Descriptive Analytics as a Foundation for Advanced Supply Chain Analytics

⇨ Descriptive analytics is one of the many analytics methodologies that organizations leverage today.

⇨ While a relatively fundamental analytics approach, descriptive analytics is a critical analytics category.

⇨ Organizations must build expertise in this type to build foundation for their analytics maturity journey.

Descriptive Analytics

Supply chain analytics have rapidly developed in the last few years, supported by progress in technology and computing power. We can also attribute this evolution to increasing supply chain complexities, and challenges companies are increasingly running into. SAPinsiders have consistently highlighted their increased focus on leveraging analytics and automation in the supply chain in our research, like the most recent research, Supply Chain Planning in The Cloud,  and in our conversations with them, leading to “Supply Chain Analytics and Data Management” being the theme of our upcoming December 2022 research report.

Supply chain analytics can be further classified into a few different types, as shown in the illustration below. As figure 1 shows, the maturity of supply chain analytics capabilities starts with descriptive analytics and then matures into more advanced methods.

Figure 1. Analytics maturity curve.

We have considered descriptive analytics a foundational analytics type in most analytics maturity models. While many attribute it to this method being one of the oldest ones used since business intelligence (BI) tools, there are also additional aspects. Some of the key reasons you must master descriptive analytics are:

You can’t improve it till you measure it. This quote from Deming fits perfectly in descriptive analytics. Descriptive analytics help you understand your current state. Even though you are looking at historical data with some lag, it is still a picture of your current state, answering the question- “What Happened”? Descriptive analytics track primarily business performance and non-performance, leveraging many business intelligence tools available today.

Helps define the next stages. Until you find the answer to the “What Happened” question, you can’t get to the stage of “Why that happened” and mitigate those drivers. So executing diagnostic analytics, which helps you get an answer to the question “Why did that happen” you need first to understand “What happened.” Even for prescriptive analytics, which answers “What is the best way…”, you need to understand what is currently not optimal. Descriptive analytics help build the groundwork for these.

Test your “culture of analytics”. Successful analytics implementations are as much about people as they are about analytics tools and solutions. Since descriptive analytics tools like BI tools have been around for a long time, the absorption of these tools into your organization’s decision-making process is a good indicator of your organization’s overall readiness for embracing more advanced analytics approaches.

The good news is that most leading tools, whether SAP Analytics Cloud or third-party solutions from vendors like Pyramid Analytics, support multiple analytics methods. The key is understanding how to mature your capabilities by leveraging these tools.