Supply Chain Analytics: What It Is, Why It Matters, and More

Supply chain analytics uses data analytics to manage, improve, and support supply chain operations. Today, supply chains are critical to developing and maintaining the modern economy, providing not only luxury goods to consumers but also basic necessities like fuel and food. 

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. 

Learn more about supply chain analytics, explore the different types used daily, and find a list of its benefits. Also, gain insights into the principles underlying the digital transformation of supply chains, browse a list of common tools, and explore some courses that can help get you started in this impactful career today.

If you’re ready to start learning about supply chain analytics, 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.

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. 

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 $16.82 billion by 2027, up from $4.53 billion in 2019 [1]. 

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 like supply chain analysts focus on these details daily. 

To better manage all 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 aforementioned 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). 

Benefits of supply chain analysis 

Supply chain analytics provides a wide range of benefits. Some of the most common 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. Explore how each functions 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 the reasons that trends or relationships exist within 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 allow them to prepare for likely future events, whether common, like seasonal demand fluctuations, or less common, like 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 the solutions they need to overcome the potential problems they identified using descriptive and predictive analytics. 

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

Cognitive analytics 

Cognitive analytics uses advanced analytics techniques, such as artificial intelligence and machine learning, 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 prepare you for a role in the field.

How to use analytics in supply chain: The 5 Cs of supply chain analytics

In a 2020 report by International Data Corporation (IDC) sponsored by IBM, author and IDC vice president Simon Ellis outlines the importance of creating “thinking” supply chains that are “self-learning, intervention-free system[s]” [2]. To achieve this “smart” supply chain, Ellis notes that current supply chains must undergo a digital transformation that ensures they conform with his five Cs: connected, collaborative, cyberaware, cognitively enabled, and comprehensive [2].

Supply chain analysis plays an important role in this digital transformation. Outlined below is what each of the five Cs mean to supply chain analytics.

1. Connected 

The “thinking” supply chain is connected to various sources, including social media and Internet of Things (IoT) devices that provide it with large amounts of unstructured data. At the same time, the supply chain is connected to traditional structured data sources like business-to-business (B2B) tools. 

2. Collaborative 

The “thinking” supply chain collaborates with the digital systems that relevant suppliers and manufacturers use. Using cloud technology, modern digitally integrated supply chains can communicate with systems used by other organizations to ensure the most efficient collaboration between all relevant parties. 

3. Cyberaware

While the “thinking” supply chain provides the opportunity for improved operations and collaboration, it also becomes vulnerable to cyberattacks and intrusions. As a result, Ellis notes that it’s important for modern supply chains to have hardened systems and databases that protect them from outside actors.

4. Cognitively enabled 

The “thinking” supply chain uses artificial intelligence (AI) to automatically assess data and make decisions. Ultimately, Ellis sees the system as augmenting the work of logistical professionals, who would instead focus on specialized tasks while an AI would automatically manage the supply chain itself. 

5. Comprehensive 

The “thinking” supply chain can scale its analytic abilities with increased data. Furthermore, the system can quickly analyze this new data and make informed decisions.

Supply chain analytics software 

You can choose from numerous tools to support your work as a supply chain analyst. 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 

Stay current on industry trends with free resources

Keep up-to-data with supply chain, and data analytics, and other industry news by subscribing to our LinkedIn newsletter, Career Chat. 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: How to Prepare for AI in Your Career

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