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.

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 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. 

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. 

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, 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 tools 

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 Analytics Platform

• IBM Sterling Supply Chain Intelligence Suite

• Tableau

• PeopleSoft Supply Chain Analytics 

Get started with supply chain analytics. 

Supply chain analysts use data analytics and advanced analytics to manage and improve the supply chains that are critical to the global economy. Get ready for your career today.

With the Google Advanced Data Analytics Professional Certificate, you can develop in-demand skills like statistical analysis, Python, regression models, and machine learning. Through Rutgers University’s Supply Chain Analytics Specialization, you can better understand how to improve supply chain performance by identifying common pain points and exploring how analytics may relieve them.