The Definitive Guide to Supply Chain Data Analytics

Due to the current global supply chain complexity, it has many vulnerabilities, including natural disasters, weather events, and epidemics. At the same time, there are myriad opportunities for an organization to optimize its supply chain to improve customer satisfaction and increase profitability. Increasingly companies are using supply chain data analytics to provide the insight needed for solving problems quickly and acting on opportunities for a competitive advantage.

What is supply chain data analytics?

Supply chain data analytics is a type of analytics designed to uncover insights into an organization’s supply chain by analyzing data from its various systems and applications. Data can provide visibility into every link in the chain: procurement, inventory management, order management, warehousing and fulfillment, and shipping.

Complexities involved in the modern supply chain result in many possible points of failure. If one link in the chain experiences a bottleneck or shutdown, the entire system following the point of failure will be affected. Data analytics for the supply chain can help companies identify where they’re vulnerable and how to avoid preventable problems. It can find ways to solve problems when they do occur. It can also uncover opportunities to improve the supply chain even further.

4 types of supply chain analytics

Supply chains can benefit from four different types of analytics for a variety of insights: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

1. Descriptive analytics

Descriptive analytics for the supply chain enables companies to collect and organize historical data for a clear picture of what happened in the past. It measures performance and looks at patterns across the supply chain, from suppliers to logistics to retailers and point of sale. For example, a national retailer might look at an analytics dashboard to track demand for particular SKUs across geographic locations throughout the past year.

2. Diagnostic analytics

Identifying what has happened is typically valuable only if you also know why it happened. Diagnostic analytics steps in to identify the origin of problems and find possible solutions to prevent them from happening in the future. For this reason, diagnostic analytics is also called root cause analysis.

A manufacturer might see an order from one of its suppliers is running late. A look at the analytics could show that the region where the supplier is located is experiencing severe flooding. Diving in further, the manufacturer might see that the flooding is expected to last three days before clearing up.

3. Predictive analytics

Predictive analytics for the supply chain helps companies predict what could happen in the future and determine the probability of various outcomes. It enables better planning and realistic goal-setting as well as avoiding unnecessary risk. Ultimately, predictive analytics allows companies to more accurately anticipate future performance based on past performance and all the factors currently affecting it.

One of the most valuable forms of predictive analytics is what-if analysis, which involves changing various values to see how those changes will affect the outcome. Manufacturers implement predictive analytics to track consumer preferences and forecast demand for their products.

It’s important to note that predictive analytics is limited to identifying correlations, anomalies, and patterns;  it cannot determine cause and effect. For this reason, complex critical thinking is necessary for accurate insights.

4. Prescriptive analytics

Prescriptive analytics for the supply chain tells teams what they need to do based on the predictions made. It’s the most complex of these analyses, which is why less than 3% of companies are using it in their business.

This type of analytics might alert a retailer that one of its key vendors is likely to have difficulty sourcing materials due to political instability in the region where it currently obtains materials. The retailer could explore alternate locations to obtain the material from and work with the vendor to head off the problem in advance. Alternatively, the analytics might reveal that the safest option is to change vendors or even look at replacing the item with a different product altogether.

While using AI in prescriptive analytics is currently making headlines, the fact is that this technology has a long way to go in its ability to generate relevant, actionable information. The use of AI at scale requires running thousands of queries in search of statistical anomalies. But randomly identified anomalies don’t always point directly to business opportunities. For now, human involvement is important to achieving relevant insights.

For instance, imagine you’re a sales director at a tech company. Here are a few examples of the types of insights each of the analytical tactics discussed in this section might reveal:

5 supply chain big data analytics use cases

Companies are now using supply chain data analytics in many different ways. The following supply chain analytics examples demonstrate the potential for business transformation:

1. Predicting supply disruptions

Supply chain data analytics gives companies the ability to predict supply disruptions and make adjustments before problems impact production. Companies can also look at trending data on weather events, political instability, or financial issues that may impact a supplier’s ability to deliver on schedule. With this information, teams can make alternate plans as needed so they can maintain normal operations.

2. Quality assurance

Manufacturing companies use real-time data analysis for quality assurance. Using IoT-enabled cameras and measuring devices, a manufacturer can identify issues before a product is shipped to retailers or consumers.

3. Warehouse management

Information gathered on warehouse temperature, shelf weight, and load weight can be used to optimize warehouse operations and improve productivity. Analytics can inform receiving, tracking, and storing inventory, as well as workload planning, managing shipping, and monitoring the movement of items in the warehouse.

4. Logistics

Data analytics is used in logistics to plan more efficient delivery routes and reduce fuel consumption. Companies can use data to identify the ideal mode of transportation for their loads.

5. Sales, inventory, and operations planning

Retailers can analyze point-of-sale (POS), inventory, and production volume data to identify misalignment in supply and demand. As a result, they can determine when to place orders with suppliers, which products to put on sale, and when to launch new product offerings.

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By leveraging supply chain data analytics, companies can proactively address vulnerabilities, optimize operations, and gain a competitive edge. From predicting disruptions to improving warehouse management and logistics, data-driven insights empower organizations to make informed decisions that enhance efficiency and profitability. 

Businesses integrating advanced analytics into supply chain strategies will be better able to navigate challenges and capitalize on opportunities in an increasingly complex global market.

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