Supply Chain Analytics: What It Is, Why It Matters, and How to Get Started

Key takeaways

• Supply chain analytics is the process of using data analysis tools to maximize business efficiency and operations.

• Predictive analytics in supply chain involves using statistical models to forecast future demand and potential maintenance needs.

• Supply chain analytics works by having supply chain professionals, such as supply chain analysts, analyze historical data to forecast future supply trends.

Big data and analytics are transforming supply chain management by increasing efficiency, reducing operational costs, enhancing risk management, and improving planning. Learn more about supply chain analytics, explore the different types used daily, and find a list of its benefits in this article.

If you’re ready to start gaining supply chain analytics skills right away, consider enrolling in the Unilever Supply Chain Data Analyst Professional Certificate. You’ll have the opportunity to learn about demand planning, process improvement, and descriptive analytics in as little as four months. By the end, you’ll have earned a career credential that showcases your supply chain knowledge to employers.

What is supply chain analytics? 

Supply chain analytics uses data analytics methodologies and tools to improve supply chain management, operations, and efficiency. As distribution networks grow, so does the need for data professionals to ensure they run smoothly. That’s where supply chain managers and analysts come in.

Due to its extensive reach and complex organization, the modern supply chain network generates vast amounts of big data. Supply chain analysts analyze this data to understand trends, identify inefficiencies, and develop insightful solutions. They use analytics to make data-driven decisions about the development, maintenance, and optimization of global distribution networks.

What is an example of supply chain analysis?

When you buy a pair of shoes, you likely don’t consider where the materials come from, how manufacturers made the shoes, or how they transported them to the store. However, logistics professionals, such as supply chain analysts, focus on these details daily.

To better manage these factors, logistics professionals use data analytics to find trends and patterns in the big data produced by their supply chain. For example, a supply chain analyst working for the shoe manufacturer might analyze historical sales data to predict when consumer demand for the shoes will rise and fall in the foreseeable future. Known as demand forecasting, this common supply chain analytics method ensures that businesses can effectively plan their material sourcing, manufacturing, and distribution to meet customer demand, a process known as demand planning.

The business of supply chains

As e-commerce grows in size and importance, so does the need for well-trained analysts capable of understanding the supply chains that undergird it. Allied Market Research projects that the global supply chain analytics market will reach $93.9 billion by 2034, up from $36.9 billion in 2025 [1].

Benefits of supply chain analysis

Supply chain analytics provides a wide range of benefits, including efficient navigation of complex global supply chains and real-time, on-demand insights. Some additional benefits include:

• More efficient supply chain management

• Reduced operational costs

• Improved planning 

• Better risk management

• Greater understanding of future events

Types of supply chain data analytics

The five primary types of supply chain analytics are descriptive, diagnostic, predictive, prescriptive, and cognitive. Each performs a different task to ensure the data you collect optimizes efficiency and workflow. Explore how each functions in more detail below.

Descriptive analytics

Descriptive analytics uses data to describe trends and relationships, such as supply chain performance or a warehouse’s inventory levels. Consequently, logistics professionals use descriptive analytics to understand how a supply chain and its parts are currently working.

Diagnostic analytics 

Diagnostic analytics uses data to diagnose a supply chain problem, such as the causes of delayed shipments or missed sales targets. Logistics professionals use diagnostic analytics to better understand why trends or relationships exist in the data, as well as the factors contributing to them.

Predictive analytics 

Predictive analytics uses data to predict future outcomes, such as forecasting future demand or anticipating possible maintenance needs. Logistics professionals use predictive analytics to construct statistical models that help them prepare for likely future events, whether common, such as seasonal demand fluctuations, or less common, such as global disruptions.

Prescriptive analytics 

Prescriptive analytics uses data to prescribe a course of action, such as improving inventory management or optimizing operational efficiency. Logistics professionals use prescriptive analytics to design solutions to overcome the potential problems they identified using descriptive and predictive analytics.

Read more: Descriptive, Predictive, and Prescriptive Analytics: Understanding the Differences

Cognitive analytics 

Cognitive analytics uses advanced analytics techniques, such as artificial intelligence (AI) and machine learning (ML), to quickly process large amounts of data and produce the most accurate answer. Logistics professionals use cognitive analytics to manage and understand the big data produced by supply chains every day.

What skills are needed for supply chain analytics?

To excel in supply chain analytics, you’ll need to have strong data analysis and data visualization skills. You should be an effective problem solver, strong communicator, and team player. You should be familiar with inventory management, demand planning, and risk management as well. Additionally, experience working with common supply chain analytics tools can help you prepare for a role in the field.

How to use supply chain analytics

Using supply chain analytics helps you identify seasonal trends that require additional inventory, manage risk, mitigate future risks, create more sustainable practices, and enhance warehouse productivity. Gartner’s top supply chain technology trends for 2026 highlight agentic and physical artificial intelligence as key drivers of supply chain transformation, including autonomy and agency, specialization and intelligence, and trust and governance [2]. Furthermore, Allied Market Research emphasizes the need for companies to become more aware of supply chain management software and to take steps to mitigate cyberattacks [1].

Consider following this step-by-step process for taking your current processes through a digital transformation with supply chain analytics, beginning with identifying your business’s goals and progressing through expanding your analytics program:

1. Determine your goals.

The first step in supply chain analytics is to determine what you, as a business, want to track and improve. This may help you decide which type of analytics to use, how to track them, and eventually implement changes.

2. Track your data.

Next, begin tracking your data. This involves identifying your key performance indicators (KPIs), where your data comes from, and the actions you’ll take to ensure your data is clean and consistent.

3. Create dashboards.

A dashboard helps you review your analytics and assess your metrics' performance. If you’re tracking multiple KPIs, ensure your dashboard displays each metric and can filter data to create unique reports.

4. Continue growing your analytics system.

After successfully tracking and presenting your supply chain analytics data, you may choose to expand your system to include additional metrics, whether niche or broad, and implement new tools to access that data.

Supply chain analytics software 

You can choose from numerous tools to support your work as a supply chain analyst. The type of supply chain analytics software that you choose may depend on the type of analysis you want to complete, for example, whether you want to focus on predictive analytics or prescriptive analytics.

Some of the most common supply chain software you’re likely to encounter include:

• Deloitte Data and Analytics Platform

• IBM Supply Chain Intelligence Suite

• Tableau

• PeopleSoft Supply Chain Analytics

• SAP Supply Chain Management

• Oracle Fusion Cloud Supply Chain and Manufacturing (SCM)

• Kinaxis Maestro

Stay current on industry trends with our free resources

Keep up-to-date with supply chain, data analytics, and other industry news by subscribing to our LinkedIn newsletter, Career Chat. Then, learn more about supply chain data analytics and prepare for a career in the field by exploring these resources:

• Build your knowledge: Data Analysis Terms & Definitions

• Hear from an insider: Meet the Data Analyst Using His Creativity to Tell Visual Stories

• Watch on YouTube: AI in Supply Chain: Skills Professionals Need Now

Accelerate your career growth with a Coursera Plus subscription. When you enroll in either the monthly or annual option, you’ll get access to over 10,000 courses.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial