Over the years, data analytics has blossomed into a robust field that underpins decision-making not just in supply chains and ecommerce, but across all industries. In this article, I’ll help to demystify supply chain analytics for you. This includes unveiling six distinct types of data analytics, and highlighting the essential elements necessary for businesses to harness the power of supply chain analytics effectively. Ready to find out what supply chain analytics really means? Let’s get started with its definition.

Supply Chain Analytics Definition.

Below, is a concise definition of supply chain analytics:

“… [it] is the bridge between data and decision-making. It is the process of analyzing large amounts of data to identify patterns and uncover insights for informed decision-making in supply chain management.”

ThroughPut

This definition clearly defines the boundaries of supply chain analytics. Namely, it is the link between large data sets and decision-making. Also, it emphasizes how important data is in enabling supply chains to decipher and gleam critical insights for effective decision-making. Thus, this also means more and more that organizations must use data analytic software tools to make informed decisions and stay competitive.

6 Types of Data Analytics As Applied To Supply Chains.

Now, what makes supply chain analytics unique is the amount of data involved and that the data comes from many sources and systems. In fact, data about even one shipment can consist of hundreds of data elements coming from scores of systems across the globe. Specifically, these disperse systems can include order fulfillment, warehousing, shipping, carriers’ systems, 3rd party logistics (3PL) tracking systems, financial systems, and Internet of Things (IoT) networks to name a few. So, to leverage this data, supply chains can use the following six types of supply chain analytics.

6 Types of Data Analytics

A Data Analytics Perspective To Better Empower Supply Chain Managers

1. Descriptive Data Analytics: What Happened?

2. Diagnostic Data Analytics: Why Did This Happen?

3. Predictive Data Analytics: What Is Most Likely To Happen?

4. Prescriptive Data Analytics: What Action Should We Take?

5. Real-Time, On-Demand Analytics: What Do I Do Now?

6. AI-Powered Analytics: What Questions Did I Not Know to Ask?

For a more detail discussion on these distinct types of data analytics, see my article, A Data Analytics Perspective To Better Empower Supply Chain Managers.

Typical Data Infrastructure that Businesses Use for Supply Chain Analytics.

The next question to ask is how do these different types of data analytics work in most supply chain organizations? As an example of how supply chain analytics works, see Qlik’s supply chain analytics diagram below.

So, the supply chain analytics chart above provides a breakdown of its components that span from data sources through actionable insights or the triggering of application events. See below for a description of each component within a data analytics process.

The Elements of Supply Chain Analytics

1. Operational Data Sources.

Typically these data sources are from operational systems that help manage various activities within the supply chain. This includes such functions as procurement, inventory, orders, warehousing, fulfillment, and transportation. Also, this can include data from third-parties such as suppliers, carriers, and merchants.

2. Data Repositories.

For instance, data from these sources are then extracted, transformed and loaded (ETL) into a data warehouse or date lake, typically in the cloud. For more information on the technical aspects of data access, see my article, The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.

3. Supply Chain Analytics.

Then supply chain analysts use software tools and different types of data analytics. For example, this could include using predictive analysis for demand forecasting. Another possible option is for organizations to setup AI agent to act autonomously on analytical tasks.

4. Deliverables: Actionable Insights and Application Events.

As a result of these analyses, insights can then be presented in graphs using key performance indicators (KPI) to support decision-making. Also, businesses can elect to fully automate the data analytics. For instance, a system could trigger an application event such as automated replenishment of inventory. Another option is for supply chains to use a Decision Intelligence platform. This type of system enables decision-makers to interact directly with the full spectrum of analytics to make rapid, superior decisions.

For a more detail discussion of the components of supply chain analytics, see Qlik’s Supply Chain Analytics. Also for a more comprehensive discussion, see ThroughPut’s Supply Chain Analytics: The Complete Guide for Improving Supply Chain KPIs. Additionally, for more on Decision Intelligence platforms, see my article, An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes.

Data Analysis Examples To Best Overcome The Challenge Of Supply Chains

For more from SC Tech Insights, see the latest articles on Data, Decision Science, and Supply Chain.