What is Descriptive Analytics?


Descriptive Analytics is the interpretation and presentation of historical data to help organizations understand the states and changes over a given period of time in a business.

Descriptive analytics helps organizations understand the states and changes that have occurred in their business. Data is usually presented visually in the form of tables, pie charts, bar charts and timelines, etc.

Descriptive analytics tell a business what occurred in the past, or what is happening now. They help a business understand the past and make decisions concerning the future. For example, it can help answer business questions like, how many units of a certain type were sold in the previous year, and how much they spent on expediting freight.

One Network's Perspective on Descriptive Analytics

Descriptive analytics is a valuable tool in the business arsenal, but there are some major challenges with making effective use of it in the supply chain. For example:

  • Each party in the supply chain has their own sources of data, and are capturing different data points. Often, key data is missing altogether.
  • The analytics tell you what happened, but not why it happened. It can be difficult to extract key insights into problems and their root causes.
  • It is difficult to decide which is the best path of action going forward.
  • The analytics themselves do not help you resolve issues that they present.

So while descriptive analytics is helpful, it really needs to be presented in context and with guidance to be really useful. (See Predictive Analytics and Prescriptive Analytics.)

Descriptive Analytics Resources

AI and Machine Learning in the Supply Chain

AI and Supply Chain Problem Solving

What does it take to effectively address challenges in the supply chain? What’s really required is real-time “always on” planning and execution capabilities that eliminate the information lead times between unplanned shifts in consumer demand or supply capability.


Webinar: 8 Keys to Achieving Success with AI in the Supply Chain

The success of AI in supply chains is heavily dependent on information accuracy, data timeliness, and selection of the correct decision space for AI applications. In this webinar, Joe Bellini explains the basics of AI that supply chain pros need to know, and how they can get the most from their AI implementations.


8 Keys to Success with Artificial Intelligence in the Supply Chain

Artificial Intelligence (AI) can offer a huge benefit to supply chain managers, but only if it is based on solid fundamentals that take into account the diverse and dynamic nature of today’s modern supply chains. This report identifies the eight fundamentals that need to be in place in order to achieve the dramatic returns that AI is capable of delivering.