We share why it’s essential to monitor and analyze inventory data. Learn about the types of inventory analytics and how to use them to achieve customer satisfaction and peak profitability.

What Are Inventory Analytics?

Inventory analytics are measurements that focus on current goods, property or raw materials. The goal of the analytics is to monitor stock levels. Current inventory levels impact business processes and highlight areas needing improvement.

Key Takeaways:

  • Inventory management solutions monitor goods throughout the supply chain, from raw materials to the sale of the final product.
  • ABC classification categorizes inventory items based on how much value they contribute to the business.
  • To remain competitive and respond to consumers' on-demand expectations, companies must avoid out-of-stock merchandise, backorders and delivery delays.

How Do You Analyze Inventory?

Inventory includes raw materials, works in progress and finished goods. Managers study inventory quantity and quality to forecast future demand. This data provides insights into current and future needs.

How do you analyze inventory performance?

Understanding and improving inventory performance is one of the most common reasons to monitor inventory analytics. An inventory manager monitors inventory management key performance indicators (KPIs) to identify areas that are performing well or processes that require attention. Many businesses will define KPI goals and regularly audit progress.

The data necessary to monitor inventory performance typically resides in an inventory management system. These systems track goods throughout the supply chain, from raw material purchase to sale. The goal of monitoring is to avoid over- or under-stock situations. Real-time, historical, and customer data are often available in inventory management solutions.

What data can you find in an inventory system?

Critical data found in inventory management solutions include:


  • Product ID
  • Item name
  • Item location
  • Item numbers
  • Measurement units
  • Starting inventory
  • Minimum required


  • First name
  • Last name
  • Product ID
  • Number shipped
  • Order date


  • Supplier ID
  • Supplier name
  • Supplier email
  • Supplier phone


  • Supplier ID
  • Product ID
  • Number received
  • Purchase date

Types of Inventory Analytics

A company will use goals to categorize inventory metrics, while others employ metrics to monitor a department's performance. For example, order cycle time is useful for measuring shipping efficiency.

Inventory analytics categories

Another way to look at inventory analytics categories is by the value the data provides. Descriptive, diagnostic, predictive and prescriptive inventory analytics offer value in a variety of ways.

  • Descriptive inventory analytics: Descriptive inventory analytics tell you what is happening. These analytics are the easiest to obtain, with most inventory management solutions providing predefined descriptive analytics out-of-the-box. Analytics include metrics commonly found on dashboards, such as the number of items on hand and cost per unit.

  • Diagnostic inventory analytics: Diagnostic inventory analytics tell you why something is happening. Root cause analysis is a diagnostic analytic — something that’s fundamental when presenting data. For example, merely knowing that a company experienced month-over-month growth is not helpful. Leaders need to understand why there was growth to apply the successes to other departments or product lines.

  • Predictive inventory analytics: Predictive inventory analytics tells an inventory manager what is likely to happen in the future. Use these analytics to prepare and make adjustments to meet needs. Machine learning, for example, is all about using technology to learn from the past and predict the future. Experts may perform predictive analysis by looking at seasonal demands from the previous year, or they may use prior knowledge of an event, such as Brexit. The UK's plan to leave the EU gave many companies early warning of a potential supply chain interruption. In contrast, the global pandemic was not predicted early enough to adjust PPE production to meet a spike in demand from healthcare providers.

  • Prescriptive inventory analytics: Prescriptive inventory analytics is a more advanced inventory analytic method. These analytics tell you what you need to do. For example, with the right data, inventory management analytics solutions can provide the quantities of an item necessary to fill 90% of orders in a three-day time frame.

Inventory Analytics Dashboard Examples

Inventory dashboards are a visual way to monitor descriptive inventory metrics. Dashboards gather data from systems and compile and display information in one easy-to-understand view.

For example, an inventory dashboard may display on-hand, top-selling and out-of-stock items.

The dashboard example below includes inventory, volume, out-of-stock and time-to-sell analytics. A glance at this dashboard provides management a birds-eye-view of the current state of stock for easy planning.

  • Inventory by category
  • Volume today
  • Out of stock items
  • Average time to sell in days

Our guide to inventory planning digs deeper into the elements a company might include in a dashboard.

Inventory Analytics Best Practices

By establishing analytics best practices, finding the right talent and adopting enabling technology from the start, companies can avoid spending money on data efforts that steer them wrong. Best practices include:

  • Centralizing data: When data resides in a single system versus disconnected silos, all stakeholders can collaborate and communicate across departments.

  • Classify inventory: All inventory items should be classified or categorized for easy monitoring. Classification may start with the ABC ranking, where A-products are the most closely monitored because they have a critical impact on profit. B products don't contribute to profit as much as A items, while C products account for the smallest profits. Then drill down into more granular details, such as apparel, electronics, home goods and outdoor. Classification allows you to view data based on a variety of parameters.

  • Get accurate information across systems: Using technology to centralize the data associated with multiple moving parts of the supply chain will ensure the information on which the company is basing decisions is accurate and complete.

  • Use real-time dashboards: What good is data if it’s not current? In inventory management, you always want to know current inventory levels to calculate needs and perform forecasting accurately. Technology solutions that provide real-time, useful metrics in a centralized view make it easy for staff and leadership to view status and productivity quickly.

How Predictive Analytics Are Used in Inventory Management

Using predictive analytics in inventory management will help a company succeed in a fast-paced market with a high demand for its products. Predicting demand based on weather, holidays and economic trends, for example, reduces backorders or excess stock levels.

Unfortunately, many companies either manually track inventory data or rely on descriptive analytics, which relays only what is happening at that time. When a company adopts a solution that predicts future trends and patterns, it reduces the cost of overstocks, minimizes out-of-stock situations and forecasts demand and profitability more accurately.

Predictive inventory analytics also enable the inventory manager to:

  • Prioritize stock based on profitability and demand;
  • Determine accurate procurement levels for production, manufacturers and suppliers;
  • Smooth out supply chain disruptions;
  • Forecast demand and revenue;
  • Optimize transportation routes and adjust when necessary;
  • Reduce waste when the solution detects a trend in damaged items from a supplier; and
  • Enable the marketing team to configure suggestive selling based on buyer trends, excess stock or most popular items.

Ways Inventory Analytics Improves Inventory Optimization

Big data coupled with the ability to measure and analyze provides for predictable demand and the ability to overcome common inventory challenges. With accurate forecasting, data-driven decisions prevent inventory shortages, overselling and shrinkage — all of which increase costs and directly impact profitability.

Consumers demand immediate gratification and expect to have almost any item on their doorsteps in 24 to 48 hours. To compete in this environment, retailers must avoid out-of-stock merchandise and delivery delays. If a vendor vows delivery in two days, the consumer expects the item in 48 hours or less. If it does not arrive, the buyer may switch to a competitor next time.

Loyalty is not a buyer's priority. The ability to purchase the exact product they want, when they want, is critical.

Meeting current consumer demands is challenging. It requires lots of data, stored in a central warehouse, connected to a modern inventory management solution. Customer, product, order, supplier and procurement data are all necessary for intelligent decision-making. Using a prediction-based model that includes historical big data helps optimize inventory management, improving customer satisfaction and profitability.

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Use NetSuite Inventory Analytics to Optimize Business Performance

Inventory analytics systems highlight what is right, what is wrong and how to improve inventory management. NetSuite’s robust inventory management solution provides real-time, customizable inventory analytics to accelerate success. With the appropriate foundational data, modern inventory management systems can use analytics to answer "what should we do" to improve satisfaction, maximize profits and reduce costs. Look for a configurable inventory management solution that integrates with all of your business systems.