What is Supply Chain Analytics? Everything You Need to Know

A growing number of businesses are looking beyond the walls of their organization and extending their analytics capabilities across the greater supply chain.  Recent advancements in supply chain analytics make it possible to discover latent insight across all areas of a value chain including sourcing, manufacturing, distribution, logistics, customer service, and more. 

By leveraging supply chain analytics, a business can drastically improve its forecasting capabilities—reducing risk while improving customer service and driving greater profits.  

What is Supply Chain Analytics?

It involves making data-driven decisions in order to improve operational and tactical efficiency between vendor touchpoints. Supply chain analytics can be broken down into the following categories.

1. Predictive analytics

Predictive analytics takes into account historical and current facts in order to predict future events. 

As an example, a business may study consumer purchase habits over time to determine whether their customer base would buy items made either overseas or from certain materials. Using this data, they could gain a better sense of how their customer base might respond to sourcing or manufacturing decisions.

In one study, the number of supply chain professionals using predictive analytics grew by 76 percent from 2017 to 2019. In fact, 30 percent of respondents are now using the technology to improve operations. What’s more, 90 percent of respondents indicated that predictive analytics would impact the supply chain in the next 10 years. 

While predictive analytics are helpful, though, they don’t provide a complete picture for retailers. 

“Predictive analytics forecasts what will happen in the future. Prescriptive analytics can help companies alter the future,” says MetroStar Systems web analytics engineer Immanuel Lee. “They’re both necessary to improve decision-making and business outcomes.”

2. Prescriptive analytics

Retailers use prescriptive analytics to determine the best possible course of action for a particular situation. This is a step up from predictive analytics because it involves using data to support a specific decision.

As an example, a department store may predict the arrival of a major snowstorm in advance and use that intelligence to load up on items like shovels, gloves, and salt across a chain of regional stores so they don’t run out during a period of peak demand. 

In another example, a business may use navigation data to study real-time weather and traffic patterns in order to anticipate delays and make more efficient deliveries. 

Retailers can’t just look ahead into the future, though. It’s also necessary to look into the past and discover what happened at various points of the supply chain—and that’s where descriptive analytics come in handy.

3. Descriptive analytics 

Descriptive analytics involves engaging in data aggregation and mining in order to study past events and learn how they may predict future outcomes. 

For example, a retailer may study year-over-year changes in sales to predict their next year in business. Descriptive analytics can pool insight from a variety of different reports including financial, operational, sales, and inventory data. 

There’s another developing area beyond predictive, prescriptive, and descriptive analysis which is rapidly gaining steam: cognitive analysis.

4. Cognitive analytics 

Just as the name sounds, cognitive analytics describes the process of using artificial intelligence and machine learning to help retailers make rapid business decisions.

Unlike linear data distribution systems, cognitive analytics continuously monitors data across all available areas of the supply chain in order to make snap judgements that can drastically reduce risk. 

An example of cognitive supply chain analytics can be seen with Walmart, which now requires leafy salad providers to use blockchain technology to track items as they move across the supply chain. Using this tracking system, Walmart can determine the exact origin of a contaminated food item, enabling the company to take action and cut off distribution when required to prevent tainted items from spreading. 

The Benefits of Using Supply Chain Analytics 

As you can see, supply chain analytics is rapidly advancing. This makes it easier for businesses to conduct short- and long-term planning. 

Here are some of the top benefits to using supply chain analytics: 

1. Larger profits 

Supply chain analytics make it possible to maximize revenue. By digging into historical and real-time data, companies can better understand how their supply chain actions will drive future profits—from sourcing to manufacturing to distribution. 

2. Reduced waste 

Supply chain analytics can also drive a leaner and more responsible supply chain. For example, a company may use prescriptive analytics to optimize a delivery route for perishable items, getting products into stores faster and increasing their shelf life. 

3. Improved relationships 

Supply chain analytics can also help vendors improve relationships with partner companies and customers by sharing data and intelligence and getting products to market faster and more efficiently. It can benefit all parts of the supply chain, leading to better customer reviews and stronger relationships with suppliers. 

How an Analytics Hub Can Help

All of the above may sound easy. But the truth is that supply chains are very complex. Information pours in from a variety of unconnected data collection points, making it difficult to visualize and optimize data. 

An analytics hub like Datameer can help simplify data management by serving as a centralized platform where information can be processed, visualized, and distributed to team managers and engineers. By using an analytics hub, organizations can move from collecting data to making it actionable—enabling a far greater return on investment. 

To see what an analytics hub can do for you, Sign up for your free trial today!