into separate tools. When analytics is built inside existing workflows, teams may be more likely to use it and spend less time reconciling different spreadsheets.
• Real-time monitoring: Not every supply chain metric needs live tracking. Focus real-time monitoring on critical inventory, bottlenecks, and other high-risk areas where early warnings help teams mitigate larger disruptions. 

How AI is transforming supply chain analytics

AI can help supply chain teams make faster, more informed decisions. Companies are using it for demand forecasting, supplier risk, reorder points, shipment rerouting, and capacity planning.

Traditional forecasts rely heavily on past sales, but AI brings in newer signals, such as weather changes. 

Changes in delivery time and order performance can reveal supplier trouble before a missed shipment. With AI, a team can pick up on those patterns earlier and work on a solution before things escalate into a major crisis. Today’s tools can extract insights from large volumes of unstructured text to identify recurring issues and their causes — a task that would be much more time-consuming for a human.

AI can also simulate events such as supplier failures or sudden demand spikes. Running those scenarios before a disruption exposes weak points in backup plans. A model built on outdated capacity figures, however, may create confidence in a plan that fails under real pressure.

In general, AI helps teams spot trouble earlier, but planners still need to decide whether the recommended response could work in practice.

Streamline your supply chain data with Fivetran

Supply chain analytics requires a reliable pipeline from source systems into the analytics layer through four stages: ingestion, cleaning, modeling, and operationalization. 

Most teams invest in analytics tools first, but even a sophisticated forecasting model can produce unreliable results when the data behind it is stale or disconnected. When teams use information scattered across ERP, WMS, TMS, and supplier systems, they may overlook disruptions or take too long to make decisions. 

Fivetran moves data from supply chain systems into a central warehouse or data lake using more than 750 pre-built connectors, including SAP, NetSuite, and Oracle. It also automates extraction and incremental updates. Teams dedicate less time to pipeline maintenance and more to forecasting models and dashboards that support day-to-day decisions. 

Fivetran’s data transformation capabilities also let teams prepare supply chain data close to the consumption layer. Clean datasets then feed demand forecasting, inventory, optimization, and risk modeling. 

Start a free trial with Fivetran to see for yourself.

FAQ

What’s the difference between supply chain analytics and supply chain management?

Supply chain management covers the planning and movement of goods, information, and money across the supply chain. Supply chain data analytics is one component of supply chain management. It uses data to guide decisions, such as how much inventory to keep or which supplier to prioritize. 

Can supply chain analytics work with existing ERP and supply chain systems?

Yes. Supply chain analytics software and platforms often connect to existing ERP, WMS, TMS, CRM, and other operational systems. They combine data from these sources into a single view, giving teams better visibility without requiring a new technology stack.

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