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, global supply chains are critical to developing and maintaining the modern economy, providing not only luxury goods to consumers but also necessities like fuel and food. 

As supply chains grow more complex, so does the need for data professionals to ensure the system runs smoothly. That’s where supply chain analytics comes in. 

In this article, you’ll learn more about supply chain analytics, explore different types used daily, and find a list of its benefits. You’ll also learn the principles underlying the digital transformation of supply chains, browse a list of standard tools, and encounter some courses that can help get you started on 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 their extensive reach and complex organisation, modern supply chains produce a wealth of big data, which can be analysed to understand trends, identify inefficiencies, and develop insightful solutions. Supply chain analysts use supply chain analytics to produce actionable insights that can help guide critical decision-making concerning the development, maintenance, and optimisation 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. According to Allied Market Research, for example, the global supply chain analytics market is projected to be valued at US$16.82 billion by 2027, up from a valuation of US$4.53 billion in 2019 [1]. 

Supply chain analytics example 

When most people purchase shoes in a department store, they rarely consider the origin of the shoes’ materials, how the shoes were manufactured, or how they were transported to the store. But, 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 shoe manufacturer might analyse historical sales data to predict when shoe demand will rise and fall in the foreseeable future. Known as demand forecasting, this standard 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). 

Supply chain analytics benefits 

There are many benefits to using supply chain analytics. Some of the most common benefits include: 

• More efficient supply chain management

• Reduced operational costs

• Improved planning 

• Better risk management 

• Greater understanding of future events

Types of supply chain analytics 

There are five primary kinds of supply chain analytics: descriptive, diagnostic, predictive, prescriptive, and cognitive. While each of these types corresponds with those used in data analytics more generally, they each have unique focuses when applied to supply chains that make them unique. Here’s how each function works:

Descriptive analytics

Descriptive analytics uses data to describe trends and relationships, such as a 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 why shipments were delayed or sales targets were not made. Logistics professionals use diagnostic analytics to understand better why trends or relationships exist within the data and the factors contributing to them. 

Predictive analytics 

Predictive analytics uses supply chain data to predict future outcomes, such as forecasting 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 expected, like seasonal demand fluctuations, or less common, like global supply chain disruptions. 

Prescriptive analytics 

Prescriptive analytics uses data to prescribe a course of action, such as the best way to improve warehouse management or to optimise a supply chain to make it more efficient. 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 supply chains produce daily. 

The five Cs of Supply chain analytics

In a 2020 report by International Data Corporation (IDC) sponsored by IBM, author Simon Ellis outlines the importance of creating "thinking" supply chains that are "self-learning, intervention-free system[s]". To achieve this ‘smart’ supply chain, Ellis notes that current supply chains must undergo a "digital transformation" that conforms with his five C’s: connected, cyber aware, cognitively enabled, and comprehensive [2].  

Supply chain analytics plays an important role in this digital transformation. Here’s what each of the five C’s means to supply chain analytics:  

Connected 

The "thinking" supply chain is connected to various sources, including social media and Internet of Things (IoT) devices that provide 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. 

Collaborative 

The "thinking" supply chain collaborates with the digital systems used by relevant suppliers and manufacturers. Using cloud technology, modern digitally-integrated supply chains should be able to speak with the systems used by other organisations to ensure the most efficient communication between all relevant parties. 

Cyber aware

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.  

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 specialised tasks, while an AI would automatically manage the supply chain itself. 

Comprehensive 

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

Supply chain analytics tools 

There are many supply chain analytics tools available to professionals. Some of the most common you are likely to encounter include: 

• Deloitte Lead Time Analytics 

• IBM Sterling Supply Chain Insights with Watson 

• 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 critical to the global economy. As a result, a career in supply chain analytics starts with first learning the skills you need to work with data effectively. 

Get ready for your career today. Google’s Data Analytics Professional Certificate teaches key analytical skills and tools, such as data cleaning, SQL, and R, to course takers without prior experience in just six months. Rutgers Supply Chain Analytics Specialisation prepares course takers to improve supply chain performance by understanding common pain points and how analytics may relieve them.